Subjects -> INSTRUMENTS (Total: 62 journals)
Showing 1 - 16 of 16 Journals sorted alphabetically
Annali dell'Istituto e Museo di storia della scienza di Firenze     Hybrid Journal  
Applied Mechanics Reviews     Full-text available via subscription   (Followers: 27)
Bulletin of Social Informatics Theory and Application     Open Access   (Followers: 1)
Computational Visual Media     Open Access   (Followers: 4)
Devices and Methods of Measurements     Open Access  
Documenta & Instrumenta - Documenta et Instrumenta     Open Access  
EPJ Techniques and Instrumentation     Open Access  
European Journal of Remote Sensing     Open Access   (Followers: 9)
Experimental Astronomy     Hybrid Journal   (Followers: 39)
Flow Measurement and Instrumentation     Hybrid Journal   (Followers: 18)
Geoscientific Instrumentation, Methods and Data Systems     Open Access   (Followers: 4)
Geoscientific Instrumentation, Methods and Data Systems Discussions     Open Access   (Followers: 1)
IEEE Journal on Miniaturization for Air and Space Systems     Hybrid Journal   (Followers: 2)
IEEE Sensors Journal     Hybrid Journal   (Followers: 103)
IEEE Sensors Letters     Hybrid Journal   (Followers: 3)
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems)     Open Access   (Followers: 3)
Imaging & Microscopy     Hybrid Journal   (Followers: 9)
InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan     Open Access  
Instrumentation Science & Technology     Hybrid Journal   (Followers: 7)
Instruments and Experimental Techniques     Hybrid Journal   (Followers: 1)
International Journal of Applied Mechanics     Hybrid Journal   (Followers: 7)
International Journal of Instrumentation Science     Open Access   (Followers: 40)
International Journal of Measurement Technologies and Instrumentation Engineering     Full-text available via subscription   (Followers: 2)
International Journal of Metrology and Quality Engineering     Full-text available via subscription   (Followers: 4)
International Journal of Remote Sensing     Hybrid Journal   (Followers: 274)
International Journal of Remote Sensing Applications     Open Access   (Followers: 43)
International Journal of Sensor Networks     Hybrid Journal   (Followers: 4)
International Journal of Testing     Hybrid Journal   (Followers: 1)
Journal of Applied Remote Sensing     Hybrid Journal   (Followers: 83)
Journal of Astronomical Instrumentation     Open Access   (Followers: 3)
Journal of Instrumentation     Hybrid Journal   (Followers: 32)
Journal of Instrumentation Technology & Innovations     Full-text available via subscription   (Followers: 1)
Journal of Medical Devices     Full-text available via subscription   (Followers: 5)
Journal of Medical Signals and Sensors     Open Access   (Followers: 3)
Journal of Optical Technology     Full-text available via subscription   (Followers: 5)
Journal of Sensors and Sensor Systems     Open Access   (Followers: 11)
Journal of Vacuum Science & Technology B     Hybrid Journal   (Followers: 2)
Jurnal Informatika Upgris     Open Access  
Measurement : Sensors     Open Access   (Followers: 3)
Measurement and Control     Open Access   (Followers: 36)
Measurement Instruments for the Social Sciences     Open Access  
Measurement Science and Technology     Hybrid Journal   (Followers: 7)
Measurement Techniques     Hybrid Journal   (Followers: 3)
Medical Devices & Sensors     Hybrid Journal  
Medical Instrumentation     Open Access  
Metrology and Measurement Systems     Open Access   (Followers: 6)
Microscopy     Hybrid Journal   (Followers: 8)
Modern Instrumentation     Open Access   (Followers: 50)
Optoelectronics, Instrumentation and Data Processing     Hybrid Journal   (Followers: 4)
PFG : Journal of Photogrammetry, Remote Sensing and Geoinformation Science     Hybrid Journal  
Photogrammetric Engineering & Remote Sensing     Full-text available via subscription   (Followers: 29)
Remote Sensing     Open Access   (Followers: 54)
Remote Sensing Applications : Society and Environment     Full-text available via subscription   (Followers: 8)
Remote Sensing of Environment     Hybrid Journal   (Followers: 93)
Remote Sensing Science     Open Access   (Followers: 24)
Review of Scientific Instruments     Hybrid Journal   (Followers: 22)
Sensors and Materials     Open Access   (Followers: 2)
Solid State Nuclear Magnetic Resonance     Hybrid Journal   (Followers: 3)
Standards     Open Access  
Transactions of the Institute of Measurement and Control     Hybrid Journal   (Followers: 13)
Труды СПИИРАН     Open Access  
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Remote Sensing
Journal Prestige (SJR): 1.386
Citation Impact (citeScore): 4
Number of Followers: 54  

  This is an Open Access Journal Open Access journal
ISSN (Print) 2072-4292
Published by MDPI Homepage  [233 journals]
  • Remote Sensing, Vol. 13, Pages 197: A Novel Method for Automated
           Supraglacial Lake Mapping in Antarctica Using Sentinel-1 SAR Imagery and
           Deep Learning

    • Authors: Mariel Dirscherl, Andreas J. Dietz, Christof Kneisel, Claudia Kuenzer
      First page: 197
      Abstract: Supraglacial meltwater accumulation on ice sheets can be a main driver for accelerated ice discharge, mass loss, and global sea-level-rise. With further increasing surface air temperatures, meltwater-induced hydrofracturing, basal sliding, or surface thinning will cumulate and most likely trigger unprecedented ice mass loss on the Greenland and Antarctic ice sheets. While the Greenland surface hydrological network as well as its impacts on ice dynamics and mass balance has been studied in much detail, Antarctic supraglacial lakes remain understudied with a circum-Antarctic record of their spatio-temporal development entirely lacking. This study provides the first automated supraglacial lake extent mapping method using Sentinel-1 synthetic aperture radar (SAR) imagery over Antarctica and complements the developed optical Sentinel-2 supraglacial lake detection algorithm presented in our companion paper. In detail, we propose the use of a modified U-Net for semantic segmentation of supraglacial lakes in single-polarized Sentinel-1 imagery. The convolutional neural network (CNN) is implemented with residual connections for optimized performance as well as an Atrous Spatial Pyramid Pooling (ASPP) module for multiscale feature extraction. The algorithm is trained on 21,200 Sentinel-1 image patches and evaluated in ten spatially or temporally independent test acquisitions. In addition, George VI Ice Shelf is analyzed for intra-annual lake dynamics throughout austral summer 2019/2020 and a decision-level fused Sentinel-1 and Sentinel-2 maximum lake extent mapping product is presented for January 2020 revealing a more complete supraglacial lake coverage (~770 km2) than the individual single-sensor products. Classification results confirm the reliability of the proposed workflow with an average Kappa coefficient of 0.925 and a F1-score of 93.0% for the supraglacial water class across all test regions. Furthermore, the algorithm is applied in an additional test region covering supraglacial lakes on the Greenland ice sheet which further highlights the potential for spatio-temporal transferability. Future work involves the integration of more training data as well as intra-annual analyses of supraglacial lake occurrence across the whole continent and with focus on supraglacial lake development throughout a summer melt season and into Antarctic winter.
      Citation: Remote Sensing
      PubDate: 2021-01-08
      DOI: 10.3390/rs13020197
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 198: Adaptive Weighting Feature Fusion
           Approach Based on Generative Adversarial Network for Hyperspectral Image
           Classification

    • Authors: Hongbo Liang, Wenxing Bao, Xiangfei Shen
      First page: 198
      Abstract: Recently, generative adversarial network (GAN)-based methods for hyperspectral image (HSI) classification have attracted research attention due to their ability to alleviate the challenges brought by having limited labeled samples. However, several studies have demonstrated that existing GAN-based HSI classification methods are limited in redundant spectral knowledge and cannot extract discriminative characteristics, thus affecting classification performance. In addition, GAN-based methods always suffer from the model collapse, which seriously hinders their development. In this study, we proposed a semi-supervised adaptive weighting feature fusion generative adversarial network (AWF2-GAN) to alleviate these problems. We introduced unlabeled data to address the issue of having a small number of samples. First, to build valid spectral–spatial feature engineering, the discriminator learns both the dense global spectrum and neighboring separable spatial context via well-designed extractors. Second, a lightweight adaptive feature weighting component is proposed for feature fusion; it considers four predictive fusion options, that is, adding or concatenating feature maps with similar or adaptive weights. Finally, for the mode collapse, the proposed AWF2-GAN combines supervised central loss and unsupervised mean minimization loss for optimization. Quantitative results on two HSI datasets show that our AWF2-GAN achieves superior performance over state-of-the-art GAN-based methods.
      Citation: Remote Sensing
      PubDate: 2021-01-08
      DOI: 10.3390/rs13020198
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 199: Random Forests for Landslide
           Prediction in Tsengwen River Watershed, Central Taiwan

    • Authors: Youg-Sin Cheng, Teng-To Yu, Nguyen-Thanh Son
      First page: 199
      Abstract: Landslides have been identified as one of the costliest and deadliest natural disasters, causing tremendous damage to humans and societies. Information regarding the spatial extent of landslides is thus important to allow officials to devise successful strategies to mitigate landslide hazards. This study aims to develop a machine-learning approach for predicting landslide areas in the Tsengwen River Watershed (TRW), which is one of the most landslide-prone areas in Central Taiwan. Various spatial datasets were collected from 2009 to 2015 to derive 36 predictive variables used for landslide modeling with random forests (RF). The results of landslide prediction, compared with ground reference data, indicated an overall accuracy of 91.4% and Kappa coefficient of 0.83, respectively. The findings achieved from estimates of predictor importance also indicated to officials that the land-use/land-cover (LULC) type, distance to previous landslides, distance to roads, bank erosion, annual groundwater recharge, geological line density, aspect, and slope are the most influential factors that trigger landslides in the study region.
      Citation: Remote Sensing
      PubDate: 2021-01-08
      DOI: 10.3390/rs13020199
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 200: Cascade Object Detection and Remote
           Sensing Object Detection Method Based on Trainable Activation Function

    • Authors: S. N. Shivappriya, M. Jasmine Pemeena Priyadarsini, Andrzej Stateczny, C. Puttamadappa, B. D. Parameshachari
      First page: 200
      Abstract: Object detection is an important process in surveillance system to locate objects and it is considered as major application in computer vision. The Convolution Neural Network (CNN) based models have been developed by many researchers for object detection to achieve higher performance. However, existing models have some limitations such as overfitting problem and lower efficiency in small object detection. Object detection in remote sensing hasthe limitations of low efficiency in detecting small object and the existing methods have poor localization. Cascade Object Detection methods have been applied to increase the learning process of the detection model. In this research, the Additive Activation Function (AAF) is applied in a Faster Region based CNN (RCNN) for object detection. The proposed AAF-Faster RCNN method has the advantage of better convergence and clear bounding variance. The Fourier Series and Linear Combination of activation function are used to update the loss function. The Microsoft (MS) COCO datasets and Pascal VOC 2007/2012 are used to evaluate the performance of the AAF-Faster RCNN model. The proposed AAF-Faster RCNN is also analyzed for small object detection in the benchmark dataset. The analysis shows that the proposed AAF-Faster RCNN model has higher efficiency than state-of-art Pay Attention to Them (PAT) model in object detection. To evaluate the performance of AAF-Faster RCNN method of object detection in remote sensing, the NWPU VHR-10 remote sensing data set is used to test the proposed method. The AAF-Faster RCNN model has mean Average Precision (mAP) of 83.1% and existing PAT-SSD512 method has the 81.7%mAP in Pascal VOC 2007 dataset.
      Citation: Remote Sensing
      PubDate: 2021-01-08
      DOI: 10.3390/rs13020200
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 201: Impacts of the Tropical Cyclone Idai
           in Mozambique: A Multi-Temporal Landsat Satellite Imagery Analysis

    • Authors: Alberto Bento Charrua, Rajchandar Padmanaban, Pedro Cabral, Salomão Bandeira, Maria M. Romeiras
      First page: 201
      Abstract: The Central Region of Mozambique (Sofala Province) bordering on the active cyclone area of the southwestern Indian Ocean has been particularly affected by climate hazards. The Cyclone Idai, which hit the region in March 2019 with strong winds causing extensive flooding and a massive loss of life, was the strongest recorded tropical cyclone in the Southern Hemisphere. The aim of this study was to use pre- and post-cyclone Idai Landsat satellite images to analyze temporal changes in Land Use and Land Cover (LULC) across the Sofala Province. Specifically, we aimed—(i) to quantify and map the changes in LULC between 2012 and 2019; (ii) to investigate the correlation between the distance to Idai’s trajectory and the degree of vegetation damage, and (iii) to determine the damage caused by Idai on different LULC. We used Landsat 7 and 8 images (with 30 m resolution) taken during the month of April for the 8-year period. The April Average Normalized Difference Vegetation Index (NDVI) over the aforementioned period (2012–2018, pre-cyclone) was compared with the values of April 2019 (post-cyclone). The results showed a decreasing trend of the productivity (NDVI 0.5 to 0.8) and an abrupt decrease after the cyclone. The most devastated land use classes were dense vegetation (decreased by 59%), followed by wetland vegetation (−57%) and shrub land (−56%). The least damaged areas were barren land (−23%), barren vegetation (−27%), and grassland and dambos (−27%). The Northeastern, Central and Southern regions of Sofala were the most devastated areas. The Pearson Correlation Coefficient between the relative vegetation change activity after Idai (NDVI%) and the distance to Idai’s trajectory was 0.95 (R-square 0.91), suggesting a strong positive linear correlation. Our study also indicated that the LULC type (vegetation physiognomy) might have influenced the degree of LULC damage. This study provides new insights for the management and conservation of natural habitats threatened by climate hazards and human factors and might accelerate ongoing recovery processes in the Sofala Province.
      Citation: Remote Sensing
      PubDate: 2021-01-08
      DOI: 10.3390/rs13020201
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 202: Assessment of Near-Real-Time Satellite
           Precipitation Products from GSMaP in Monitoring Rainfall Variations over
           Taiwan

    • Authors: Huang, Liu, Hsu, Li, Deng
      First page: 202
      Abstract: This study assessed four near-real-time satellite precipitation products (NRT SPPs) of Global Satellite Mapping of Precipitation (GSMaP)—NRT v6 (hereafter NRT6), NRT v7 (hereafter NRT7), Gauge-NRT v6 (hereafter GNRT6), and Gauge-NRT v7 (hereafter GNRT7)— in representing the daily and monthly rainfall variations over Taiwan, an island with complex terrain. The GNRT products are the gauge-adjusted version of NRT products. Evaluations for warm (May–October) and cold months (November–April) were conducted from May 2017 to April 2020. By using observations from more than 400 surface gauges in Taiwan as a reference, our evaluations showed that GNRT products had a greater error than NRT products in underestimating the monthly mean rainfall, especially during the warm months. Among SPPs, NRT7 performed best in quantitative monthly mean rainfall estimation; however, when examining the daily scale, GNRT6 and GNRT7 were superior, particularly for monitoring stronger (i.e., more intense) rainfall events during warm and cold months, respectively. Spatially, the major improvement from NRT6 to GNRT6 (from NRT7 to GNRT7) in monitoring stronger rainfall events over southwestern Taiwan was revealed during warm (cold) months. From NRT6 to NRT7, the improvement in daily rainfall estimation primarily occurred over southwestern and northwestern Taiwan during the warm and cold months, respectively. Possible explanations for the differences between the ability of SPPs are attributed to the algorithms used in SPPs. These findings highlight that different NRT SPPs of GSMaP should be used for studying or monitoring the rainfall variations over Taiwan for different purposes (e.g., warning of floods in different seasons, studying monthly or daily precipitation features in different seasons, etc.).
      Citation: Remote Sensing
      PubDate: 2021-01-08
      DOI: 10.3390/rs13020202
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 203: A Preliminary Study of Wave Energy
           Resource Using an HF Marine Radar, Application to an Eastern Southern
           Pacific Location: Advantages and Opportunities

    • Authors: Mundaca-Moraga, Abarca-del-Rio, Figueroa, Morales
      First page: 203
      Abstract: As climate change is of global concern, the electric generation through fossil fuel is progressively shifted to renewable energies. Among the renewables, the most common solar and wind, the wave energy stands for its high-power density. Studies about wave energy resource have been increasing over the years, especially in coastal countries. Several research investigations have assessed the global wave power, with higher values at high latitudes. However, to have a precise assessment of this resource, the measurement systems need to provide a high temporal and spatial resolution, and due to the lack of in-situ measurements, the way to estimate this value is numerical. Here, we use a high-frequency radar to estimate the wave energy resource in a nearshore central Chile at a high resolution. The study focuses near Concepción city (36.5° S), using a WERA (WavE RAdar) high frequency (HF) radar. The amount of annual energy collected is calculated. Analysis of coefficient of variation (COV), seasonal variability (SV), and monthly variability (MV) shows the area's suitability for installing a wave energy converter device due to a relatively low variability and the high concentration of wave power obtained. The utility of HF radars in energy terms relies on its high resolution, both temporal and spatial. It can then compare the location of interest within small areas and use them as a complement to satellite measurements or numerical models, demonstrating its versatility.
      Citation: Remote Sensing
      PubDate: 2021-01-08
      DOI: 10.3390/rs13020203
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 204: Multi-Exposure Fusion of Gray Images
           Under Low Illumination Based on Low-Rank Decomposition

    • Authors: Nie, Huang, Liu, Li, Zhao, Yuan, Song, He
      First page: 204
      Abstract: Existing multi-exposure fusion (MEF) algorithms for gray images under low-illumination cannot preserve details in dark and highlighted regions very well, and the fusion image noise is large. To address these problems, an MEF method is proposed. First, the latent low-rank representation (LatLRR) is used on low-dynamic images to generate low-rank parts and saliency parts to reduce noise after fusion. Then, two components are fused separately in Laplace multi-scale space. Two different weight maps are constructed according to features of gray images under low illumination. At the same time, an energy equation is designed to obtain the optimal ratio of different weight factors. An improved guided filtering based on an adaptive regularization factor is proposed to refine the weight maps to maintain spatial consistency and avoid artifacts. Finally, a high dynamic image is obtained by the inverse transform of low-rank part and saliency part. The experimental results show that the proposed method has advantages both in subjective and objective evaluation over state-of-the-art multi-exposure fusion methods for gray images under low-illumination imaging.
      Citation: Remote Sensing
      PubDate: 2021-01-08
      DOI: 10.3390/rs13020204
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 205: Fully Automated Detection of
           Supraglacial Lake Area for Northeast Greenland Using Sentinel-2
           Time-Series

    • Authors: Hochreuther, Neckel, Reimann, Humbert, Braun
      First page: 205
      Abstract: The usability of multispectral satellite data for detecting and monitoring supraglacial meltwater ponds has been demonstrated for western Greenland. For a multitemporal analysis of large regions or entire Greenland, largely automated processing routines are required. Here, we present a sequence of algorithms that allow for an automated Sentinel-2 data search, download, processing, and generation of a consistent and dense melt pond area time-series based on open-source software. We test our approach for a ~82,000 km2 area at the 79°N Glacier (Nioghalvfjerdsbrae) in northeast Greenland, covering the years 2016, 2017, 2018 and 2019. Our lake detection is based on the ratio of the blue and red visible bands using a minimum threshold. To remove false classification caused by the similar spectra of shadow and water on ice, we implement a shadow model to mask out topographically induced artifacts. We identified 880 individual lakes, traceable over 479 time-steps throughout 2016–2019, with an average size of 64,212 m2. Of the four years, 2019 had the most extensive lake area coverage with a maximum of 333 km2 and a maximum individual lake size of 30 km2. With 1.5 days average observation interval, our time-series allows for a comparison with climate data of daily resolution, enabling a better understanding of short-term climate-glacier feedbacks.
      Citation: Remote Sensing
      PubDate: 2021-01-08
      DOI: 10.3390/rs13020205
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 206: Mapping the Lithological Features and
           Ore-Controlling Structures Related to Ni–Cu Mineralization in the
           Eastern Tian Shan, NW China from ASTER Data

    • Authors: Shuo Zheng, Yanfei An, Pilong Shi, Tian Zhao
      First page: 206
      Abstract: The study of lithological features and tectonic evolution related to mineralization in the eastern Tian Shan is crucial for understanding the ore-controlling mechanism. In this paper, the lithological features and ore-controlling structure of the Huangshan Ni–Cu ore belt in the eastern Tian Shan are documented using advanced spaceborne thermal emission and reflection radiometer (ASTER) multispectral data based on spectral image processing algorithms, mineral indices and directional filter technology. Our results show that the algorithms of b2/b1, b6/b7 and b4/b8 from ASTER visible and near-infrared (VNIR)- shortwave infrared (SWIR) bands and of mafic index (MI), carbonate index (CI) and silica index (SI) from thermal infrared (TIR) bands are helpful to extract regional pyroxenite, external foliated gabbro bearing Ni–Cu ore bodies as well as the country rocks in the study area. The detailed interpretations and analyses of the geometrical feature of fault system and intrusive facies suggest that the Ni–Cu metallogenic belts are related to Carboniferous arc intrusive rocks and Permian wrench tectonics locating at the intersection of EW- and NEE-striking dextral strike-slip fault system, and the emplacement at the releasing bends in the southern margin of Kanggur Fault obviously controlled by secondary faults orthogonal or oblique to the Kanggur Fault in the post-collision extensional environment. Therefore, the ASTER data-based approach to map lithological features and ore-controlling structures related to the Ni–Cu mineralization are well performed. Moreover, a 3D geodynamic sketch map proposes that the strike-slip movement of Kanggur Fault in Huangshan-Kanggur Shear Zone (HKSZ) during early Permian controlled the migration and emplacement of three mafic/ultramafic intrusions bearing Ni–Cu derived from partial mantle melting and also favored CO2-rich fluids leaking to the participation of metallogenic processes.
      Citation: Remote Sensing
      PubDate: 2021-01-08
      DOI: 10.3390/rs13020206
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 207: Estimation of Urban Ecosystem Services
           Value: A Case Study of Chengdu, Southwestern China

