Subjects -> INSTRUMENTS (Total: 63 journals)
Showing 1 - 16 of 16 Journals sorted alphabetically
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: 6)
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: 282)
International Journal of Remote Sensing Applications     Open Access   (Followers: 45)
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: 2)
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: 3)
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 Instruments / Метрологія та прилади     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: 55)
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: 23)
Science of Remote Sensing     Open Access  
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  
Similar Journals
Journal Cover
Remote Sensing
Journal Prestige (SJR): 1.386
Citation Impact (citeScore): 4
Number of Followers: 55  

  This is an Open Access Journal Open Access journal
ISSN (Print) 2072-4292
Published by MDPI Homepage  [233 journals]
  • Remote Sensing, Vol. 13, Pages 1837: Towards Vine Water Status Monitoring
           on a Large Scale Using Sentinel-2 Images

    • Authors: Eve Laroche-Pinel, Sylvie Duthoit, Mohanad Albughdadi, Anne D. Costard, Jacques Rousseau, Véronique Chéret, Harold Clenet
      First page: 1837
      Abstract: Wine growing needs to adapt to confront climate change. In fact, the lack of water becomes more and more important in many regions. Whereas vineyards have been located in dry areas for decades, so they need special resilient varieties and/or a sufficient water supply at key development stages in case of severe drought. With climate change and the decrease of water availability, some vineyard regions face difficulties because of unsuitable variety, wrong vine management or due to the limited water access. Decision support tools are therefore required to optimize water use or to adapt agronomic practices. This study aimed at monitoring vine water status at a large scale with Sentinel-2 images. The goal was to provide a solution that would give spatialized and temporal information throughout the season on the water status of the vines. For this purpose, thirty six plots were monitored in total over three years (2018, 2019 and 2020). Vine water status was measured with stem water potential in field measurements from pea size to ripening stage. Simultaneously Sentinel-2 images were downloaded and processed to extract band reflectance values and compute vegetation indices. In our study, we tested five supervised regression machine learning algorithms to find possible relationships between stem water potential and data acquired from Sentinel-2 images (bands reflectance values and vegetation indices). Regression model using Red, NIR, Red-Edge and SWIR bands gave promising result to predict stem water potential (R2=0.40, RMSE=0.26).
      Citation: Remote Sensing
      PubDate: 2021-05-09
      DOI: 10.3390/rs13091837
      Issue No: Vol. 13, No. 9 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1838: Assessing the Accuracy of
           ALOS/PALSAR-2 and Sentinel-1 Radar Images in Estimating the Land
           Subsidence of Coastal Areas: A Case Study in Alexandria City, Egypt

    • Authors: Noura Darwish, Mona Kaiser, Magaly Koch, Ahmed Gaber
      First page: 1838
      Abstract: Recently, the Differential Interferometric Synthetic Aperture Radar (DInSAR) technique is widely used for quantifying the land surface deformation, which is very important to assess the potential impact on social and economic activities. Radar satellites operate in different wavelengths and each provides different levels of vertical displacement accuracy. In this study, the accuracies of Sentinel-1 (C-band) and ALOS/PALSAR-2 (L-band) were investigated in terms of estimating the land subsidence rate along the study area of Alexandria City, Egypt. A total of nine Sentinel-1 and 11 ALOS/PALSAR-2 scenes were used for such assessment. The small baseline subset (SBAS) processing scheme, which detects the land deformation with a high spatial and temporal coverage, was performed. The results show that the threshold coherence values of the generated interferograms from ALOS-2 data are highly concentrated between 0.2 and 0.3, while a higher threshold value of 0.4 shows no coherent pixels for about 80% of Alexandria’s urban area. However, the coherence values of Sentinel-1 interferograms ranged between 0.3 and 1, with most of the urban area in Alexandria showing coherent pixels at a 0.4 value. In addition, both data types produced different residual topography values of almost 0 m with a standard deviation of 13.5 m for Sentinel-1 and −20.5 m with a standard deviation of 33.24 m for ALOS-2 using the same digital elevation model (DEM) and wavelet number. Consequently, the final deformation was estimated using high coherent pixels with a threshold of 0.4 for Sentinel-1, which is comparable to a threshold of about 0.8 when using ALOS-2 data. The cumulative vertical displacement along the study area from 2017 to 2020 reached −60 mm with an average of −12.5 mm and mean displacement rate of −1.73 mm/year. Accordingly, the Alexandrian coastal plain and city center are found to be relatively stable, with land subsidence rates ranging from 0 to −5 mm/year. The maximum subsidence rate reached −20 mm/yr and was found along the boundary of Mariout Lakes and former Abu Qir Lagoon. Finally, the affected buildings recorded during the field survey were plotted on the final land subsidence maps and show high consistency with the DInSAR results. For future developmental urban plans in Alexandria City, it is recommended to expand towards the western desert fringes instead of the south where the present-day ground lies on top of the former wetland areas.
      Citation: Remote Sensing
      PubDate: 2021-05-09
      DOI: 10.3390/rs13091838
      Issue No: Vol. 13, No. 9 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1839: Seasonal Trends in Movement Patterns
           of Birds and Insects Aloft Simultaneously Recorded by Radar

    • Authors: Xu Shi, Baptiste Schmid, Philippe Tschanz, Gernot Segelbacher, Felix Liechti
      First page: 1839
      Abstract: Airspace is a key but not well-understood habitat for many animal species. Enormous amounts of insects and birds use the airspace to forage, disperse, and migrate. Despite numerous studies on migration, the year-round flight activities of both birds and insects are still poorly studied. We used a 2 year dataset from a vertical-looking radar in Central Europe and developed an iterative hypothesis-testing algorithm to investigate the general temporal pattern of migratory and local movements. We estimated at least 3 million bird and 20 million insect passages over a 1 km transect annually. Most surprisingly, peak non-directional bird movement intensities during summer were of the same magnitude as seasonal directional movement peaks. Birds showed clear peaks in seasonally directional movements during day and night, coinciding well with the main migration period documented in this region. Directional insect movements occurred throughout the year, paralleling non-directional movements. In spring and summer, insect movements were non-directional; in autumn, their movements concentrated toward the southwest, similar to birds. Notably, the nocturnal movements of insects did not appear until April, while directional movements mainly occurred in autumn. This simple monitoring reveals how little we still know about the movement of biomass through airspace.
      Citation: Remote Sensing
      PubDate: 2021-05-09
      DOI: 10.3390/rs13091839
      Issue No: Vol. 13, No. 9 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1840: The Intra-Tidal Characteristics of
           Tidal Front and Their Spring–Neap Tidal and Seasonal Variations in Bungo
           Channel, Japan

    • Authors: Menghong Dong, Xinyu Guo
      First page: 1840
      Abstract: The intra-tidal variations of a tidal front in Bungo Channel, Japan and their dependence on the spring–neap tidal cycle and month were analyzed utilizing high-resolution (~2 km) hourly sea surface temperature (SST) data obtained from a Himawari-8 geostationary satellite from April 2016 to August 2020. A gradient-based front detection method was utilized to define the position and intensity of the front. Similar to previous ship-based studies, SST data were utilized to identify tidal fronts between a well-mixed strait and its surrounding stratified area. The hourly SST data confirmed the theoretical intra-tidal movement of the tidal front, which is mainly controlled by tidal current advection. Notably, the intensity of the front increases during the ebb current phase, which carries the front toward the stratified area, but decreases during the flood current phase that drives the front in the opposite direction. Due to a strong dependence on tidal currents, the intra-tidal variations appear in a fortnight cycle, and the fortnightly variations of the front are dependent on the month in which the background stratification and residual current changes occur. Additionally, tidal current convergence and divergence are posited to cause tidal front intensification and weakening.
      Citation: Remote Sensing
      PubDate: 2021-05-09
      DOI: 10.3390/rs13091840
      Issue No: Vol. 13, No. 9 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1841: Impact of Assimilating FY-3D MWTS-2
           Upper Air Sounding Data on Forecasting Typhoon Lekima (2019)

    • Authors: Zeyi Niu, Lei Zhang, Peiming Dong, Fuzhong Weng, Wei Huang
      First page: 1841
      Abstract: In this study, the Fengyun-3D (FY-3D) clear-sky microwave temperature sounder-2 (MWTS-2) radiances were directly assimilated in the regional mesoscale Weather Research and Forecasting (WRF) model using the Gridpoint Statistical Interpolation (GSI) data assimilation system. The assimilation experiments were conducted to compare the track errors of typhoon Lekima from uses of the Advanced Microwave Sounding Unit-A (AMSU-A) radiances (EXP_AD) with those from FY-3D MWTS-2 upper-air sounding data at channels 5–7 (EXP_AMD). The clear-sky mean bias-corrected observation-minus-background (O-B) values of FY-3D MWTS-2 channels 5, 6, and 7 are 0.27, 0.10 and 0.57 K, respectively, which are smaller than those without bias corrections. Compared with the control experiment, which was the forecast of the WRF model without use of satellite data, the assimilation of satellite radiances can improve the forecast performance and reduce the mean track error by 8.7% (~18.4 km) and 30% (~58.6 km) beyond 36 h through the EXP_AD and EXP_AMD, respectively. The direction of simulated steering flow changed from southwest in the EXP_AD to southeast in the EXP_AMD, which can be pivotal to forecasting the landfall of typhoon Lekima (2019) three days in advance. Assimilation of MWTS-2 upper-troposphere channels 5–7 has great potential to improve the track forecasts for typhoon Lekima.
      Citation: Remote Sensing
      PubDate: 2021-05-09
      DOI: 10.3390/rs13091841
      Issue No: Vol. 13, No. 9 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1842: Analyzing the Performance of GPS Data
           for Earthquake Prediction

    • Authors: Valeri Gitis, Alexander Derendyaev, Konstantin Petrov
      First page: 1842
      Abstract: The results of earthquake prediction largely depend on the quality of data and the methods of their joint processing. At present, for a number of regions, it is possible, in addition to data from earthquake catalogs, to use space geodesy data obtained with the help of GPS. The purpose of our study is to evaluate the efficiency of using the time series of displacements of the Earth’s surface according to GPS data for the systematic prediction of earthquakes. The criterion of efficiency is the probability of successful prediction of an earthquake with a limited size of the alarm zone. We use a machine learning method, namely the method of the minimum area of alarm, to predict earthquakes with a magnitude greater than 6.0 and a hypocenter depth of up to 60 km, which occurred from 2016 to 2020 in Japan, and earthquakes with a magnitude greater than 5.5. and a hypocenter depth of up to 60 km, which happened from 2013 to 2020 in California. For each region, we compare the following results: random forecast of earthquakes, forecast obtained with the field of spatial density of earthquake epicenters, forecast obtained with spatio-temporal fields based on GPS data, based on seismological data, and based on combined GPS data and seismological data. The results confirm the effectiveness of using GPS data for the systematic prediction of earthquakes.
      Citation: Remote Sensing
      PubDate: 2021-05-09
      DOI: 10.3390/rs13091842
      Issue No: Vol. 13, No. 9 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1843: Distribution and Attribution of
           Terrestrial Snow Cover Phenology Changes over the Northern Hemisphere
           during 2001–2020

    • Authors: Xiaona Chen, Yaping Yang, Yingzhao Ma, Huan Li
      First page: 1843
      Abstract: Snow cover phenology has exhibited dramatic changes in the past decades. However, the distribution and attribution of the hemispheric scale snow cover phenology anomalies remain unclear. Using satellite-retrieved snow cover products, ground observations, and reanalysis climate variables, this study explored the distribution and attribution of snow onset date, snow end date, and snow duration days over the Northern Hemisphere from 2001 to 2020. The latitudinal and altitudinal distributions of the 20-year averaged snow onset date, snow end date, and snow duration days are well represented by satellite-retrieved snow cover phenology matrixes. The validation results by using 850 ground snow stations demonstrated that satellite-retrieved snow cover phenology matrixes capture the spatial variability of the snow onset date, snow end date, and snow duration days at the 95% significance level during the overlapping period of 2001–2017. Moreover, a delayed snow onset date and an earlier snow end date (1.12 days decade−1, p < 0.05) are detected over the Northern Hemisphere during 2001–2020 based on the satellite-retrieved snow cover phenology matrixes. In addition, the attribution analysis indicated that snow end date dominates snow cover phenology changes and that an increased melting season temperature is the key driving factor of snow end date anomalies over the NH during 2001–2020. These results are helpful in understanding recent snow cover change and can contribute to climate projection studies.
      Citation: Remote Sensing
      PubDate: 2021-05-09
      DOI: 10.3390/rs13091843
      Issue No: Vol. 13, No. 9 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1844: Terrain Proxy-Based Site
           Classification for Seismic Zonation in North Korea within a Geospatial
           Data-Driven Workflow

    • Authors: Han-Saem Kim, Chang-Guk Sun, Moon-Gyo Lee, Hyung-Ik Cho
      First page: 1844
      Abstract: Numerous seismic activities occur in North Korea. However, it is difficult to perform seismic hazard assessment and obtain zonal data in the Korean Peninsula, including North Korea, when applying parametric or nonparametric methods. Remote sensing can be implemented for soil characterization or spatial zonation studies on irregular, surficial, and subsurface systems of inaccessible areas. Herein, a data-driven workflow for extracting the principal features using a digital terrain model (DTM) is proposed. In addition, geospatial grid information containing terrain features and the average shear wave velocity in the top 30 m of the subsurface (VS30) are employed using geostatistical interpolation methods; machine learning (ML)-based regression models were optimized and VS30-based seismic zonation in the test areas in North Korea were forecasted. The interrelationships between VS30 and terrain proxy (elevation, slope, and landform class) in the training area in South Korea were verified to define the input layer in regression models. The landform class represents a new proxy of VS30 and was subgrouped according to the correlation with grid-based VS30. The geospatial grid information was generated via the optimum geostatistical interpolation method (i.e., sequential Gaussian simulation (SGS)). The best-fitting model among four ML methods was determined by evaluating cost function-based prediction performance, performing uncertainty analysis for the empirical correlations of VS30, and studying spatial correspondence with the borehole-based VS30 map. Subsequently, the best-fitting regression models were designed by training the geospatial grid in South Korea. Then, DTM and its terrain features were constructed along with VS30 maps for three major cities (Pyongyang, Kaesong, and Nampo) in North Korea. A similar distribution of the VS30 grid obtained using SGS was shown in the multilayer perceptron-based VS30 map.
      Citation: Remote Sensing
      PubDate: 2021-05-09
      DOI: 10.3390/rs13091844
      Issue No: Vol. 13, No. 9 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1845: Quantifying the Response of German
           Forests to Drought Events via Satellite Imagery

    • Authors: Marius Philipp, Martin Wegmann, Carina Kübert-Flock
      First page: 1845
      Abstract: Forest systems provide crucial ecosystem functions to our environment, such as balancing carbon stocks and influencing the local, regional and global climate. A trend towards an increasing frequency of climate change induced extreme weather events, including drought, is hereby a major challenge for forest management. Within this context, the application of remote sensing data provides a powerful means for fast, operational and inexpensive investigations over large spatial scales and time. This study was dedicated to explore the potential of satellite data in combination with harmonic analyses for quantifying the vegetation response to drought events in German forests. The harmonic modelling method was compared with a z-score standardization approach and correlated against both, meteorological and topographical data. Optical satellite imagery from Landsat and the Moderate Resolution Imaging Spectroradiometer (MODIS) was used in combination with three commonly applied vegetation indices. Highest correlation scores based on the harmonic modelling technique were computed for the 6th harmonic degree. MODIS imagery in combination with the Normalized Difference Vegetation Index (NDVI) generated hereby best results for measuring spectral response to drought conditions. Strongest correlation between remote sensing data and meteorological measures were observed for soil moisture and the self-calibrated Palmer Drought Severity Index (scPDSI). Furthermore, forests regions over sandy soils with pine as the dominant tree type were identified to be particularly vulnerable to drought. In addition, topographical analyses suggested mitigated drought affects along hill slopes. While the proposed approaches provide valuable information about vegetation dynamics as a response to meteorological weather conditions, standardized in-situ measurements over larger spatial scales and related to drought quantification are required for further in-depth quality assessment of the used methods and data.
      Citation: Remote Sensing
      PubDate: 2021-05-09
      DOI: 10.3390/rs13091845
      Issue No: Vol. 13, No. 9 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1846: Identifying Spatial and Temporal
           Variations in Concrete Bridges with Ground Penetrating Radar Attributes

    • Authors: Vivek Kumar, Isabel M. Morris, Santiago A. Lopez, Branko Glisic
      First page: 1846
      Abstract: Estimating variations in material properties over space and time is essential for the purposes of structural health monitoring (SHM), mandated inspection, and insurance of civil infrastructure. Properties such as compressive strength evolve over time and are reflective of the overall condition of the aging infrastructure. Concrete structures pose an additional challenge due to the inherent spatial variability of material properties over large length scales. In recent years, nondestructive approaches such as rebound hammer and ultrasonic velocity have been used to determine the in situ material properties of concrete with a focus on the compressive strength. However, these methods require personnel expertise, careful data collection, and high investment. This paper presents a novel approach using ground penetrating radar (GPR) to estimate the variability of in situ material properties over time and space for assessment of concrete bridges. The results show that attributes (or features) of the GPR data such as raw average amplitudes can be used to identify differences in compressive strength across the deck of a concrete bridge. Attributes such as instantaneous amplitudes and intensity of reflected waves are useful in predicting the material properties such as compressive strength, porosity, and density. For compressive strength, one alternative approach of the Maturity Index (MI) was used to estimate the present values and compare with GPR estimated values. The results show that GPR attributes could be successfully used for identifying spatial and temporal variation of concrete properties. Finally, discussions are presented regarding their suitability and limitations for field applications.
      Citation: Remote Sensing
      PubDate: 2021-05-09
      DOI: 10.3390/rs13091846
      Issue No: Vol. 13, No. 9 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1847: Comparing PlanetScope to Landsat-8
           and Sentinel-2 for Sensing Water Quality in Reservoirs in Agricultural
           Watersheds