    • Authors: Dai, Johnson, Luo, Yang, Dong, Wang, Liu, Li, Lu, Ma, Yang, Yao
      First page: 207
      Abstract: Research on the service values of urban ecosystems is a hot topic of ecological studies in the current era of rapid urbanization. To quantitatively estimate the ecosystem service value in Chengdu, China from the perspectives of natural ecology and social ecology, the technologies of remote sensing (RS) and geographic information system (GIS) are utilized in this study to extract the land use type information from RS images of Chengdu in 2003, 2007, 2013 and 2018. Subsequently, a driver analysis of the ecosystem services of Chengdu was performed based on socioeconomic data from the last 16 years. The results indicated that: (1) from 2003 to 2018, the land utilization in Chengdu changed significantly, with the area of cultivated lands, forest lands and water decreasing remarkably, while the area of construction lands dramatically increased. (2) The ecosystem services value (ESV) of Chengdu decreased by 30.92% in the last 16 years, from CNY 2.4078 × 1010 in 2003 to CNY 1.6632 × 1010 in 2018. Based on a future simulation, the ESV is further predicted to be reduced to CNY 1.4261 × 1010 by 2033. (3) The ESV of Chengdu showed a negative correlation with the total population, the urbanization rate and the per capita GDP of the region, indicating that the ESV of the studied region was inter-coupled with the socioeconomic development and can be maintained at a high level through rationally regulating the socioeconomic structure.
      Citation: Remote Sensing
      PubDate: 2021-01-08
      DOI: 10.3390/rs13020207
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 208: Spatial Temporal Analysis of Traffic
           Patterns during the COVID-19 Epidemic by Vehicle Detection Using Planet
           Remote-Sensing Satellite Images

    • Authors: Chen, Qin, Zhang, Albanwan
      First page: 208
      Abstract: The spread of the COVID-19 since the end of 2019 has reached an epidemic level and has quickly become a global public health crisis. During this period, the responses for COVID-19 were highly diverse and decentralized across countries and regions. Understanding the dynamics of human mobility change at high spatial temporal resolution is critical for assessing the impacts of non-pharmaceutical interventions (such as stay-at-home orders, regional lockdowns and travel restrictions) during the pandemic. However, this requires collecting traffic data at scale, which is time-consuming, cost-prohibitive and often not available (e.g., in underdeveloped countries). Therefore, spatiotemporal analysis through processing periodical remote-sensing images is very beneficial to enable efficient monitoring at the global scale. In this paper, we present a novel study that utilizes high temporal Planet multispectral images (from November 2019 to September 2020, on average 7.1 days of frequency) to detect traffic density in multiple cities through a proposed morphology-based vehicle detection method and evaluate how the traffic data collected in such a manner reflect mobility pattern changes in response to COVID-19. Our experiments at city-scale detection, demonstrate that our proposed vehicle detection method over this 3 m resolution data is able to achieve a detection level at an accuracy of 68.26% in most of the images, and the observations’ trends coincide with existing public data of where available (lockdown duration, traffic volume, etc.), further suggesting that such high temporal Planet data with global coverage (although not with the best resolution), with well-devised detection algorithms, can sufficiently provide traffic details for trend analysis to better facilitate informed decision making for extreme events at the global level.
      Citation: Remote Sensing
      PubDate: 2021-01-08
      DOI: 10.3390/rs13020208
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 209: Evaluation of TMPA 3B42-V7 Product on
           Extreme Precipitation Estimates

    • Authors: Jiachao Chen, Zhaoli Wang, Xushu Wu, Chengguang Lai, Xiaohong Chen
      First page: 209
      Abstract: Availability of precipitation data at high spatial and temporal resolution is crucial for the understanding of precipitation behaviors that are determinant for environmental aspects such as hydrology, ecology, and social aspects like agriculture, food security, or health issues. This study evaluates the performance of 3B42-V7 satellite-based precipitation product on extreme precipitation estimates in China, by using the Fuzzy C-Means algorithm and L-moment-based regional frequency analysis method. The China Gauge-based Daily Precipitation Analysis (CGDPA) product is employed to measure the estimation biases of 3B42-V7. Results show that: (1) for most regions of China, the Generalized Extreme Value and Generalized Normal distributions are preferable for extreme precipitation estimates; (2) the extreme precipitation estimations of 3B42-V7 for different return periods have a high correlation with those of CGDPA, with biases within 25% for a majority of China on extreme precipitation estimates.
      Citation: Remote Sensing
      PubDate: 2021-01-09
      DOI: 10.3390/rs13020209
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 210: A Distributed Modular Data Processing
           Chain Applied to Simulated Satellite Ozone Observations

    • Authors: Marco Gai, Flavio Barbara, Simone Ceccherini, Ugo Cortesi, Samuele Del Bianco, Cecilia Tirelli, Nicola Zoppetti, Claudio Belotti, Bruno Canessa, Vincenzo Farruggia, Andrea Masini, Arno Keppens, Jean-Christopher Lambert, Antti Arola, Antti Lipponen, Olaf Tuinder
      First page: 210
      Abstract: Remote sensing of the atmospheric composition from current and future satellites, such as the Sentinel missions of the Copernicus programme, yields an unprecedented amount of data to monitor air quality, ozone, UV radiation and other climate variables. Hence, full exploitation of the growing wealth of information delivered by spaceborne observing systems requires addressing the technological challenges for developing new strategies and tools that are capable to deal with these huge data volumes. The H2020 AURORA (Advanced Ultraviolet Radiation and Ozone Retrieval for Applications) project investigated a novel approach for synergistic use of ozone profile measurements acquired at different frequencies (ultraviolet, visible, thermal infrared) by sensors onboard Geostationary Equatorial Orbit (GEO) and Low Earth Orbit (LEO) satellites in the framework of the Copernicus Sentinel-4 and Sentinel-5 missions. This paper outlines the main features of the technological infrastructure, designed and developed to support the AURORA data processing chain as a distributed data processing and describes in detail the key components of the infrastructure and the software prototype. The latter demonstrates the technical feasibility of the automatic execution of the full processing chain with simulated data. The Data Processing Chain (DPC) presented in this work thus replicates a processing system that, starting from the operational satellite retrievals, carries out their fusion and results in the assimilation of the fused products. These consist in ozone vertical profiles from which further modules of the chain deliver tropospheric ozone and UV radiation at the Earth’s surface. The conclusions highlight the relevance of this novel approach to the synergistic use of operational satellite data and underline that the infrastructure uses general-purpose technologies and is open for applications in different contexts.
      Citation: Remote Sensing
      PubDate: 2021-01-09
      DOI: 10.3390/rs13020210
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 211: Hindcast and Near Real-Time Monitoring
           of Green Macroalgae Blooms in Shallow Coral Reef Lagoons Using Sentinel-2:
           A New-Caledonia Case Study

    • Authors: Maële Brisset, Simon Van Wynsberge, Serge Andréfouët, Claude Payri, Benoît Soulard, Emmanuel Bourassin, Romain Le Gendre, Emmanuel Coutures
      First page: 211
      Abstract: Despite the necessary trade-offs between spatial and temporal resolution, remote sensing is an effective approach to monitor macroalgae blooms, understand their origins and anticipate their developments. Monitoring of small tropical lagoons is challenging because they require high resolutions. Since 2017, the Sentinel-2 satellites has provided new perspectives, and the feasibility of monitoring green algae blooms was investigated in this study. In the Poé-Gouaro-Déva lagoon, New Caledonia, recent Ulva blooms are the cause of significant nuisances when beaching. Spectral indices using the blue and green spectral bands were confronted with field observations of algal abundances using images concurrent with fieldwork. Depending on seabed compositions and types of correction applied to reflectance data, the spectral indices explained between 1 and 64.9% of variance. The models providing the best statistical fit were used to revisit the algal dynamics using Sentinel-2 data from January 2017 to December 2019, through two image segmentation approaches: unsupervised and supervised. The latter accurately reproduced the two algal blooms that occurred in the area in 2018. This paper demonstrates that Sentinel-2 data can be an effective source to hindcast and monitor the dynamics of green algae in shallow lagoons.
      Citation: Remote Sensing
      PubDate: 2021-01-09
      DOI: 10.3390/rs13020211
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 212: Estimating Artificial Impervious
           Surface Percentage in Asia by Fusing Multi-Temporal MODIS and VIIRS
           Nighttime Light Data

    • Authors: Li, Li, Zhang, Samat, Liu, Li, Atkinson
      First page: 212
      Abstract: Impervious surfaces have important effects on the natural environment, including promoting hydrological run-off and impeding evapotranspiration, as well as increasing the urban heat island effect. Obtaining accurate and timely information on the spatial distribution and dynamics of urban surfaces is, thus, of paramount importance for socio-economic analysis, urban planning, and environmental modeling and management. Previous studies have indicated that the fusion of multi-source remotely sensed imagery can increase the accuracy of prediction for impervious surface information across large areas. However, the majority of them are limited to the use of specific data sources to construct a few features with which it can be challenging to characterize adequately the variation in impervious surfaces over large areas. Thus, impervious surface maps are often presented with high uncertainty. In response to this problem, we proposed the use of multi-temporal MODIS and Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light data to construct a more general and robust feature set for large-area artificial impervious surface percentage (AISP) prediction. Three fusion methods were proposed for application to multi-temporal MODIS surface reflectance product (MOD09A1) and Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) Day/Night Band (DNB) data to construct three different types of features: spectral features, index features (band calculations), and fusion features. These features were then used as variables in a random-forest-based AISP prediction model. The model was fitted to China and then applied to predict AISP across Asia. Fifteen typical cities from different regions of Asia were selected to assess the accuracy of the prediction model. The use of multi-temporal MODIS and VIIRS DNB data was found to significantly increase the accuracy of prediction for large-area AISP. The feature set constructed in this research was demonstrated to be suitable for large-area AISP prediction, and the random forest model based on optimization of the selected features achieved the highest accuracy, amongst benchmarks, with testing R2 of 0.690, and testing RMSE of 0.044 in 2018, respectively. In addition, to further test the performance of the proposed method, three existing impervious products (GAIA, HBASE, and NUACI) were used to compare quantitatively. The results showed that the predicted AISP achieved superior performance in comparison with others in some areas (e.g., arid areas and cloudy areas).
      Citation: Remote Sensing
      PubDate: 2021-01-09
      DOI: 10.3390/rs13020212
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 213: A Novel Four-Stage Method for
           Vegetation Height Estimation with Repeat-Pass PolInSAR Data via Temporal
           Decorrelation Adaptive Estimation and Distance Transformation

    • Authors: Cheng Xing, Tao Zhang, Hongmiao Wang, Liang Zeng, Junjun Yin, Jian Yang
      First page: 213
      Abstract: Vegetation height estimation plays a pivotal role in forest mapping, which significantly promotes the study of environment and climate. This paper develops a general forest structure model for vegetation height estimation using polarimetric interferometric synthetic aperture radar (PolInSAR) data. In simple terms, the temporal decorrelation factor of the random volume over ground model with volumetric temporal decorrelation (RVoG-vtd) is first modeled by random motions of forest scatterers to solve the problem of ambiguity. Then, a novel four-stage algorithm is proposed to improve accuracy in forest height estimation. In particular, to compensate for the temporal decorrelation mainly caused by changes between multiple observations, one procedure of temporal decorrelation adaptive estimation via Expectation-Maximum (EM) algorithm is added into the novel method. On the other hand, to extract the features of amplitude and phase more effectively, in the proposed method, we also convert Euclidean distance to a generalized distance for the first time. Assessments of different algorithms are given based on the repeat-pass PolInSAR data of Gabon Lope Park acquired in AfriSAR campaign of German Aerospace Center (DLR). The experimental results show that the proposed method presents a significant improvement of vegetation height estimation accuracy with a root mean square error (RMSE) of 6.23 m and a bias of 1.28 m against LiDAR heights, compared to the results of the three-stage method (RMSE: 8.69 m, bias: 4.81 m) and the previous four-stage method (RMSE: 7.72 m, bias: −2.87 m).
      Citation: Remote Sensing
      PubDate: 2021-01-09
      DOI: 10.3390/rs13020213
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 214: Distinguishing between Warm and
           Stratiform Rain Using Polarimetric Radar Measurements

    • Authors: Sergey Y. Matrosov
      First page: 214
      Abstract: Modeled statistical differential reflectivity–reflectivity (i.e., ZDR–Ze) correspondences for no bright-band warm rain and stratiform bright-band rain are evaluated using measurements from an operational polarimetric weather radar and independent information about rain types from a vertically pointing profiler. It is shown that these relations generally fit observational data satisfactorily. Due to a relative abundance of smaller drops, ZDR values for warm rain are, on average, smaller than those for stratiform rain of the same reflectivity by a factor of about two (in the logarithmic scale). A ZDR–Ze relation, representing a mean of such relations for warm and stratiform rains, can be utilized to distinguish between warm and stratiform rain types using polarimetric radar measurements. When a mean offset of observational ZDR data is accounted for and reflectivities are greater than 16 dBZ, about 70% of stratiform rains and approximately similar amounts of warm rains are classified correctly using the mean ZDR–Ze relation when applied to averaged data. Since rain rate estimators for warm rain are quite different from other common rain types, identifying and treating warm rain as a separate precipitation category can lead to better quantitative precipitation estimations.
      Citation: Remote Sensing
      PubDate: 2021-01-10
      DOI: 10.3390/rs13020214
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 215: Analysis of Forward Model, Data Type,
           and Prior Information in Probabilistic Inversion of Crosshole GPR Data

    • Authors: Hui Qin, Zhengzheng Wang, Yu Tang, Tiesuo Geng
      First page: 215
      Abstract: The crosshole ground penetrating radar (GPR) is a widely used tool to map subsurface properties, and inversion methods are used to derive electrical parameters from crosshole GPR data. In this paper, a probabilistic inversion algorithm that uses Markov chain Monte Carlo (MCMC) simulations within the Bayesian framework is implemented to infer the posterior distribution of the relative permittivity of the subsurface medium. Close attention is paid to the critical elements of this method, including the forward model, data type and prior information, and their influence on the inversion results are investigated. First, a uniform prior distribution is used to reflect the lack of prior knowledge of model parameters, and inversions are performed using the straight-ray model with first-arrival traveltime data, the finite-difference time-domain (FDTD) model with first-arrival traveltime data, and the FDTD model with waveform data, respectively. The cases using first-arrival traveltime data require an unreasonable number of model evaluations to converge, yet are not able to recover the real relative permittivity field. In contrast, the inversion using the FDTD model with waveform data successfully infers the correct model parameters. Then, the smooth constraint of model parameters is employed as the prior distribution. The inversion results demonstrate that the prior information barely affects the inversion results using the FDTD model with waveform data, but significantly improves the inversion results using first-arrival traveltime data by decreasing the computing time and reducing uncertainties of the posterior distribution of model parameters.
      Citation: Remote Sensing
      PubDate: 2021-01-10
      DOI: 10.3390/rs13020215
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 216: Classification of Street Tree Species
           Using UAV Tilt Photogrammetry

    • Authors: Yutang Wang, Jia Wang, Shuping Chang, Lu Sun, Likun An, Yuhan Chen, Jiangqi Xu
      First page: 216
      Abstract: As an important component of the urban ecosystem, street trees have made an outstanding contribution to alleviating urban environmental pollution. Accurately extracting tree characteristics and species information can facilitate the monitoring and management of street trees, as well as aiding landscaping and studies of urban ecology. In this study, we selected the suburban areas of Beijing and Zhangjiakou and investigated six representative street tree species using unmanned aerial vehicle (UAV) tilt photogrammetry. We extracted five tree attributes and four combined attribute parameters and used four types of commonly-used machine learning classification algorithms as classifiers for tree species classification. The results show that random forest (RF), support vector machine (SVM), and back propagation (BP) neural network provide better classification results when using combined parameters for tree species classification, compared with those using individual tree attributes alone; however, the K-nearest neighbor (KNN) algorithm produced the opposite results. The best combination for classification is the BP neural network using combined attributes, with a classification precision of 89.1% and F-measure of 0.872, and we conclude that this approach best meets the requirements of street tree surveys. The results also demonstrate that optical UAV tilt photogrammetry combined with a machine learning classification algorithm is a low-cost, high-efficiency, and high-precision method for tree species classification.
      Citation: Remote Sensing
      PubDate: 2021-01-10
      DOI: 10.3390/rs13020216
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 217: Challenges of the Polarimetric Update
           on Operational Radars in China—Ground Clutter Contamination of Weather
           Radar Observations

    • Authors: Chong Wu, Liping Liu, Chao Chen, Chian Zhang, Guangxin He, Juan Li
      First page: 217
      Abstract: China New Generation Doppler Weather Radar (CINRAD) plans to upgrade its hardware and software to achieve polarimetric function. However, the small-magnitude polarimetric measurements were negatively affected by the scattering characteristics of ground clutter and the filter’s response to the ground clutter. This polarimetric contamination was characterized by decreased differential reflectivity (ZDR) and cross-correlation coefficient (ρhv), as well as an increased standard deviation of the differential phase (ΦDP), generating a large-area and long-term observational anomaly for eight polarimetric radars in South China. Considering that outliers simultaneously appeared in the radar mainlobe and sidelobe, the variations in the reflectivity before and after clutter mitigation (ΔZH) and ρhv were used for quantitatively describing the random dispersion caused by mainlobe and sidelobe clutters. The performance of polarimetric algorithms was also reduced by clutter contamination. The deteriorated membership functions in the hydrometeor classification algorithm changed the proportion of classified echoes. The empirical relations of R(ZH, ZDR) and R(KDP) were broken in the quantitative precipitation estimation algorithm and the extra error considerably exceeded the uncertainty caused by the drop-size distribution (DSD) variability of R(ZH). The above results highlighted the negative impact of clutter contamination on polarimetric applications that need to be further investigated.
      Citation: Remote Sensing
      PubDate: 2021-01-10
      DOI: 10.3390/rs13020217
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 218: Random Forest Regression Model for
           Estimation of the Growing Stock Volumes in Georgia, USA, Using Dense
           Landsat Time Series and FIA Dataset

    • Authors: Shingo Obata, Chris J. Cieszewski, Roger C. Lowe III, Pete Bettinger
      First page: 218
      Abstract: The forest volumes are essential as they are directly related to the economic and environmental values of the forests. Satellite-based forest volume estimation was first developed in the 1990s, and the accuracy of the estimation has been improved over time. One of the satellite-based forest volume estimation issues is that it tends to overestimate the large volume class and underestimate the small volume class. Free availability of the major satellite imagery and the development of cloud-based computational platforms facilitate an immense amount of satellite imagery in the estimation. In this paper, we set three objectives: (1) to examine whether the long Landsat time series contributes to the improvement of the estimation accuracy, (2) to explore the effectiveness of forest disturbance record and land cover data as ancillary spatial data on the accuracy of the estimation, and (3) to apply the bias correction method to reduce the bias of the estimation. We computed three Tasseled-cap components from the Landsat data for preparation of short (2014–2016) and long (1984–2016) time series. Each data entity was analyzed with harmonic regressions resulting in the coefficients and the fitted values recorded as pixel values in a multilayer raster database. Data included Forest Inventory and Analysis (FIA) unit field inventory measurements provided by the United States Department of Agriculture Forest Service and the National Land Cover Database and disturbance history data added as ancillary information. The totality of the available data was organized into seven distinct Random Forest (RF) models with different variables compared against each other to identify the ones with the most satisfactory performance. A bias correction method was then applied to all the RF models to examine the effectiveness of the method. Among the seven models, the worst one used the coefficients and fitted values of the short Landsat time series only, and the best one used coefficients and fitted values of both short and long Landsat time series. Using the Out-of-bag (OOB) score, the best model was found to be 34.4% better than the worst one. The model that used only the long time series data had almost the same OOB score as the best model. The results indicate that the use of the long Landsat time series improves model performance. Contrary to the previous research employing forest disturbance data as a feature variable had almost no effect on OOB. The bias correction method reduced the relative size of the bias in the estimates of the best model from 3.79% to −1.47%, the bottom 10% bias by 12.5 points, and the top 10% bias by 9.9 points. Depending on the types of forest, important feature variables were differed, reflecting the relationship between the time series remote sensing data we computed for this research and the forests’ phenological characteristics. The availability of Light Detection And Ranging (LiDAR) data and accessibility of the precise locations of the FIA data are likely to improve the model estimates further.
      Citation: Remote Sensing
      PubDate: 2021-01-10
      DOI: 10.3390/rs13020218
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 219: Deep Localization of Static Scans in
           Mobile Mapping Point Clouds