    • Authors: Abubakarr S. Mansaray, Andrew R. Dzialowski, Meghan E. Martin, Kevin L. Wagner, Hamed Gholizadeh, Scott H. Stoodley
      First page: 1847
      Abstract: Agricultural runoff transports sediments and nutrients that deteriorate water quality erratically, posing a challenge to ground-based monitoring. Satellites provide data at spatial-temporal scales that can be used for water quality monitoring. PlanetScope nanosatellites have spatial (3 m) and temporal (daily) resolutions that may help improve water quality monitoring compared to coarser-resolution satellites. This work compared PlanetScope to Landsat-8 and Sentinel-2 in their ability to detect key water quality parameters. Spectral bands of each satellite were regressed against chlorophyll a, turbidity, and Secchi depth data from 13 reservoirs in Oklahoma over three years (2017–2020). We developed significant regression models for each satellite. Landsat-8 and Sentinel-2 explained more variation in chlorophyll a than PlanetScope, likely because they have more spectral bands. PlanetScope and Sentinel-2 explained relatively similar amounts of variations in turbidity and Secchi Disk data, while Landsat-8 explained less variation in these parameters. Since PlanetScope is a commercial satellite, its application may be limited to cases where the application of coarser-resolution satellites is not feasible. We identified scenarios where PS may be more beneficial than Landsat-8 and Sentinel-2. These include measuring water quality parameters that vary daily, in small ponds and narrow coves of reservoirs, and at reservoir edges.
      Citation: Remote Sensing
      PubDate: 2021-05-09
      DOI: 10.3390/rs13091847
      Issue No: Vol. 13, No. 9 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1848: Estimation of Long-Term Surface
           Downward Longwave Radiation over the Global Land from 2000 to 2018

    • Authors: Chunjie Feng, Xiaotong Zhang, Yu Wei, Weiyu Zhang, Ning Hou, Jiawen Xu, Shuyue Yang, Xianhong Xie, Bo Jiang
      First page: 1848
      Abstract: It is of great importance for climate change studies to construct a worldwide, long-term surface downward longwave radiation (Ld, 4–100 μm) dataset. Although a number of global Ld datasets are available, their low accuracies and coarse spatial resolutions limit their applications. This study generated a daily Ld dataset with a 5-km spatial resolution over the global land surface from 2000 to 2018 using atmospheric parameters, which include 2-m air temperature (Ta), relative humidity (RH) at 1000 hPa, total column water vapor (TCWV), surface downward shortwave radiation (Sd), and elevation, based on the gradient boosting regression tree (GBRT) method. The generated Ld dataset was evaluated using ground measurements collected from AmeriFlux, AsiaFlux, baseline surface radiation network (BSRN), surface radiation budget network (SURFRAD), and FLUXNET networks. The validation results showed that the root mean square error (RMSE), mean bias error (MBE), and correlation coefficient (R) values of the generated daily Ld dataset were 17.78 W m−2, 0.99 W m−2, and 0.96 (p < 0.01). Comparisons with other global land surface radiation products indicated that the generated Ld dataset performed better than the clouds and earth’s radiant energy system synoptic (CERES-SYN) edition 4.1 dataset and ERA5 reanalysis product at the selected sites. In addition, the analysis of the spatiotemporal characteristics for the generated Ld dataset showed an increasing trend of 1.8 W m−2 per decade (p < 0.01) from 2003 to 2018, which was closely related to Ta and water vapor pressure. In general, the generated Ld dataset has a higher spatial resolution and accuracy, which can contribute to perfect the existing radiation products.
      Citation: Remote Sensing
      PubDate: 2021-05-09
      DOI: 10.3390/rs13091848
      Issue No: Vol. 13, No. 9 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1849: Evaluation of Light Pollution in
           Global Protected Areas from 1992 to 2018

    • Authors: Haowei Mu, Xuecao Li, Xiaoping Du, Jianxi Huang, Wei Su, Tengyun Hu, Yanan Wen, Peiyi Yin, Yuan Han, Fei Xue
      First page: 1849
      Abstract: Light pollution, a phenomenon in which artificial nighttime light (NTL) changes the form of brightness and darkness in natural areas such as protected areas (PAs), has become a global concern due to its threat to global biodiversity. With ongoing global urbanization and climate change, the light pollution status in global PAs deserves attention for mitigation and adaptation. In this study, we developed a framework to evaluate the light pollution status in global PAs, using the global NTL time series data. First, we classified global PAs (30,624) into three pollution categories: non-polluted (5974), continuously polluted (8141), and discontinuously polluted (16,509), according to the time of occurrence of lit pixels in/around PAs from 1992 to 2018. Then, we explored the NTL intensity (e.g., digital numbers) and its trend in those polluted PAs and identified those hotspots of PAs at the global scale with consideration of global urbanization. Our study shows that global light pollution is mainly distributed within the range of 30°N and 60°N, including Europe, north America, and East Asia. Although the temporal trend of NTL intensity in global PAs is increasing, Japan and the United States of America (USA) have opposite trends due to the implementation of well-planned ecological conservation policies and declining population growth. For most polluted PAs, the lit pixels are close to their boundaries (i.e., less than 10 km), and the NTL in/around these lit areas has become stronger over the past decades. The identified hotspots of PAs (e.g., Europe, the USA, and East Asia) help support decisions on global biodiversity conservation, particularly with global urbanization and climate change.
      Citation: Remote Sensing
      PubDate: 2021-05-09
      DOI: 10.3390/rs13091849
      Issue No: Vol. 13, No. 9 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1850: Towards the Spectral Mapping of
           Plastic Debris on Beaches

    • Authors: Jenna A. Guffogg, Mariela Soto-Berelov, Simon D. Jones, Chris J. Bellman, Jennifer L. Lavers, Andrew K. Skidmore
      First page: 1850
      Abstract: Floating and washed ashore marine plastic debris (MPD) is a growing environmental challenge. It has become evident that secluded locations including the Arctic, Antarctic, and remote islands are being impacted by plastic pollution generated thousands of kilometers away. Optical remote sensing of MPD is an emerging field that can aid in monitoring remote environments where in-person observation and data collection is not always feasible. Here we evaluate MPD spectral features in the visible to shortwave infrared regions for detecting varying quantities of MPD that have accumulated on beaches using a spectroradiometer. Measurements were taken from a range of in situ MPD accumulations ranging from 0.08% to 7.94% surface coverage. Our results suggest that spectral absorption features at 1215 nm and 1732 nm are useful for detecting varying abundance levels of MPD in a complex natural environment, however other absorption features at 931 nm, 1045 nm and 2046 nm could not detect in situ MPD. The reflectance of some in situ MPD accumulations was statistically different from samples that only contained organic debris and sand between 1.56% and 7.94% surface cover; however other samples with similar surface cover did not have reflectance that was statistically different from samples containing no MPD. Despite MPD being detectable against a background of sand and organic beach debris, a clear relationship between the surface cover of MPD and the strength of key absorption features could not be established. Additional research is needed to advance our understanding of the factors, such as type of MPD assemblage, that contribute to the bulk reflectance of MPD contaminated landscapes.
      Citation: Remote Sensing
      PubDate: 2021-05-10
      DOI: 10.3390/rs13091850
      Issue No: Vol. 13, No. 9 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1851: Monitoring Terrestrial Water Storage
           Changes with the Tongji-Grace2018 Model in the Nine Major River Basins of
           the Chinese Mainland

    • Authors: Zhiwei Chen, Xingfu Zhang, Jianhua Chen
      First page: 1851
      Abstract: Data from the Gravity Recovery and Climate Experiment (GRACE) satellite mission can be used to monitor changes in terrestrial water storage (TWS). In this study, we exploit the TWS observations from a new temporal gravity field model, Tongji-Grace2018, which was developed using an optimized short-arc approach at Tongji University. We analyzed the changes in the TWS and groundwater storage (GWS) in each of the nine major river basins of the Chinese mainland from April 2002 to August 2016, using Tongji-Grace2018, the Global Land Data Assimilation System (GLDAS) hydrological model, in situ observations, and additional auxiliary data (such as precipitation and temperature). Our results indicate that the TWS of the Songliao, Yangtze, Pearl, and Southeastern River Basins are all increasing, with the most drastic TWS growth occurring in the Southeastern River Basin. The TWS of the Yellow, Haihe, Huaihe, and Southwestern River Basins are all decreasing, with the most drastic TWS loss occurring in the Haihe River Basin. The Continental River Basin TWS has remained largely unchanged over time. With the exception of the Songliao and Pearl River Basins, the GWS results produced by the Tongji-Grace2018 model are consistent with the in situ observations of these basins. The correlation coefficients for the Tongji-Grace2018 model results and the in situ observations for the Yellow, Huaihe, Yangtze, Southwestern, and Continental River Basins are higher than 0.710. Overall, the GWS results for the Songliao, Yellow, Haihe, Huaihe, Southwestern, and Continental River Basins all exhibit a downward trend, with the most severe groundwater loss occurring in the Haihe and Huaihe River Basins. However, the Yangtze and Southeastern River Basins both have upward-trending modeled and measured GWS values. This study demonstrates the effectiveness of the Tongji-Grace2018 model for the reliable estimation of TWS and GWS changes on the Chinese mainland, and may contribute to the management of available water resources.
      Citation: Remote Sensing
      PubDate: 2021-05-10
      DOI: 10.3390/rs13091851
      Issue No: Vol. 13, No. 9 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1852: Day and Night Clouds Detection Using
           a Thermal-Infrared All-Sky-View Camera

    • Authors: Yiren Wang, Dong Liu, Wanyi Xie, Ming Yang, Zhenyu Gao, Xinfeng Ling, Yong Huang, Congcong Li, Yong Liu, Yingwei Xia
      First page: 1852
      Abstract: The formation and evolution of clouds are associated with their thermodynamical and microphysical progress. Previous studies have been conducted to collect images using ground-based cloud observation equipment to provide important cloud characteristics information. However, most of this equipment cannot perform continuous observations during the day and night, and their field of view (FOV) is also limited. To address these issues, this work proposes a day and night clouds detection approach integrated into a self-made thermal-infrared (TIR) all-sky-view camera. The TIR camera consists of a high-resolution thermal microbolometer array and a fish-eye lens with a FOV larger than 160°. In addition, a detection scheme was designed to directly subtract the contamination of the atmospheric TIR emission from the entire infrared image of such a large FOV, which was used for cloud recognition. The performance of this scheme was validated by comparing the cloud fractions retrieved from the infrared channel with those from the visible channel and manual observation. The results indicated that the current instrument could obtain accurate cloud fraction from the observed infrared image, and the TIR all-sky-view camera developed in this work exhibits good feasibility for long-term and continuous cloud observation.
      Citation: Remote Sensing
      PubDate: 2021-05-10
      DOI: 10.3390/rs13091852
      Issue No: Vol. 13, No. 9 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1853: Sequence Image Datasets Construction
           via Deep Convolution Networks

    • Authors: Xing Jin, Ping Tang, Zheng Zhang
      First page: 1853
      Abstract: Remote-sensing time-series datasets are significant for global 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 transformation is the method that is often used to deal with this issue. This paper considers the deep convolution networks to learn the complex mapping between sequence images, called adaptive filter generation network (AdaFG), convolution long short-term memory network (CLSTM), and cycle-consistent generative adversarial network (CyGAN) for construction of sequence image datasets. AdaFG 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. CLSTM network can map between different images using the state information of multiple time-series images. CyGAN network can map an image from a source domain to a target domain without additional information. Our experiments, which were performed with unmanned aerial vehicle (UAV) and Landsat-8 datasets, show that the deep convolution networks are effective to produce high-quality time-series image datasets, and the data-driven deep convolution networks can better simulate complex and diverse nonlinear data information.
      Citation: Remote Sensing
      PubDate: 2021-05-10
      DOI: 10.3390/rs13091853
      Issue No: Vol. 13, No. 9 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1854: Small Object Detection in Remote
           Sensing Images with Residual Feature Aggregation-Based Super-Resolution
           and Object Detector Network

    • Authors: Syed Muhammad Arsalan Bashir, Yi Wang
      First page: 1854
      Abstract: This paper deals with detecting small objects in remote sensing images from satellites or any aerial vehicle by utilizing the concept of image super-resolution for image resolution enhancement using a deep-learning-based detection method. This paper provides a rationale for image super-resolution for small objects by improving the current super-resolution (SR) framework by incorporating a cyclic generative adversarial network (GAN) and residual feature aggregation (RFA) to improve detection performance. The novelty of the method is threefold: first, a framework is proposed, independent of the final object detector used in research, i.e., YOLOv3 could be replaced with Faster R-CNN or any object detector to perform object detection; second, a residual feature aggregation network was used in the generator, which significantly improved the detection performance as the RFA network detected complex features; and third, the whole network was transformed into a cyclic GAN. The image super-resolution cyclic GAN with RFA and YOLO as the detection network is termed as SRCGAN-RFA-YOLO, which is compared with the detection accuracies of other methods. Rigorous experiments on both satellite images and aerial images (ISPRS Potsdam, VAID, and Draper Satellite Image Chronology datasets) were performed, and the results showed that the detection performance increased by using super-resolution methods for spatial resolution enhancement; for an IoU of 0.10, AP of 0.7867 was achieved for a scale factor of 16.
      Citation: Remote Sensing
      PubDate: 2021-05-10
      DOI: 10.3390/rs13091854
      Issue No: Vol. 13, No. 9 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1855: Airborne LiDAR-Derived Digital
           Elevation Model for Archaeology

    • Authors: Benjamin Štular, Edisa Lozić, Stefan Eichert
      First page: 1855
      Abstract: The use of topographic airborne LiDAR data has become an essential part of archaeological prospection, and the need for an archaeology-specific data processing workflow is well known. It is therefore surprising that little attention has been paid to the key element of processing: an archaeology-specific DEM. Accordingly, the aim of this paper is to describe an archaeology-specific DEM in detail, provide a tool for its automatic precision assessment, and determine the appropriate grid resolution. We define an archaeology-specific DEM as a subtype of DEM, which is interpolated from ground points, buildings, and four morphological types of archaeological features. We introduce a confidence map (QGIS plug-in) that assigns a confidence level to each grid cell. This is primarily used to attach a confidence level to each archaeological feature, which is useful for detecting data bias in archaeological interpretation. Confidence mapping is also an effective tool for identifying the optimal grid resolution for specific datasets. Beyond archaeological applications, the confidence map provides clear criteria for segmentation, which is one of the unsolved problems of DEM interpolation. All of these are important steps towards the general methodological maturity of airborne LiDAR in archaeology, which is our ultimate goal.
      Citation: Remote Sensing
      PubDate: 2021-05-10
      DOI: 10.3390/rs13091855
      Issue No: Vol. 13, No. 9 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1856: SNR-Based Water Height Retrieval in
           Rivers: Application to High Amplitude Asymmetric Tides in the Garonne
           River

    • Authors: Pierre Zeiger, Frédéric Frappart, José Darrozes, Nicolas Roussel, Philippe Bonneton, Natalie Bonneton, Guillaume Detandt
      First page: 1856
      Abstract: Signal-to-noise ratio (SNR) time series acquired by a geodetic antenna were analyzed to retrieve water heights during asymmetric tides on a narrow river using the Interference Pattern Technique (IPT) from Global Navigation Satellite System Reflectometry (GNSS-R). The dynamic SNR method was selected because the elevation rate of the reflecting surface during rising tides is high in the Garonne River with macro tidal conditions. A new process was developed to filter out the noise introduced by the environmental conditions on the reflected signal due to the narrowness of the river compared to the size of the Fresnel areas, the presence of vegetation on the river banks, and the presence of boats causing multiple reflections. This process involved the removal of multipeaks in the Lomb-Scargle Periodogram (LSP) output and an iterative least square estimation (LSE) of the output heights. Evaluation of the results was performed against pressure-derived water heights. The best results were obtained using all GNSS bands (L1, L2, and L5) simultaneously: R = 0.99, ubRMSD = 0.31 m. We showed that the quality of the retrieved heights was consistent, whatever the vertical velocity of the reflecting surface, and was highly dependent on the number of satellites visible. The sampling period of our solution was 1 min with a 5-min moving window, and no tide models or fit were used in the inversion process. This highlights the potential of the dynamic SNR method to detect and monitor extreme events with GNSS-R, including those affecting inland waters such as flash floods.
      Citation: Remote Sensing
      PubDate: 2021-05-10
      DOI: 10.3390/rs13091856
      Issue No: Vol. 13, No. 9 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1857: Develop of New Tools for 4D
           Monitoring: Case Study of Cliff in Apulia Region (Italy)

    • Authors: Domenica Costantino, Francesco Settembrini, Massimiliano Pepe, Vincenzo Saverio Alfio
      First page: 1857
      Abstract: The monitoring of areas at risk is one of the topics of great interest in the scientific world in order to preserve natural areas of particular environmental value. The present work aims to develop a suitable survey and analysis methodology, in order to optimise multi-temporal processing. In particular, the phenomenon investigated the monitoring of cliffs in southern Apulia (Italy). To achieve this objective, different algorithms were tested and implemented in an in-house software called ICV. The implementation involved the use of different calculation procedures, combined and aimed at the analysis of the phenomenon in question. The validation of the experimentation was shown through the elaboration of a series of datasets of a particular area within the investigated coastline.
      Citation: Remote Sensing
      PubDate: 2021-05-10
      DOI: 10.3390/rs13091857
      Issue No: Vol. 13, No. 9 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1858: Optical Remote Sensing Image
           Denoising and Super-Resolution Reconstructing Using Optimized Generative
           Network in Wavelet Transform Domain

    • Authors: Xubin Feng, Wuxia Zhang, Xiuqin Su, Zhengpu Xu
      First page: 1858
      Abstract: High spatial quality (HQ) optical remote sensing images are very useful for target detection, target recognition and image classification. Due to the influence of imaging equipment accuracy and atmospheric environment, HQ images are difficult to acquire, while low spatial quality (LQ) remote sensing images are very easy to acquire. Hence, denoising and super-resolution (SR) reconstruction technology are the most important solutions to improve the quality of remote sensing images very effectively, which can lower the cost as much as possible. Most existing methods usually only employ denoising or SR technology to obtain HQ images. However, due to the complex structure and the large noise of remote sensing images, the quality of the remote sensing image obtained only by denoising method or SR method cannot meet the actual needs. To address these problems, a method of reconstructing HQ remote sensing images based on Generative Adversarial Network (GAN) named “Restoration Generative Adversarial Network with ResNet and DenseNet” (RRDGAN) is proposed, which can acquire better quality images by incorporating denoising and SR into a unified framework. The generative network is implemented by fusing Residual Neural Network (ResNet) and Dense Convolutional Network (DenseNet) in order to consider denoising and SR problems at the same time. Then, total variation (TV) regularization is used to furthermore enhance the edge details, and the idea of Relativistic GAN is explored to make the whole network converge better. Our RRDGAN is implemented in wavelet transform (WT) domain, since different frequency parts could be handled separately in the wavelet domain. The experimental results on three different remote sensing datasets shows the feasibility of our proposed method in acquiring remote sensing images.
      Citation: Remote Sensing
      PubDate: 2021-05-10
      DOI: 10.3390/rs13091858
      Issue No: Vol. 13, No. 9 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1859: Canopy Parameter Estimation of Citrus
           grandis var. Longanyou Based on LiDAR 3D Point Clouds

    • Authors: Xiangyang Liu, Yaxiong Wang, Feng Kang, Yang Yue, Yongjun Zheng
      First page: 1859
      Abstract: The characteristic parameters of Citrus grandis var. Longanyou canopies are important when measuring yield and spraying pesticides. However, the feasibility of the canopy reconstruction method based on point clouds has not been confirmed with these canopies. Therefore, LiDAR point cloud data for C. grandis var. Longanyou were obtained to facilitate the management of groves of this species. Then, a cloth simulation filter and European clustering algorithm were used to realize individual canopy extraction. After calculating canopy height and width, canopy reconstruction and volume calculation were realized using six approaches: by a manual method and using five algorithms based on point clouds (convex hull, CH; convex hull by slices; voxel-based, VB; alpha-shape, AS; alpha-shape by slices, ASBS). ASBS is an innovative algorithm that combines AS with slices optimization, and can best approximate the actual canopy shape. Moreover, the CH algorithm had the shortest run time, and the R2 values of VCH, VVB, VAS, and VASBS algorithms were above 0.87. The volume with the highest accuracy was obtained from the ASBS algorithm, and the CH algorithm had the shortest computation time. In addition, a theoretical but preliminarily system suitable for the calculation of the canopy volume of C. grandis var. Longanyou was developed, which provides a theoretical reference for the efficient and accurate realization of future functional modules such as accurate plant protection, orchard obstacle avoidance, and biomass estimation.
      Citation: Remote Sensing
      PubDate: 2021-05-10
      DOI: 10.3390/rs13091859
      Issue No: Vol. 13, No. 9 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1860: NDFTC: A New Detection Framework of
           Tropical Cyclones from Meteorological Satellite Images with Deep Transfer
           Learning

    • Authors: Shanchen Pang, Pengfei Xie, Danya Xu, Fan Meng, Xixi Tao, Bowen Li, Ying Li, Tao Song
      First page: 1860
      Abstract: Accurate detection of tropical cyclones (TCs) is important to prevent and mitigate natural disasters associated with TCs. Deep transfer learning methods have advantages in detection tasks, because they can further improve the stability and accuracy of the detection model. Therefore, on the basis of deep transfer learning, we propose a new detection framework of tropical cyclones (NDFTC) from meteorological satellite images by combining the deep convolutional generative adversarial networks (DCGAN) and You Only Look Once (YOLO) v3 model. The algorithm process of NDFTC consists of three major steps: data augmentation, a pre-training phase, and transfer learning. First, to improve the utilization of finite data, DCGAN is used as the data augmentation method to generate images simulated to TCs. Second, to extract the salient characteristics of TCs, the generated images obtained from DCGAN are inputted into the detection model YOLOv3 in the pre-training phase. Furthermore, based on the network-based deep transfer learning method, we train the detection model with real images of TCs and its initial weights are transferred from the YOLOv3 trained with generated images. Training with real images helps to extract universal characteristics of TCs and using transferred weights as initial weights can improve the stability and accuracy of the model. The experimental results show that the NDFTC has a better performance, with an accuracy (ACC) of 97.78% and average precision (AP) of 81.39%, in comparison to the YOLOv3, with an ACC of 93.96% and AP of 80.64%.
      Citation: Remote Sensing
      PubDate: 2021-05-10
      DOI: 10.3390/rs13091860
      Issue No: Vol. 13, No. 9 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1861: Analysis of Activity in an Open-Pit
           Mine by Using InSAR Coherence-Based Normalized Difference Activity Index

    • Authors: Jihyun Moon, Hoonyol Lee
      First page: 1861
      Abstract: In this study, time-series of Sentinel-1A/B Interferometric Synthetic Aperture Radar (InSAR) coherence images were used to monitor the mining activity of Musan open-pit mine, the largest iron mine in North Korea. First, the subtraction of SRTM DEM (2000) from TanDEM-X DEM (2010–2015) has identified two major accumulation areas, one in the east (+112.33 m) and the other in the west (+84.03 m), and a major excavation area (−42.54 m) at the center of the mine. A total of 89 high-quality coherence images with a 12-day baseline from 2015 to 2020 were converted to the normalized difference activity index (NDAI), a newly developed activity indicator robust to spatial and temporal decorrelation. An RGB composite of annually averaged NDAI maps (red for 2019, green for 2018, and blue for 2017) showed that overall activity has diminished since 2018. Dumping slopes were categorized into shrinking, expanding, or transitional, according to the color pattern. Migration and expansion of excavation sites were also found on the pit floor. Time series of 12-day NDAI graphs revealed the date of activities with monthly accuracy. It is believed that NDAI with continuous acquisition of Sentinel-1A/B data can provide detailed monitoring of various types of activities in open-pit mines especially with limited in situ data.
      Citation: Remote Sensing
      PubDate: 2021-05-10
      DOI: 10.3390/rs13091861
      Issue No: Vol. 13, No. 9 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1862: Lifting Scheme-Based Sparse Density
           Feature Extraction for Remote Sensing Target Detection

    • Authors: Ling Tian, Yu Cao, Zishan Shi, Bokun He, Chu He, Deshi Li
      First page: 1862
      Abstract: The design of backbones is of great significance for enhancing the location and classification precision in the remote sensing target detection task. Recently, various approaches have been proposed on altering the feature extraction density in the backbones to enlarge the receptive field, make features prominent, and reduce computational complexity, such as dilated convolution and deformable convolution. Among them, one of the most widely used methods is strided convolution, but it loses the information about adjacent feature points which leads to the omission of some useful features and the decrease of detection precision. This paper proposes a novel sparse density feature extraction method based on the relationship between the lifting scheme and convolution, which improves the detection precision while keeping the computational complexity almost the same as the strided convolution. Experimental results on remote sensing target detection indicate that our proposed method improves both detection performance and network efficiency.
      Citation: Remote Sensing
      PubDate: 2021-05-10
      DOI: 10.3390/rs13091862
      Issue No: Vol. 13, No. 9 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1863: Classifying Forest Type in the
           National Forest Inventory Context with Airborne Hyperspectral and Lidar
           Data

    • Authors: Caileigh Shoot, Hans-Erik Andersen, L. Monika Moskal, Chad Babcock, Bruce D. Cook, Douglas C. Morton
      First page: 1863
      Abstract: Forest structure and composition regulate a range of ecosystem services, including biodiversity, water and nutrient cycling, and wood volume for resource extraction. Forest type is an important metric measured in the US Forest Service Forest Inventory and Analysis (FIA) program, the national forest inventory of the USA. Forest type information can be used to quantify carbon and other forest resources within specific domains to support ecological analysis and forest management decisions, such as managing for disease and pests. In this study, we developed a methodology that uses a combination of airborne hyperspectral and lidar data to map FIA-defined forest type between sparsely sampled FIA plot data collected in interior Alaska. To determine the best classification algorithm and remote sensing data for this task, five classification algorithms were tested with six different combinations of raw hyperspectral data, hyperspectral vegetation indices, and lidar-derived canopy and topography metrics. Models were trained using forest type information from 632 FIA subplots collected in interior Alaska. Of the thirty model and input combinations tested, the random forest classification algorithm with hyperspectral vegetation indices and lidar-derived topography and canopy height metrics had the highest accuracy (78% overall accuracy). This study supports random forest as a powerful classifier for natural resource data. It also demonstrates the benefits from combining both structural (lidar) and spectral (imagery) data for forest type classification.
      Citation: Remote Sensing
      PubDate: 2021-05-11
      DOI: 10.3390/rs13101863
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1864: GIS-Based Urban Flood Resilience
           Assessment Using Urban Flood Resilience Model: A Case Study of Peshawar
           City, Khyber Pakhtunkhwa, Pakistan

    • Authors: Muhammad Tayyab, Jiquan Zhang, Muhammad Hussain, Safi Ullah, Xingpeng Liu, Shah Nawaz Khan, Muhammad Aslam Baig, Waqas Hassan, Bazel Al-Shaibah
      First page: 1864
      Abstract: Urban flooding has been an alarming issue in the past around the globe, particularly in South Asia. Pakistan is no exception from this situation where urban floods with associated damages are frequently occurring phenomena. In Pakistan, rapid urbanization is the key factor for urban flooding, which is not taken into account. This study aims to identify flood sensitivity and coping capacity while assessing urban flood resilience and move a step toward the initialization of resilience, specifically for Peshawar city and generally for other cities of Pakistan. To achieve this aim, an attempt has been made to propose an integrated approach named the “urban flood resilience model (UFResi-M),” which is based on geographical information system(GIS), remote sensing (RS), and the theory of analytical hierarchy process (AHP). The UFResi-M incorporates four main factors—urban flood hazard, exposure, susceptibility, and coping capacity into two parts, i.e., sensitivity and coping capacity. The first part consists of three factors—IH, IE, and IS—that represent sensitivity, while the second part represents coping capacity (ICc). All four indicators were weighted through AHP to obtain product value for each indicator. The result showed that in the Westzone of the study area, the northwestern and central parts have very high resilience, whereas the southern and southwestern parts have very low resilience. Similarly, in the East zone of the study area, the northwest and southwest parts have very high resilience, while the northern and western parts have very low resilience. The likelihood of the proposed model was also determined using the receiver operating characteristic (ROC) curve method; the area under the curve acquired for the model was 0.904. The outcomes of these integrated assessments can help in tracking community performance and can provide a tool to decision makers to integrate the resilience aspect into urban flood management, urban development, and urban planning.
      Citation: Remote Sensing
      PubDate: 2021-05-11
      DOI: 10.3390/rs13101864
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1865: Industrial Plume Properties Retrieved
           by Optimal Estimation Using Combined Hyperspectral and Sentinel-2 Data

    • Authors: Gabriel Calassou, Pierre-Yves Foucher, Jean-François Léon
      First page: 1865
      Abstract: Stack emissions from the industrial sector are a subject of concern for air quality. However, the characterization of the stack emission plume properties from in situ observations remains a challenging task. This paper focuses on the characterization of the aerosol properties of a steel plant stack plume through the use of hyperspectral (HS) airborne remote sensing imagery. We propose a new method, based on the combination of HS airborne acquisition and surface reflectance imagery derived from the Sentinel-2 Multi-Spectral Instrument (MSI). The proposed method detects the plume footprint and estimates the surface reflectance under the plume, the aerosol optical thickness (AOT), and the modal radius of the plume. Hyperspectral surface reflectances are estimated using the coupled non-negative matrix factorization (CNMF) method combining HS and MSI data. The CNMF reduces the error associated with estimating the surface reflectance below the plume, particularly for heterogeneous classes. The AOT and modal radius are retrieved using an optimal estimation method (OEM), based on the forward model and allowing for uncertainties in the observations and in the model parameters. The a priori state vector is provided by a sequential method using the root mean square error (RMSE) metric, which outperforms the previously used cluster tuned matched filter (CTMF). The OEM degrees of freedom are then analysed, in order to refine the mask plume and to enhance the quality of the retrieval. The retrieved mean radii of aerosol particles in the plume is 0.125 μμm, with an uncertainty of 0.05 μμm. These results are close to the ultra-fine mode (modal radius around 0.1 μμm) observed from in situ measurements within metallurgical plant plumes from previous studies. The retrieved AOT values vary between 0.07 (near the source point) and 0.01, with uncertainties of 0.005 for the darkest surfaces and above 0.010 for the brightest surfaces.
      Citation: Remote Sensing
      PubDate: 2021-05-11
      DOI: 10.3390/rs13101865
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1866: Drone-Based Hyperspectral and Thermal
           Imagery for Quantifying Upland Rice Productivity and Water Use Efficiency
           after Biochar Application

    • Authors: Hongxiao Jin, Christian Josef Köppl, Benjamin M. C. Fischer, Johanna Rojas-Conejo, Mark S. Johnson, Laura Morillas, Steve W. Lyon, Ana M. Durán-Quesada, Andrea Suárez-Serrano, Stefano Manzoni, Monica Garcia
      First page: 1866
      Abstract: Miniature hyperspectral and thermal cameras onboard lightweight unmanned aerial vehicles (UAV) bring new opportunities for monitoring land surface variables at unprecedented fine spatial resolution with acceptable accuracy. This research applies hyperspectral and thermal imagery from a drone to quantify upland rice productivity and water use efficiency (WUE) after biochar application in Costa Rica. The field flights were conducted over two experimental groups with bamboo biochar (BC1) and sugarcane biochar (BC2) amendments and one control (C) group without biochar application. Rice canopy biophysical variables were estimated by inverting a canopy radiative transfer model on hyperspectral reflectance. Variations in gross primary productivity (GPP) and WUE across treatments were estimated using light-use efficiency and WUE models respectively from the normalized difference vegetation index (NDVI), canopy chlorophyll content (CCC), and evapotranspiration rate. We found that GPP was increased by 41.9 ± 3.4% in BC1 and 17.5 ± 3.4% in BC2 versus C, which may be explained by higher soil moisture after biochar application, and consequently significantly higher WUEs by 40.8 ± 3.5% in BC1 and 13.4 ± 3.5% in BC2 compared to C. This study demonstrated the use of hyperspectral and thermal imagery from a drone to quantify biochar effects on dry cropland by integrating ground measurements and physical models.
      Citation: Remote Sensing
      PubDate: 2021-05-11
      DOI: 10.3390/rs13101866
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1867: Extreme Wind Speeds Retrieval Using
           Sentinel-1 IW Mode SAR Data

    • Authors: Yuan Gao, Jian Sun, Jie Zhang, Changlong Guan
      First page: 1867
      Abstract: With the improvement in microwave radar technology, spaceborne synthetic aperture radar (SAR) is widely used to observe the tropical cyclone (TC) wind field. Based on European Space Agency Sentinel-1 Interferometric Wide swath (IW) mode imagery, this paper evaluates the correlation between vertical transmitting–horizontal receiving (VH) polarization signals and extreme ocean surface wind speeds (>40 m/s) under strong TC conditions. A geophysical model function (GMF) Sentinel-1 IW mode wind retrieval model after noise removal (S1IW.NR) was proposed, according to the SAR images of nine TCs and collocated stepped frequency microwave radiometer (SFMR) and soil moisture active passive (SMAP) radiometer wind speed measurements. Through curve fitting and regression correction, the new GMF exploits the relationships between VH-polarization normalized radar cross section, incident angle, and wind speed in each sub-swath and covers wind speeds up to 74 m/s. Based on collocated SAR and SFMR measurements of four TCs, the new GMF was validated in the wind speed range from 2 to 53 m/s. Results show that the correlation coefficient, bias, and root mean squared error were 0.89, −0.89 m/s, and 4.13 m/s, respectively, indicating that extreme winds can be retrieved accurately by the new model. In addition, we investigated the relationship between the S1IW.NR wind retrieval bias and the SFMR-measured rain rate. The S1IW.NR model tended to overestimate wind speeds under high rain rates.
      Citation: Remote Sensing
      PubDate: 2021-05-11
      DOI: 10.3390/rs13101867
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1868: An Evaluation of Pixel- and
           Object-Based Tree Species Classification in Mixed Deciduous Forests Using
           Pansharpened Very High Spatial Resolution Satellite Imagery

    • Authors: Martina Deur, Mateo Gašparović, Ivan Balenović
      First page: 1868
      Abstract: Quality tree species information gathering is the basis for making proper decisions in forest management. By applying new technologies and remote sensing methods, very high resolution (VHR) satellite imagery can give sufficient spatial detail to achieve accurate species-level classification. In this study, the influence of pansharpening of the WorldView-3 (WV-3) satellite imagery on classification results of three main tree species (Quercus robur L., Carpinus betulus L., and Alnus glutinosa (L.) Geartn.) has been evaluated. In order to increase tree species classification accuracy, three different pansharpening algorithms (Bayes, RCS, and LMVM) have been conducted. The LMVM algorithm proved the most effective pansharpening technique. The pixel- and object-based classification were applied to three pansharpened imageries using a random forest (RF) algorithm. The results showed a very high overall accuracy (OA) for LMVM pansharpened imagery: 92% and 96% for tree species classification based on pixel- and object-based approach, respectively. As expected, the object-based exceeded the pixel-based approach (OA increased by 4%). The influence of fusion on classification results was analyzed as well. Overall classification accuracy was improved by the spatial resolution of pansharpened images (OA increased by 7% for pixel-based approach). Also, regardless of pixel- or object-based classification approaches, the influence of the use of pansharpening is highly beneficial to classifying complex, natural, and mixed deciduous forest areas.
      Citation: Remote Sensing
      PubDate: 2021-05-11
      DOI: 10.3390/rs13101868
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1869: Can Commercial Low-Cost Drones and
           Open-Source GIS Technologies Be Suitable for Semi-Automatic Weed Mapping
           for Smart Farming' A Case Study in NE Italy

    • Authors: Pietro Mattivi, Salvatore Eugenio Pappalardo, Nebojša Nikolić, Luca Mandolesi, Antonio Persichetti, Massimo De Marchi, Roberta Masin
      First page: 1869
      Abstract: Weed management is a crucial issue in agriculture, resulting in environmental in-field and off-field impacts. Within Agriculture 4.0, adoption of UASs combined with spatially explicit approaches may drastically reduce doses of herbicides, increasing sustainability in weed management. However, Agriculture 4.0 technologies are barely adopted in small-medium size farms. Recently, small and low-cost UASs, together with open-source software packages, may represent a low-cost spatially explicit system to map weed distribution in crop fields. The general aim is to map weed distribution by a low-cost UASs and a replicable workflow, completely based on open GIS software and algorithms: OpenDroneMap, QGIS, SAGA and OpenCV classification algorithms. Specific objectives are: (i) testing a low-cost UAS for weed mapping; (ii) assessing open-source packages for semi-automatic weed classification; (iii) performing a sustainable management scenario by prescription maps. Results showed high performances along the whole process: in orthomosaic generation at very high spatial resolution (0.01 m/pixel), in testing weed detection (Matthews Correlation Coefficient: 0.67–0.74), and in the production of prescription maps, reducing herbicide treatment to only 3.47% of the entire field. This study reveals the feasibility of low-cost UASs combined with open-source software, enabling a spatially explicit approach for weed management in small-medium size farmlands.
      Citation: Remote Sensing
      PubDate: 2021-05-11
      DOI: 10.3390/rs13101869
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1870: Using Sentinel-1, Sentinel-2, and
           Planet Imagery to Map Crop Type of Smallholder Farms