    • Authors: Yufu Zang, Fancong Meng, Roderik Lindenbergh, Linh Truong-Hong, Bijun Li
      First page: 219
      Abstract: Mobile laser scanning (MLS) systems are often used to efficiently acquire reference data covering a large-scale scene. The terrestrial laser scanner (TLS) can easily collect high point density data of local scene. Localization of static TLS scans in mobile mapping point clouds can afford detailed geographic information for many specific tasks especially in autonomous driving and robotics. However, large-scale MLS reference data often have a huge amount of data and many similar scene data; significant differences may exist between MLS and TLS data. To overcome these challenges, this paper presents a novel deep neural network-based localization method in urban environment, divided by place recognition and pose refinement. Firstly, simple, reliable primitives, cylinder-like features were extracted to describe the global features of a local urban scene. Then, a probabilistic framework is applied to estimate a similarity between TLS and MLS data, under a stable decision-making strategy. Based on the results of a place recognition, we design a patch-based convolution neural network (CNN) (point-based CNN is used as kernel) for pose refinement. The input data unit is the batch consisting of several patches. One patch goes through three main blocks: feature extraction block (FEB), the patch correspondence search block and the pose estimation block. Finally, a global refinement was proposed to tune the predicted transformation parameters to realize localization. The research aim is to find the most similar scene of MLS reference data compared with the local TLS scan, and accurately estimate the transformation matrix between them. To evaluate the performance, comprehensive experiments were carried out. The experiments demonstrate that the proposed method has good performance in terms of efficiency, i.e., the runtime of processing a million points is 5 s, robustness, i.e., the success rate of place recognition is 100% in the experiments, accuracy, i.e., the mean rotation and translation error is (0.24 deg, 0.88 m) and (0.03 deg, 0.06 m) on TU Delft campus and Shanghai urban datasets, respectively, and outperformed some commonly used methods (e.g., iterative closest point (ICP), coherent point drift (CPD), random sample consensus (RANSAC)-based method).
      Citation: Remote Sensing
      PubDate: 2021-01-10
      DOI: 10.3390/rs13020219
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 220: Wildfire Damage Assessment over
           Australia Using Sentinel-2 Imagery and MODIS Land Cover Product within the
           Google Earth Engine Cloud Platform

    • Authors: Seyd Teymoor Seydi, Mehdi Akhoondzadeh, Meisam Amani, Sahel Mahdavi
      First page: 220
      Abstract: Wildfires are major natural disasters negatively affecting human safety, natural ecosystems, and wildlife. Timely and accurate estimation of wildfire burn areas is particularly important for post-fire management and decision making. In this regard, Remote Sensing (RS) images are great resources due to their wide coverage, high spatial and temporal resolution, and low cost. In this study, Australian areas affected by wildfire were estimated using Sentinel-2 imagery and Moderate Resolution Imaging Spectroradiometer (MODIS) products within the Google Earth Engine (GEE) cloud computing platform. To this end, a framework based on change analysis was implemented in two main phases: (1) producing the binary map of burned areas (i.e., burned vs. unburned); (2) estimating burned areas of different Land Use/Land Cover (LULC) types. The first phase was implemented in five main steps: (i) preprocessing, (ii) spectral and spatial feature extraction for pre-fire and post-fire analyses; (iii) prediction of burned areas based on a change detection by differencing the pre-fire and post-fire datasets; (iv) feature selection; and (v) binary mapping of burned areas based on the selected features by the classifiers. The second phase was defining the types of LULC classes over the burned areas using the global MODIS land cover product (MCD12Q1). Based on the test datasets, the proposed framework showed high potential in detecting burned areas with an overall accuracy (OA) and kappa coefficient (KC) of 91.02% and 0.82, respectively. It was also observed that the greatest burned area among different LULC classes was related to evergreen needle leaf forests with burning rate of over 25 (%). Finally, the results of this study were in good agreement with the Landsat burned products.
      Citation: Remote Sensing
      PubDate: 2021-01-10
      DOI: 10.3390/rs13020220
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 221: Satellite-based Precipitation Datasets
           Evaluation Using Gauge Observation and Hydrological Modeling in a Typical
           Arid Land Watershed of Central Asia

    • Authors: Peng, Liu, Huang, Ling, Li, Bao, Chen, Kurban, De Maeyer
      First page: 221
      Abstract: Hydrological modeling has always been a challenge in the data-scarce watershed, especially in the areas with complex terrain conditions like the inland river basin in Central Asia. Taking Bosten Lake Basin in Northwest China as an example, the accuracy and the hydrological applicability of satellite-based precipitation datasets were evaluated. The gauge-adjusted version of six widely used datasets was adopted; namely, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (CDR), Climate Hazards Group Infrared Precipitation with Stations (CHIRPS), Global Precipitation Measurement Ground Validation National Oceanic and Atmospheric Administration Climate Prediction Center (NOAA CPC) Morphing Technique (CMORPH), Integrated Multi-Satellite Retrievals for GPM (GPM), Global Satellite Mapping of Precipitation (GSMaP), the Tropical Rainfall Measuring Mission (TRMM) and Multi-satellite Precipitation Analysis (TMPA). Seven evaluation indexes were used to compare the station data and satellite datasets, the soil and water assessment tool (SWAT) model, and four indexes were used to evaluate the hydrological performance. The main results were as follows: (1) The GPM and CDR were the best datasets for the daily scale and monthly scale rainfall accuracy evaluations, respectively. (2) The performance of CDR and GPM was more stable than others at different locations in a watershed, and all datasets tended to perform better in the humid regions. (3) All datasets tended to perform better in the summer of a year, while the CDR and CHIRPS performed well in winter compare to other datasets. (4) The raw data of CDR and CMORPH performed better than others in monthly runoff simulations, especially CDR. (5) Integrating the hydrological performance of the uncorrected and corrected data, all datasets have the potential to provide valuable input data in hydrological modeling. This study is expected to provide a reference for the hydrological and meteorological application of satellite precipitation datasets in Central Asia or even the whole temperate zone.
      Citation: Remote Sensing
      PubDate: 2021-01-11
      DOI: 10.3390/rs13020221
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 222: Applying Deep Learning to Clear-Sky
           Radiance Simulation for VIIRS with Community Radiative Transfer
           Model—Part 1: Develop AI-Based Clear-Sky Mask

    • Authors: Liang, Liu
      First page: 222
      Abstract: A fully connected deep neural network (FCDN) clear-sky mask (CSM) algorithm (FCDN_CSM) was developed to assist the FCDN-based Community Radiative Transfer Model (FCDN_CRTM) to reproduce the Visible Infrared Imaging Radiometer Suite (VIIRS) clear-sky radiances in five thermal emission M (TEB/M) bands. The model design was referenced and enhanced from its earlier version (version 1), and was trained and tested in the global ocean clear-sky domain using six dispersion days’ data from 2019 to 2020 as inputs and a modified NOAA Advanced Clear-Sky Processor over Ocean (ACSPO) CSM product as reference labels. The improved FCDN_CSM (version 2) was further enhanced by including daytime data, which was not collected in version 1. The trained model was then employed to predict VIIRS CSM over multiple days in 2020 as an accuracy and stability check. The results were validated against the biases between the sensor observations and CRTM calculations (O-M). The objectives were to (1) enhance FCDN_CSM performance to include daytime analysis, and improve model stability, accuracy, and efficiency; and (2) further understand the model performance based on a combination of the statistics and physical interpretation. According to the analyses of the F-score, the prediction result showed ~96% and ~97% accuracy for day and night, respectively. The type Cloud was the most accurate, followed by Clear-Sky. The O-M mean biases are comparable to the ACSPO CSM for all bands, both day and night. The standard deviations (STD) were slightly degraded in long wave IRs (M14, M15, and M16), mainly due to contamination by a 3% misclassification of the type Cloud, which may require the model to be further fine-tuned to improve prediction accuracy in the future. However, the consistent O-M means and STDs persist throughout the prediction period, suggesting that FCDN_CSM version 2 is robust and does not have significant overfitting. Given its high F-scores, spatial and long-term stability for both day and night, high efficiency, and acceptable O-M means and STDs, FCDN_CSM version 2 is deemed to be ready for use in the FCDN_CRTM.
      Citation: Remote Sensing
      PubDate: 2021-01-11
      DOI: 10.3390/rs13020222
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 223: Individual Tree Extraction from
           Terrestrial LiDAR Point Clouds Based on Transfer Learning and Gaussian
           Mixture Model Separation

    • Authors: Zhenyang Hui, Shuanggen Jin, Dajun Li, Yao Yevenyo Ziggah, Bo Liu
      First page: 223
      Abstract: Individual tree extraction is an important process for forest resource surveying and monitoring. To obtain more accurate individual tree extraction results, this paper proposed an individual tree extraction method based on transfer learning and Gaussian mixture model separation. In this study, transfer learning is first adopted in classifying trunk points, which can be used as clustering centers for tree initial segmentation. Subsequently, principal component analysis (PCA) transformation and kernel density estimation are proposed to determine the number of mixed components in the initial segmentation. Based on the number of mixed components, the Gaussian mixture model separation is proposed to separate canopies for each individual tree. Finally, the trunk stems corresponding to each canopy are extracted based on the vertical continuity principle. Six tree plots with different forest environments were used to test the performance of the proposed method. Experimental results show that the proposed method can achieve 87.68% average correctness, which is much higher than that of other two classical methods. In terms of completeness and mean accuracy, the proposed method also outperforms the other two methods.
      Citation: Remote Sensing
      PubDate: 2021-01-11
      DOI: 10.3390/rs13020223
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 224: Characterizing the Development Pattern
           of a Colluvial Landslide Based on Long-Term Monitoring in the Three Gorges
           Reservoir

    • Authors: Liang, Gui, Wang, Du, Ma, Yin
      First page: 224
      Abstract: Since the impoundment of the Three Gorges Reservoir (TGR) in June 2003, the fluctuation of the reservoir water level coupled with rainfall has resulted in more than 2500 landslides in this region. Among these instability problems, most colluvial landslides exhibit slow-moving patterns and pose a significant threat to local people and channel navigation. Advanced monitoring techniques are therefore implemented to investigate landslide deformation and provide insights for the subsequent countermeasures. In this study, the development pattern of a large colluvial landslide, locally named the Ganjingzi landslide, is analyzed on the basis of long-term monitoring. To understand the kinematic characteristics of the landslide, an integrated analysis based on real-time and multi-source monitoring, including the global navigation satellite system (GNSS), crackmeters, inclinometers, and piezometers, was conducted. The results indicate that the Ganjingzi landslide exhibits a time-variable response to the reservoir water fluctuation and rainfall. According to the supplement of community-based monitoring, the evolution of the landslide consists of three stages, namely the stable stage before reservoir impoundment, the initial movement stage of retrogressive failure, and the shallow movement stage with stepwise acceleration. The latter two stages are sensitive to the drawdown of reservoir water level and rainfall infiltration, respectively. All of the monitoring approaches used in this study are significant for understanding the time-variable pattern of colluvial landslides and are essential for landslide mechanism analysis and early warning for risk mitigation.
      Citation: Remote Sensing
      PubDate: 2021-01-11
      DOI: 10.3390/rs13020224
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 225: Estimating Daily Actual
           Evapotranspiration at a Landsat-Like Scale Utilizing Simulated and Remote
           Sensing Surface Temperature

    • Authors: Dakang Wang, Tao Yu, Yan Liu, Xingfa Gu, Xiaofei Mi, Shuaiyi Shi, Meihong Ma, Xinran Chen, Yin Zhang, Qixin Liu, Faisal Mumtaz, Yulin Zhan
      First page: 225
      Abstract: Actual evapotranspiration (ET) with high spatiotemporal resolution is very important for the research on agricultural water resource management and the water cycle processes, and it is helpful to realize precision agriculture and smart agriculture, and provides critical references for agricultural layout planning. Due to the impact of the clouds, weather environment, and the orbital period of optical satellite, there are difficulties in providing daily remote sensing data that are not contaminated by clouds for estimating daily ET with high spatial-temporal resolution. By improving the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), this manuscript proposes the method to fuse high temporal and low spatial resolution Weather Research and Forecasting (WRF) model surface skin temperature (TSK) with the low temporal and high spatial resolution remote sensing surface temperature for obtaining high spatiotemporal resolution daily surface temperature to be used in the estimation of the high spatial resolution daily ET (ET_WRFHR). The distinction of this study from the previous literatures can be summarized as the novel application of the fusion of WRF-simulated TSK and remote sensing surface temperature, giving full play to the availability of model surface skin temperature data at any time and region, making up for the shortcomings of the remote sensing data, and combining the high spatial resolution of remote sensing data to obtain ET with high spatial (Landsat-like scale) and temporal (daily) resolution. The ET_WRFHR were cross-validated and quantitatively verified with MODIS ET products (MOD16) and observations (ET_Obs) from eddy covariance system. Results showed that ET_WRFHR not only better reflects the difference and dynamic evolution process of ET for different land types but also better identifies the details of various fine geographical objects. It also represented a high correlation with the ET_Obs by the R2 amount reaching 0.9186. Besides, the RMSE and BIAS between ET_WRFHR and the ET_Obs are obtained as 0.77 mm/d and −0.08 mm/d respectively. High R2, as well as the small RMSE and BIAS amounts, indicate that ET_WRFHR has achieved a very good performance.
      Citation: Remote Sensing
      PubDate: 2021-01-11
      DOI: 10.3390/rs13020225
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 226: Exploring the Relationship between
           River Discharge and Coastal Erosion: An Integrated Approach Applied to the
           Pisa Coastal Plain (Italy)

    • Authors: Monica Bini, Nicola Casarosa, Marco Luppichini
      First page: 226
      Abstract: Coastal erosion coupled with human-induced pressure has severely affected the coastal areas of the Mediterranean region in the past and continues to do so with increasing intensity today. In this context, the Pisa coastal plain shows a long history of erosion, which started at the beginning of the nineteenth century. In this work, shoreline positions derived from historical maps as well as airborne and DGPS (Differential Global Positioning System) surveys were analyzed in a GIS (Geographic Information System) environment to identify the main changes that have occurred in the last 142 years. These analyses were compared with 100 years of discharge data measured at the S. Giovanni alla Vena gauge to identify a possible correlation between the two sets of information. Finally, Sentinel-2 and Landsat images were studied to identify the dispersion of sediments transported by the Arno River. In particular, we found a minimum of fluvial discharge in the years 1954, 1978, and 2012 corresponding to a peak of erosion, while the reduced erosion rate and the fluvial discharge increased in the years 1928–1944, 1954–1975, and after 2012. The qualitative anticorrelation between discharge and erosion is particularly true if we take into account flood events with a value of discharge greater than 700 m3/s, which are those able to transport suspended sand. The remote sensing analyses of Sentinel-2 images acquired during the floods of 6 February 2019 and 3 December 2019, under the most typical wind and sea state conditions for this area (wind coming from SW and storms coming from W/SW and SW) show that during these events a consistent amount of sediment was transported by the river. However, the majority of these sediments are not deposited along the coastline but are dispersed offshore. Grain-size analyses on the transported sediment show that plumes are formed by coarse-to-medium sand, suitable for coastal nourishment, but the reconstructed sediment dispersion lines show that some sectors of the coastline are constantly in the shade. These areas are the most affected by erosion.
      Citation: Remote Sensing
      PubDate: 2021-01-11
      DOI: 10.3390/rs13020226
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 227: Consequences of the 2019 Greenland Ice
           Sheet Melt Episode on Albedo

    • Authors: Arthur Elmes, Charlotte Levy, Angela Erb, Dorothy K. Hall, Ted A. Scambos, Nicolo DiGirolamo, Crystal Schaaf
      First page: 227
      Abstract: In mid-June 2019, the Greenland ice sheet (GrIS) experienced an extreme early-season melt event. This, coupled with an earlier-than-average melt onset and low prior winter snowfall over western Greenland, led to a rapid decrease in surface albedo and greater solar energy absorption over the melt season. The 2019 melt season resulted in significantly more melt than other recent years, even compared to exceptional melt years previously identified in the moderate-resolution imaging spectroradiometer (MODIS) record. The increased solar radiation absorbance in 2019 warmed the surface and increased the rate of meltwater production. We use two decades of satellite-derived albedo from the MODIS MCD43 record to show a significant and extended decrease in albedo in Greenland during 2019. This decrease, early in the melt season and continuing during peak summer insolation, caused increased radiative forcing of the ice sheet of 2.33 Wm−2 for 2019. Radiative forcing is strongly influenced by the dramatic seasonal differences in surface albedo experienced by any location experiencing persistent and seasonal snow-cover. We also illustrate the utility of the newly developed Landsat-8 albedo product for better capturing the detailed spatial heterogeneity of the landscape, leading to a more refined representation of the surface energy budget. While the MCD43 data accurately capture the albedo for a given 500 m pixel, the higher spatial resolution 30 m Landsat-8 albedos more fully represent the detailed landscape variations.
      Citation: Remote Sensing
      PubDate: 2021-01-11
      DOI: 10.3390/rs13020227
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 228: Mapping High Spatiotemporal-Resolution
           Soil Moisture by Upscaling Sparse Ground-Based Observations Using a
           Bayesian Linear Regression Method for Comparison with Microwave Remotely
           Sensed Soil Moisture Products

    • Authors: Jian Kang, Rui Jin, Xin Li, Yang Zhang
      First page: 228
      Abstract: In recent decades, microwave remote sensing (RS) has been used to measure soil moisture (SM). Long-term and large-scale RS SM datasets derived from various microwave sensors have been used in environmental fields. Understanding the accuracies of RS SM products is essential for their proper applications. However, due to the mismatched spatial scale between the ground-based and RS observations, the truth at the pixel scale may not be accurately represented by ground-based observations, especially when the spatial density of in situ measurements is low. Because ground-based observations are often sparsely distributed, temporal upscaling was adopted to transform a few in situ measurements into SM values at a pixel scale of 1 km by introducing the temperature vegetation dryness index (TVDI) related to SM. The upscaled SM showed high consistency with in situ SM observations and could accurately capture rainfall events. The upscaled SM was considered as the reference data to evaluate RS SM products at different spatial scales. In regard to the validation results, in addition to the correlation coefficient (R) of the Soil Moisture Active Passive (SMAP) SM being slightly lower than that of the Climate Change Initiative (CCI) SM, SMAP had the best performance in terms of the root-mean-square error (RMSE), unbiased RMSE and bias, followed by the CCI. The Soil Moisture and Ocean Salinity (SMOS) products were in worse agreement with the upscaled SM and were inferior to the R value of the X-band SM of the Advanced Microwave Scanning Radiometer 2 (AMSR2). In conclusion, in the study area, the SMAP and CCI SM are more reliable, although both products were underestimated by 0.060 cm3 cm−3 and 0.077 cm3 cm−3, respectively. If the biases are corrected, then the improved SMAP with an RMSE of 0.043 cm3 cm−3 and the CCI with an RMSE of 0.039 cm3 cm−3 will hopefully reach the application requirement for an accuracy with an RMSE less than 0.040 cm3 cm−3.
      Citation: Remote Sensing
      PubDate: 2021-01-11
      DOI: 10.3390/rs13020228
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 229: Benefits of Combining ALOS/PALSAR-2
           and Sentinel-2A Data in the Classification of Land Cover Classes in the
           Santa Catarina Southern Plateau

    • Authors: Jessica da Silva Costa, Veraldo Liesenberg, Marcos Benedito Schimalski, Raquel Valério de Sousa, Leonardo Josoé Biffi, Alessandra Rodrigues Gomes, Sílvio Luís Rafaeli Neto, Edson Mitishita, Polyanna da Conceição Bispo
      First page: 229
      Abstract: The Santa Catarina Southern Plateau is located in Southern Brazil and is a region that has gained considerable attention due to the rapid conversion of the typical landscape of natural grasslands and wetlands into agriculture, reforestation, pasture, and more recently, wind farms. This study’s main goal was to characterize the polarimetric attributes of the experimental quad-polarization acquisition mode of the Advanced Land Observing Satellite/Phased Array type L-band Synthetic Aperture Radar (ALOS/PALSAR-2) for mapping seven land cover classes. The polarimetric attributes were evaluated alone and combined with SENTINEL-2A using a supervised classification method based on the Support Vector Machine (SVM) algorithm. The results showed that the intensity backscattering alone reached an overall classification accuracy of 37.48% and a Kappa index of 0.26. Interestingly, the addition of polarimetric features increased to 71.35% and 0.66, respectively. It shows that the use of polarimetric decomposition features was relatively efficient in discriminating land cover classes. SENTINEL-2A data alone performed better and achieved a weighted overall accuracy and Kappa index of 85.56% and 0.82. This increase was also significant for the Z-test. However, the addition of ALOS/PALSAR-2 derived features to SENTINEL-2A slightly improved accuracy and was marginally significant at a 95% confidence level only when all features were considered. Possible implications for that performance are the accumulated precipitation prior to SAR data acquisition, which coincides with the rainy season period. The experimental quad-polarization mode of ALOS/PALSAR- 2 shall be evaluated in the near future over different seasonal conditions to confirm results. Alternatively, further studies are then suggested by focusing on additional features derived from SAR data such as texture and interferometric coherence to increase classification accuracy. These measures would be an interesting data source for monitoring specific land cover classes such as the threatened grasslands and wetlands during periods of frequent cloud coverage. Future investigations could also address multitemporal approaches employing either single or multifrequency SAR.
      Citation: Remote Sensing
      PubDate: 2021-01-11
      DOI: 10.3390/rs13020229
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 230: Real-Time Detection and Spatial
           Localization of Insulators for UAV Inspection Based on Binocular Stereo
           Vision