    • Authors: Preeti Rao, Weiqi Zhou, Nishan Bhattarai, Amit K. Srivastava, Balwinder Singh, Shishpal Poonia, David B. Lobell, Meha Jain
      First page: 1870
      Abstract: Remote sensing offers a way to map crop types across large spatio-temporal scales at low costs. However, mapping crop types is challenging in heterogeneous, smallholder farming systems, such as those in India, where field sizes are often smaller than the resolution of historically available imagery. In this study, we examined the potential of relatively new, high-resolution imagery (Sentinel-1, Sentinel-2, and PlanetScope) to identify four major crop types (maize, mustard, tobacco, and wheat) in eastern India using support vector machine (SVM). We found that a trained SVM model that included all three sensors led to the highest classification accuracy (85%), and the inclusion of Planet data was particularly helpful for classifying crop types for the smallest farms (<600 m2). This was likely because its higher spatial resolution (3 m) could better account for field-level variations in smallholder systems. We also examined the impact of image timing on the classification accuracy, and we found that early-season images did little to improve our models. Overall, we found that readily available Sentinel-1, Sentinel-2, and Planet imagery were able to map crop types at the field-scale with high accuracy in Indian smallholder systems. The findings from this study have important implications for the identification of the most effective ways to map crop types in smallholder systems.
      Citation: Remote Sensing
      PubDate: 2021-05-11
      DOI: 10.3390/rs13101870
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1871: Attenuation of Seismic Multiples in
           Very Shallow Water: An Application in Archaeological Prospection Using
           Data Driven Approaches

    • Authors: Michaela Schwardt, Dennis Wilken, Wolfgang Rabbel
      First page: 1871
      Abstract: Water-layer multiples pose a major problem in shallow water seismic investigations as they interfere with primaries reflected from layer boundaries or archaeology buried only a few meters below the water bottom. In the present study we evaluate two model-driven approaches (“Prediction and Subtraction” and “RTM-Deco”) to attenuate water-layer multiple reflections in very shallow water using synthetic and field data. The tests comprise both multi- and constant-offset data. We compare the multiple removal efficiency of the evaluated methods with two traditional methods (Predictive Deconvolution and SRME). Both model-driven approaches yield satisfactory results concerning the enhancement of primary energy and the attenuation of multiple energy. For the synthetic test cases, the multiple energy is reduced by at least 80% for the Prediction and Subtraction approach, and by more than 60% for the RTM-Deco approach. The application to two field data sets shows a significant amplification of primaries formerly hidden by the first water-layer multiple, with a reduction of multiple energy of up to 50%. The waveforms obtained from FD modeling match the true waveforms of the field data well and small deviations in time and amplitude can be removed by a time shift of the traces as well as an amplitude adaption to the field data. The field data examples should be emphasized, where the tested Prediction and Subtraction approach works significantly better than the traditional methods: the multiples are effectively predicted and attenuated while primary signals are highlighted. In conclusion, this shows that this method is particularly suitable in shallow water applications. Both evaluated multiple attenuation approaches could be successfully transferred to two other 3D systems used in shallow water near surface investigations. Especially the Prediction and Subtraction approach is able to enhance the primaries for both tested 3D systems with the multiple energy being reduced by more than 50%.
      Citation: Remote Sensing
      PubDate: 2021-05-11
      DOI: 10.3390/rs13101871
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1872: Evaluation of Global Surface Water
           Temperature Data Sets for Use in Passive Remote Sensing of Soil Moisture

    • Authors: Runze Zhang, Steven Chan, Rajat Bindlish, Venkataraman Lakshmi
      First page: 1872
      Abstract: Inland open water bodies often pose a systematic error source in the passive remote sensing retrievals of soil moisture. Water temperature is a necessary variable used to compute water emissions that is required to be subtracted from satellite observation to yield actual emissions from the land portion, which in turn generates accurate soil moisture retrievals. Therefore, overestimation of soil moisture can often be corrected using concurrent water temperature data in the overall mitigation procedure. In recent years, several data sets of lake water temperature have become available, but their specifications and accuracy have rarely been investigated in the context of passive soil moisture remote sensing on a global scale. For this reason, three lake temperature products were evaluated against in-situ measurements from 2007 to 2011. The data sets include the lake surface water temperature (LSWT) from Global Observatory of Lake Responses to Environmental Change (GloboLakes), the Copernicus Global Land Operations Cryosphere and Water (C-GLOPS), as well as the lake mix-layer temperature (LMLT) from the European Centers for Medium-Range Weather Forecast (ECMWF) ERA5 Land Reanalysis. GloboLakes, C-GLOPS, and ERA5 Land have overall comparable performance with Pearson correlations (R) of 0.87, 0.92 and 0.88 in comparison with in-situ measurements. LSWT products exhibit negative median biases of −0.27 K (GloboLakes) and −0.31 K (C-GLOPS), whereas the median bias of LMLT is 1.56 K. When mapped from their respective native resolutions to a common 9 km Equal-Area Scalable Earth (EASE) Grid 2.0 projection, similar relative performance was observed. LMLT and LSWT data are closer in performance over the 9 km grid cells that exhibit a small range of lake cover fractions (0.05–0.5). Despite comparable relative performance, ERA5 Land shows great advantages in spatial coverage and temporal resolution. In summary, an integrated evaluation on data accuracy, long-term availability, global coverage, temporal resolution, and regular forward processing with modest data latency led us to conclude that LMLT from the ERA5 Land Reanalysis product represents the most optimal path for use in the development of a long-term soil moisture product.
      Citation: Remote Sensing
      PubDate: 2021-05-11
      DOI: 10.3390/rs13101872
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1873: Analysis of the Response of Long-Term
           Vegetation Dynamics to Climate Variability Using the Pruned Exact Linear
           Time (PELT) Method and Disturbance Lag Model (DLM) Based on Remote Sensing
           Data: A Case Study in Guangdong Province (China)

    • Authors: Sai Wang, Fenglei Fan
      First page: 1873
      Abstract: The dynamic change and spatial–temporal distribution of vegetation coverage are of great significance for regional ecological evolution, especially in the subtropics and tropics. Identifying the heterogeneity in vegetation activities and its response to climate factors is crucial for projecting ecosystem dynamics. We used long-term (2001–2018) satellite-derived enhanced vegetation index (EVI) datasets and climatic factors to analyze the spatiotemporal patterns of vegetation activities in an experimental area in Guangdong Province (China), as well as their links to changes in temperature (TEM), relative humidity (HUM), precipitation (PRE), sunshine duration (SUN), and surface runoff. The pruned exact linear time change point detection method (PELT) and the disturbance lag model (DLM) were used to understand the detailed ecological coverage status and time lag relationships between the EVI and climatic factors. The results indicate the following. (1) At the whole regional scale, a significant overall upward trend in the EVI variation was observed in 2001–2018. More specifically, there were two distinct periods with different trends, which were split by a turning point in 2005. PRE was the main climate-related driver of the rising EVI pre-2005, and the increase in TEM was the main climate factor influencing the forest EVI variation post-2006. (2) A three-month time lag effect was observed in the EVI response to relative humidity. The same phenomenon was found in the sunshine duration factor. (3) The EVI of farmlands (one type of land use) exhibited the largest lags between relative humidity and the sunshine duration factor, followed by grasslands and forests. (4) The comprehensive index of surface runoff could explain the time lags of vegetation activities, and the surface runoff value showed an apparently negative relationship with the vegetation coverage change.
      Citation: Remote Sensing
      PubDate: 2021-05-11
      DOI: 10.3390/rs13101873
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1874: Wave-Filtered Surf Zone Circulation
           under High-Energy Waves Derived from Video-Based Optical Systems

    • Authors: Isaac Rodríguez-Padilla, Bruno Castelle, Vincent Marieu, Philippe Bonneton, Arthur Mouragues, Kevin Martins, Denis Morichon
      First page: 1874
      Abstract: This paper examines the potential of an optical flow video-based technique to estimate wave-filtered surface currents in the nearshore where wave-breaking induced foam is present. This approach uses the drifting foam, left after the passage of breaking waves, as a quasi-passive tracer and tracks it to estimate the surface water flow. The optical signature associated with sea-swell waves is first removed from the image sequence to avoid capturing propagating waves instead of the desired foam motion. Waves are removed by applying a temporal Fourier low-pass filter to each pixel of the image. The low-pass filtered images are then fed into an optical flow algorithm to estimate the foam displacement and to produce mean velocity fields (i.e., wave-filtered surface currents). We use one week of consecutive 1-Hz sampled frames collected during daylight hours from a single fixed camera located at La Petite Chambre d’Amour beach (Anglet, SW France) under high-energy conditions with significant wave height ranging from 0.8 to 3.3 m. Optical flow-computed velocities are compared against time-averaged in situ measurements retrieved from one current profiler installed on a submerged reef. The computed circulation patterns are also compared against surf-zone drifter trajectories under different field conditions. Optical flow time-averaged velocities show a good agreement with current profiler measurements: coefficient of determination (r2)= 0.5–0.8; root mean square error (RMSE) = 0.12–0.24 m/s; mean error (bias) =−0.09 to −0.17 m/s; regression slope =1±0.15; coherence2 = 0.4–0.6. Despite an underestimation of offshore-directed velocities under persistent wave breaking across the reef, the optical flow was able to correctly reproduce the mean flow patterns depicted by drifter trajectories. Such patterns include rip-cell circulation, dominant onshore-directed surface flow and energetic longshore current. Our study suggests that open-source optical flow algorithms are a promising technique for coastal imaging applications, particularly under high-energy wave conditions when in situ instrument deployment can be challenging.
      Citation: Remote Sensing
      PubDate: 2021-05-11
      DOI: 10.3390/rs13101874
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1875: Spatial and Temporal Variability of
           Soil Salinity in the Yangtze River Estuary Using Electromagnetic Induction
           

    • Authors: Wenping Xie, Jingsong Yang, Rongjiang Yao, Xiangping Wang
      First page: 1875
      Abstract: Soil salt-water dynamics in the Yangtze River Estuary (YRE) is complex and soil salinity is an obstacle to regional agricultural production and the ecological environment in the YRE. Runoff into the sea is reduced during the impoundment period as the result of the water-storing process of the Three Gorges Reservoir (TGR) in the upper reaches of the Yangtze River, which causes serious seawater intrusion. Soil salinity is a problem due to shallow and saline groundwater under serious seawater intrusion in the YRE. In this research, we focused on the temporal variation and spatial distribution characteristics of soil salinity in the YRE using geostatistics combined with proximally sensed information obtained by an electromagnetic induction (EM) survey method in typical years under the impoundment of the TGR. The EM survey with proximal sensing method was applied to perform soil salinity survey in field in the Yangtze River Estuary, allowing quick determination and quantitative assessment of spatial and temporal variation of soil salinity from 2006 to 2017. We developed regional soil salinity survey and mapping by coupling limited laboratory data with proximal sensed data obtained from EM. We interpreted the soil electrical conductivity by constructing a linear model between the apparent electrical conductivity data measured by an EM 38 device and the soil electrical conductivity (EC) of soil samples measured in laboratory. Then, soil electrical conductivity was converted to soil salt content (soil salinity g kg−1) through established linear regression model based on the laboratory data of soil salinity and soil EC. Semivariograms of regional soil salinity in the survey years were fitted and ordinary kriging interpolation was applied in interpolation and mapping of regional soil salinity. The cross-validation results showed that the prediction results were acceptable. The soil salinity distribution under different survey years was presented and the area of salt affected soil was calculated using geostatistics method. The results of spatial distribution of soil salinity showed that soil salinity near the riverbanks and coastlines was higher than that of inland. The spatial distribution of groundwater depth and salinity revealed that shallow groundwater and high groundwater salinity influenced the spatial distribution characteristics of soil salinity. Under long-term impoundment of the Three Gorges Reservoir, the variation of soil salinity in different hydrological years was analyzed. Results showed that the area affected by soil salinity gradually increased in different hydrological year types under the impoundment of the TGR.
      Citation: Remote Sensing
      PubDate: 2021-05-11
      DOI: 10.3390/rs13101875
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1876: Estimating the Suitability for the
           Reintroduced Arabian Oryx (Oryx leucoryx, Pallas 1777) of Two Desert
           Environments by NIRS-Aided Fecal Chemistry

    • Authors: Serge Yan Landau, Ido Isler, Levana Dvash, Benny Shalmon, Amir Arnon, David Saltz
      First page: 1876
      Abstract: The re-introduction paradigm is that Arabian Oryx (Oryx leucoryx) herds adjust the size of their home ranges depending on the availability of vegetation, which is directly related to rainfall. In Israel, Arabian oryx were released in two hyper-arid sites: the Arava Valley and in the Paran wilderness, belonging to the Sudanese and the Saharo–Arabian biogeographic zones, respectively. While post-release survival was similar in both, reproductive success in the Paran wilderness reintroduction site was extremely low, resulting in an acute decline of the reintroduced population over time. The hypothesis that impaired nutrition might be associated with this finding was assessed with near-infrared spectroscopy (NIRS)-aided chemistry of monthly sampled fecal pellets, used as remote sensing evidence of ingested diets, throughout a year. Fecal nitrogen (FN), used as an estimate of nutritional status, was consistently higher in the Arava. Grass was never the sole or even a major dietary component. The dietary contribution of tannin-rich browse was high and steady all year-round in the Arava and increased steadily in Paran from winter to summer, corresponding to the period of availability of Acacia raddiana pods in both regions. The oryx in Paran had a home-range that was ten-fold, compared to the Arava, suggesting less feed availability. Acacia browsing may mitigate the effects of temporal variance in primary production. Under such conditions, oryx should be preferably released in areas that support significant acacia stands.
      Citation: Remote Sensing
      PubDate: 2021-05-12
      DOI: 10.3390/rs13101876
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1877: Assessment of Tropospheric
           Concentrations of NO2 from the TROPOMI/Sentinel-5 Precursor for the
           Estimation of Long-Term Exposure to Surface NO2 over South Korea

    • Authors: Ukkyo Jeong, Hyunkee Hong
      First page: 1877
      Abstract: Since April 2018, the TROPOspheric Monitoring Instrument (TROPOMI) has provided data on tropospheric NO2 column concentrations (CTROPOMI) with unprecedented spatial resolution. This study aims to assess the capability of TROPOMI to acquire high spatial resolution data regarding surface NO2 mixing ratios. In general, the instrument effectively detected major and moderate sources of NO2 over South Korea with a clear weekday–weekend distinction. We compared the CTROPOMI with surface NO2 mixing ratio measurements from an extensive ground-based network over South Korea operated by the Korean Ministry of Environment (SKME; more than 570 sites), for 2019. Spatiotemporally collocated CTROPOMI and SKME showed a moderate correlation (correlation coefficient, r = 0.67), whereas their annual mean values at each site showed a higher correlation (r = 0.84). The CTROPOMI and SKME were well correlated around the Seoul metropolitan area, where significant amounts of NO2 prevailed throughout the year, whereas they showed lower correlation at rural sites. We converted the tropospheric NO2 from TROPOMI to the surface mixing ratio (STROPOMI) using the EAC4 (ECMWF Atmospheric Composition Reanalysis 4) profile shape, for quantitative comparison with the SKME. The estimated STROPOMI generally underestimated the in-situ value obtained, SKME (slope = 0.64), as reported in previous studies.
      Citation: Remote Sensing
      PubDate: 2021-05-11
      DOI: 10.3390/rs13101877
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1878: Using UAV-Based Photogrammetry to
           Obtain Correlation between the Vegetation Indices and Chemical Analysis of
           Agricultural Crops

    • Authors: Janoušek, Jambor, Marcoň, Dohnal, Synková, Fiala
      First page: 1878
      Abstract: The optimum corn harvest time differs between individual harvest scenarios, depending on the intended use of the crop and on the technical equipment of the actual farm. It is therefore economically significant to specify the period as precisely as possible. The harvest maturity of silage corn is currently determined from the targeted sampling of plants cultivated over large areas. In this context, the paper presents an alternative, more detail-oriented approach for estimating the correct harvest time; the method focuses on the relationship between the ripeness data obtained via photogrammetry and the parameters produced by the chemical analysis of corn. The relevant imaging methodology utilizing a spectral camera-equipped unmanned aerial vehicle (UAV) allows the user to acquire the spectral reflectance values and to compute the vegetation indices. Furthermore, the authors discuss the statistical data analysis centered on both the nutritional values found in the laboratory corn samples and on the information obtained from the multispectral images. This discussion is associated with a detailed insight into the computation of correlation coefficients. Statistically significant linear relationships between the vegetation indices, the normalized difference red edge index (NDRE) and the normalized difference vegetation index (NDVI) in particular, and nutritional values such as dry matter, starch, and crude protein are evaluated to indicate different aspects of and paths toward predicting the optimum harvest time. The results are discussed in terms of the actual limitations of the method, the benefits for agricultural practice, and planned research.
      Citation: Remote Sensing
      PubDate: 2021-05-11
      DOI: 10.3390/rs13101878
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1879: Characterisation of the Historic
           Urban Landscape through the Aristotelian Four Causes: Towards
           Comprehensive GIS Databases

    • Authors: Rafael Ramírez Eudave, Tiago Miguel Ferreira
      First page: 1879
      Abstract: The Historic Urban Landscape provides a basis to comprehensively study the city, considering the numerous agents and stakeholders involved in the urban phenomenon. However, the characterisation of the city is challenging, due to the numerous ways of reading and using the city. Although several theoretical approaches address the process of documenting the city, there is still a gap related to the design of a generalised, holistic, and comprehensive framework. This article aims to contribute to this purpose by discussing the concept of the Historic Urban Landscape (HUL) and its implications for the characterisation of the urban phenomena. The Aristotelian theory of the causes is proposed here as a suitable approach for the description, characterisation, and analysis of virtually any entity by first discussing its theoretical basis and then testing it in a real building located in the historical city, Guimarães, Portugal. A set of tools related to Geographic Information System databases are comprehensively explored during the implementation process of the approach, allowing to identify and discuss a set of limitations, challenges, and opportunities.
      Citation: Remote Sensing
      PubDate: 2021-05-11
      DOI: 10.3390/rs13101879
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1880: Advanced Power Line Diagnostics Using
           Point Cloud Data—Possible Applications and Limits

    • Authors: Marek Siranec, Marek Höger, Alena Otcenasova
      First page: 1880
      Abstract: The advance in remote sensing techniques, especially the development of LiDAR scanning systems, allowed the development of new methods for power line corridor surveys using a digital model of the powerline and its surroundings. The advanced diagnostic techniques based on the acquired conductor geometry recalculation to extreme operating and climatic conditions were proposed using this digital model. Although the recalculation process is relatively easy and straightforward, the uncertainties of input parameters used for the recalculation can significantly compromise such recalculation accuracy. This paper presents a systematic analysis of the accuracy of the recalculation affected by the inaccuracies of the conductor state equation input variables. The sensitivity of the recalculation to the inaccuracy of five basic input parameters was tested (initial temperature and mechanical tension, elasticity modulus, specific gravity load and tower span) by comparing the conductor sag values when input parameters were affected by a specific inaccuracy with an ideal sag-tension table. The presented tests clearly showed that the sag recalculation inaccuracy must be taken into account during the safety assessment process, as the sag deviation can, in some cases, reach values comparable to the minimal clearance distances specified in the technical standards.
      Citation: Remote Sensing
      PubDate: 2021-05-11
      DOI: 10.3390/rs13101880
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1881: Integration of Satellite
           Interferometric Data in Civil Protection Strategies for Landslide Studies
           at a Regional Scale

    • Authors: Silvia Bianchini, Lorenzo Solari, Davide Bertolo, Patrick Thuegaz, Filippo Catani
      First page: 1881
      Abstract: Multi-Temporal Satellite Interferometry (MTInSAR) is gradually evolving from being a tool developed by the scientific community exclusively for research purposes to a real operational technique that can meet the needs of different users involved in geohazard mitigation. This work aims at showing the innovative operational use of satellite radar interferometric products in Civil Protection Authority (CPA) practices for monitoring slow-moving landslides. We present the example of the successful ongoing monitoring system in the Valle D’Aosta Region (VAR-Northern Italy). This system exploits well-combined MTInSAR products and ground-based instruments for landslide management and mitigation strategies over the whole regional territory. Due to the critical intrinsic constraints of MTInSAR data, a robust regional satellite monitoring integrated into CPA practices requires the support of both in situ measurements and remotely sensed systems to guarantee the completeness and reliability of information. The monitoring network comprises three levels of analysis: Knowledge monitoring, Control monitoring, and Emergency monitoring. At the first monitoring level, MTInSAR data are used for the preliminary evaluation of the deformation scenario at a regional scale. At the second monitoring level, MTInSAR products support the prompt detection of trend variations of radar benchmarks displacements with bi-weekly temporal frequency to identify active critical situations where follow-up studies must be carried out. In the third monitoring level, MTInSAR data integrated with ground-based data are exploited to confirm active slow-moving deformations detected by on-site instruments. At this level, MTInSAR data are also used to carry out back analysis that cannot be performed by any other tool. From the example of the Valle D’Aosta Region integrated monitoring network, which is one of the few examples of this kind around Europe, it is evident that MTInSAR provides a great opportunity to improve monitoring capabilities within CPA activities.
      Citation: Remote Sensing
      PubDate: 2021-05-11
      DOI: 10.3390/rs13101881
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1882: RegARD: Symmetry-Based Coarse
           Registration of Smartphone’s Colorful Point Clouds with CAD Drawings for
           Low-Cost Digital Twin Buildings

    • Authors: Yijie Wu, Jianga Shang, Fan Xue
      First page: 1882
      Abstract: Coarse registration of 3D point clouds plays an indispensable role for parametric, semantically rich, and realistic digital twin buildings (DTBs) in the practice of GIScience, manufacturing, robotics, architecture, engineering, and construction. However, the existing methods have prominently been challenged by (i) the high cost of data collection for numerous existing buildings and (ii) the computational complexity from self-similar layout patterns. This paper studies the registration of two low-cost data sets, i.e., colorful 3D point clouds captured by smartphones and 2D CAD drawings, for resolving the first challenge. We propose a novel method named ‘Registration based on Architectural Reflection Detection’ (RegARD) for transforming the self-symmetries in the second challenge from a barrier of coarse registration to a facilitator. First, RegARD detects the innate architectural reflection symmetries to constrain the rotations and reduce degrees of freedom. Then, a nonlinear optimization formulation together with advanced optimization algorithms can overcome the second challenge. As a result, high-quality coarse registration and subsequent low-cost DTBs can be created with semantic components and realistic appearances. Experiments showed that the proposed method outperformed existing methods considerably in both effectiveness and efficiency, i.e., 49.88% less error and 73.13% less time, on average. The RegARD presented in this paper first contributes to coarse registration theories and exploitation of symmetries and textures in 3D point clouds and 2D CAD drawings. For practitioners in the industries, RegARD offers a new automatic solution to utilize ubiquitous smartphone sensors for massive low-cost DTBs.
      Citation: Remote Sensing
      PubDate: 2021-05-11
      DOI: 10.3390/rs13101882
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1883: Development of a Radar Reflector Kit
           for Older Adults to Use to Signal Their Location and Needs in a
           Large-Scale Earthquake Disaster

    • Authors: Morisaki, Fujiu, Furuta, Takayama
      First page: 1883
      Abstract: In Japan, older adults account for the highest proportion of the population of any country in the world. When large-scale earthquake disasters strike, large numbers of casualties are known to particularly occur among seniors. Many are physically or mentally vulnerable and require assistance during the different phases of disaster response, including rescue, evacuation, and living in an evacuation center. However, the growing number of older adults has made it difficult, after a disaster, to quickly gather information on their locations and assess their needs. The authors are developing a proposal to enable vulnerable people to signal their location and needs in the aftermath of a disaster to response teams by deploying radar reflectors that can be detected in synthetic aperture radar (SAR) satellite imagery. The purpose of this study was to develop a radar reflector kit that seniors could easily assemble in order to make this proposal feasible in practice. Three versions of the reflector were tested for detectability, and a sample of older adults was asked to assemble the kits and provide feedback regarding problems they encountered and regarding their interest in using the reflectors in the event of a large-scale disaster.
      Citation: Remote Sensing
      PubDate: 2021-05-11
      DOI: 10.3390/rs13101883
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1884: Temperature and Relative Humidity
           Profile Retrieval from Fengyun-3D/HIRAS in the Arctic Region

    • Authors: Jingjing Hu, Yansong Bao, Jian Liu, Hui Liu, George P. Petropoulos, Petros Katsafados, Liuhua Zhu, Xi Cai
      First page: 1884
      Abstract: The acquisition of real-time temperature and relative humidity (RH) profiles in the Arctic is of great significance for the study of the Arctic’s climate and Arctic scientific research. However, the operational algorithm of Fengyun-3D only takes into account areas within 60°N, the innovation of this work is that a new technique based on Neural Network (NN) algorithm was proposed, which can retrieve these parameters in real time from the Fengyun-3D Hyperspectral Infrared Radiation Atmospheric Sounding (HIRAS) observations in the Arctic region. Considering the difficulty of obtaining a large amount of actual observation (such as radiosonde) in the Arctic region, collocated ERA5 data from European Centre for Medium-Range Weather Forecasts (ECMWF) and HIRAS observations were used to train the neural networks (NNs). Brightness temperature and training targets were classified using two variables: season (warm season and cold season) and surface type (ocean and land). NNs-based retrievals were compared with ERA5 data and radiosonde observations (RAOBs) independent of the NN training sets. Results showed that (1) the NNs retrievals accuracy is generally higher on warm season and ocean; (2) the root-mean-square error (RMSE) of retrieved profiles is generally slightly higher in the RAOB comparisons than in the ERA5 comparisons, but the variation trend of errors with height is consistent; (3) the retrieved profiles by the NN method are closer to ERA5, comparing with the AIRS products. All the results demonstrated the potential value in time and space of NN algorithm in retrieving temperature and relative humidity profiles of the Arctic region from HIRAS observations under clear-sky conditions. As such, the proposed NN algorithm provides a valuable pathway for retrieving reliably temperature and RH profiles from HIRAS observations in the Arctic region, providing information of practical value in a wide spectrum of practical applications and research investigations alike.All in all, our work has important implications in broadening Fengyun-3D’s operational implementation range from within 60°N to the Arctic region.
      Citation: Remote Sensing
      PubDate: 2021-05-11
      DOI: 10.3390/rs13101884
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1885: Inferring Grassland Drought Stress
           with Unsupervised Learning from Airborne Hyperspectral VNIR Imagery

    • Authors: Floris Hermanns, Felix Pohl, Corinna Rebmann, Gundula Schulz, Ulrike Werban, Angela Lausch
      First page: 1885
      Abstract: The 2018–2019 Central European drought had a grave impact on natural and managed ecosystems, affecting their health and productivity. We examined patterns in hyperspectral VNIR imagery using an unsupervised learning approach to improve ecosystem monitoring and the understanding of grassland drought responses. The main objectives of this study were (1) to evaluate the application of simplex volume maximisation (SiVM), an unsupervised learning method, for the detection of grassland drought stress in high-dimensional remote sensing data at the ecosystem scale and (2) to analyse the contributions of different spectral plant and soil traits to the computed stress signal. The drought status of the research site was assessed with a non-parametric standardised precipitation–evapotranspiration index (SPEI) and soil moisture measurements. We used airborne HySpex VNIR-1800 data from spring 2018 and 2019 to compare vegetation condition at the onset of the drought with the state after one year. SiVM, an interpretable matrix factorisation technique, was used to derive typical extreme spectra (archetypes) from the hyperspectral data. The classification of archetypes allowed for the inference of qualitative drought stress levels. The results were evaluated using a set of geophysical measurements and vegetation indices as proxy variables for drought-inhibited vegetation growth. The successful application of SiVM for grassland stress detection at the ecosystem canopy scale was verified in a correlation analysis. The predictor importance was assessed with boosted beta regression. In the resulting interannual stress model, carotenoid-related variables had among the highest coefficient values. The significance of the photochemical reflectance index that uses 512 nm as reference wavelength (PRI512) demonstrates the value of combining imaging spectrometry and unsupervised learning for the monitoring of vegetation stress. It also shows the potential of archetypical reflectance spectra to be used for the remote estimation of photosynthetic efficiency. More conclusive results could be achieved by using vegetation measurements instead of proxy variables for evaluation. It must also be investigated how the method can be generalised across ecosystems.
      Citation: Remote Sensing
      PubDate: 2021-05-11
      DOI: 10.3390/rs13101885
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1886: Spatiotemporal Modeling of Coniferous
           Forests Dynamics Along the Southern Edge of Their Range in the Central
           Russian Plain

    • Authors: Tatiana Chernenkova, Ivan Kotlov, Nadezhda Belyaeva, Elena Suslova
      First page: 1886
      Abstract: Forests with predominance of Norway spruce (Picea abies (L.) H. Karst.) and Scots pine (Pinus sylvestris L.) within the hemiboreal zone are considered as secondary communities formed under long-term human activity (logging, plowing, fires and silviculture). This study raises the question—how stable is current state of coniferous forests on the southern border of their natural distribution in the center of Eastern Europe using the example of the Moscow region (MR)' The object of the study are spruce and pine forests in different periods of Soviet and post-Soviet history within the Moscow Region (MR). The current proportion of spruce forests is 21.7%, and the proportion of pine forests is 18.5% from total forest area according to our estimates. The direction and rate of forest succession were analyzed based on current composition of populations of the main forest-forming species (spruce, pine, birch, aspen, oak, linden, and ash) based on ground-based research materials collected in 2006–2019. This allowed to develop the dynamic model (DM) of forest communities with the participation of Norway spruce and Scots pine for several decades. Assessment of the spatial distribution of coniferous communities is based on field data and spatial modeling using remote sensing data—Landsat 8 mosaic for 2020. In parallel, a retrospective model (RM) of the spatial-temporal organization of spruce and pine forests for a 30-year period was developed using two Landsat 5 mosaics. For this, nine different algorithms were tested and the best one for this task was found—random forest. Geobotanical relevés were used as a training sample combined with the 2006–2012 mosaic; the obtained spectral signatures were used for modeling based on the 1984–1990 mosaic. Thus, two multi-temporal spatial models of coniferous formations have been developed. Detailed analysis of the structure of spruce and pine forests based on field data made it possible to track trends of successional dynamics for the first time, considering the origin of communities and the ecological conditions of habitats. As a result, ideas about the viability of spruce and pine cenopopulations in different types of communities were formulated, which made possible to develop a dynamic model (DM) of changes in forest communities for future. Comparison of the areas and nature of changes in the spatial structure of coniferous formations made possible to develop the RM. Comparison of two different-time models of succession dynamics (DM and RM) makes possible to correct the main trends in the transformation of coniferous forests of natural and artificial origin under the existing regime of forestry. A set of features was identified that indicates risk factors for coniferous forests in the region. A further decrease of the spruce and pine plantations and increase of the spruce-small-leaved and deciduous formations are expected in the study area. The proportion of pine-spruce forests does not exceed 3% of the area and can be considered as the most vulnerable type of forest.
      Citation: Remote Sensing
      PubDate: 2021-05-11
      DOI: 10.3390/rs13101886
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1887: Selection of Lee Filter Window Size
           Based on Despeckling Efficiency Prediction for Sentinel SAR Images

    • Authors: Oleksii Rubel, Vladimir Lukin, Andrii Rubel, Karen Egiazarian
      First page: 1887
      Abstract: Radar imaging has many advantages. Meanwhile, SAR images suffer from a noise-like phenomenon called speckle. Many despeckling methods have been proposed to date but there is still no common opinion as to what the best filter is and/or what are its parameters (window or block size, thresholds, etc.). The local statistic Lee filter is one of the most popular and best-known despeckling techniques in radar image processing. Using this filter and Sentinel-1 images as a case study, we show how filter parameters, namely scanning window size, can be selected for a given image based on filter efficiency prediction. Such a prediction can be carried out using a set of input parameters that can be easily and quickly calculated and employing a trained neural network that allows determining one or several criteria of filtering efficiency with high accuracy. The statistical analysis of the obtained results is carried out. This characterizes improvements due to the adaptive selection of the filter window size, both potential and based on prediction. We also analyzed what happens if, due to prediction errors, erroneous decisions are undertaken. Examples for simulated and real-life images are presented.
      Citation: Remote Sensing
      PubDate: 2021-05-12
      DOI: 10.3390/rs13101887
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1888: Elevation Spatial Variation Analysis
           and Compensation in GEO SAR Imaging

    • Authors: Faguang Chang, Dexin Li, Zhen Dong, Yang Huang, Zhihua He, Xing Chen
      First page: 1888
      Abstract: Due to geosynchronous synthetic aperture radar’s (GEO SAR) high orbit and low relative speed, the integration time reaches up to hundreds of seconds for a fine resolution. The short revisit cycle is essential for remote sensing applications such as disaster monitoring and vegetation measurements. Three-dimensional (3D) scene imaging mode is crucial for long-term observation using GEO SAR. However, this mode will bring a new kind of space-variant error in elevation. In this paper, we focus on the analysis of the elevation space-variant error. First, the decorrelation problems caused by the spatial variation are presented. Second, by combining with the SAR imaging geometry, the elevation spatial variation is decomposed into two-dimensional (2D) space variation of range and azimuth. Third, an imaging algorithm is proposed to solve the 3D space variation and improve the focusing depth. Finally, simulations with dot-matrix targets and distributed targets are performed to validate the imaging method.
      Citation: Remote Sensing
      PubDate: 2021-05-12
      DOI: 10.3390/rs13101888
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1889: Towards Effective BIM/GIS Data
           Integration for Smart City by Integrating Computer Graphics Technique

    • Authors: Junxiang Zhu, Peng Wu
      First page: 1889
      Abstract: The development of a smart city and digital twin requires the integration of Building Information Modeling (BIM) and Geographic Information Systems (GIS), where BIM models are to be integrated into GIS for visualization and/or analysis. However, the intrinsic differences between BIM and GIS have led to enormous problems in BIM-to-GIS data conversion, and the use of City Geography Markup Language (CityGML) has further escalated this issue. This study aims to facilitate the use of BIM models in GIS by proposing using the shapefile format, and a creative approach for converting Industry Foundation Classes (IFC) to shapefile was developed by integrating a computer graphics technique. Thirteen building models were used to validate the proposed method. The result shows that: (1) the IFC-to-shapefile conversion is easier and more flexible to realize than the IFC-to-CityGML conversion, and (2) the computer graphics technique can improve the efficiency and reliability of BIM-to-GIS data conversion. This study can facilitate the use of BIM information in GIS and benefit studies working on digital twins and smart cities where building models are to be processed and integrated in GIS, or any other studies that need to manipulate IFC geometry in depth.
      Citation: Remote Sensing
      PubDate: 2021-05-12
      DOI: 10.3390/rs13101889
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1890: Phenological Changes of Mongolian Oak
           Depending on the Micro-Climate Changes Due to Urbanization

    • Authors: A Reum Kim, Chi Hong Lim, Bong Soon Lim, Jaewon Seol, Chang Seok Lee
      First page: 1890
      Abstract: Urbanization and the resulting increase in development areas and populations cause micro-climate changes such as the urban heat island (UHI) effect. This micro-climate change can affect vegetation phenology. It can advance leaf unfolding and flowering and delay the timing of fallen leaves. This study was carried out to clarify the impact of urbanization on the leaf unfolding of Mongolian oak. The survey sites for this study were established in the urban center (Mts. Nam, Mido, and Umyeon in Seoul), suburbs (Mts. Cheonggye and Buram in Seoul), a rural area (Gwangneung, Mt. Sori in Gyeonggi-do), and a natural area (Mt. Jeombong in Gangwon-do). Green-up dates derived from the analyses of digital camera images and MODIS satellite images were the earliest in the urban center and delayed through the suburbs and rural area to the natural area. The difference in the observed green-up date compared to the expected one, which was determined by regarding the Mt. Jeombong site located in the natural area as the reference site, was the biggest in the urban center and decreased through the suburbs and rural area to the natural area. Green-up dates in the rural area, suburbs, and urban center were earlier by 11.0, 14.5, and 16.3 days than the expected ones. If these results are transformed into the air temperature based on previous research results, it could be deduced that the air temperature in the urban center, suburbs, and rural area rose by 3.8 to 4.6 °C, 3.3 to 4.1 °C, and 2.5 to 3.1 °C, respectively. Green-up dates derived based on the accumulated growing degree days (AGDD) showed the same trend as those derived from the image interpretation. Green-up dates derived from the change in sap flow as a physiological response of the plant showed a difference within one day from the green-up dates derived from digital camera and MODIS satellite image analyses. The change trajectory of the curvature K value derived from the sap flow also showed a very similar trend to that of the curvature K value derived from the vegetation phenology. From these results, we confirm the availability of AGDD and sap flow as tools predicting changes in ecosystems due to climate change including phenology. Meanwhile, the green-up dates in survey sites were advanced in proportion to the land use intensity of each survey site. Green-up dates derived based on AGDD were also negatively correlated with the land use intensity of the survey site. This result implies that differences in green-up dates among the survey sites and between the expected and observed green-up dates in the urban center, suburbs, and rural area were due to the increased temperature due to land use in the survey sites. Based on these results, we propose conservation and restoration of nature as measures to reduce the impact of climate change.
      Citation: Remote Sensing
      PubDate: 2021-05-12
      DOI: 10.3390/rs13101890
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1891: A Hybrid Approach of Combining Random
           Forest with Texture Analysis and VDVI for Desert Vegetation Mapping Based
           on UAV RGB Data