    • Authors: Yunpeng Ma, Qingwu Li, Lulu Chu, Yaqin Zhou, Chang Xu
      First page: 230
      Abstract: Unmanned aerial vehicles (UAVs) have become important tools for power transmission line inspection. Cameras installed on the platforms can efficiently obtain aerial images containing information about power equipment. However, most of the existing inspection systems cannot perform automatic real-time detection of transmission line components. In this paper, an automatic transmission line inspection system incorporating UAV remote sensing with binocular visual perception technology is developed to accurately detect and locate power equipment in real time. The system consists of a UAV module, embedded industrial computer, binocular visual perception module, and control and observation module. Insulators, which are key components in power transmission lines as well as fault-prone components, are selected as the detection targets. Insulator detection and spatial localization in aerial images with cluttered backgrounds are interesting but challenging tasks for an automatic transmission line inspection system. A two-stage strategy is proposed to achieve precise identification of insulators. First, candidate insulator regions are obtained based on RGB-D saliency detection. Then, the skeleton structure of candidate insulator regions is extracted. We implement a structure search to realize the final accurate detection of insulators. On the basis of insulator detection results, we further propose a real-time object spatial localization method that combines binocular stereo vision and a global positioning system (GPS). The longitude, latitude, and height of insulators are obtained through coordinate conversion based on the UAV’s real-time flight data and equipment parameters. Experiment results in the actual inspection environment (220 kV power transmission line) show that the presented system meets the requirement of robustness and accuracy of insulator detection and spatial localization in practical engineering.
      Citation: Remote Sensing
      PubDate: 2021-01-11
      DOI: 10.3390/rs13020230
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 231: Links between Phenology of Large
           Phytoplankton and Fisheries in the Northern and Central Red Sea

    • Authors: John A. Gittings, Dionysios. E. Raitsos, Robert J. W. Brewin, Ibrahim Hoteit
      First page: 231
      Abstract: Phytoplankton phenology and size structure are key ecological indicators that influence the survival and recruitment of higher trophic levels, marine food web structure, and biogeochemical cycling. For example, the presence of larger phytoplankton cells supports food chains that ultimately contribute to fisheries resources. Monitoring these indicators can thus provide important information to help understand the response of marine ecosystems to environmental change. In this study, we apply the phytoplankton size model of Gittings et al. (2019b) to 20-years of satellite-derived ocean colour observations in the northern and central Red Sea, and investigate interannual variability in phenology metrics for large phytoplankton (>2 µm in cell diameter). Large phytoplankton consistently bloom in the winter. However, the timing of bloom initiation and termination (in autumn and spring, respectively) varies between years. In the autumn/winter of 2002/2003, we detected a phytoplankton bloom, which initiated ~8 weeks earlier and lasted ~11 weeks longer than average. The event was linked with an eddy dipole in the central Red Sea, which increased nutrient availability and enhanced the growth of large phytoplankton. The earlier timing of food availability directly impacted the recruitment success of higher trophic levels, as represented by the maximum catch of two commercially important fisheries (Sardinella spp. and Teuthida) in the following year. The results of our analysis are essential for understanding trophic linkages between phytoplankton and fisheries and for marine management strategies in the Red Sea.
      Citation: Remote Sensing
      PubDate: 2021-01-11
      DOI: 10.3390/rs13020231
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 232: Sugarcane Yield Mapping Using
           High-Resolution Imagery Data and Machine Learning Technique

    • Authors: Tatiana Fernanda Canata, Marcelo Chan Fu Wei, Leonardo Felipe Maldaner, José Paulo Molin
      First page: 232
      Abstract: Yield maps provide essential information to guide precision agriculture (PA) practices. Yet, on-board yield monitoring for sugarcane can be challenging. At the same time, orbital images have been widely used for indirect crop yield estimation for many crops like wheat, corn, and rice, but not for sugarcane. Due to this, the objective of this study is to explore the potential of multi-temporal imagery data as an alternative for sugarcane yield mapping. The study was based on developing predictive sugarcane yield models integrating time-series orbital imaging and a machine learning technique. A commercial sugarcane site was selected, and Sentinel-2 images were acquired from the beginning of the ratoon sprouting until harvesting of two consecutive cropping seasons. The predictive yield models RF (Random forest) and MLR (Multiple Linear Regression) were developed using orbital images and yield maps generated by a commercial sensor-system on harvesting. Original yield data were filtered and interpolated with the same spatial resolution of the orbital images. The entire dataset was divided into training and testing datasets. Spectral bands, especially the near-infrared at tillering crop stage showed greater contribution to predicting sugarcane yield than the use of derived spectral vegetation indices. The Root Mean Squared Error (RMSE) obtained for the RF regression based on multiple spectral bands was 4.63 Mg ha−1 with an R2 of 0.70 for the testing dataset. Overall, the RF regression had better performance than the MLR to predict sugarcane yield.
      Citation: Remote Sensing
      PubDate: 2021-01-12
      DOI: 10.3390/rs13020232
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 233: Assessing Leaf Biomass of Agave
           sisalana Using Sentinel-2 Vegetation Indices

    • Authors: Ilja Vuorinne, Janne Heiskanen, Petri K. E. Pellikka
      First page: 233
      Abstract: Biomass is a principal variable in crop monitoring and management and in assessing carbon cycling. Remote sensing combined with field measurements can be used to estimate biomass over large areas. This study assessed leaf biomass of Agave sisalana (sisal), a perennial crop whose leaves are grown for fibre production in tropical and subtropical regions. Furthermore, the residue from fibre production can be used to produce bioenergy through anaerobic digestion. First, biomass was estimated for 58 field plots using an allometric approach. Then, Sentinel-2 multispectral satellite imagery was used to model biomass in an 8851-ha plantation in semi-arid south-eastern Kenya. Generalised Additive Models were employed to explore how well biomass was explained by various spectral vegetation indices (VIs). The highest performance (explained deviance = 76%, RMSE = 5.15 Mg ha−1) was achieved with ratio and normalised difference VIs based on the green (R560), red-edge (R740 and R783), and near-infrared (R865) spectral bands. Heterogeneity of ground vegetation and resulting background effects seemed to limit model performance. The best performing VI (R740/R783) was used to predict plantation biomass that ranged from 0 to 46.7 Mg ha−1 (mean biomass 10.6 Mg ha−1). The modelling showed that multispectral data are suitable for assessing sisal leaf biomass at the plantation level and in individual blocks. Although these results demonstrate the value of Sentinel-2 red-edge bands at 20-m resolution, the difference from the best model based on green and near-infrared bands at 10-m resolution was rather small.
      Citation: Remote Sensing
      PubDate: 2021-01-12
      DOI: 10.3390/rs13020233
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 234: An Efficient Downscaling Scheme for
           

    • Authors: Na Zhao
      First page: 234
      Abstract: Satellites are capable of observing precipitation over large areas and are particularly suitable for estimating precipitation in high mountains and poorly gauged regions. However, the coarse resolution and relatively low accuracy of satellites limit their applications. In this study, a downscaling scheme was developed to obtain precipitation estimates with high resolution and high accuracy in the Heihe watershed. Shannon’s entropy, together with a semi-variogram, was applied to establish the optimal precipitation station network. A combination of the random forest (RF) method and the residual correction approach with the established rain gauge network was applied to downscale monthly precipitation products from Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG). The results indicated that the RF model showed little improvement in the accuracy of IMERG-based precipitation downscaling. Including residual modification could improve the results of the RF model. The mean absolute error (MAE) and root mean square error (RMSE) values decreased by 19% and 21%, respectively, after residual corrections were added to the RF approach. Moreover, we found that enough rain gauge records are necessary for and remain an important component of tuning model performance. The application of more rain gauges improves the performance of the combined RF and residual modification methods, with the MAE and RMSE values reduced by 8% and 9%, respectively. Residual correction, together with enough precipitation stations, can effectively enhance the quality of the precipitation patterns and magnitudes obtained in the RF downscaling process. The proposed downscaling scheme is an effective tool for increasing the accuracy and spatial resolution of precipitation fields in the Heihe watershed.
      Citation: Remote Sensing
      PubDate: 2021-01-12
      DOI: 10.3390/rs13020234
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 235: The Assessment of Hydrologic- and
           Flood-Induced Land Deformation in Data-Sparse Regions Using GRACE/GRACE-FO
           Data Assimilation

    • Authors: Natthachet Tangdamrongsub, Michal Šprlák
      First page: 235
      Abstract: The vertical motion of the Earth’s surface is dominated by the hydrologic cycle on a seasonal scale. Accurate land deformation measurements can provide constructive insight into the regional geophysical process. Although the Global Positioning System (GPS) delivers relatively accurate measurements, GPS networks are not uniformly distributed across the globe, posing a challenge to obtaining accurate deformation information in data-sparse regions, e.g., Central South-East Asia (CSEA). Model simulations and gravity data (from the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO)) have been successfully used to improve the spatial coverage. While combining model estimates and GRACE/GRACE-FO data via the GRACE/GRACE-FO data assimilation (DA) framework can potentially improve the accuracy and resolution of deformation estimates, the approach has rarely been considered or investigated thus far. This study assesses the performance of vertical displacement estimates from GRACE/GRACE-FO, the PCRaster Global Water Balance (PCR-GLOBWB) hydrology model, and the GRACE/GRACE-FO DA approach (assimilating GRACE/GRACE-FO into PCR-GLOBWB) in CSEA, where measurements from six GPS sites are available for validation. The results show that GRACE/GRACE-FO, PCR-GLOBWB, and GRACE/GRACE-FO DA accurately capture regional-scale hydrologic- and flood-induced vertical displacements, with the correlation value and RMS reduction relative to GPS measurements up to 0.89 and 53%, respectively. The analyses also confirm the GRACE/GRACE-FO DA’s effectiveness in providing vertical displacement estimates consistent with GRACE/GRACE-FO data while maintaining high-spatial details of the PCR-GLOBWB model, highlighting the benefits of GRACE/GRACE-FO DA in data-sparse regions.
      Citation: Remote Sensing
      PubDate: 2021-01-12
      DOI: 10.3390/rs13020235
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 236: A New Machine Learning Approach in
           Detecting the Oil Palm Plantations Using Remote Sensing Data

    • Authors: Kaibin Xu, Jing Qian, Zengyun Hu, Zheng Duan, Chaoliang Chen, Jun Liu, Jiayu Sun, Shujie Wei, Xiuwei Xing
      First page: 236
      Abstract: The rapid expansion of oil palm is a major driver of deforestation and other associated damage to the climate and ecosystem in tropical regions, especially Southeast Asia. It is therefore necessary to precisely detect and monitor oil palm plantations to safeguard the ecosystem services and biodiversity of tropical forests. Compared with optical data, which are vulnerable to cloud cover, the Sentinel-1 dual-polarization C-band synthetic aperture radar (SAR) acquires global observations under all weather conditions and times of day and shows good performance for oil palm detection in the humid tropics. However, because accurately distinguishing mature and young oil palm trees by using optical and SAR data is difficult and considering the strong dependence on the input parameter values when detecting oil palm plantations by employing existing classification algorithms, we propose an innovative method to improve the accuracy of classifying the oil palm type (mature or young) and detecting the oil palm planting area in Sumatra by fusing Landsat-8 and Sentinel-1 images. We extract multitemporal spectral characteristics, SAR backscattering values, vegetation indices, and texture features to establish different feature combinations. Then, we use the random forest algorithm based on improved grid search optimization (IGSO-RF) and select optimal feature subsets to establish a classification model and detect oil palm plantations. Based on the IGSO-RF classifier and optimal features, our method improved the oil palm detection accuracy and obtained the best model performance (OA = 96.08% and kappa = 0.9462). Moreover, the contributions of different features to oil palm detection are different; nevertheless, the optimal feature subset performed the best and demonstrated good potential for the detection of oil palm plantations.
      Citation: Remote Sensing
      PubDate: 2021-01-12
      DOI: 10.3390/rs13020236
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 237: Test Charts for Evaluating Imaging and
           Point Cloud Quality of Mobile Mapping Systems for Urban Street Space
           Acquisition

    • Authors: Norbert Pfeifer, Johannes Falkner, Andreas Bayr, Lothar Eysn, Camillo Ressl
      First page: 237
      Abstract: Mobile mapping is in the process of becoming a routinely applied standard tool to support administration of cities. For ensuring the usability of the mobile mapping data it is necessary to have a practical method to evaluate the quality of different systems, which reaches beyond 3D accuracy of individual points. Such a method must be objective, easy to implement, and provide quantitative results to be used in tendering processes. We present such an approach which extracts quality figures for point density, point distribution, point cloud planarity, image resolution, and street sign legibility. In its practical application for the mobile mapping campaign of the City of Vienna (Austria) in 2020 the proposed test method proved to fulfill the above requirements. As an additional result, quality figures are reported for the panorama images and point clouds of three different mobile mapping systems.
      Citation: Remote Sensing
      PubDate: 2021-01-12
      DOI: 10.3390/rs13020237
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 238: Landslide Susceptibility Mapping Using
           Rotation Forest Ensemble Technique with Different Decision Trees in the
           Three Gorges Reservoir Area, China

    • Authors: Zhice Fang, Yi Wang, Gonghao Duan, Ling Peng
      First page: 238
      Abstract: This study presents a new ensemble framework to predict landslide susceptibility by integrating decision trees (DTs) with the rotation forest (RF) ensemble technique. The proposed framework mainly includes four steps. First, training and validation sets are randomly selected according to historical landslide locations. Then, landslide conditioning factors are selected and screened by the gain ratio method. Next, several training subsets are produced from the training set and a series of trained DTs are obtained by using a DT as a base classifier couple with different training subsets. Finally, the resultant landslide susceptibility map is produced by combining all the DT classification results using the RF ensemble technique. Experimental results demonstrate that the performance of all the DTs can be effectively improved by integrating them with the RF ensemble technique. Specifically, the proposed ensemble methods achieved the predictive values of 0.012–0.121 higher than the DTs in terms of area under the curve (AUC). Furthermore, the proposed ensemble methods are better than the most popular ensemble methods with the predictive values of 0.005–0.083 in terms of AUC. Therefore, the proposed ensemble framework is effective to further improve the spatial prediction of landslides.
      Citation: Remote Sensing
      PubDate: 2021-01-12
      DOI: 10.3390/rs13020238
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 239: MRENet: Simultaneous Extraction of
           Road Surface and Road Centerline in Complex Urban Scenes from Very
           High-Resolution Images

    • Authors: Zhenfeng Shao, Zifan Zhou, Xiao Huang, Ya Zhang
      First page: 239
      Abstract: Automatic extraction of the road surface and road centerline from very high-resolution (VHR) remote sensing images has always been a challenging task in the field of feature extraction. Most existing road datasets are based on data with simple and clear backgrounds under ideal conditions, such as images derived from Google Earth. Therefore, the studies on road surface extraction and road centerline extraction under complex scenes are insufficient. Meanwhile, most existing efforts addressed these two tasks separately, without considering the possible joint extraction of road surface and centerline. With the introduction of multitask convolutional neural network models, it is possible to carry out these two tasks simultaneously by facilitating information sharing within a multitask deep learning model. In this study, we first design a challenging dataset using remote sensing images from the GF-2 satellite. The dataset contains complex road scenes with manually annotated images. We then propose a two-task and end-to-end convolution neural network, termed Multitask Road-related Extraction Network (MRENet), for road surface extraction and road centerline extraction. We take features extracted from the road as the condition of centerline extraction, and the information transmission and parameter sharing between the two tasks compensate for the potential problem of insufficient road centerline samples. In the network design, we use atrous convolutions and a pyramid scene parsing pooling module (PSP pooling), aiming to expand the network receptive field, integrate multilevel features, and obtain more abundant information. In addition, we use a weighted binary cross-entropy function to alleviate the background imbalance problem. Experimental results show that the proposed algorithm outperforms several comparative methods in the aspects of classification precision and visual interpretation.
      Citation: Remote Sensing
      PubDate: 2021-01-12
      DOI: 10.3390/rs13020239
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 240: Multi-Image-Feature-Based Hierarchical
           Concrete Crack Identification Framework Using Optimized SVM
           Multi-Classifiers and D–S Fusion Algorithm for Bridge Structures

    • Authors: Yu, Rashidi, Samali, Yousefi, Wang
      First page: 240
      Abstract: Cracks in concrete can cause the degradation of stiffness, bearing capacity and durability of civil infrastructure. Hence, crack diagnosis is of great importance in concrete research. On the basis of multiple image features, this work presents a novel approach for crack identification of concrete structures. Firstly, the non-local means method is adopted to process the original image, which can effectively diminish the noise influence. Then, to extract the effective features sensitive to the crack, different filters are employed for crack edge detection, which are subsequently tackled by integral projection and principal component analysis (PCA) for optimal feature selection. Moreover, support vector machine (SVM) is used to design the classifiers for initial diagnosis of concrete surface based on extracted features. To raise the classification accuracy, enhanced salp swarm algorithm (ESSA) is applied to the SVM for meta-parameter optimization. The Dempster–Shafer (D–S) fusion algorithm is utilized to fuse the diagnostic results corresponding to different filters for decision making. Finally, to demonstrate the effectiveness of the proposed framework, a total of 1200 images are collected from a real concrete bridge including intact (without crack), longitudinal crack, transverse crack and oblique crack cases. The results validate the performance of proposed method with promising results of diagnosis accuracy as high as 96.25%.
      Citation: Remote Sensing
      PubDate: 2021-01-12
      DOI: 10.3390/rs13020240
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 241: Through-Wall Human Pose Reconstruction
           via UWB MIMO Radar and 3D CNN

    • Authors: Song, Jin, Dai, Song, Zhou
      First page: 241
      Abstract: Human pose reconstruction has been a fundamental research in computer vision. However, existing pose reconstruction methods suffer from the problem of wall occlusion that cannot be solved by a traditional optical sensor. This article studies a novel human target pose reconstruction framework using low-frequency ultra-wideband (UWB) multiple-input multiple-output (MIMO) radar and a convolutional neural network (CNN), which is used to detect targets behind the wall. In the proposed framework, first, we use UWB MIMO radar to capture the human body information. Then, target detection and tracking are used to lock the target position, and the back-projection algorithm is adopted to construct three-dimensional (3D) images. Finally, we take the processed 3D image as input to reconstruct the 3D pose of the human target via the designed 3D CNN model. Field detection experiments and comparison results show that the proposed framework can achieve pose reconstruction of human targets behind a wall, which indicates that our research can make up for the shortcomings of optical sensors and significantly expands the application of the UWB MIMO radar system.
      Citation: Remote Sensing
      PubDate: 2021-01-12
      DOI: 10.3390/rs13020241
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 242: Application of Ecosystem Service
           Bundles and Tour Experience in Land Use Management: A Case Study of
           Xiaohuangshan Mountain (China)

    • Authors: Zhao, Chen, Cuan, Zhang, Li, Wan, Li
      First page: 242
      Abstract: With the deterioration of human-terrestrial relations and the intensification of global warming, development in all countries is facing difficulties. Whether in highly urbanized countries or in rapidly urbanizing developing countries such as China, the research on ecosystem services (ES) and land use management has attracted increasing attention. The general management of land use unilaterally pursues economic benefits and neglects ecological benefits, which aggravates the disparity between ecological development and the economic benefits of land resources. How to strike up a balance between ecologic protection and economic development remains a difficult problem during urbanization. It may be a better choice to formulate regional development strategies by combining natural conditions with humanistic and social tendencies. Identifying regional cultural ecosystem services (CES) and other important ES while performing zoning planning for regional land use can be a viable approach in land use management. Here, our study quantitatively evaluates the tourism experience of Xiaohuangshan Mountain (XHSM) and various ES, including recreation, biodiversity, history, aesthetics, soil conservation, surface water regulation, and soil nutrition. All ES were classified into four bundles for XHSM. Different ES bundles generated are suitable for different land use management methods and development forms according to their outstanding ES. The results show that quantifying and mapping regional ES bundles can provide the necessary information to support a win-win solution and provide decision support for land and spatial planning in areas with different social and ecological characteristics.
      Citation: Remote Sensing
      PubDate: 2021-01-12
      DOI: 10.3390/rs13020242
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 243: Improving the Accuracy of Multiple
           Algorithms for Crop Classification by Integrating Sentinel-1 Observations
           with Sentinel-2 Data

    • Authors: Chakhar, Hernández-López, Ballesteros, Moreno
      First page: 243
      Abstract: The availability of an unprecedented amount of open remote sensing data, such as Sentinel-1 and -2 data within the Copernicus program, has boosted the idea of combining the use of optical and radar data to improve the accuracy of agricultural applications such as crop classification. Sentinel-1’s Synthetic Aperture Radar (SAR) provides co- and cross-polarized backscatter, which offers the opportunity to monitor agricultural crops using radar at high spatial and temporal resolution. In this study, we assessed the potential of integrating Sentinel-1 information (VV and VH backscatter and their ratio VH/VV with Sentinel-2A data (NDVI) to perform crop classification and to define which are the most important input data that provide the most accurate classification results. Further, we examined the temporal dynamics of remote sensing data for cereal, horticultural, and industrial crops, perennials, deciduous trees, and legumes. To select the best SAR input feature, we tried two approaches, one based on classification with only SAR features and one based on integrating SAR with optical data. In total, nine scenarios were tested. Furthermore, we evaluated the performance of 22 nonparametric classifiers on which most of these algorithms had not been tested before with SAR data. The results revealed that the best performing scenario was the one integrating VH and VV with normalized difference vegetation index (NDVI) and cubic support vector machine (SVM) (the kernel function of the classifier is cubic) as the classifier with the highest accuracy among all those tested.
      Citation: Remote Sensing
      PubDate: 2021-01-12
      DOI: 10.3390/rs13020243
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 244: Fusion and Correction of Multi-Source
           Land Cover Products Based on Spatial Detection and Uncertainty Reasoning
           Methods in Central Asia