    • Authors: Huoyan Zhou, Ram P. Sharma, Yuancai Lei, Jinping Guo, Liyong Fu
      First page: 1891
      Abstract: Desert vegetation is an important part of arid and semi-arid areas, which plays an important role in preventing wind and fixing sand, conserving water and soil, maintaining the balanced ecosystem. Therefore, mapping the vegetation accurately is necessary to conserve rare desert plants in the fragile ecosystems that are easily damaged and slow to recover. In mapping desert vegetation, there are some weaknesses by using traditional digital classification algorithms from high resolution data. The traditional approach is to use spectral features alone, without spatial information. With the rapid development of drones, cost-effective visible light data is easily available, and the data would be non-spectral but with spatial information. In this study, a method of mapping the desert rare vegetation was developed based on the pixel classifiers and use of Random Forest (RF) algorithm with the feature of VDVI and texture. The results indicated the accuracy of mapping the desert rare vegetation were different with different methods and the accuracy of the method proposed was higher than the traditional method. The most commonly used decision rule in the traditional method, named Maximum Likelihood classifier, produced overall accuracy (76.69%). The inclusion of texture and VDVI features with RGB (Red Green Blue) data could increase the separability, thus improved the precision. The overall accuracy could be up to 84.19%, and the Kappa index with 79.96%. From the perspective of features, VDVI is less important than texture features. The texture features appeared more important than spectral features in desert vegetation mapping. The RF method with the RGB+VDVI+TEXTURE would be better method for desert vegetation mapping compared with the common method. This study is the first attempt of classifying the desert vegetation based on the RGB data, which will help to inform management and conservation of Ulan Buh desert vegetation.
      Citation: Remote Sensing
      PubDate: 2021-05-12
      DOI: 10.3390/rs13101891
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1892: One-Class Classification of Natural
           Vegetation Using Remote Sensing: A Review

    • Authors: Rapinel, Hubert-Moy
      First page: 1892
      Abstract: Advances in remote sensing (RS) technology in recent years have increased the interest in including RS data into one-class classifiers (OCCs). However, this integration is complex given the interdisciplinary issues involved. In this context, this review highlights the advances and current challenges in integrating RS data into OCCs to map vegetation classes. A systematic review was performed for the period 2013–2020. A total of 136 articles were analyzed based on 11 topics and 30 attributes that address the ecological issues, properties of RS data, and the tools and parameters used to classify natural vegetation. The results highlight several advances in the use of RS data in OCCs: (i) mapping of potential and actual vegetation areas, (ii) long-term monitoring of vegetation classes, (iii) generation of multiple ecological variables, (iv) availability of open-source data, (v) reduction in plotting effort, and (vi) quantification of over-detection. Recommendations related to interdisciplinary issues were also suggested: (i) increasing the visibility and use of available RS variables, (ii) following good classification practices, (iii) bridging the gap between spatial resolution and site extent, and (iv) classifying plant communities.
      Citation: Remote Sensing
      PubDate: 2021-05-12
      DOI: 10.3390/rs13101892
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1893: Remote and Proximal Assessment of
           Plant Traits

    • Authors: Ittai Herrmann, Katja Berger
      First page: 1893
      Abstract: The inference of functional vegetation traits from remotely sensed signals is key to providing efficient information for multiple plant-based applications and to solve related problems [...]
      Citation: Remote Sensing
      PubDate: 2021-05-12
      DOI: 10.3390/rs13101893
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1894: Remote Sensing Image Augmentation
           Based on Text Description for Waterside Change Detection

    • Authors: Chen Chen, Hongxiang Ma, Guorun Yao, Ning Lv, Hua Yang, Cong Li, Shaohua Wan
      First page: 1894
      Abstract: Since remote sensing images are difficult to obtain and need to go through a complicated administrative procedure for use in China, it cannot meet the requirement of huge training samples for Waterside Change Detection based on deep learning. Recently, data augmentation has become an effective method to address the issue of an absence of training samples. Therefore, an improved Generative Adversarial Network (GAN), i.e., BTD-sGAN (Text-based Deeply-supervised GAN), is proposed to generate training samples for remote sensing images of Anhui Province, China. The principal structure of our model is based on Deeply-supervised GAN(D-sGAN), and D-sGAN is improved from the point of the diversity of the generated samples. First, the network takes Perlin Noise, image segmentation graph, and encoded text vector as input, in which the size of image segmentation graph is adjusted to 128 × 128 to facilitate fusion with the text vector. Then, to improve the diversity of the generated images, the text vector is used to modify the semantic loss of the downsampled text. Finally, to balance the time and quality of image generation, only a two-layer Unet++ structure is used to generate the image. Herein, “Inception Score”, “Human Rank”, and “Inference Time” are used to evaluate the performance of BTD-sGAN, StackGAN++, and GAN-INT-CLS. At the same time, to verify the diversity of the remote sensing images generated by BTD-sGAN, this paper compares the results when the generated images are sent to the remote sensing interpretation network and when the generated images are not added; the results show that the generated image can improve the precision of soil-moving detection by 5%, which proves the effectiveness of the proposed model.
      Citation: Remote Sensing
      PubDate: 2021-05-12
      DOI: 10.3390/rs13101894
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1895: Using Uncrewed Aerial Vehicles for
           Identifying the Extent of Invasive Phragmites australis in Treatment Areas
           Enrolled in an Adaptive Management Program

    • Authors: Colin Brooks, Charlotte Weinstein, Andrew Poley, Amanda Grimm, Nicholas Marion, Laura Bourgeau-Chavez, Dana Hansen, Kurt Kowalski
      First page: 1895
      Abstract: Higher spatial and temporal resolutions of remote sensing data are likely to be useful for ecological monitoring efforts. There are many different treatment approaches for the introduced European genotype of Phragmites australis, and adaptive management principles are being integrated in at least some long-term monitoring efforts. In this paper, we investigated how natural color and a smaller set of near-infrared (NIR) images collected with low-cost uncrewed aerial vehicles (UAVs) could help quantify the aboveground effects of management efforts at 20 sites enrolled in the Phragmites Adaptive Management Framework (PAMF) spanning the coastal Laurentian Great Lakes region. We used object-based image analysis and field ground truth data to classify the Phragmites and other cover types present at each of the sites and calculate the percent cover of Phragmites, including whether it was alive or dead, in the UAV images. The mean overall accuracy for our analysis with natural color data was 91.7% using four standardized classes (Live Phragmites, Dead Phragmites, Other Vegetation, Other Non-vegetation). The Live Phragmites class had a mean user’s accuracy of 90.3% and a mean producer’s accuracy of 90.1%, and the Dead Phragmites class had a mean user’s accuracy of 76.5% and a mean producer’s accuracy of 85.2% (not all classes existed at all sites). These results show that UAV-based imaging and object-based classification can be a useful tool to measure the extent of dead and live Phragmites at a series of sites undergoing management. Overall, these results indicate that UAV sensing appears to be a useful tool for identifying the extent of Phragmites at management sites.
      Citation: Remote Sensing
      PubDate: 2021-05-12
      DOI: 10.3390/rs13101895
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1896: Thresholding Analysis and Feature
           Extraction from 3D Ground Penetrating Radar Data for Noninvasive
           Assessment of Peanut Yield

    • Authors: Iliyana D. Dobreva, Henry A. Ruiz-Guzman, Ilse Barrios-Perez, Tyler Adams, Brody L. Teare, Paxton Payton, Mark E. Everett, Mark D. Burow, Dirk B. Hays
      First page: 1896
      Abstract: This study explores the efficacy of utilizing a novel ground penetrating radar (GPR) acquisition platform and data analysis methods to quantify peanut yield for breeding selection, agronomic research, and producer management and harvest applications. Sixty plots comprising different peanut market types were scanned with a multichannel, air-launched GPR antenna. Image thresholding analysis was performed on 3D GPR data from four of the channels to extract features that were correlated to peanut yield with the objective of developing a noninvasive high-throughput peanut phenotyping and yield-monitoring methodology. Plot-level GPR data were summarized using mean, standard deviation, sum, and the number of nonzero values (counts) below or above different percentile threshold values. Best results were obtained for data below the percentile threshold for mean, standard deviation and sum. Data both below and above the percentile threshold generated good correlations for count. Correlating individual GPR features to yield generated correlations of up to 39% explained variability, while combining GPR features in multiple linear regression models generated up to 51% explained variability. The correlations increased when regression models were developed separately for each peanut type. This research demonstrates that a systematic search of thresholding range, analysis window size, and data summary statistics is necessary for successful application of this type of analysis. The results also establish that thresholding analysis of GPR data is an appropriate methodology for noninvasive assessment of peanut yield, which could be further developed for high-throughput phenotyping and yield-monitoring, adding a new sensor and new capabilities to the growing set of digital agriculture technologies.
      Citation: Remote Sensing
      PubDate: 2021-05-12
      DOI: 10.3390/rs13101896
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1897: Seasonal Net Shortwave Radiation of
           Bare Arable Land in Poland and Israel According to Roughness and
           Atmospheric Irradiance

    • Authors: Jerzy Cierniewski, Jean-Louis Roujean, Jarosław Jasiewicz, Sławomir Królewicz
      First page: 1897
      Abstract: Tillage of arable fields, using for instance a smoothing harrow, may increase the magnitude of albedo of such soil surfaces depending on the location, the sun’s illumination and atmospheric components. As these soil surfaces absorb less shortwave radiation compared to plowed soils, the result is an atmospheric cooling and a positive effect on the Earth’s climate. This paper is the follow-on of a previous study aimed at quantifying the seasonal dynamics of net shortwave radiation reflected by bare air-dried arable land areas located in contrasting environments, i.e. Poland and Israel. Soil tillage includes a plow, a disk harrow, and a smoothing harrow. Previous work concentrated on the estimate of net shortwave radiation under clear-sky theoretical scenarios, whereas the present study deals with a realistic atmosphere throughout the year 2014. This latter is characterized by the observations of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument on board the Meteosat Second Generation (MSG). The variations of the net shortwave radiation for the selected bare arable land areas were assessed in combining observations from Landsat 8 images and digital maps of land use and soil, plus model equations that calculate the diurnal variations of the broadband blue-sky albedo with roughness inclusive. The daily amount of net shortwave radiation for air-dried bare arable land in Poland and Israel for the time their spatial coverage is the largest was found to be about 40–50% and 10% lower, respectively, in cloudy-sky conditions compared to clear-sky conditions.
      Citation: Remote Sensing
      PubDate: 2021-05-12
      DOI: 10.3390/rs13101897
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1898: Use of GNSS Tropospheric Delay
           Measurements for the Parameterization and Validation of WRF
           High-Resolution Re-Analysis over the Western Gulf of Corinth, Greece: The
           PaTrop Experiment

    • Authors: Nikolaos Roukounakis, Dimitris Katsanos, Pierre Briole, Panagiotis Elias, Ioannis Kioutsioukis, Athanassios A. Argiriou, Adrianos Retalis
      First page: 1898
      Abstract: In the last thirty years, Synthetic Aperture Radar interferometry (InSAR) and the Global Navigation Satellite System (GNSS) have become fundamental space geodetic techniques for mapping surface deformations due to tectonic movements. One major limiting factor to those techniques is the effect of the troposphere, as surface velocities are of the order of a few mm yr−1, and high accuracy (to mm level) is required. The troposphere introduces a path delay in the microwave signal, which, in the case of GNSS Precise Point Positioning (PPP), can nowadays be partly removed with the use of specialized mapping functions. Moreover, tropospheric stratification and short wavelength spatial turbulences produce an additive noise to the low amplitude ground deformations calculated by the (multitemporal) InSAR methodology. InSAR atmospheric phase delay corrections are much more challenging, as opposed to GNSS PPP, due to the single pass geometry and the gridded nature of the acquired data. Thus, the precise knowledge of the tropospheric parameters along the propagation medium is extremely useful for the estimation and correction of the atmospheric phase delay. In this context, the PaTrop experiment aims to maximize the potential of using a high-resolution Limited-Area Model for the calculation and removal of the tropospheric noise from InSAR data, by following a synergistic approach and integrating all the latest advances in the fields of remote sensing meteorology (GNSS and InSAR) and Numerical Weather Forecasting (WRF). In the first phase of the experiment, presented in the current paper, we investigate the extent to which a high-resolution 1 km WRF weather re-analysis can produce detailed tropospheric delay maps of the required accuracy, by coupling its output (in terms of Zenith Total Delay or ZTD) with the vertical delay component in GNSS measurements. The model is initially operated with varying parameterization, with GNSS measurements providing a benchmark of real atmospheric conditions. Subsequently, the final WRF daily re-analysis run covers an extended period of one year, based on the optimum model parameterization scheme demonstrated by the parametric analysis. The two datasets (predicted and observed) are compared and statistically evaluated, in order to investigate the extent to which meteorological parameters that affect ZTD can be simulated accurately by the model under different weather conditions. Results demonstrate a strong correlation between predicted and observed ZTDs at the 19 GNSS stations throughout the year (R ranges from 0.91 to 0.93), with an average mean bias (MB) of –19.2 mm, indicating that the model tends to slightly underestimate the tropospheric ZTD as compared to the GNSS derived values. With respect to the seasonal component, model performance is better during the autumn period (October–December), followed by spring (April–June). Setting the acceptable bias range at ±23 mm (equal to the amplitude of one Sentinel-1 C-band phase cycle when projected to the zenithal distance), it is demonstrated that the model produces satisfactory results, with a percentage of ZTD values within the bias margin ranging from 57% in summer to 63% in autumn.
      Citation: Remote Sensing
      PubDate: 2021-05-13
      DOI: 10.3390/rs13101898
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1899: A Review of Passive and Active
           Ultra-Wideband Baluns for Use in Ground Penetrating Radar

    • Authors: Wouter van Verre, Frank J. W. Podd, Xianyang Gao, David J. Daniels, Anthony J. Peyton
      First page: 1899
      Abstract: Microwave ultra-wideband technology has been widely adopted in instrumentation and measurement systems, including ground-penetrating radar (GPR) sensors. Baluns are essential components in these systems to feed balanced antennas from unbalanced feed cables. Baluns are typically introduced to avoid issues with return signals, asymmetrical radiation patterns and radiation from cables. In GPR systems, these issues can cause poor sensitivity due to a reduction in radiated power, blind spots due to changes in the radiation pattern and additional clutter from common mode radiation. The different balun technologies currently available exhibit a wide variation in performance characteristics such as insertion loss, reflection coefficient and phase balance, as well as physical properties such as size and manufacturability. In this study, the performance of two magnetic transformer baluns, two tapered microstrip baluns and an active balun based on high-speed amplifiers were investigated, all up to frequencies of 6 GHz. A radio frequency current probe was used to measure the common mode currents on the feed cables that occur with poor performing baluns. It was found that commercially available magnetic transformer baluns have the best phase linearity, while also having the highest insertion losses. The active balun design has the best reflection coefficient at low frequencies, while, at high frequencies, its performance is similar to the other baluns tested. It was found that the active balun had the lowest common mode current on the feed cables.
      Citation: Remote Sensing
      PubDate: 2021-05-13
      DOI: 10.3390/rs13101899
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1900: Mapping Impervious Surface Areas
           Using Time-Series Nighttime Light and MODIS Imagery

    • Authors: Yun Tang, Zhenfeng Shao, Xiao Huang, Bowen Cai
      First page: 1900
      Abstract: Mapping impervious surface area (ISA) dynamics at the regional and global scales is an important task that supports the management of the urban environment and urban ecological systems. In this study, we aimed to develop a new method for ISA percentage (ISA%) mapping using Nighttime Light (NTL) and MODIS products. The proposed method consists of three major steps. First, we calculated the Enhanced Vegetation Index (EVI)-adjusted NTL index (EANTLI) and performed intra-annual and inter-annual corrections on the DMSP-OLS data. Second, based on the geographically weighted regression (GWR) model, we built a consistent NTL product from 2000 to 2019 by performing an intercalibration between DMSP-OLS and VIIRS images. Third, we adopted a GA-BP neural network model to monitor ISA% dynamics using NTL imagery, MODIS imagery, and population data. Taking the Guangdong–Hong Kong–Macao Greater Bay as the study area, our results indicate that the ISA% in our study area increased from 7.97% in 2000 to 17.11% in 2019, with a mean absolute error (MAE) of 0.0647, root mean square error (RMSE) of 0.1003, Pearson’s coefficient of 0.9613, and R2 (R-squared) of 0.9239. Specifically, these results demonstrate the effectiveness of the proposed method in mapping ISA and investigating ISA dynamics using temporal features extracted from consistent NTL and MODIS products. The proposed method is feasible when generating ISA% at a large scale at high frequency, given the ease of implementation and the availability of input data sources.
      Citation: Remote Sensing
      PubDate: 2021-05-13
      DOI: 10.3390/rs13101900
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1901: Estimation of Liquid Fraction of Wet
           Snow by Using 2-D Video Disdrometer and S-Band Weather Radar

    • Authors: Sung-Ho Suh, Hong-Il Kim, Eun-Ho Choi, Cheol-Hwan You
      First page: 1901
      Abstract: Wet snow may cause significant damage to humans and property, and thus, it is necessary to estimate the corresponding liquid fraction (FL). Consequently, the FL of wet snow was estimated using a novel technique; specifically, the particle shape irregularity (Ir) was estimated through the particle coordinate information obtained using 2-D video disdrometer (2DVD) measurements. Moreover, the possibility of quantitively estimating FL via Ir, based on the temperature (T), was examined. Eight snowfall cases from 2014 to 2016 were observed through a 2DVD installed in Jincheon, South Korea, to analyze the dominant properties of physical variables of snowflakes (i.e., the terminal velocity (VT), particle density (ρs), Ir, and FL) and the corresponding relationships according to the T ranges (−4.5 < T (°C) < 2.5) in which wet snow can occur. It was clarified that the volume-equivalent particle diameter (D)–FL and D–Ir relationships depended on T, and a relationship existed between Ir and FL. The analysis results were verified using the Yong-In Testbed (YIT) S-band weather radar and T-matrix scattering simulation. The D–FL relationship was implemented in the scattering simulation, and the results indicated that the simulated reflectivity (ZS) was highly correlated with the observed reflectivity (ZO) under all T classes. These features can provide a basis for radar analysis and quantitative snowfall estimation for wet snow with various FL values.
      Citation: Remote Sensing
      PubDate: 2021-05-13
      DOI: 10.3390/rs13101901
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1902: Combination of Sentinel-2 and
           PALSAR-2 for Local Climate Zone Classification: A Case Study of Nanchang,
           China

    • Authors: Chaomin Chen, Hasi Bagan, Xuan Xie, Yune La, Yoshiki Yamagata
      First page: 1902
      Abstract: Local climate zone (LCZ) maps have been used widely to study urban structures and urban heat islands. Because remote sensing data enable automated LCZ mapping on a large scale, there is a need to evaluate how well remote sensing resources can produce fine LCZ maps to assess urban thermal environments. In this study, we combined Sentinel-2 multispectral imagery and dual-polarized (HH + HV) PALSAR-2 data to generate LCZ maps of Nanchang, China using a random forest classifier and a grid-cell-based method. We then used the classifier to evaluate the importance scores of different input features (Sentinel-2 bands, PALSAR-2 channels, and textural features) for the classification model and their contribution to each LCZ class. Finally, we investigated the relationship between LCZs and land surface temperatures (LSTs) derived from summer nighttime ASTER thermal imagery by spatial statistical analysis. The highest classification accuracy was 89.96% when all features were used, which highlighted the potential of Sentinel-2 and dual-polarized PALSAR-2 data. The most important input feature was the short-wave infrared-2 band of Sentinel-2. The spectral reflectance was more important than polarimetric and textural features in LCZ classification. PALSAR-2 data were beneficial for several land cover LCZ types when Sentinel-2 and PALSAR-2 were combined. Summer nighttime LSTs in most LCZs differed significantly from each other. Results also demonstrated that grid-cell processing provided more homogeneous LCZ maps than the usual resampling methods. This study provided a promising reference to further improve LCZ classification and quantitative analysis of local climate.
      Citation: Remote Sensing
      PubDate: 2021-05-13
      DOI: 10.3390/rs13101902
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1903: A Disparity Refinement Algorithm for
           Satellite Remote Sensing Images Based on Mean-Shift Plane Segmentation

    • Authors: Zhihui Li, Jiaxin Liu, Yang Yang, Jing Zhang
      First page: 1903
      Abstract: Objects in satellite remote sensing image sequences often have large deformations, and the stereo matching of this kind of image is so difficult that the matching rate generally drops. A disparity refinement method is needed to correct and fill the disparity. A method for disparity refinement based on the results of plane segmentation is proposed in this paper. The plane segmentation algorithm includes two steps: Initial segmentation based on mean-shift and alpha-expansion-based energy minimization. According to the results of plane segmentation and fitting, the disparity is refined by filling missed matching regions and removing outliers. The experimental results showed that the proposed plane segmentation method could not only accurately fit the plane in the presence of noise but also approximate the surface by plane combination. After the proposed plane segmentation method was applied to the disparity refinement of remote sensing images, many missed matches were filled, and the elevation errors were reduced. This proved that the proposed algorithm was effective. For difficult evaluations resulting from significant variations in remote sensing images of different satellites, the edge matching rate and the edge matching map are proposed as new stereo matching evaluation and analysis tools. Experiment results showed that they were easy to use, intuitive, and effective.
      Citation: Remote Sensing
      PubDate: 2021-05-13
      DOI: 10.3390/rs13101903
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1904: From Remote Sensing to Species
           Distribution Modelling: An Integrated Workflow to Monitor Spreading
           Species in Key Grassland Habitats

    • Authors: Walter De Simone, Marina Allegrezza, Anna Rita Frattaroli, Silvia Montecchiari, Giulio Tesei, Vincenzo Zuccarello, Michele Di Musciano
      First page: 1904
      Abstract: Remote sensing (RS) has been widely adopted as a tool to investigate several biotic and abiotic factors, directly and indirectly, related to biodiversity conservation. European grasslands are one of the most biodiverse habitats in Europe. Most of these habitats are subject to priority conservation measure, and several human-induced processes threaten them. The broad expansions of few dominant species are usually reported as drivers of biodiversity loss. In this context, using Sentinel-2 (S2) images, we investigate the distribution of one of the most spreading species in the Central Apennine: Brachypodium genuense. We performed a binary Random Forest (RF) classification of B. genuense using RS images and field-sampled presence/absence data. Then, we integrate the occurrences obtained from RS classification into species distribution models to identify the topographic drivers of B. genuense distribution in the study area. Lastly, the impact of B. genuense distribution in the Natura 2000 (N2k) habitats (Annex I of the European Habitat Directive) was assessed by overlay analysis. The RF classification process detected cover of B. genuense with an overall accuracy of 94.79%. The topographic species distribution model shows that the most relevant topographic variables that influence the distribution of B. genuense are slope, elevation, solar radiation, and topographic wet index (TWI) in order of importance. The overlay analysis shows that 74.04% of the B. genuense identified in the study area falls on the semi-natural dry grasslands. The study highlights the RS classification and the topographic species distribution model’s importance as an integrated workflow for mapping a broad-expansion species such as B. genuense. The coupled techniques presented in this work should apply to other plant communities with remotely recognizable characteristics for more effective management of N2k habitats.
      Citation: Remote Sensing
      PubDate: 2021-05-13
      DOI: 10.3390/rs13101904
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1905: Voxel Grid-Based Fast Registration of
           Terrestrial Point Cloud

    • Authors: Biao Xiong, Weize Jiang, Dengke Li, Man Qi
      First page: 1905
      Abstract: Terrestrial laser scanning (TLS) is an important part of urban reconstruction and terrain surveying. In TLS applications, 4-point congruent set (4PCS) technology is widely used for the global registration of point clouds. However, TLS point clouds usually enjoy enormous data and uneven density. Obtaining the congruent set of tuples in a large point cloud scene can be challenging. To address this concern, we propose a registration method based on the voxel grid of the point cloud in this paper. First, we establish a voxel grid structure and index structure for the point cloud and eliminate uneven point cloud density. Then, based on the point cloud distribution in the voxel grid, keypoints are calculated to represent the entire point cloud. Fast query of voxel grids is used to restrict the selection of calculation points and filter out 4-point tuples on the same surface to reduce ambiguity in building registration. Finally, the voxel grid is used in our proposed approach to perform random queries of the array. Using different indoor and outdoor data to compare our proposed approach with other 4-point congruent set methods, according to the experimental results, in terms of registration efficiency, the proposed method is more than 50% higher than K4PCS and 78% higher than Super4PCS.
      Citation: Remote Sensing
      PubDate: 2021-05-13
      DOI: 10.3390/rs13101905
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1906: Near-Ultraviolet to Near-Infrared
           Band Thresholds Cloud Detection Algorithm for TANSAT-CAPI

    • Authors: Ning Ding, Jianbing Shao, Changxiang Yan, Junqiang Zhang, Yanfeng Qiao, Yun Pan, Jing Yuan, Youzhi Dong, Bo Yu
      First page: 1906
      Abstract: Cloud and aerosol polarization imaging detector (CAPI) is one of the important payloads on the China Carbon Dioxide Observation Satellite (TANSAT), which can realize multispectral polarization detection and accurate on-orbit calibration. The main function of the instrument is to identify the interference of clouds and aerosols in the atmospheric detection path and to improve the retrieval accuracy of greenhouse gases. Therefore, it is of great significance to accurately identify the clouds in remote sensing images. However, in order to meet the requirement of lightweight design, CAPI is only equipped with channels in the near-ultraviolet to near-infrared bands. It is difficult to achieve effective cloud recognition using traditional visible light to thermal infrared band spectral threshold cloud detection algorithms. In order to solve the above problem, this paper innovatively proposes a cloud detection method based on different threshold tests from near ultraviolet to near infrared (NNDT). This algorithm first introduces the 0.38 μm band and the ratio of 0.38 μm band to 1.64 μm band, to realize the separation of cloud pixels and clear sky pixels, which can take advantage of the obvious difference in radiation characteristics between clouds and ground objects in the near-ultraviolet band and the advantages of the band ratio in identifying clouds on the snow surface. The experimental results show that the cloud recognition hit rate (PODcloud) reaches 0.94 (ocean), 0.98 (vegetation), 0.99 (desert), and 0.86 (polar), which therefore achieve the application standard of CAPI data cloud detection The research shows that the NNDT algorithm replaces the demand for thermal infrared bands for cloud detection, gets rid of the dependence on the minimum surface reflectance database that is embodied in traditional cloud recognition algorithms, and lays the foundation for aerosol and CO2 parameter inversion.
      Citation: Remote Sensing
      PubDate: 2021-05-13
      DOI: 10.3390/rs13101906
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1907: Ambiguity Suppression Based on Joint
           Optimization for Multichannel Hybrid and ±π/4 Quad-Pol SAR Systems

    • Authors: Pengfei Zhao, Yunkai Deng, Wei Wang, Yongwei Zhang, Robert Wang
      First page: 1907
      Abstract: Hybrid and ±π/4 quadrature-polarimetric (quad-pol) synthetic aperture radar (SAR) systems operating from space can obtain all polarimetric components simultaneously but suffer from severe azimuth ambiguities in the cross-polarized (cross-pol) measurement channels. In this paper, the hybrid and ±π/4 quad-pol SAR systems with multiple receive channels in azimuth are widely investigated to suppress the azimuth ambiguities of the cross-pol components. We first provide a more thorough analysis of the multichannel hybrid and ±π/4 quad-pol SAR systems. Then, the multichannel signal processing is briefly discussed for the reconstruction of the quad-pol SAR signal from the aliased signals, in which the conventional reconstruction algorithm causes extremely severe azimuth ambiguities. To this end, an improved reconstruction method is proposed based on a joint optimization, which allows for the minimization of ambiguities from the desired polarization and the simultaneous power of undesired polarized signal. This method can largely suppress azimuth ambiguities compared with the conventional reconstruction algorithm. Finally, to verify the advantages and effectiveness of the proposed approach, the azimuth ambiguity-to-signal ratio (AASR), the range ambiguity-to-signal ratio (RASR) and signal-to-noise ratio (SNR) of all polarizations, as well as a set of imaging simulation results, are given to describe the effects of reconstruction on the multichannel hybrid and ±π/4 quad-pol SAR systems.
      Citation: Remote Sensing
      PubDate: 2021-05-13
      DOI: 10.3390/rs13101907
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1908: Impacts of Urban Expansion Forms on
           Ecosystem Services in Urban Agglomerations: A Case Study of
           Shanghai-Hangzhou Bay Urban Agglomeration

    • Authors: Sinan Li, Youyong He, Hanliang Xu, Congmou Zhu, Baiyu Dong, Yue Lin, Bo Si, Jinsong Deng, Ke Wang
      First page: 1908
      Abstract: Exploring impacts of urban expansion on ecosystem services has become a hot topic for regional sustainable development, while analyzing the ecological effects of urban expansion forms under different expansion intensities and city sizes is relatively rare. Therefore, taking a typical urban agglomeration, Shanghai-Hangzhou Bay Urban Agglomeration, as a case study, this study first analyzed the dynamics of urban expansion forms (leapfrogging, edge-expansion, and infilling) and four critical ecosystem services (carbon sequestration, food supply, habitat quality, and soil retention) in three periods from 1990 to 2019. The multiple linear regression model and zonal statistics analysis model were used to quantitatively identify the impacts of urban expansion forms on ecosystem services, taking into account different expansion intensities and city sizes. The results showed that the urban expansion trend in the study area experienced a morphological change from integration to diffusion and then to integration in 1990–2019; edge-expansion was the dominant expansion form. Food supply decreased continuously while other ecosystem services had fluctuating changes, and they all had spatial heterogeneity. The leapfrogging, edge-expansion, and infilling all had negative impacts on ecosystem services, and among them, the edge-expansion intensity had the highest influence degree in the early expansion, and the leapfrogging intensity occupied the dominant position in all influences with the expansion of urban scales. For different city sizes, the impact of edge-expansion in large-scale cities was greater than in small-scale cities in the early expansion, and the impact of leapfrogging in large-scale cities exceeded the edge-expansion in the subsequent expansion. These findings will help further understand the influential mechanisms between urban expansion and ecosystem services and provide a scientific basis for formulating reasonable urban planning.
      Citation: Remote Sensing
      PubDate: 2021-05-13
      DOI: 10.3390/rs13101908
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1909: High-Speed Lightweight Ship Detection
           Algorithm Based on YOLO-V4 for Three-Channels RGB SAR Image

    • Authors: Jiahuan Jiang, Xiongjun Fu, Rui Qin, Xiaoyan Wang, Zhifeng Ma
      First page: 1909
      Abstract: Synthetic Aperture Radar (SAR) has become one of the important technical means of marine monitoring in the field of remote sensing due to its all-day, all-weather advantage. National territorial waters to achieve ship monitoring is conducive to national maritime law enforcement, implementation of maritime traffic control, and maintenance of national maritime security, so ship detection has been a hot spot and focus of research. After the development from traditional detection methods to deep learning combined methods, most of the research always based on the evolving Graphics Processing Unit (GPU) computing power to propose more complex and computationally intensive strategies, while in the process of transplanting optical image detection ignored the low signal-to-noise ratio, low resolution, single-channel and other characteristics brought by the SAR image imaging principle. Constantly pursuing detection accuracy while ignoring the detection speed and the ultimate application of the algorithm, almost all algorithms rely on powerful clustered desktop GPUs, which cannot be implemented on the frontline of marine monitoring to cope with the changing realities. To address these issues, this paper proposes a multi-channel fusion SAR image processing method that makes full use of image information and the network’s ability to extract features; it is also based on the latest You Only Look Once version 4 (YOLO-V4) deep learning framework for modeling architecture and training models. The YOLO-V4-light network was tailored for real-time and implementation, significantly reducing the model size, detection time, number of computational parameters, and memory consumption, and refining the network for three-channel images to compensate for the loss of accuracy due to light-weighting. The test experiments were completed entirely on a portable computer and achieved an Average Precision (AP) of 90.37% on the SAR Ship Detection Dataset (SSDD), simplifying the model while ensuring a lead over most existing methods. The YOLO-V4-lightship detection algorithm proposed in this paper has great practical application in maritime safety monitoring and emergency rescue.
      Citation: Remote Sensing
      PubDate: 2021-05-13
      DOI: 10.3390/rs13101909
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1910: 46-Year (1973–2019) Permafrost
           Landscape Changes in the Hola Basin, Northeast China Using Machine
           Learning and Object-Oriented Classification

    • Authors: Raul-David Șerban, Mihaela Șerban, Ruixia He, Huijun Jin, Yan Li, Xinyu Li, Xinbin Wang, Guoyu Li
      First page: 1910
      Abstract: Land use and cover changes (LUCC) in permafrost regions have significant consequences on ecology, engineered systems, and the environment. Obtaining more details about LUCC is crucial for sustainable development, land conservation, and environment management. The Hola Basin (957 km2) in the northernmost part of Northeast China, a boreal forest landscape underlain by discontinuous, sporadic, and isolated permafrost, was selected for the case study. The LUCC was analyzed using the Landsat archive of satellite images from 1973 to 2019. A thematic change detection analysis was performed by combining the object-based image analysis (OBIA) and the Support Vector Machine (SVM) algorithm. Four types of LUCC (forest, grass, water, and anthropic) were extracted with an overall accuracy of 80% for 1973 and >90% for 1986, 2000, and 2019. Forest, the dominant class (750 km2 in 1973), declined by 88 km2 (11.8%) from 1973 to 1986 but had a recovery of 78 km2 (12.5%) from 2000 to 2019. Grass, the second-largest class (187 km2 in 1973), increased by 86 km2 (46.5%) between 1973 and 1986 and decreased by 90 km2 (40%) between 2000 and 2019. The anthropic class continuously increased from 10 km2 (1973) to 37 km2 (2019). Major features in LUCC are attributed to rapid population growth, resource exploitation, agriculture intensification, economic development, and frequent forest fires. Under a pronounced climate warming, these drivers have been accelerating the degradation of permafrost, subsequently triggering natural hazards and deteriorating the ecological environment. This study represents a benchmark for sustainable LUCC management in the Hola Basin, Northeast China.
      Citation: Remote Sensing
      PubDate: 2021-05-13
      DOI: 10.3390/rs13101910
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1911: Quantifying the Compound Factors of
           Forest Land Changes in the Pearl River Delta, China

    • Authors: Xinchuang Chen, Feng Li, Xiaoqian Li, Yinhong Hu, Panpan Hu
      First page: 1911
      Abstract: Forestland has been a focus of urbanization research, yet the effect of urbanization on forest land change on an urban agglomeration scale still remains unclear. Screening and quantifying the main factors affecting forest land changes have practical significance for land planning and management. Considering the characteristics of the region and referring to related studies, 26 natural, social, and economic factors were screened in the Pearl River Delta (PRD), where land-use changes are intense. Geographically weighted regression and the relative importance were used to quantify the spatial heterogeneity of these main factors. There was still a large area of deforestation evident in the PRD with its afforestation area of 604.3 km2 (mainly converted from cropland) and a deforestation area of 1544.6 km2 (mainly converted from built-up land). The effects of socio-economic factors were the main factors for these forest land changes, especially the rural population and migration. Deforestation mainly occurs in urban growth boundaries, which will be the focus area for further land management. These main factors have the potential to provide a methodological contribution to land-use changes, and the results of this study can provide a solid theoretical basis for forest land management and urban planning (e.g., balancing expansion of built-up land and ecological protection that advances forest land protection and restoration).
      Citation: Remote Sensing
      PubDate: 2021-05-13
      DOI: 10.3390/rs13101911
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1912: Rich CNN Features for Water-Body
           Segmentation from Very High Resolution Aerial and Satellite Imagery