    • Authors: Liu, Xu
      First page: 244
      Abstract: Land cover products are an indispensable data source in land surface process research, and their accuracy directly affects the reliability of related research. Due to the differences in factors such as satellite sensors, the temporal–spatial resolution of remote sensing images, and landcover interpretation technologies, various recently released land cover products are inconsistent, and their accuracy is usually insufficient to meet application requirements. This study, therefore, established a fusion and correction method for multi-source landcover products by combining them with landcover statistics from the Food and Agriculture Organization of the United Nations (FAO), introducing a spatial consistency discrimination technique, and applying an improved Dempster–Shafer evidence fusion method. The five countries in Central Asia were used for a method application and verification assessment. The nine products selected (CCI-LC, CGLS, FROM-GLC, GLCNMO, MCD12Q, GFSAD30, PALSAR, GSWD, and GHS-BUILT) were consistent in time and covered the study area. Based on the interpretation of 1437 high-definition image verification areas, the overall accuracy of the fusion landcover result was 85.32%, and the kappa coefficient was 0.80, which was better than that of the existing comprehensive products. The spatial consistency fusion method had the advantage of an improved statistical fitting, with an overall similarity statistic of 0.999. The improved Dempster–Shafer evidence theory fusion method had an accuracy that was 4.86% higher than the spatial consistency method, and the kappa coefficient increased by 0.07. Combining these two methods improved the consistency of the multi-source data fusion and correction method established in this paper and will also provide more reliable basic data for future research in Central Asia.
      Citation: Remote Sensing
      PubDate: 2021-01-12
      DOI: 10.3390/rs13020244
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 245: Mapping Large-Scale Mangroves along
           the Maritime Silk Road from 1990 to 2015 Using a Novel Deep Learning Model
           and Landsat Data

    • Authors: Yujuan Guo, Jingjuan Liao, Guozhuang Shen
      First page: 245
      Abstract: Mangroves are important ecosystems and their distribution and dynamics can provide an understanding of the processes of ecological change. Meanwhile, mangroves protection is also an important element of the Maritime Silk Road (MSR) Cooperation Project. Large amounts of accessible satellite remote sensing data can provide timely and accurate information on the dynamics of mangroves, offering significant advantages in space, time, and characterization. In view of the capability of deep learning in processing massive data in recent years, we developed a new deep learning model—Capsules-Unet, which introduces the capsule concept into U-net to extract mangroves with high accuracy by learning the spatial relationship between objects in images. This model can significantly reduce the number of network parameters to improve the efficiency of data processing. This study uses Landsat data combined with Capsules-Unet to map the dynamics of mangrove changes over the 25 years (1990–2015) along the MSR. The results show that there was a loss in the mangrove area of 1,356,686 ha (about 21.5%) between 1990 and 2015, with anthropic activities such as agriculture, aquaculture, tourism, urban development, and over-development appearing to be the likely drivers of this decline. This information contributes to the understanding of ecological conditions, variability characteristics, and influencing factors along the MSR.
      Citation: Remote Sensing
      PubDate: 2021-01-13
      DOI: 10.3390/rs13020245
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 246: Fusion of Rain Radar Images and Wind
           Forecasts in a Deep Learning Model Applied to Rain Nowcasting

    • Authors: Vincent Bouget, Dominique Béréziat, Julien Brajard, Anastase Charantonis, Arthur Filoche
      First page: 246
      Abstract: Short- or mid-term rainfall forecasting is a major task with several environmental applications such as agricultural management or flood risk monitoring. Existing data-driven approaches, especially deep learning models, have shown significant skill at this task, using only rainfall radar images as inputs. In order to determine whether using other meteorological parameters such as wind would improve forecasts, we trained a deep learning model on a fusion of rainfall radar images and wind velocity produced by a weather forecast model. The network was compared to a similar architecture trained only on radar data, to a basic persistence model and to an approach based on optical flow. Our network outperforms by 8% the F1-score calculated for the optical flow on moderate and higher rain events for forecasts at a horizon time of 30 min. Furthermore, it outperforms by 7% the same architecture trained using only rainfall radar images. Merging rain and wind data has also proven to stabilize the training process and enabled significant improvement especially on the difficult-to-predict high precipitation rainfalls.
      Citation: Remote Sensing
      PubDate: 2021-01-13
      DOI: 10.3390/rs13020246
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 247: A Remote Sensing-Based Assessment of
           Water Resources in the Arabian Peninsula

    • Authors: Youssef Wehbe, Marouane Temimi
      First page: 247
      Abstract: A better understanding of the spatiotemporal distribution of water resources is crucial for the sustainable development of hyper-arid regions. Here, we focus on the Arabian Peninsula (AP) and use remotely sensed data to (i) analyze the local climatology of total water storage (TWS), precipitation, and soil moisture; (ii) characterize their temporal variability and spatial distribution; and (iii) infer recent trends and change points within their time series. Remote sensing data for TWS, precipitation, and soil moisture are obtained from the Gravity Recovery and Climate Experiment (GRACE), the Tropical Rainfall Measuring Mission (TRMM), and the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E), respectively. The study relies on trend analysis, the modified Mann–Kendall test, and change point detection statistics. We first derive 10-year (2002–2011) seasonal averages from each of the datasets and intercompare their spatial organization. In the absence of large-scale in situ data, we then compare trends from GRACE TWS retrievals to in situ groundwater observations locally over the subdomain of the United Arab Emirates (UAE). TWS anomalies vary between −6.2 to 3.2 cm/month and −6.8 to −0.3 cm/month during the winter and summer periods, respectively. Trend analysis shows decreasing precipitation trends (−2.3 × 10−4 mm/day) spatially aligned with decreasing soil moisture trends (−1.5 × 10−4 g/cm3/month) over the southern part of the AP, whereas the highest decreasing TWS trends (−8.6 × 10−2 cm/month) are recorded over areas of excessive groundwater extraction in the northern AP. Interestingly, change point detection reveals increasing precipitation trends pre- and post-change point breaks over the entire AP region. Significant spatial dependencies are observed between TRMM and GRACE change points, particularly over Yemen during 2010, revealing the dominant impact of climatic changes on TWS depletion.
      Citation: Remote Sensing
      PubDate: 2021-01-13
      DOI: 10.3390/rs13020247
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 248: Decision Support System Based on
           Indoor Location for Personnel Management

    • Authors: Néstor Álvarez-Díaz, Pino Caballero-Gil
      First page: 248
      Abstract: A wide variety of business areas organize their work based on the location of their employees because only by taking these locations into account, they can schedule activities properly. However, in a large number of cases, the requirement of immediacy, such as the need to help an injured person in a hospital or to dry up water in a busy hallway to prevent people from slipping, is a major constraint. This work is based on a proof of concept in which we used Bluetooth Low Energy devices to track the location of each employee in an indoor environment. Among other factors, the location of each individual is assigned a large percentage of the weight to assign a task. This proposal is intended to cover some scenarios of great interest, guaranteeing the correctness of measurement and the privacy of staff tracking.
      Citation: Remote Sensing
      PubDate: 2021-01-13
      DOI: 10.3390/rs13020248
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 249: Geo-Object-Based Vegetation Mapping
           via Machine Learning Methods with an Intelligent Sample Collection Scheme:
           A Case Study of Taibai Mountain, China

    • Authors: Tianjun Wu, Jiancheng Luo, Lijing Gao, Yingwei Sun, Wen Dong, Ya’nan Zhou, Wei Liu, Xiaodong Hu, Jiangbo Xi, Changpeng Wang, Yun Yang
      First page: 249
      Abstract: Precise vegetation maps of mountainous areas are of great significance to grasp the situation of an ecological environment and forest resources. In this paper, while multi-source geospatial data can generally be quickly obtained at present, to realize effective vegetation mapping in mountainous areas when samples are difficult to collect due to their perilous terrain and inaccessible deep forest, we propose a novel and intelligent method of sample collection for machine-learning (ML)-based vegetation mapping. First, we employ geo-objects (i.e., polygons) from topographic partitioning and constrained segmentation as basic mapping units and formalize the problem as a supervised classification process using ML algorithms. Second, a previously available vegetation map with rough-scale label information is overlaid on the geo-object-level polygons, and candidate geo-object-based samples can be identified when all the grids’ labels of vegetation types within the geo-objects are the same. Third, various kinds of geo-object-level features are extracted according to high-spatial-resolution remote sensing (HSR-RS) images and multi-source geospatial data. Some unreliable geo-object-based samples are rejected in the candidate set by comparing their features and the rules based on local expert knowledge. Finally, based on these automatically collected samples, we train the model using a random forest (RF)-based algorithm and classify all the geo-objects with labels of vegetation types. A case experiment of Taibai Mountain in China shows that the methodology has the ability to achieve good vegetation mapping results with the rapid and convenient sample collection scheme. The map with a finer geographic distribution pattern of vegetation could clearly promote the vegetation resources investigation and monitoring of the study area; thus, the methodological framework is worth popularizing in the mapping areas such as mountainous regions where the field survey sampling is difficult to implement.
      Citation: Remote Sensing
      PubDate: 2021-01-13
      DOI: 10.3390/rs13020249
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 250: Relating Hyperspectral Vegetation
           Indices with Soil Salinity at Different Depths for the Diagnosis of Winter
           Wheat Salt Stress

    • Authors: Kangying Zhu, Zhigang Sun, Fenghua Zhao, Ting Yang, Zhenrong Tian, Jianbin Lai, Wanxue Zhu, Buju Long
      First page: 250
      Abstract: Abundant shallow underground brackish water resources could help in alleviating the shortage of fresh water resources and the crisis concerning agricultural water resources in the North China Plain. Improper brackish water irrigation will increase soil salinity and decrease the final yield due to salt stress affecting the crops. Therefore, it is urgent to develop a practical and low-cost method to monitor the soil salinity of brackish irrigation systems. Remotely sensed spectral vegetation indices (SVIs) of crops are promising proxies for indicating the salinity of the surface soil layer. However, there is still a challenge concerning quantitatively correlating SVIs with the salinity of deeper soil layers, in which crop roots are mainly distributed. In this study, a field experiment was conducted to investigate the relationship between SVIs and salinity measurements at four soil depths within six winter wheat plots irrigated using three salinity levels at the Yucheng Comprehensive Experimental Station of the Chinese Academy of Sciences during 2017–2019. The hyperspectral reflectance was measured during the grain-filling stage of winter wheat, since it is more sensitive to soil salinity during this period. The SVIs derived from the observed hyperspectral data of winter wheat were compared with the salinity at four soil depths. The results showed that the optimized SVIs, involving soil salt-sensitive blue, red-edge, and near-infrared wavebands, performed better when retrieving the soil salinity (R2 ≥ 0.58, root mean square error (RMSE) ≤ 0.62 g/L), especially at the 30-cm depth (R2 = 0.81, RMSE = 0.36 g/L). For practical applications, linear or quadratic models based on the screened SVIs in the form of normalized differential vegetation indices (NDVIs) could be used to retrieve soil salinity (R2 ≥ 0.63, RMSE ≤ 0.62 g/L) at all soil depths and then diagnose salt stress in winter wheat. This could provide a practical technique for evaluating regional brackish water irrigation systems.
      Citation: Remote Sensing
      PubDate: 2021-01-13
      DOI: 10.3390/rs13020250
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 251: A New Urban Functional Zone-Based
           Climate Zoning System for Urban Temperature Study

    • Authors: Zhaowu Yu, Yongcai Jing, Gaoyuan Yang, Ranhao Sun
      First page: 251
      Abstract: The urban heat island (UHI) effect has been recognized as one of the most significant terrestrial surface climate-related consequences of urbanization. However, the traditional definition of the urban–rural (UR) division and the newly established local climate zone (LCZ) classification for UHI and urban climate studies do not adequately express the pattern and intensity of UHI. Moreover, these definitions of UHI find it hard to capture the human activity-induced anthropogenic heat that is highly correlated with urban functional zones (UFZ). Therefore, in this study, with a comparison (theory, technology, and application) of the previous definition (UR and LCZ) of UHI and integration of computer programming technology, social sensing, and remote sensing, we develop a new urban functional zone-based urban temperature zoning system (UFZC). The UFZC system is generally a social-based, planning-oriented, and data-driven classification system associated with the urban function and temperature; it can also be effectively used in city management (e.g., urban planning and energy saving). Moreover, in the Beijing case, we tested the UFZC system and preliminarily analyzed the land surface temperature (LST) difference patterns and causes of the 11 UFZC types. We found that, compared to other UFZCs, the PGZ (perseveration green zone)-UFZC has the lowest LST, while the CBZ (center business district zone)-UFZC and GCZ (general commercial zone)-UFZC contribute the most and stable heat sources. This implies that reducing the heat generated by the function of commercial (and industrial) activities is an effective measure to reduce the UHI effect. We also proposed that multi-source temperature datasets with a high spatiotemporal resolution are needed to obtain more accurate results; thus providing more accurate recommendations for mitigating UHI effects. In short, as a new and finer urban temperature zoning system, although UFZC is not intended to supplant the UR and LCZ classifications, it can facilitate more detailed and coupled urban climate studies.
      Citation: Remote Sensing
      PubDate: 2021-01-13
      DOI: 10.3390/rs13020251
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 252: Efficient Hybrid Supervision for
           Instance Segmentation in Aerial Images

    • Authors: Linwei Chen, Ying Fu, Shaodi You, Hongzhe Liu
      First page: 252
      Abstract: Instance segmentation in aerial images is of great significance for remote sensing applications, and it is inherently more challenging because of cluttered background, extremely dense and small objects, and objects with arbitrary orientations. Besides, current mainstream CNN-based methods often suffer from the trade-off between labeling cost and performance. To address these problems, we present a pipeline of hybrid supervision. In the pipeline, we design an ancillary segmentation model with the bounding box attention module and bounding box filter module. It is able to generate accurate pseudo pixel-wise labels from real-world aerial images for training any instance segmentation models. Specifically, bounding box attention module can effectively suppress the noise in cluttered background and improve the capability of segmenting small objects. Bounding box filter module works as a filter which removes the false positives caused by cluttered background and densely distributed objects. Our ancillary segmentation model can locate object pixel-wisely instead of relying on horizontal bounding box prediction, which has better adaptability to arbitrary oriented objects. Furthermore, oriented bounding box labels are utilized for handling arbitrary oriented objects. Experiments on iSAID dataset show that the proposed method can achieve comparable performance (32.1 AP) to fully supervised methods (33.9 AP), which is obviously higher than weakly supervised setting (26.5 AP), when using only 10% pixel-wise labels.
      Citation: Remote Sensing
      PubDate: 2021-01-13
      DOI: 10.3390/rs13020252
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 253: Characteristics of the Global Radio
           Frequency Interference in the Protected Portion of L-Band

    • Authors: Mustafa Aksoy, Hamid Rajabi, Pranjal Atrey, Imara Mohamed Nazar
      First page: 253
      Abstract: The National Aeronautics and Space Administration’s (NASA’s) Soil Moisture Active–Passive (SMAP) radiometer has been providing geolocated power moments measured within a 24 MHz band in the protected portion of L-band, i.e., 1400–1424 MHz, with 1.2 ms and 1.5 MHz time and frequency resolutions, as its Level 1A data. This paper presents important spectral and temporal properties of the radio frequency interference (RFI) in the protected portion of L-band using SMAP Level 1A data. Maximum and average bandwidth and duration of RFI signals, average RFI-free spectrum availability, and variations in such properties between ascending and descending satellite orbits have been reported across the world. The average bandwidth and duration of individual RFI sources have been found to be usually less than 4.5 MHz and 4.8 ms; and the average RFI-free spectrum is larger than 20 MHz in most regions with exceptions over the Middle East and Central and Eastern Asia. It has also been shown that, the bandwidth and duration of RFI signals can vary as much as 10 MHz and 10 ms, respectively, between ascending and descending orbits over certain locations. Furthermore, to identify frequencies susceptible to RFI contamination in the protected portion of L-band, observed RFI signals have been assigned to individual 1.5 MHz SMAP channels according to their frequencies. It has been demonstrated that, contrary to common perception, the center of the protected portion can be as RFI contaminated as its edges. Finally, there have been no significant correlations noted among different RFI properties such as amplitude, bandwidth, and duration within the 1400–1424 MHz band.
      Citation: Remote Sensing
      PubDate: 2021-01-13
      DOI: 10.3390/rs13020253
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 254: Validation of CHIRPS Precipitation
           Estimates over Taiwan at Multiple Timescales

    • Authors: Jie Hsu, Wan-Ru Huang, Pin-Yi Liu, Xiuzhen Li
      First page: 254
      Abstract: The Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), which incorporates satellite imagery and in situ station information, is a new high-resolution long-term precipitation dataset available since 1981. This study aims to understand the performance of the latest version of CHIRPS in depicting the multiple timescale precipitation variation over Taiwan. The analysis is focused on examining whether CHIRPS is better than another satellite precipitation product—the Integrated Multi-satellitE Retrievals for Global Precipitation Mission (GPM) final run (hereafter IMERG)—which is known to effectively capture the precipitation variation over Taiwan. We carried out the evaluations made for annual cycle, seasonal cycle, interannual variation, and daily variation during 2001–2019. Our results show that IMERG is slightly better than CHIRPS considering most of the features examined; however, CHIRPS performs better than that of IMERG in representing the (1) magnitude of the annual cycle of monthly precipitation climatology, (2) spatial distribution of the seasonal mean precipitation for all four seasons, (3) quantitative precipitation estimation of the interannual variation of area-averaged winter precipitation in Taiwan, and (4) occurrence frequency of the non-rainy grids in winter. Notably, despite the fact that CHIRPS is not better than IMERG for many examined features, CHIRPS can depict the temporal variation in precipitation over Taiwan on annual, seasonal, and interannual timescales with 95% significance. This highlights the potential use of CHIRPS in studying the multiple timescale variation in precipitation over Taiwan during the years 1981–2000, for which there are no data available in the IMERG database.
      Citation: Remote Sensing
      PubDate: 2021-01-13
      DOI: 10.3390/rs13020254
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 255: An Accurate GEO SAR Range Model for
           Ultralong Integration Time Based on mth-Order Taylor Expansion

    • Authors: Binbin Zhou, Xiangyang Qi, Heng Zhang
      First page: 255
      Abstract: As the Geosynchronous Earth Orbital Synthetic Aperture Radar (GEO SAR) allows a wide area viewing combined with a short revisit cycle, it is suitable for many applications that require high timeliness, such as natural disaster monitoring, weather supervision, and military reconnaissance. However, the ultralong integration time and the invalidation of “stop-and-go” assumption caused by the raise of orbital height also greatly increase the difficulty of signal processing. In this paper, a generalized method for calculating the accurate propagation distance between a GEO satellite and a target with ultralong integration time is proposed. This range model is mainly composed of an accurate pulse transmitting distance and an error compensation term for “stop-and-go” assumption failure. The transmitting distance is obtained by Taylor expansion, and the specific derivation process of the general formula of the mth-order expansion is given, in this paper. As for the compensation term, this is achieved by approximately calculating the pulse receiving distance based on twice Taylor expansion, the first expansion is for fast-time and the other is for slow-time. Finally, a series of simulation experiments were conducted to verify the effectiveness and superiority of this new range model for an ultralong integration time.
      Citation: Remote Sensing
      PubDate: 2021-01-13
      DOI: 10.3390/rs13020255
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 256: Spatio-Temporal Trends of Surface
           Energy Budget in Tibet from Satellite Remote Sensing Observations and
           Reanalysis Data

    • Authors: Usman Mazhar, Shuanggen Jin, Wentao Duan, Muhammad Bilal, Md. Arfan Ali, Hasnain Farooq
      First page: 256
      Abstract: Being the highest and largest land mass of the earth, the Tibetan Plateau has a strong impact on the Asian climate especially on the Asian monsoon. With high downward solar radiation, the Tibetan Plateau is a climate sensitive region and the main water source for many rivers in South and East Asia. Although many studies have analyzed energy fluxes in the Tibetan Plateau, a long-term detailed spatio-temporal variability of all energy budget parameters is not clear for understanding the dynamics of the regional climate change. In this paper, satellite remote sensing and reanalysis data are used to quantify spatio-temporal trends of energy budget parameters, net radiation, latent heat flux, and sensible heat flux over the Tibetan Plateau from 2001 to 2019. The validity of both data sources is analyzed from in situ ground measurements of the FluxNet micrometeorological tower network, which verifies that both datasets are valid and reliable. It is found that the trend of net radiation shows a slight increase. The latent heat flux increases continuously, while the sensible heat flux decreases continuously throughout the study period over the Tibetan Plateau. Varying energy fluxes in the Tibetan plateau will affect the regional hydrological cycle. Satellite LE product observation is limited to certain land covers. Thus, for larger spatial areas, reanalysis data is a more appropriate choice. Normalized difference vegetation index proves a useful indicator to explain the latent heat flux trend. Despite the reduction of sensible heat, the atmospheric temperature increases continuously resulting in the warming of the Tibetan Plateau. The opposite trend of sensible heat flux and air temperature is an interesting and explainable phenomenon. It is also concluded that the surface evaporative cooling is not the indicator of atmospheric cooling/warming. In the future, more work shall be done to explain the mechanism which involves the complete heat cycle in the Tibetan Plateau.
      Citation: Remote Sensing
      PubDate: 2021-01-13
      DOI: 10.3390/rs13020256
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 257: Leveraging TLS as a Calibration and
           Validation Tool for MLS and ULS Mapping of Savanna Structure and Biomass
           at Landscape-Scales

    • Authors: Shaun R. Levick, Tim Whiteside, David A. Loewensteiner, Mitchel Rudge, Renee Bartolo
      First page: 257
      Abstract: Savanna ecosystems are challenging to map and monitor as their vegetation is highly dynamic in space and time. Understanding the structural diversity and biomass distribution of savanna vegetation requires high-resolution measurements over large areas and at regular time intervals. These requirements cannot currently be met through field-based inventories nor spaceborne satellite remote sensing alone. UAV-based remote sensing offers potential as an intermediate scaling tool, providing acquisition flexibility and cost-effectiveness. Yet despite the increased availability of lightweight LiDAR payloads, the suitability of UAV-based LiDAR for mapping and monitoring savanna 3D vegetation structure is not well established. We mapped a 1 ha savanna plot with terrestrial-, mobile- and UAV-based laser scanning (TLS, MLS, and ULS), in conjunction with a traditional field-based inventory (n = 572 stems > 0.03 m). We treated the TLS dataset as the gold standard against which we evaluated the degree of complementarity and divergence of structural metrics from MLS and ULS. Sensitivity analysis showed that MLS and ULS canopy height models (CHMs) did not differ significantly from TLS-derived models at spatial resolutions greater than 2 m and 4 m respectively. Statistical comparison of the resulting point clouds showed minor over- and under-estimation of woody canopy cover by MLS and ULS, respectively. Individual stem locations and DBH measurements from the field inventory were well replicated by the TLS survey (R2 = 0.89, RMSE = 0.024 m), which estimated above-ground woody biomass to be 7% greater than field-inventory estimates (44.21 Mg ha−1 vs 41.08 Mg ha−1). Stem DBH could not be reliably estimated directly from the MLS or ULS, nor indirectly through allometric scaling with crown attributes (R2 = 0.36, RMSE = 0.075 m). MLS and ULS show strong potential for providing rapid and larger area capture of savanna vegetation structure at resolutions suitable for many ecological investigations; however, our results underscore the necessity of nesting TLS sampling within these surveys to quantify uncertainty. Complementing large area MLS and ULS surveys with TLS sampling will expand our options for the calibration and validation of multiple spaceborne LiDAR, SAR, and optical missions.
      Citation: Remote Sensing
      PubDate: 2021-01-13
      DOI: 10.3390/rs13020257
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 258: Effects of the COVID-19 Lockdown on
           Urban Light Emissions: Ground and Satellite Comparison

    • Authors: Máximo Bustamante-Calabria, Sánchez de Miguel, Susana Martín-Ruiz, Jose-Luis Ortiz, Vílchez, Alicia Pelegrina, Antonio García, Jaime Zamorano, Jonathan Bennie, Kevin J. Gaston
      First page: 258
      Abstract: ’Lockdown’ periods in response to COVID-19 have provided a unique opportunity to study the impacts of economic activity on environmental pollution (e.g., NO2, aerosols, noise, light). The effects on NO2 and aerosols have been very noticeable and readily demonstrated, but that on light pollution has proven challenging to determine. The main reason for this difficulty is that the primary source of nighttime satellite imagery of the earth is the SNPP-VIIRS/DNB instrument, which acquires data late at night after most human nocturnal activity has already occurred and much associated lighting has been turned off. Here, to analyze the effect of lockdown on urban light emissions, we use ground and satellite data for Granada, Spain, during the COVID-19 induced confinement of the city’s population from 14 March until 31 May 2020. We find a clear decrease in light pollution due both to a decrease in light emissions from the city and to a decrease in anthropogenic aerosol content in the atmosphere which resulted in less light being scattered. A clear correlation between the abundance of PM10 particles and sky brightness is observed, such that the more polluted the atmosphere the brighter the urban night sky. An empirical expression is determined that relates PM10 particle abundance and sky brightness at three different wavelength bands.
      Citation: Remote Sensing
      PubDate: 2021-01-13
      DOI: 10.3390/rs13020258
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 259: Remote Sensing Supported Sea Surface
           pCO2 Estimation and Variable Analysis in the Baltic Sea

    • Authors: Shuping Zhang, Anna Rutgersson, Petra Philipson, Marcus B. Wallin
      First page: 259
      Abstract: Marginal seas are a dynamic and still to large extent uncertain component of the global carbon cycle. The large temporal and spatial variations of sea-surface partial pressure of carbon dioxide (pCO2) in these areas are driven by multiple complex mechanisms. In this study, we analyzed the variable importance for the sea surface pCO2 estimation in the Baltic Sea and derived monthly pCO2 maps for the marginal sea during the period of July 2002–October 2011. We used variables obtained from remote sensing images and numerical models. The random forest algorithm was employed to construct regression models for pCO2 estimation and produce the importance of different input variables. The study found that photosynthetically available radiation (PAR) was the most important variable for the pCO2 estimation across the entire Baltic Sea, followed by sea surface temperature (SST), absorption of colored dissolved organic matter (aCDOM), and mixed layer depth (MLD). Interestingly, Chlorophyll-a concentration (Chl-a) and the diffuse attenuation coefficient for downwelling irradiance at 490 nm (Kd_490nm) showed relatively low importance for the pCO2 estimation. This was mainly attributed to the high correlation of Chl-a and Kd_490nm to other pCO2-relevant variables (e.g., aCDOM), particularly in the summer months. In addition, the variables’ importance for pCO2 estimation varied between seasons and sub-basins. For example, the importance of aCDOM were large in the Gulf of Finland but marginal in other sub-basins. The model for pCO2 estimate in the entire Baltic Sea explained 63% of the variation and had a root of mean squared error (RMSE) of 47.8 µatm. The pCO2 maps derived with this model displayed realistic seasonal variations and spatial features of sea surface pCO2 in the Baltic Sea. The spatially and seasonally varying variables’ importance for the pCO2 estimation shed light on the heterogeneities in the biogeochemical and physical processes driving the carbon cycling in the Baltic Sea and can serve as an important basis for future pCO2 estimation in marginal seas using remote sensing techniques. The pCO2 maps derived in this study provided a robust benchmark for understanding the spatiotemporal patterns of CO2 air-sea exchange in the Baltic Sea.
      Citation: Remote Sensing
      PubDate: 2021-01-13
      DOI: 10.3390/rs13020259
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 260: Individual Sick Fir Tree (Abies
           mariesii) Identification in Insect Infested Forests by Means of UAV Images
           and Deep Learning

    • Authors: Ha Trang Nguyen, Maximo Larry Lopez Caceres, Koma Moritake, Sarah Kentsch, Hase Shu, Yago Diez
      First page: 260
      Abstract: Insect outbreaks are a recurrent natural phenomenon in forest ecosystems expected to increase due to climate change. Recent advances in Unmanned Aerial Vehicles (UAV) and Deep Learning (DL) Networks provide us with tools to monitor them. In this study we used nine orthomosaics and normalized Digital Surface Models (nDSM) to detect and classify healthy and sick Maries fir trees as well as deciduous trees. This study aims at automatically classifying treetops by means of a novel computer vision treetops detection algorithm and the adaptation of existing DL architectures. Considering detection alone, the accuracy results showed 85.70% success. In terms of detection and classification, we were able to detect/classify correctly 78.59% of all tree classes (39.64% for sick fir). However, with data augmentation, detection/classification percentage of the sick fir class rose to 73.01% at the cost of the result accuracy of all tree classes that dropped 63.57%. The implementation of UAV, computer vision and DL techniques contribute to the development of a new approach to evaluate the impact of insect outbreaks in forest.
      Citation: Remote Sensing
      PubDate: 2021-01-13
      DOI: 10.3390/rs13020260
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 261: Regional Modeling of Forest Fuels and
           Structural Attributes Using Airborne Laser Scanning Data in Oregon

    • Authors: Mauro, Hudak, Fekety, Frank, Temesgen, Bell, Gregory, McCarley
      First page: 261
      Abstract: Airborne laser scanning (ALS) acquisitions provide piecemeal coverage across the western US, as collections are organized by local managers of individual project areas. In this study, we analyze different factors that can contribute to developing a regional strategy to use information from completed ALS data acquisitions and develop maps of multiple forest attributes in new ALS project areas in a rapid manner. This study is located in Oregon, USA, and analyzes si× forest structural attributes for differences between: (1) synthetic (i.e., not-calibrated), and calibrated predictions, (2) parametric linear and semiparametric models, and (3) models developed with predictors computed for point clouds enclosed in the areas where field measurements were taken, i.e., “point-cloud predictors”, and models developed using predictors e×tracted from pre-rasterized layers, i.e., “rasterized predictors”. Forest structural attributes under consideration are aboveground biomass, downed woody biomass, canopy bulk density, canopy height, canopy base height, and canopy fuel load. Results from our study indicate that semiparametric models perform better than parametric models if no calibration is performed. However, the effect of the calibration is substantial in reducing the bias of parametric models but minimal for the semiparametric models and, once calibrations are performed, differences between parametric and semiparametric models become negligible for all responses. In addition, minimal differences between models using point-cloud predictors and models using rasterized predictors were found. We conclude that the approach that applies semiparametric models and rasterized predictors, which represents the easiest workflow and leads to the most rapid results, is justified with little loss in accuracy or precision even if no calibration is performed.
      Citation: Remote Sensing
      PubDate: 2021-01-13
      DOI: 10.3390/rs13020261
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 262: Predicting Future Urban Flood Risk
           Using Land Change and Hydraulic Modeling in a River Watershed in the
           Central Province of Vietnam

    • Authors: Huu Duy Nguyen, Dennis Fox, Dinh Kha Dang, Le Tuan Pham, Quan Vu Viet Du, Thi Ha Thanh Nguyen, Thi Ngoc Dang, Van Truong Tran, Phuong Lan Vu, Quoc-Huy Nguyen, Tien Giang Nguyen, Quang-Thanh Bui, Alexandru-Ionut Petrisor
      First page: 262
      Abstract: Flood risk is a significant challenge for sustainable spatial planning, particularly concerning climate change and urbanization. Phrasing suitable land planning strategies requires assessing future flood risk and predicting the impact of urban sprawl. This study aims to develop an innovative approach combining land use change and hydraulic models to explore future urban flood risk, aiming to reduce it under different vulnerability and exposure scenarios. SPOT-3 and Sentinel-2 images were processed and classified to create land cover maps for 1995 and 2019, and these were used to predict the 2040 land cover using the Land Change Modeler Module of Terrset. Flood risk was computed by combining hazard, exposure, and vulnerability using hydrodynamic modeling and the Analytic Hierarchy Process method. We have compared flood risk in 1995, 2019, and 2040. Although flood risk increases with urbanization, population density, and the number of hospitals in the flood plain, especially in the coastal region, the area exposed to high and very high risks decreases due to a reduction in poverty rate. This study can provide a theoretical framework supporting climate change related to risk assessment in other metropolitan regions. Methodologically, it underlines the importance of using satellite imagery and the continuity of data in the planning-related decision-making process.
      Citation: Remote Sensing
      PubDate: 2021-01-13
      DOI: 10.3390/rs13020262
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 263: A Novel Vegetation Index for Coffee
           Ripeness Monitoring Using Aerial Imagery

    • Authors: Rodrigo Nogueira Martins, Francisco de Assis de Carvalho Pinto, Daniel Marçal de Queiroz, Domingos Sárvio Magalhães Valente, Jorge Tadeu Fim Rosas
      First page: 263
      Abstract: Coffee ripeness monitoring is a key indicator for defining the moment of starting the harvest, especially because the coffee quality is related to the fruit ripeness degree. The most used method to define the start of harvesting is by visual inspection, which is time-consuming, labor-intensive, and does not provide information on the entire area. There is a lack of new techniques or alternative methodologies to provide faster measurements that can support harvest planning. Based on that, this study aimed at developing a vegetation index (VI) for coffee ripeness monitoring using aerial imagery. For this, an experiment was set up in five arabica coffee fields in Minas Gerais State, Brazil. During the coffee ripeness stage, four flights were carried out to acquire spectral information on the crop canopy using two quadcopters, one equipped with a five-band multispectral camera and another with an RGB (Red, Green, Blue) camera. Prior to the flights, manual counts of the percentage of unripe fruits were carried out using irregular sampling grids on each day for validation purposes. After image acquisition, the coffee ripeness index (CRI) and other five VIs were obtained. The CRI was developed combining reflectance from the red band and from a ground-based red target placed on the study area. The effectiveness of the CRI was compared under different analyses with traditional VIs. The CRI showed a higher sensitivity to discriminate coffee plants ready for harvest from not-ready for harvest in all coffee fields. Furthermore, the highest R2 and lowest RMSE values for estimating the coffee ripeness were also presented by the CRI (R2: 0.70; 12.42%), whereas the other VIs showed R2 and RMSE values ranging from 0.22 to 0.67 and from 13.28 to 16.50, respectively. Finally, the study demonstrated that the time-consuming fieldwork can be replaced by the methodology based on VIs.
      Citation: Remote Sensing
      PubDate: 2021-01-13
      DOI: 10.3390/rs13020263
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 264: Non-Cooperative Passive Direct
           Localization Based on Waveform Estimation

    • Authors: Tao Zhou, Wei Yi, Lingjiang Kong
      First page: 264
      Abstract: This paper considers a non-cooperative passive localization system wherein widely distributed receivers are used to localize a transmitter radiating a periodical pulse pair signal. Two possible pulse modulation models, noncoherent and coherent pulses, are fully considered for practical application, and are effectively unified as a general model for the algorithm design. To achieve highly accurate and robust localization performance, an enhanced direct position determination (DPD) algorithm based on waveform estimation (WE) is devised to jointly estimate the transmitter position and the waveform profile. The optimal objective function based on a least square (LS) principle is first derived to directly determine the position of the transmitter. Due to the complete lack of knowledge on the transmitted signal, the processing center calculates the objective function at each searched grid of interest by using estimated pulses instead of the real ones, while extraction of pulse samples and estimation of waveform are executed. Theoretical derivation gives the solution to estimate the non-parameterized waveform with a structure of maximum Rayleigh quotient. Additionally, simulation results verify the effectiveness of the proposed algorithm for many common waveform types in the cases of transmitting noncoherent and coherent pulses, and also show the excellent advantage over the classical DPD algorithm at low signal-to-noise ratio (SNR).
      Citation: Remote Sensing
      PubDate: 2021-01-13
      DOI: 10.3390/rs13020264
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 265: Spatio-Temporal Patterns of Mass
           Changes in Himalayan Glaciated Region from EOF Analyses of GRACE Data

    • Authors: Harika Munagapati, Virendra M. Tiwari
      First page: 265
      Abstract: The nature of hydrological seasonality over the Himalayan Glaciated Region (HGR) is complex due to varied precipitation patterns. The present study attempts to exemplify the spatio-temporal variation of hydrological mass over the HGR using time-variable gravity from the Gravity Recovery and Climate Experiment (GRACE) satellite for the period of 2002–2016 on seasonal and interannual timescales. The mass signal derived from GRACE data is decomposed using empirical orthogonal functions (EOFs), allowing us to identify the three broad divisions of HGR, i.e., western, central, and eastern, based on the seasonal mass gain or loss that corresponds to prevailing climatic changes. Further, causative relationships between climatic variables and the EOF decomposed signals are explored using the Granger causality algorithm. It appears that a causal relationship exists between total precipitation and total water storage from GRACE. EOF modes also indicate certain regional anomalies such as the Karakoram mass gain, which represents ongoing snow accumulation. Our causality result suggests that the excessive snowfall in 2005–2008 has initiated this mass gain. However, as our results indicate, despite the dampening of snowfall rates after 2008, mass has been steadily increasing in the Karakorum, which is attributed to the flattening of the temperature anomaly curve and subsequent lower melting after 2008.
      Citation: Remote Sensing
      PubDate: 2021-01-14
      DOI: 10.3390/rs13020265
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 266: Assessing the Accuracy of
           Landsat-MODIS NDVI Fusion with Limited Input Data: A Strategy for Base
           Data Selection

    • Authors: Yiting Wang, Donghui Xie, Yinggang Zhan, Huan Li, Guangjian Yan, Yuanyuan Chen
      First page: 266
      Abstract: Despite its wide applications, the spatiotemporal fusion of coarse- and fine-resolution satellite images is limited primarily to the availability of clear-sky fine-resolution images, which are commonly scarce due to unfavorable weather, and such a limitation might cause errors in spatiotemporal fusion. Thus, the effective use of limited fine-resolution images, while critical, remains challenging. To address this issue, in this paper we propose a new phenological similarity strategy (PSS) to select the optimal combination of image pairs for a prediction date. The PSS considers the temporal proximity and phenological similarity between the base and prediction images and computes a weight for identifying the optimal combination of image pairs. Using the PSS, we further evaluate the influence of input data on the fusion accuracy by varying the number and temporal distribution of input images. The results show that the PSS (mean R = 0.827 and 0.760) outperforms the nearest date (mean R = 0.786 and 0.742) and highest correlation (mean R = 0.821 and 0.727) strategies in both the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and the linear mixing growth model (LMGM), respectively, for fusing Landsat 8 OLI and MODIS NDVI datasets. Furthermore, base images adequately covering different growth stages yield better predictability than simply increasing the number of base images.
      Citation: Remote Sensing
      PubDate: 2021-01-14
      DOI: 10.3390/rs13020266
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 267: Characterizing Forest Dynamics with
           Landsat-Derived Phenology Curves

    • Authors: M. Brooke Rose, Nicholas N. Nagle
      First page: 267
      Abstract: Landsat is among the most popular satellites used for forest change assessments. Traditionally, Landsat data users relied on annual or biennial images to measure forest recovery after disturbance, a process that is difficult to monitor at broad scales. With the availability of free Landsat data, intra-annual change analyses are now possible. Phenology, the timing of cyclical vegetation events, can be estimated using indices derived from intra-annual remote sensing data and used to classify different vegetation types after a disturbance. We used a smoothed harmonic modelling approach to estimate NDVI and NBR phenology patterns in pre- and post-fire Landsat sample pixels for two forest groups in South Carolina, using nearby unburned samples as an approximate control group. These methods take advantage of all available images collected by Landsat 5, 7, and 8 for the study area. We found that within burned samples, there were differences in phenology for the two forest groups, while the unburned samples showed no forest group differences. Phenology patterns also differed based on fire severity. These methods take advantage of the freely available Landsat archive and can be used to characterize intra-annual fluctuations in vegetation following a variety of disturbances in the southeastern U.S. and other regions. Our approach builds on other harmonic approaches that use the Landsat archive to detect forest change, such as the Continuous Change Detection and Classification (CCDC) algorithm, and provides a tool to describe post-disturbance forest change.
      Citation: Remote Sensing
      PubDate: 2021-01-14
      DOI: 10.3390/rs13020267
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 268: Cluster-Wise Weighted NMF for
           Hyperspectral Images Unmixing with Imbalanced Data

    • Authors: Xiaochen Lv, Wenhong Wang, Hongfu Liu
      First page: 268
      Abstract: Hyperspectral unmixing is an important technique for analyzing remote sensing images which aims to obtain a collection of endmembers and their corresponding abundances. In recent years, non-negative matrix factorization (NMF) has received extensive attention due to its good adaptability for mixed data with different degrees. The majority of existing NMF-based unmixing methods are developed by incorporating additional constraints into the standard NMF based on the spectral and spatial information of hyperspectral images. However, they neglect to exploit the nature of imbalanced pixels included in the data, which may cause the pixels mixed with imbalanced endmembers to be ignored, and thus the imbalanced endmembers generally cannot be accurately estimated due to the statistical property of NMF. To exploit the information of imbalanced samples in hyperspectral data during the unmixing procedure, in this paper, a cluster-wise weighted NMF (CW-NMF) method for the unmixing of hyperspectral images with imbalanced data is proposed. Specifically, based on the result of clustering conducted on the hyperspectral image, we construct a weight matrix and introduce it into the model of standard NMF. The proposed weight matrix can provide an appropriate weight value to the reconstruction error between each original pixel and the reconstructed pixel in the unmixing procedure. In this way, the adverse effect of imbalanced samples on the statistical accuracy of NMF is expected to be reduced by assigning larger weight values to the pixels concerning imbalanced endmembers and giving smaller weight values to the pixels mixed by majority endmembers. Besides, we extend the proposed CW-NMF by introducing the sparsity constraints of abundance and graph-based regularization, respectively. The experimental results on both synthetic and real hyperspectral data have been reported, and the effectiveness of our proposed methods has been demonstrated by comparing them with several state-of-the-art methods.
      Citation: Remote Sensing
      PubDate: 2021-01-14
      DOI: 10.3390/rs13020268
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 269: Phase Imbalance Analysis of GF-3
           Along-Track InSAR Data and Ocean Current Measurements

    • Authors: Junxin Yang, Xinzhe Yuan, Bing Han, Liangbo Zhao, Jili Sun, Mingyang Shang, Xiaochen Wang, Chibiao Ding
      First page: 269
      Abstract: There are two useful methods of current measurement based on synthetic aperture radar (SAR): one is along-track interferometry (ATI), and the other is Doppler centroid analysis (DCA). For the ATI method, the interferometric phase must be accurate enough for ocean current measurements. Therefore, the space-varying of phase imbalances along the range, caused by antenna phase center position error, attitude error, antenna electronic miss pointing, antenna pattern mismatch, and other reasons, cannot be ignored. Firstly, this paper mainly analyzes the above possible factors by using real GF-3 ATI data and error model simulation results. Secondly, the ocean current is measured by the ATI method and the DCA method using the GF-3 ATI data of the ocean scene near Qingdao, China, which is up to around −1.45 m/s. The results of the two methods are in good agreement with the correlation coefficient of 0.98, the mean difference of −0.010 m/s, and the root mean squared error (RMSE) of 0.062 m/s. Moreover, the current measured by GF-3 ATI data has a standard deviation of around 0.38 m/s concerning that measured by high-frequency surface wave radar (HFSWR), which is enough to demonstrate the ability of GF-3 data to measure ocean current.
      Citation: Remote Sensing
      PubDate: 2021-01-14
      DOI: 10.3390/rs13020269
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 270: A Proof-of-Concept Algorithm for the
           Retrieval of Total Column Amount of Trace Gases in a Multi-Dimensional
           Atmosphere

    • Authors: Adrian Doicu, Dmitry S. Efremenko, Thomas Trautmann
      First page: 270
      Abstract: An algorithm for the retrieval of total column amount of trace gases in a multi-dimensional atmosphere is designed. The algorithm uses (i) certain differential radiance models with internal and external closures as inversion models, (ii) the iteratively regularized Gauss–Newton method as a regularization tool, and (iii) the spherical harmonics discrete ordinate method (SHDOM) as linearized radiative transfer model. For efficiency reasons, SHDOM is equipped with a spectral acceleration approach that combines the correlated k-distribution method with the principal component analysis. The algorithm is used to retrieve the total column amount of nitrogen for two- and three-dimensional cloudy scenes. Although for three-dimensional geometries, the computational time is high, the main concepts of the algorithm are correct and the retrieval results are accurate.
      Citation: Remote Sensing
      PubDate: 2021-01-14
      DOI: 10.3390/rs13020270
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 271: SAR Image Classification Using Fully
           Connected Conditional Random Fields Combined with Deep Learning and
           Superpixel Boundary Constraint

    • Authors: Zhensheng Sun, Miao Liu, Peng Liu, Juan Li, Tao Yu, Xingfa Gu, Jian Yang, Xiaofei Mi, Weijia Cao, Zhouwei Zhang
      First page: 271
      Abstract: As one of the most important active remote sensing technologies, synthetic aperture radar (SAR) provides advanced advantages of all-day, all-weather, and strong penetration capabilities. Due to its unique electromagnetic spectrum and imaging mechanism, the dimensions of remote sensing data have been considerably expanded. Important for fundamental research in microwave remote sensing, SAR image classification has been proven to have great value in many remote sensing applications. Many widely used SAR image classification algorithms rely on the combination of hand-designed features and machine learning classifiers, which still experience many issues that remain to be resolved and overcome, including optimized feature representation, the fuzzy confusion of speckle noise, the widespread applicability, and so on. To mitigate some of the issues and to improve the pattern recognition of high-resolution SAR images, a ConvCRF model combined with superpixel boundary constraint is developed. The proposed algorithm can successfully combine the local and global advantages of fully connected conditional random fields and deep models. An optimizing strategy using a superpixel boundary constraint in the inference iterations more efficiently preserves structure details. The experimental results demonstrate that the proposed method provides competitive advantages over other widely used models. In the land cover classification experiments using the MSTAR, E-SAR and GF-3 datasets, the overall accuracy of our proposed method achieves 90.18 ± 0.37, 91.63 ± 0.27, and 90.91 ± 0.31, respectively. Regarding the issues of SAR image classification, a novel integrated learning containing local and global image features can bring practical implications.
      Citation: Remote Sensing
      PubDate: 2021-01-14
      DOI: 10.3390/rs13020271
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 272: Integrated Drought Monitoring and
           Evaluation through Multi-Sensor Satellite-Based Statistical Simulation

    • Authors: Jong-Suk Kim, Seo-Yeon Park, Joo-Heon Lee, Jie Chen, Si Chen, Tae-Woong Kim
      First page: 272
      Abstract: To proactively respond to changes in droughts, technologies are needed to properly diagnose and predict the magnitude of droughts. Drought monitoring using satellite data is essential when local hydrogeological information is not available. The characteristics of meteorological, agricultural, and hydrological droughts can be monitored with an accurate spatial resolution. In this study, a remote sensing-based integrated drought index was extracted from 849 sub-basins in Korea’s five major river basins using multi-sensor collaborative approaches and multivariate dimensional reduction models that were calculated using monthly satellite data from 2001 to 2019. Droughts that occurred in 2001 and 2014, which are representative years of severe drought since the 2000s, were evaluated using the integrated drought index. The Bayesian principal component analysis (BPCA)-based integrated drought index proposed in this study was analyzed to reflect the timing, severity, and evolutionary pattern of meteorological, agricultural, and hydrological droughts, thereby enabling a comprehensive delivery of drought information.
      Citation: Remote Sensing
      PubDate: 2021-01-14
      DOI: 10.3390/rs13020272
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 273: UAV-Based Heating Requirement
           Determination for Frost Management in Apple Orchard

    • Authors: Wenan Yuan, Daeun Choi
      First page: 273
      Abstract: Frost is a natural disaster that can cause catastrophic damages in agriculture, while traditional temperature monitoring in orchards has disadvantages such as being imprecise and laborious, which can lead to inadequate or wasteful frost protection treatments. In this article, we presented a heating requirement assessment methodology for frost protection in an apple orchard utilizing unmanned aerial vehicle (UAV)-based thermal and RGB cameras. A thermal image stitching algorithm using the BRISK feature was developed for creating georeferenced orchard temperature maps, which attained a sub-centimeter map resolution and a stitching speed of 100 thermal images within 30 s. YOLOv4 classifiers for six apple flower bud growth stages in various network sizes were trained based on 5040 RGB images, and the best model achieved a 71.57% mAP for a test dataset consisted of 360 images. A flower bud mapping algorithm was developed to map classifier detection results into dense growth stage maps utilizing RGB image geoinformation. Heating requirement maps were created using artificial flower bud critical temperatures to simulate orchard heating demands during frost events. The results demonstrated the feasibility of the proposed orchard heating requirement determination methodology, which has the potential to be a critical component of an autonomous, precise frost management system in future studies.
      Citation: Remote Sensing
      PubDate: 2021-01-14
      DOI: 10.3390/rs13020273
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 274: Matching Large Baseline Oblique Stereo
           Images Using an End-to-End Convolutional Neural Network

    • Authors: Guobiao Yao, Alper Yilmaz, Li Zhang, Fei Meng, Haibin Ai, Fengxiang Jin
      First page: 274
      Abstract: The available stereo matching algorithms produce large number of false positive matches or only produce a few true-positives across oblique stereo images with large baseline. This undesired result happens due to the complex perspective deformation and radiometric distortion across the images. To address this problem, we propose a novel affine invariant feature matching algorithm with subpixel accuracy based on an end-to-end convolutional neural network (CNN). In our method, we adopt and modify a Hessian affine network, which we refer to as IHesAffNet, to obtain affine invariant Hessian regions using deep learning framework. To improve the correlation between corresponding features, we introduce an empirical weighted loss function (EWLF) based on the negative samples using K nearest neighbors, and then generate deep learning-based descriptors with high discrimination that is realized with our multiple hard network structure (MTHardNets). Following this step, the conjugate features are produced by using the Euclidean distance ratio as the matching metric, and the accuracy of matches are optimized through the deep learning transform based least square matching (DLT-LSM). Finally, experiments on Large baseline oblique stereo images acquired by ground close-range and unmanned aerial vehicle (UAV) verify the effectiveness of the proposed approach, and comprehensive comparisons demonstrate that our matching algorithm outperforms the state-of-art methods in terms of accuracy, distribution and correct ratio. The main contributions of this article are: (i) our proposed MTHardNets can generate high quality descriptors; and (ii) the IHesAffNet can produce substantial affine invariant corresponding features with reliable transform parameters.
      Citation: Remote Sensing
      PubDate: 2021-01-14
      DOI: 10.3390/rs13020274
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 275: A Comparison of Machine Learning
           Approaches to Improve Free Topography Data for Flood Modelling

    • Authors: Michael Meadows, Matthew Wilson
      First page: 275
      Abstract: Given the high financial and institutional cost of collecting and processing accurate topography data, many large-scale flood hazard assessments continue to rely instead on freely-available global Digital Elevation Models, despite the significant vertical biases known to affect them. To predict (and thereby reduce) these biases, we apply a fully-convolutional neural network (FCN), a form of artificial neural network originally developed for image segmentation which is capable of learning from multi-variate spatial patterns at different scales. We assess its potential by training such a model on a wide variety of remote-sensed input data (primarily multi-spectral imagery), using high-resolution, LiDAR-derived Digital Terrain Models published by the New Zealand government as the reference topography data. In parallel, two more widely used machine learning models are also trained, in order to provide benchmarks against which the novel FCN may be assessed. We find that the FCN outperforms the other models (reducing root mean square error in the testing dataset by 71%), likely due to its ability to learn from spatial patterns at multiple scales, rather than only a pixel-by-pixel basis. Significantly for flood hazard modelling applications, corrections were found to be especially effective along rivers and their floodplains. However, our results also suggest that models are likely to be biased towards the land cover and relief conditions most prevalent in their training data, with further work required to assess the importance of limiting training data inputs to those most representative of the intended application area(s).
      Citation: Remote Sensing
      PubDate: 2021-01-14
      DOI: 10.3390/rs13020275
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 276: Development of a Miniaturized Mobile
           Mapping System for In-Row, Under-Canopy Phenotyping

    • Authors: Raja Manish, Yi-Chun Lin, Radhika Ravi, Seyyed Meghdad Hasheminasab, Tian Zhou, Ayman Habib
      First page: 276
      Abstract: This paper focuses on the development of a miniaturized mobile mapping platform with advantages over current agricultural phenotyping systems in terms of acquiring data that facilitate under-canopy plant trait extraction. The system is based on an unmanned ground vehicle (UGV) for in-row, under-canopy data acquisition to deliver accurately georeferenced 2D and 3D products. The paper addresses three main aspects pertaining to the UGV development: (a) architecture of the UGV mobile mapping system (MMS), (b) quality assessment of acquired data in terms of georeferencing information as well as derived 3D point cloud, and (c) ability to derive phenotypic plant traits using data acquired by the UGV MMS. The experimental results from this study demonstrate the ability of the UGV MMS to acquire dense and accurate data over agricultural fields that would facilitate highly accurate plant phenotyping (better than above-canopy platforms such as unmanned aerial systems and high-clearance tractors). Plant centers and plant count with an accuracy in the 90% range have been achieved.
      Citation: Remote Sensing
      PubDate: 2021-01-14
      DOI: 10.3390/rs13020276
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 277: Intra-Annual Sentinel-2 Time-Series
           Supporting Grassland Habitat Discrimination

    • Authors: Cristina Tarantino, Luigi Forte, Palma Blonda, Saverio Vicario, Valeria Tomaselli, Carl Beierkuhnlein, Maria Adamo
      First page: 277
      Abstract: The present study aims to discriminate four semi-arid grassland habitats in a Mediterranean Natura 2000 site, Southern Italy, involving 6210/E1.263, 62A0/E1.55, 6220/E1.434 and X/E1.61-E1.C2-E1.C4 (according to Annex I of the European Habitat Directive/EUropean Nature Information System (EUNIS) taxonomies). For this purpose, an intra-annual time-series of 30 Sentinel-2 images, embedding phenology information, were investigated for 2018. The methodology adopted was based on a two-stage workflow employing a Support Vector Machine classifier. In the first stage only four Sentinel-2 multi-season images were analyzed, to provide an updated land cover map from where the grassland layer was extracted. The layer obtained was then used for masking the input features to the second stage. The latter stage discriminated the four grassland habitats by analyzing several input features configurations. These included multiple spectral indices selected from the time-series and the Digital Terrain Model. The results obtained from the different input configurations selected were compared to evaluate if the phenology information from time-series could improve grassland habitats discrimination. The highest F1 values (95.25% and 80.27%) were achieved for 6210/E1.263 and 6220/E1.434, respectively, whereas the results remained stable (97,33%) for 62A0/E1.55 and quite low (75,97%) for X/E1.61-E1.C2-E1.C4. However, since for all the four habitats analyzed no single configuration resulted effective, a Majority Vote algorithm was applied to achieve a reduction in classification uncertainty.
      Citation: Remote Sensing
      PubDate: 2021-01-14
      DOI: 10.3390/rs13020277
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 278: Integrating Spectral Information and
           Meteorological Data to Monitor Wheat Yellow Rust at a Regional Scale: A
           Case Study

    • Authors: Qiong Zheng, Huichun Ye, Wenjiang Huang, Yingying Dong, Hao Jiang, Chongyang Wang, Dan Li, Li Wang, Shuisen Chen
      First page: 278
      Abstract: Wheat yellow rust has a severe impact on wheat production and threatens food security in China; as such, an effective monitoring method is necessary at the regional scale. We propose a model for yellow rust monitoring based on Sentinel-2 multispectral images and a series of two-stage vegetation indices and meteorological data. Sensitive spectral vegetation indices (single- and two-stage indices) and meteorological features for wheat yellow rust discrimination were selected using the random forest method. Wheat yellow rust monitoring models were established using three different classification methods: linear discriminant analysis (LDA), support vector machine (SVM), and artificial neural network (ANN). The results show that models based on two-stage indices (i.e., those calculated using images from two different days) significantly outperform single-stage index models (i.e., those calculated using an image from a single day), the overall accuracy improved from 63.2% to 78.9%. The classification accuracies of models combining a vegetation index with meteorological feature are higher than those of pure vegetation index models. Among them, the model based on two-stage vegetation indices and meteorological features performs best, with a classification accuracy exceeding 73.7%. The SVM algorithm performed best for wheat yellow rust monitoring among the three algorithms; its classification accuracy (84.2%) was ~10.5% and 5.3% greater than those of LDA and ANN, respectively. Combined with crop growth and environmental information, our model has great potential for monitoring wheat yellow rust at a regional scale. Future work will focus on regional-scale monitoring and forecasting of crop disease.
      Citation: Remote Sensing
      PubDate: 2021-01-14
      DOI: 10.3390/rs13020278
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 279: Seasonal Variation of GPS-Derived the
           Principal Ocean Tidal Constituents’ Loading Displacement Parameters
           Based on Moving Harmonic Analysis in Hong Kong

    • Authors: Maosheng Zhou, Xin Liu, Jiajia Yuan, Xin Jin, Yupeng Niu, Jinyun Guo, Hao Gao
      First page: 279
      Abstract: The classical harmonic analysis (CHA) method only can be used to obtain the harmonic constants (amplitude and phase) of ocean tide loading displacement (OTLD). In fact, there are significant seasonal variations in the harmonic constants of OTLD. A moving harmonic analysis (MHA) method is proposed, which can effectively capture the seasonal variation of OTLD parameters. Based on 5 years of kinematic coordinate time series in direction U of six Global Positioning System (GPS) stations in Hong Kong, the MHA method is used to explore the seasonal variation of the OTLD parameters of the 6 principal tidal constituents (M2, S2, N2, K1, O1, Q1). The influence of mass loading on the seasonal variation of OTLD parameters is analyzed. The results show that there are obviously seasonal variations in OTLD parameters of the 6 principal tidal constituents in Hong Kong. The OTLD’s amplitude’s changes of the 6 principal tidal constituents are around 4–25.1% and the oscillation ranges of OTLD’s phase parameters vary from 8.8° to 20.4°. Among the seasonal variations of OTLD parameters, the annual signal, the semi-annual signal, and the ter-annual signal are the most significant. By analyzing the influence of atmospheric loading on the seasonal variation of OTLD parameters, it is found that atmospheric loading has certain contribution to the seasonal variation of OTLD parameters. Hydrological loading and non-tidal ocean loading have little influence on the seasonal variation of OTLD parameters.
      Citation: Remote Sensing
      PubDate: 2021-01-14
      DOI: 10.3390/rs13020279
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 280: An Improved Method for Retrieving
           Aerosol Optical Depth Using Gaofen-1 WFV Camera Data

    • Authors: Yang, Fan, Tao
      First page: 280
      Abstract: The four wide-field-of-view (WFV) cameras aboard the GaoFen-1 (GF-1) satellite launched by China in April 2013 have been applied to the studies of the atmospheric environment. To highlight the advantages of GF-1 data in the atmospheric environment monitoring, an improved deep blue (DB) algorithm using only four bands (visible–near infrared) of GF-1/WFV was adopted to retrieve the aerosol optical depth (AOD) at ~500 m resolution in this paper. An optimal reflectivity technique (ORT) method was proposed to construct monthly land surface reflectance (LSR) dataset through converting from MODIS LSR product according to the WFV and MODIS spectral response functions to make the relationship more suitable for GF-1/WFV. There is a good spatial coincidence between our retrieved GF-1/WFV AOD results and MODIS/Terra or Himawari-8/AHI AOD products at 550 nm, but GF-1/WFV AOD with higher resolution can better characterized the details of regional pollution. Additionally, our retrieved GF-1/WFV AOD (2016–2019) results showed a good agreement with AERONET ground-based AOD measurements, especially, at low levels of AOD. Based on the same LSR dataset transmitted from 2016–2018 MODIS LSR products, RORT of 2016–2018 and 2019 GF-1/WFV AOD retrievals can reach up to 0.88 and 0.94, respectively, while both of RMSEORT are smaller than 0.13. It is indicated that using the ORT method to deal with LSR information can make GF-1/WFV AOD retrieval algorithm more suitable and flexible.
      Citation: Remote Sensing
      PubDate: 2021-01-14
      DOI: 10.3390/rs13020280
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 281: Structured Object-Level Relational
           Reasoning CNN-Based Target Detection Algorithm in a Remote Sensing Image

    • Authors: Cheng, Li, Xu, Yao, Ding, Qin
      First page: 281
      Abstract: Deep learning technology has been extensively explored by existing methods to improve the performance of target detection in remote sensing images, due to its powerful feature extraction and representation abilities. However, these methods usually focus on the interior features of the target, but ignore the exterior semantic information around the target, especially the object-level relationship. Consequently, these methods fail to detect and recognize targets in the complex background where multiple objects crowd together. To handle this problem, a diversified context information fusion framework based on convolutional neural network (DCIFF-CNN) is proposed in this paper, which employs the structured object-level relationship to improve the target detection and recognition in complex backgrounds. The DCIFF-CNN is composed of two successive sub-networks, i.e., a multi-scale local context region proposal network (MLC-RPN) and an object-level relationship context target detection network (ORC-TDN). The MLC-RPN relies on the fine-grained details of objects to generate candidate regions in the remote sensing image. Then, the ORC-TDN utilizes the spatial context information of objects to detect and recognize targets by integrating an attentional message integrated module (AMIM) and an object relational structured graph (ORSG). The AMIM is integrated into the feed-forward CNN to highlight the useful object-level context information, while the ORSG builds the relations between a set of objects by processing their appearance features and geometric features. Finally, the target detection method based on DCIFF-CNN effectively represents the interior and exterior information of the target by exploiting both the multiscale local context information and the object-level relationships. Extensive experiments are conducted, and experimental results demonstrate that the proposed DCIFF-CNN method improves the target detection and recognition accuracy in complex backgrounds, showing superiority to other state-of-the-art methods.
      Citation: Remote Sensing
      PubDate: 2021-01-14
      DOI: 10.3390/rs13020281
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 282: 3D Characterization of Sorghum
           Panicles Using a 3D Point Cloud Derived from UAV Imagery

    • Authors: Anjin Chang, Jinha Jung, Junho Yeom, Juan Landivar
      First page: 282
      Abstract: Sorghum is one of the most important crops worldwide. An accurate and efficient high-throughput phenotyping method for individual sorghum panicles is needed for assessing genetic diversity, variety selection, and yield estimation. High-resolution imagery acquired using an unmanned aerial vehicle (UAV) provides a high-density 3D point cloud with color information. In this study, we developed a detecting and characterizing method for individual sorghum panicles using a 3D point cloud derived from UAV images. The RGB color ratio was used to filter non-panicle points out and select potential panicle points. Individual sorghum panicles were detected using the concept of tree identification. Panicle length and width were determined from potential panicle points. We proposed cylinder fitting and disk stacking to estimate individual panicle volumes, which are directly related to yield. The results showed that the correlation coefficient of the average panicle length and width between the UAV-based and ground measurements were 0.61 and 0.83, respectively. The UAV-derived panicle length and diameter were more highly correlated with the panicle weight than ground measurements. The cylinder fitting and disk stacking yielded R2 values of 0.77 and 0.67 with the actual panicle weight, respectively. The experimental results showed that the 3D point cloud derived from UAV imagery can provide reliable and consistent individual sorghum panicle parameters, which were highly correlated with ground measurements of panicle weight.
      Citation: Remote Sensing
      PubDate: 2021-01-15
      DOI: 10.3390/rs13020282
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 283: Drone-Based Remote Sensing for
           Research on Wind Erosion in Drylands: Possible Applications

    • Authors: Junzhe Zhang, Wei Guo, Bo Zhou, Gregory S. Okin
      First page: 283
      Abstract: With rapid innovations in drone, camera, and 3D photogrammetry, drone-based remote sensing can accurately and efficiently provide ultra-high resolution imagery and digital surface model (DSM) at a landscape scale. Several studies have been conducted using drone-based remote sensing to quantitatively assess the impacts of wind erosion on the vegetation communities and landforms in drylands. In this study, first, five difficulties in conducting wind erosion research through data collection from fieldwork are summarized: insufficient samples, spatial displacement with auxiliary datasets, missing volumetric information, a unidirectional view, and spatially inexplicit input. Then, five possible applications—to provide a reliable and valid sample set, to mitigate the spatial offset, to monitor soil elevation change, to evaluate the directional property of land cover, and to make spatially explicit input for ecological models—of drone-based remote sensing products are suggested. To sum up, drone-based remote sensing has become a useful method to research wind erosion in drylands, and can solve the issues caused by using data collected from fieldwork. For wind erosion research in drylands, we suggest that a drone-based remote sensing product should be used as a complement to field measurements.
      Citation: Remote Sensing
      PubDate: 2021-01-15
      DOI: 10.3390/rs13020283
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 284: Modeling Spatiotemporal Population
           Changes by Integrating DMSP-OLS and NPP-VIIRS Nighttime Light Data in
           Chongqing, China

    • Authors: Dan Lu, Yahui Wang, Qingyuan Yang, Kangchuan Su, Haozhe Zhang, Yuanqing Li
      First page: 284
      Abstract: The sustained growth of non-farm wages has led to large-scale migration of rural population to cities in China, especially in mountainous areas. It is of great significance to study the spatial and temporal pattern of population migration mentioned above for guiding population spatial optimization and the effective supply of public services in the mountainous areas. Here, we determined the spatiotemporal evolution of population in the Chongqing municipality of China from 2000–2018 by employing multi-period spatial distribution data, including nighttime light (NTL) data from the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) and the Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS). There was a power function relationship between the two datasets at the pixel scale, with a mean relative error of NTL integration of 8.19%, 4.78% less than achieved by a previous study at the provincial scale. The spatial simulations of population distribution achieved a mean relative error of 26.98%, improved the simulation accuracy for mountainous population by nearly 20% and confirmed the feasibility of this method in Chongqing. During the study period, the spatial distribution of Chongqing’s population has increased in the west and decreased in the east, while also increased in low-altitude areas and decreased in medium-high altitude areas. Population agglomeration was common in all of districts and counties and the population density of central urban areas and its surrounding areas significantly increased, while that of non-urban areas such as northeast Chongqing significantly decreased.
      Citation: Remote Sensing
      PubDate: 2021-01-15
      DOI: 10.3390/rs13020284
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 285: Sensing the Past: Perspectives on
           Collaborative Archaeology and Ground Penetrating Radar Techniques from
           Coastal California

    • Authors: Gabriel M. Sanchez, Michael A. Grone, Alec J. Apodaca, R. Scott Byram, Valentin Lopez, Roberta A. Jewett
      First page: 285
      Abstract: This paper summarizes over a decade of collaborative eco-archaeological research along the central coast of California involving researchers from the University of California, Berkeley, tribal citizens from the Amah Mutsun Tribal Band, and California Department of Parks and Recreation archaeologists. Our research employs remote sensing methods to document and assess cultural resources threatened by coastal erosion and geophysical methods to identify archaeological deposits, minimize impacts on sensitive cultural resources, and provide tribal and state collaborators with a suite of data to consider before proceeding with any form of invasive archaeological excavation. Our case study of recent eco-archaeological research developed to define the historical biogeography of threatened and endangered anadromous salmonids demonstrates how remote sensing technologies help identify dense archaeological deposits, remove barriers, and create bridges through equitable and inclusive research practices between archaeologists and the Amah Mutsun Tribal Band. These experiences have resulted in the incorporation of remote sensing techniques as a central approach of the Amah Mutsun Tribal Band when conducting archaeology in their traditional territories.
      Citation: Remote Sensing
      PubDate: 2021-01-15
      DOI: 10.3390/rs13020285
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 286: Vegetation Phenology in the Qilian
           Mountains and Its Response to Temperature from 1982 to 2014

    • Authors: Cancan Qiao, Shi Shen, Changxiu Cheng, Junxu Wu, Duo Jia, Changqing Song
      First page: 286
      Abstract: The vulnerability of vegetation ecosystems and hydrological systems in high-altitude areas makes their phenology more sensitive and their response to climate change more intense. The Qilian Mountains, an important geographic unit located in the northeastern Tibetan Plateau (TP), has experienced the more significant increases in temperature and precipitation in the past few decades than most areas of the TP. However, under such intense climate change, the temporal and spatial differences in phenology in the Qilian Mountains are not clear. This study explored the spatial and temporal heterogeneity of phenology in the Qilian Mountains from 1982 to 2014 and its response to three temperature indicators, including the mean daily temperature (Tmean), mean daily daytime temperature (Tmax), and mean daily nighttime temperature (Tmin). The results showed that (1) as the altitude rose from southeast to northwest, the multiyear mean of the start of the growing season (SOS) was gradually delayed mainly from 120 to 190 days, the multiyear mean of the end of the growing season (EOS) as a whole was advanced (from 290 to 260 days), and the multiyear mean of the length of the growing season (LGS) was gradually shortened (from 150 to 80 days). (2) In general, there was an advanced trend in the annual average SOS (0.2 days per decade), a delayed trend in the annual average EOS (0.15 days per decade), and an extended trend in the annual average LGS (0.36 days per decade) over the study period. However, there has been no significant phenological trend in recent years, especially for the SOS after 2000 and the EOS and LGS after 2003. (3) Higher preseason temperatures led to an advanced SOS and a delayed EOS at the regional scale. Moreover, the SOS and EOS were more triggered by Tmax than Tmin and Tmean. The LGS was significantly positively correlated with annual mean temperature (r = −0.82, p < 0.01).
      Citation: Remote Sensing
      PubDate: 2021-01-15
      DOI: 10.3390/rs13020286
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 287: A Survey of Active Learning for
           Quantifying Vegetation Traits from Terrestrial Earth Observation Data

    • Authors: Katja Berger, Juan Pablo Rivera Caicedo, Luca Martino, Matthias Wocher, Tobias Hank, Jochem Verrelst
      First page: 287
      Abstract: The current exponential increase of spatiotemporally explicit data streams from satellite-based Earth observation missions offers promising opportunities for global vegetation monitoring. Intelligent sampling through active learning (AL) heuristics provides a pathway for fast inference of essential vegetation variables by means of hybrid retrieval approaches, i.e., machine learning regression algorithms trained by radiative transfer model (RTM) simulations. In this study we summarize AL theory and perform a brief systematic literature survey about AL heuristics used in the context of Earth observation regression problems over terrestrial targets. Across all relevant studies it appeared that: (i) retrieval accuracy of AL-optimized training data sets outperformed models trained over large randomly sampled data sets, and (ii) Euclidean distance-based (EBD) diversity method tends to be the most efficient AL technique in terms of accuracy and computational demand. Additionally, a case study is presented based on experimental data employing both uncertainty and diversity AL criteria. Hereby, a a simulated training data base by the PROSAIL-PRO canopy RTM is used to demonstrate the benefit of AL techniques for the estimation of total leaf carotenoid content (Cxc) and leaf water content (Cw). Gaussian process regression (GPR) was incorporated to minimize and optimize the training data set with AL. Training the GPR algorithm on optimally AL-based sampled data sets led to improved variable retrievals compared to training on full data pools, which is further demonstrated on a mapping example. From these findings we can recommend the use of AL-based sub-sampling procedures to select the most informative samples out of large training data pools. This will not only optimize regression accuracy due to exclusion of redundant information, but also speed up processing time and reduce final model size of kernel-based machine learning regression algorithms, such as GPR. With this study we want to encourage further testing and implementation of AL sampling methods for hybrid retrieval workflows. AL can contribute to the solution of regression problems within the framework of operational vegetation monitoring using satellite imaging spectroscopy data, and may strongly facilitate data processing for cloud-computing platforms.
      Citation: Remote Sensing
      PubDate: 2021-01-15
      DOI: 10.3390/rs13020287
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 288: Compression of Remotely Sensed
           Astronomical Image Using Wavelet-Based Compressed Sensing in Deep Space
           Exploration

    • Authors: Yong Zhang, Jie Jiang, Guangjun Zhang
      First page: 288
      Abstract: Compression of remotely sensed astronomical images is an essential part of deep space exploration. This study proposes a wavelet-based compressed sensing (CS) algorithm for astronomical image compression in a miniaturized independent optical sensor system, which introduces a new framework for CS in the wavelet domain. The algorithm starts with a traditional 2D discrete wavelet transform (DWT), which provides frequency information of an image. The wavelet coefficients are rearranged in a new structured manner determined by the parent–child relationship between the sub-bands. We design scanning modes based on the direction information of high-frequency sub-bands, and propose an optimized measurement matrix with a double allocation of measurement rate. Through a single measurement matrix, higher measurement rates can be simultaneously allocated to sparse vectors containing more information and coefficients with higher energy in sparse vectors. The double allocation strategy can achieve better image sampling. At the decoding side, orthogonal matching pursuit (OMP) and inverse discrete wavelet transform (IDWT) are used to reconstruct the image. Experimental results on simulated image and remotely sensed astronomical images show that our algorithm can achieve high-quality reconstruction with a low measurement rate.
      Citation: Remote Sensing
      PubDate: 2021-01-15
      DOI: 10.3390/rs13020288
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 289: Mapping Seasonal Agricultural Land Use
           Types Using Deep Learning on Sentinel-2 Image Time Series

    • Authors: Misganu Debella-Gilo, Arnt Kristian Gjertsen
      First page: 289
      Abstract: The size and location of agricultural fields that are in active use and the type of use during the growing season are among the vital information that is needed for the careful planning and forecasting of agricultural production at national and regional scales. In areas where such data are not readily available, an independent seasonal monitoring method is needed. Remote sensing is a widely used tool to map land use types, although there are some limitations that can partly be circumvented by using, among others, multiple observations, careful feature selection and appropriate analysis methods. Here, we used Sentinel-2 satellite image time series (SITS) over the land area of Norway to map three agricultural land use classes: cereal crops, fodder crops (grass) and unused areas. The Multilayer Perceptron (MLP) and two variants of the Convolutional Neural Network (CNN), are implemented on SITS data of four different temporal resolutions. These enabled us to compare twelve model-dataset combinations to identify the model-dataset combination that results in the most accurate predictions. The CNN is implemented in the spectral and temporal dimensions instead of the conventional spatial dimension. Rather than using existing deep learning architectures, an autotuning procedure is implemented so that the model hyperparameters are empirically optimized during the training. The results obtained on held-out test data show that up to 94% overall accuracy and 90% Cohen’s Kappa can be obtained when the 2D CNN is applied on the SITS data with a temporal resolution of 7 days. This is closely followed by the 1D CNN on the same dataset. However, the latter performs better than the former in predicting data outside the training set. It is further observed that cereal is predicted with the highest accuracy, followed by grass. Predicting the unused areas has been found to be difficult as there is no distinct surface condition that is common for all unused areas.
      Citation: Remote Sensing
      PubDate: 2021-01-15
      DOI: 10.3390/rs13020289
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 290: Wildland Fire Tree Mortality Mapping
           from Hyperspatial Imagery Using Machine Learning

    • Authors: Dale A. Hamilton, Kamden L. Brothers, Samuel D. Jones, Jason Colwell, Jacob Winters
      First page: 290
      Abstract: The use of imagery from small unmanned aircraft systems (sUAS) has enabled the production of more accurate data about the effects of wildland fire, enabling land managers to make more informed decisions. The ability to detect trees in hyperspatial imagery enables the calculation of canopy cover. A comparison of hyperspatial post-fire canopy cover and pre-fire canopy cover from sources such as the LANDFIRE project enables the calculation of tree mortality, which is a major indicator of burn severity. A mask region-based convolutional neural network was trained to classify trees as groups of pixels from a hyperspatial orthomosaic acquired with a small unmanned aircraft system. The tree classification is summarized at 30 m, resulting in a canopy cover raster. A post-fire canopy cover is then compared to LANDFIRE canopy cover preceding the fire, calculating how much the canopy was reduced due to the fire. Canopy reduction allows the mapping of burn severity while also identifying where surface, passive crown, and active crown fire occurred within the burn perimeter. Canopy cover mapped through this effort was lower than the LANDFIRE Canopy Cover product, which literature indicated is typically over reported. Assessment of canopy reduction mapping on a wildland fire reflects observations made both from ground truthing efforts as well as observations made of the associated hyperspatial sUAS orthomosaic.
      Citation: Remote Sensing
      PubDate: 2021-01-15
      DOI: 10.3390/rs13020290
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 291: Unraveling the Morphological
           Constraints on Roman Gold Mining Hydraulic Infrastructure in NW Spain. A
           UAV-Derived Photogrammetric and Multispectral Approach

    • Authors: Javier Fernández-Lozano, Enoc Sanz-Ablanedo
      First page: 291
      Abstract: The province of León preserves a unique hydraulic infrastructure 1200 km-long, used for the exploitation of auriferous deposits in Roman times. It represents the most extensive waterworks in Europe and is one of the best-preserved examples of mining heritage in Antiquity. In this work, three mining exploitation sectors (upper, middle, and lower) characterized by channels and leats developed in different geological materials were examined, using Unmanned Aerial Vehicles (UAVs). A multi-approach based on a comparison of photogrammetric and multispectral data improved the identification and description of the hydraulic network. Comparison with traditional orthoimages and LiDAR data suggests that UAV-derived multispectral images are of great interest in areas where these sets of data have low resolution or areas that are densely covered by vegetation. The results showed that the size of the channel box and its width were factors that do not depend exclusively on the available water resources, as previously suggested, but also on the geological and hydraulic conditioning factors that intervene in each sector. Additionally, the detailed study allowed the establishment of a water sheet maximum height that was much lower than previously thought. All in all, these inferences might help researchers develop new strategies for mapping the Roman mining infrastructure and establishing the importance of geological inheritance on the construction of the hydraulic system that led the Romans to the accomplishment of the largest mining infrastructure ever known in Europe.
      Citation: Remote Sensing
      PubDate: 2021-01-15
      DOI: 10.3390/rs13020291
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 292: Imaging Spectroscopy for Conservation
           Applications

    • Authors: Megan Seeley, Gregory P. Asner
      First page: 292
      Abstract: As humans continue to alter Earth systems, conservationists look to remote sensing to monitor, inventory, and understand ecosystems and ecosystem processes at large spatial scales. Multispectral remote sensing data are commonly integrated into conservation decision-making frameworks, yet imaging spectroscopy, or hyperspectral remote sensing, is underutilized in conservation. The high spectral resolution of imaging spectrometers captures the chemistry of Earth surfaces, whereas multispectral satellites indirectly represent such surfaces through band ratios. Here, we present case studies wherein imaging spectroscopy was used to inform and improve conservation decision-making and discuss potential future applications. These case studies include a broad array of conservation areas, including forest, dryland, and marine ecosystems, as well as urban applications and methane monitoring. Imaging spectroscopy technology is rapidly developing, especially with regard to satellite-based spectrometers. Improving on and expanding existing applications of imaging spectroscopy to conservation, developing imaging spectroscopy data products for use by other researchers and decision-makers, and pioneering novel uses of imaging spectroscopy will greatly expand the toolset for conservation decision-makers.
      Citation: Remote Sensing
      PubDate: 2021-01-15
      DOI: 10.3390/rs13020292
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 293: Improve the Accuracy of Water Storage
           Estimation—A Case Study from Two Lakes in the Hohxil Region of North
           Tibetan Plateau

    • Authors: Qiao, Ju, Zhu, Chen, Kai, Kou
      First page: 293
      Abstract: Lake water storage is essential information for lake research. Previous studies usually used bathymetric data to acquire underwater topography by interpolation method, and to therefore estimate water storage. However, due to the large area of Tibetan Plateau (TP) lakes, the method of bathymetry was challenging to cover the whole region of one lake, and the accuracy of the underwater topography, in which no bathymetric data covered, was low, which resulted in a comparatively large error of lake water storage estimation and its change. In this study, we used Shuttle Radar Topography Mission (SRTM) and in situ bathymetric data to establish the underwater topography of Hohxil Lake (HL) and Lexiewudan Lake (LL) in the Hohxil Region of North TP and estimate and analyzed the changes of lake level and water storage. The results showed HL and LL’s water storage was 5.12 km3 and 5.31 km3 in 2019, respectively, and their level increased by 0.5 m/y and 0.57 m/y during 2003−2018, respectively. They were consistent with those (0.5 m/y and 0.5 m/y) from altimetry data, and they were much more accurate than those results (0.077 m/y and 0.156 m/y) from bathymetric data. These findings indicated that this method could improve the accuracy of lake water storage and change estimation. We estimated water storage of two lakes by combining with multitemporal Landsat images, which had doubled since 1976. Our results suggested that the increasing precipitation may dominate the lake expansion by comparing with the change of temperature and precipitation and the increasing glacial meltwater contributed approximately 4.8% and 10.7% to lake expansion of HL and LL during 2000–2019 based on the glacier mass balance data, respectively.
      Citation: Remote Sensing
      PubDate: 2021-01-15
      DOI: 10.3390/rs13020293
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 294: DR-Net: An Improved Network for
           Building Extraction from High Resolution Remote Sensing Image

    • Authors: Meng Chen, Jianjun Wu, Leizhen Liu, Wenhui Zhao, Feng Tian, Qiu Shen, Bingyu Zhao, Ruohua Du
      First page: 294
      Abstract: At present, convolutional neural networks (CNN) have been widely used in building extraction from remote sensing imagery (RSI), but there are still some bottlenecks. On the one hand, there are so many parameters in the previous network with complex structure, which will occupy lots of memories and consume much time during training process. On the other hand, low-level features extracted by shallow layers and abstract features extracted by deep layers of artificial neural network cannot be fully fused, which leads to an inaccurate building extraction from RSI. To alleviate these disadvantages, a dense residual neural network (DR-Net) was proposed in this paper. DR-Net uses a deeplabv3+Net encoder/decoder backbone, in combination with densely connected convolution neural network (DCNN) and residual network (ResNet) structure. Compared with deeplabv3+net (containing about 41 million parameters) and BRRNet (containing about 17 million parameters), DR-Net contains about 9 million parameters; So, the number of parameters reduced a lot. The experimental results for both the WHU Building Dataset and Massachusetts Building Dataset, DR-Net show better performance in building extraction than other two state-of-the-art methods. Experiments on WHU building data set showed that Intersection over Union (IoU) increased by 2.4% and F1 score increased by 1.4%; in terms of Massachusetts Building Dataset, IoU increased by 3.8% and F1 score increased by 2.9%.
      Citation: Remote Sensing
      PubDate: 2021-01-15
      DOI: 10.3390/rs13020294
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 295: A Task-Driven Invertible Projection
           Matrix Learning Algorithm for Hyperspectral Compressed Sensing

    • Authors: Dai, Liu, Wang, Li
      First page: 295
      Abstract: The high complexity of the reconstruction algorithm is the main bottleneck of the hyperspectral image (HSI) compression technology based on compressed sensing. Compressed sensing technology is an important tool for retrieving the maximum number of HSI scenes on the ground. However, the complexity of the compressed sensing algorithm is limited by the energy and hardware of spaceborne equipment. Aiming at the high complexity of compressed sensing reconstruction algorithm and low reconstruction accuracy, an equivalent model of the invertible transformation is theoretically derived by us in the paper, which can convert the complex invertible projection training model into the coupled dictionary training model. Besides, aiming at the invertible projection training model, the most competitive task-driven invertible projection matrix learning algorithm (TIPML) is proposed. In TIPML, we don’t need to directly train the complex invertible projection model, but indirectly train the invertible projection model through the training of the coupled dictionary. In order to improve the accuracy of reconstructed data, in the paper, the singular value transformation is proposed. It has been verified that the concentration of the dictionary is increased and that the expressive ability of the dictionary has not been reduced by the transformation. Besides, two-loop iterative training is established to improve the accuracy of data reconstruction. Experiments show that, compared with the traditional compressed sensing algorithm, the compressed sensing algorithm based on TIPML has higher reconstruction accuracy, and the reconstruction time is shortened by more than a hundred times. It is foreseeable that the TIPML algorithm will have a huge application prospect in the field of HSI compression.
      Citation: Remote Sensing
      PubDate: 2021-01-15
      DOI: 10.3390/rs13020295
      Issue No: Vol. 13, No. 2 (2021)
       
  • Remote Sensing, Vol. 13, Pages 296: Sequence Image Interpolation via
           Separable Convolution Network

    • Authors: Jin, Tang, Houet, Corpetti, Alvarez-Vanhard, Zhang
      First page: 296
      Abstract: Remote-sensing time-series data are significant for global environmental change research and a better understanding of the Earth. However, remote-sensing acquisitions often provide sparse time series due to sensor resolution limitations and environmental factors, such as cloud noise for optical data. Image interpolation is the method that is often used to deal with this issue. This paper considers the deep learning method to learn the complex mapping of an interpolated intermediate image from predecessor and successor images, called separable convolution network for sequence image interpolation. The separable convolution network uses a separable 1D convolution kernel instead of 2D kernels to capture the spatial characteristics of input sequence images and then is trained end-to-end using sequence images. Our experiments, which were performed with unmanned aerial vehicle (UAV) and Landsat-8 datasets, show that the method is effective to produce high-quality time-series interpolated images, and the data-driven deep model can better simulate complex and diverse nonlinear image data information.
      Citation: Remote Sensing
      PubDate: 2021-01-15
      DOI: 10.3390/rs13020296
      Issue No: Vol. 13, No. 2 (2021)
       
 
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