    • Authors: Zhili Zhang, Meng Lu, Shunping Ji, Huafen Yu, Chenhui Nie
      First page: 1912
      Abstract: Extracting water-bodies accurately is a great challenge from very high resolution (VHR) remote sensing imagery. The boundaries of a water body are commonly hard to identify due to the complex spectral mixtures caused by aquatic vegetation, distinct lake/river colors, silts near the bank, shadows from the surrounding tall plants, and so on. The diversity and semantic information of features need to be increased for a better extraction of water-bodies from VHR remote sensing images. In this paper, we address these problems by designing a novel multi-feature extraction and combination module. This module consists of three feature extraction sub-modules based on spatial and channel correlations in feature maps at each scale, which extract the complete target information from the local space, larger space, and between-channel relationship to achieve a rich feature representation. Simultaneously, to better predict the fine contours of water-bodies, we adopt a multi-scale prediction fusion module. Besides, to solve the semantic inconsistency of feature fusion between the encoding stage and the decoding stage, we apply an encoder-decoder semantic feature fusion module to promote fusion effects. We carry out extensive experiments in VHR aerial and satellite imagery respectively. The result shows that our method achieves state-of-the-art segmentation performance, surpassing the classic and recent methods. Moreover, our proposed method is robust in challenging water-body extraction scenarios.
      Citation: Remote Sensing
      PubDate: 2021-05-13
      DOI: 10.3390/rs13101912
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1913: Soil Moisture Retrievals Using
           Multi-Temporal Sentinel-1 Data over Nagqu Region of Tibetan Plateau

    • Authors: Mengying Yang, Hongquan Wang, Cheng Tong, Luyao Zhu, Xiaodong Deng, Jinsong Deng, Ke Wang
      First page: 1913
      Abstract: This paper presents an approach for retrieval of soil moisture in Nagqu region of Tibetan Plateau using VV-polarized Sentinel-1 SAR and MODIS optical data, by coupling the semi-empirical Oh-2004 model and the Water Cloud Model (WCM). The Oh model is first used to estimate the surface roughness parameter based on the hypothesis that the roughness is invariant among SAR acquisitions. Afterward, the vegetation water content (VWC) in the WCM is calculated from the daily MODIS NDVI data obtained by temporal interpolation. To improve the performance of the model, the parameters A, B, and α of the WCM are analyzed and optimized using randomly selected half of the sampled dataset. Then, the soil moisture is retrieved by minimizing a cost function between the simulated and measured backscattering coefficients. The comparison of the retrieved soil moisture with the ground measurements shows the determination coefficient R2 and the Root Mean Square Error (RMSE) are 0.46 and 0.08 m3/m3, respectively. These results demonstrate the capability and reliability of Sentinel-1 SAR data for estimating the soil moisture over the Tibetan Plateau.
      Citation: Remote Sensing
      PubDate: 2021-05-13
      DOI: 10.3390/rs13101913
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1914: New Inventories of Global Carbon
           Dioxide Emissions through Biomass Burning in 2001–2020

    • Authors: Tomohiro Shiraishi, Ryuichi Hirata, Takashi Hirano
      First page: 1914
      Abstract: Recently, the effect of large-scale fires on the global environment has attracted attention. Satellite observation data are used for global estimation of fire CO2 emissions, and available data sources are increasing. Although several CO2 emission inventories have already been released, various remote sensing data were used to create the inventories depend on the studies. We created eight global CO2 emission inventories through fires from 2001 to 2020 by combining input data sources, compared them with previous studies, and evaluated the effect of input sources on CO2 emission estimation. CO2 emissions were estimated using a method that combines the biomass density change (by the repeated fires) with the general burned area approach. The average annual CO2 emissions of the created eight inventories were 8.40 ± 0.70 Pg CO2 year−1 (±1 standard deviation), and the minimum and maximum emissions were 3.60 ± 0.67 and 14.5 ± 0.83 Pg CO2 year−1, respectively, indicating high uncertainty. CO2 Emissions obtained from four previous inventories were within ±1 standard deviation in the eight inventories created in this study. Input datasets, especially biomass density, affected CO2 emission estimation. The global annual CO2 emissions from two biomass maps differed by 60% (Maximum). This study assesses the performance of climate and fire models by revealing the uncertainty of fire emission estimation from the input sources.
      Citation: Remote Sensing
      PubDate: 2021-05-13
      DOI: 10.3390/rs13101914
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1915: Global Positioning System (GPS)
           Scintillation Associated with a Polar Cap Patch

    • Authors: Jayachandran P. Thayyil, Anthony M. McCaffrey, Yong Wang, David R. Themens, Christopher Watson, Benjamin Reid, Qinghe Zhang, Zanyang Xing
      First page: 1915
      Abstract: A Global Positioning System (GPS) network in the polar cap, along with ionosonde and SuperDARN radar measurements, are used to study GPS signal amplitude and phase scintillation associated with a polar cap patch. The patch was formed due to a north-to-south transition of the interplanetary magnetic field (IMF Bz). The patch moved antisunward with an average speed of ~600 m/s and lasted for ~2 h. Significant scintillation occurred on the leading edge of the patch, with smaller bursts of scintillation inside and on the trailing edge. As the patch moved, it maintained the integrity of the scintillation, producing irregularities (Fresnel scale) on the leading edge. There were no convection shears or changes in the direction of convection during scintillation events. Observations suggest that scintillation-producing Fresnel scale structures are generated through the non-linear evolution of the gradient drift instability mechanism.
      Citation: Remote Sensing
      PubDate: 2021-05-13
      DOI: 10.3390/rs13101915
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1916: Focus Improvement of Airborne
           High-Squint Bistatic SAR Data Using Modified Azimuth NLCS Algorithm Based
           on Lagrange Inversion Theorem

    • Authors: Chuang Li, Heng Zhang, Yunkai Deng
      First page: 1916
      Abstract: In this paper, a modified azimuth nonlinear chirp scaling (NLCS) algorithm is derived for high-squint bistatic synthetic aperture radar (BiSAR) imaging to solve its inherent difficult issues, including the large range cell migration (RCM), azimuth-dependent Doppler parameters, and the sensibility of the higher order terms. First, using the Lagrange inversion theorem, an accurate spectrum suitable for processing airborne high-squint BiSAR data is introduced. Different from the spectrum that is based on the method of series reversion (MSR), it is allowed to derive the bistatic stationary phase point while retaining the double square root (DSR) of the slant range history. Based the spectrum, a linear RCM correction is used to remove the most of the linear RCM components and mitigate the range-azimuth coupling, and, then, bulk secondary range compression is implemented to compensate the residual RCM and cross-coupling terms. Following this, a modified azimuth NLCS operation is applied to eliminate the azimuth-dependence of Doppler parameters and equalize the azimuth frequency modulation for azimuth compression. The experimental results, with better focusing performance, prove the high accuracy and effectiveness of the proposed algorithm.
      Citation: Remote Sensing
      PubDate: 2021-05-13
      DOI: 10.3390/rs13101916
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1917: Modelling the Visibility of
           Baltic-Type Crude Oil Emulsion Dispersed in the Southern Baltic Sea

    • Authors: Emilia Baszanowska, Zbigniew Otremba, Jacek Piskozub
      First page: 1917
      Abstract: This paper analyses the radiance reflectance modelling of a sea area and the case of a water column polluted with an oil emulsion in relation to various depths of the occurrence of an oil-in-water emulsion in all azimuth and zenith angles. For the radiance reflectance modelling, the simulation of large numbers of solar photons in water was performed using a Monte Carlo simulation. For the simulations, the optical properties of seawater for the open sea typical of the southern Baltic Sea were used and Petrobaltic-type crude oil (extracted in the Baltic Sea) was added. Oil pollution in the sea was considered for oil droplet concentrations of 10 ppm, which were optically represented by spectral waveforms of absorption and scattering coefficients, as well as by angular light scattering distribution determined using the Mie theory. The results of the radiance reflectance modelling in the whole spectrum of both angles, azimuth and zenith, allowed us to select 555 nm as the optimal wavelength for oil emulsion detection. Moreover, the parameter contrast was defined and determined using radiance reflectance results for eight light wavelengths in the range of 412-676 nm. The contrast is discussed in relation to the various thicknesses of polluted water layers. Changes in contrast for a thickness layer 5 m under the sea surface were noted, whereas for thicker layers the contrast remained unchanged.
      Citation: Remote Sensing
      PubDate: 2021-05-14
      DOI: 10.3390/rs13101917
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1918: Regional GNSS-Derived SPCI:
           Verification and Improvement in Yunnan, China

    • Authors: Xiongwei Ma, Yibin Yao, Qingzhi Zhao
      First page: 1918
      Abstract: From the aspect of global drought monitoring, improving the regional drought monitoring method is becoming increasingly important for the sustainable development of regional agriculture and the economy. The standardized precipitation conversion index (SPCI) calculated by the Global Navigation Satellite System (GNSS) observation is a new means for drought monitoring that has the advantages of simple calculation and real-time monitoring. However, only SPCI with a 12-month scale has been verified on a global scale, while its capability and applicability for monitoring drought at a short time scale in regional areas have never been investigated. Therefore, this study aims to evaluate the performance of SPCI at other time scales in Yunnan, China, and propose an improved method for SPCI. The data of six GNSS stations were selected to calculate SPCI; the standardized precipitation evapotranspiration index (SPEI) and composite meteorological drought index (CI) are introduced to evaluate the SPCI at a short time scale in Yunnan Province. In addition, a modified CI (MCI) was proposed to calibrate the SPCI because of its large bias in Yunnan. Experimental results show that (1) SPCI exhibits better agreement with CI in Yunnan Province when compared to SPEI; (2) the capability of SPCI for drought monitoring is superior to that of SPEI in Yunnan; and (3) the improved SPCI is more suitable for drought monitoring in Yunnan, with a relative bias of 5.43% when compared to the MCI. These results provide a new means for regional drought monitoring in Yunnan, which is significant for dealing with drought disasters and formulating related disaster prevention and mitigation policies.
      Citation: Remote Sensing
      PubDate: 2021-05-14
      DOI: 10.3390/rs13101918
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1919: Multi-Task Fusion Deep Learning Model
           for Short-Term Intersection Operation Performance Forecasting

    • Authors: Chen, Yan, Liu, Wang, Li, Li
      First page: 1919
      Abstract: Urban road intersection bottleneck has become an important factor in causing traffic delay and restricting traffic efficiency. It is essential to explore the prediction of the operating performance at intersections in real-time and formulate corresponding strategies to alleviate intersection delay. However, because of the sophisticated intersection traffic condition, it is difficult to capture the intersection traffic Spatio-temporal features by the traditional data and prediction methods. The development of big data technology and the deep learning model provides us a good chance to address this challenge. Therefore, this paper proposes a multi-task fusion deep learning (MFDL) model based on massive floating car data to effectively predict the passing time and speed at intersections over different estimation time granularity. Moreover, the grid model and the fuzzy C-means (FCM) clustering method are developed to identify the intersection area and derive a set of key Spatio-temporal traffic parameters from floating car data. In order to validate the effectiveness of the proposed model, the floating car data from ten intersections of Beijing with a sampling rate of 3s are adopted for the training and test process. The experiment result shows that the MFDL model enables us to capture the Spatio-temporal and topology feature of the traffic state efficiently. Compared with the traditional prediction method, the proposed model has the best prediction performance. The interplay between these two targeted prediction variables can significantly improve prediction accuracy and efficiency. Thereby, this method predicts the intersection operation performance in real-time and can provide valuable insights for traffic managers to improve the intersection’s operation efficiency.
      Citation: Remote Sensing
      PubDate: 2021-05-14
      DOI: 10.3390/rs13101919
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1920: Group Target Tracking Based on
           MS-MeMBer Filters

    • Authors: Zhang, Sun, Zhou, Xu
      First page: 1920
      Abstract: This paper presents a new group target tracking method based on the standard multi-sensor multi-target multi-Bernoulli (MS-MeMBer) filter. In the prediction step, the group structure is used to constrain the movement of the constituent members within the respective groups. Specifically, the group of members is considered as an undirected random graph. Combined with the virtual leader-follower model, the motion equation of the members within groups is formulated. In the update step, the partitioning problem of multiple sensors is transformed into a multi-dimensional assignment (MDA) problem. Compared with the original two-step greedy partitioning mechanism, the MDA algorithm achieves better measurement partitions in group target tracking scenarios. To evaluate the performance of the proposed method, a simulation scenario including group splitting and merging is established. Results show that, compared with the standard MS-MeMBer filter, our method can effectively estimate the cardinality of members and groups at the cost of increasing computational load. The filtering accuracy of the proposed method outperforms that of the MS-MeMBer filter.
      Citation: Remote Sensing
      PubDate: 2021-05-14
      DOI: 10.3390/rs13101920
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1921: Multi-Sector Oriented Object Detector
           for Accurate Localization in Optical Remote Sensing Images

    • Authors: Xu He, Shiping Ma, Linyuan He, Le Ru, Chen Wang
      First page: 1921
      Abstract: Oriented object detection in optical remote sensing images (ORSIs) is a challenging task since the targets in ORSIs are displayed in an arbitrarily oriented manner and on small scales, and are densely packed. Current state-of-the-art oriented object detection models used in ORSIs primarily evolved from anchor-based and direct regression-based detection paradigms. Nevertheless, they still encounter a design difficulty from handcrafted anchor definitions and learning complexities in direct localization regression. To tackle these issues, in this paper, we proposed a novel multi-sector oriented object detection framework called MSO2-Det, which quantizes the scales and orientation prediction of targets in ORSIs via an anchor-free classification-to-regression approach. Specifically, we first represented the arbitrarily oriented bounding box as four scale offsets and angles in four quadrant sectors of the corresponding Cartesian coordinate system. Then, we divided the scales and angle space into multiple discrete sectors and obtained more accurate localization information by a coarse-granularity classification to fine-grained regression strategy. In addition, to decrease the angular-sector classification loss and accelerate the network’s convergence, we designed a smooth angular-sector label (SASL) that smoothly distributes label values with a definite tolerance radius. Finally, we proposed a localization-aided detection score (LADS) to better represent the confidence of a detected box by combining the category-classification score and the sector-selection score. The proposed MSO2-Det achieves state-of-the-art results on three widely used benchmarks, including the DOTA, HRSC2016, and UCAS-AOD data sets.
      Citation: Remote Sensing
      PubDate: 2021-05-14
      DOI: 10.3390/rs13101921
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1922: Object Tracking in
           Hyperspectral-Oriented Video with Fast Spatial-Spectral Features

    • Authors: Chen, Zhao, Yao, Chen, Li, Chan, Kong
      First page: 1922
      Abstract: This paper presents a correlation filter object tracker based on fast spatial-spectral features (FSSF) to realize robust, real-time object tracking in hyperspectral surveillance video. Traditional object tracking in surveillance video based only on appearance information often fails in the presence of background clutter, low resolution, and appearance changes. Hyperspectral imaging uses unique spectral properties as well as spatial information to improve tracking accuracy in such challenging environments. However, the high-dimensionality of hyperspectral images causes high computational costs and difficulties for discriminative feature extraction. In FSSF, the real-time spatial-spectral convolution (RSSC) kernel is updated in real time in the Fourier transform domain without offline training to quickly extract discriminative spatial-spectral features. The spatial-spectral features are integrated into correlation filters to complete the hyperspectral tracking. To validate the proposed scheme, we collected a hyperspectral surveillance video (HSSV) dataset consisting of 70 sequences in 25 bands. Extensive experiments confirm the advantages and the efficiency of the proposed FSSF for object tracking in hyperspectral video tracking in challenging conditions of background clutter, low resolution, and appearance changes.
      Citation: Remote Sensing
      PubDate: 2021-05-14
      DOI: 10.3390/rs13101922
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1923: An Imaging Network Design for
           UGV-Based 3D Reconstruction of Buildings

    • Authors: Ali Hosseininaveh, Fabio Remondino
      First page: 1923
      Abstract: Imaging network design is a crucial step in most image-based 3D reconstruction applications based on Structure from Motion (SfM) and multi-view stereo (MVS) methods. This paper proposes a novel photogrammetric algorithm for imaging network design for building 3D reconstruction purposes. The proposed methodology consists of two main steps: (i) the generation of candidate viewpoints and (ii) the clustering and selection of vantage viewpoints. The first step includes the identification of initial candidate viewpoints, selecting the candidate viewpoints in the optimum range, and defining viewpoint direction stages. In the second step, four challenging approaches—named façade pointing, centre pointing, hybrid, and both centre & façade pointing—are proposed. The entire methodology is implemented and evaluated in both simulation and real-world experiments. In the simulation experiment, a building and its environment are computer-generated in the ROS (Robot Operating System) Gazebo environment and a map is created by using a simulated robot and Gmapping algorithm based on a Simultaneously Localization and Mapping (SLAM) algorithm using a simulated Unmanned Ground Vehicle (UGV). In the real-world experiment, the proposed methodology is evaluated for all four approaches for a real building with two common approaches, called continuous image capturing and continuous image capturing & clustering and selection approaches. The results of both evaluations reveal that the fusion of centre & façade pointing approach is more efficient than all other approaches in terms of both accuracy and completeness criteria.
      Citation: Remote Sensing
      PubDate: 2021-05-14
      DOI: 10.3390/rs13101923
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1924: Compressive Underwater Sonar Imaging
           with Synthetic Aperture Processing

    • Authors: Ha-min Choi, Hae-sang Yang, Woo-jae Seong
      First page: 1924
      Abstract: Synthetic aperture sonar (SAS) is a technique that acquires an underwater image by synthesizing the signal received by the sonar as it moves. By forming a synthetic aperture, the sonar overcomes physical limitations and shows superior resolution when compared with use of a side-scan sonar, which is another technique for obtaining underwater images. Conventional SAS algorithms require a high concentration of sampling in the time and space domains according to Nyquist theory. Because conventional SAS algorithms go through matched filtering, side lobes are generated, resulting in deterioration of imaging performance. To overcome the shortcomings of conventional SAS algorithms, such as the low imaging performance and the requirement for high-level sampling, this paper proposes SAS algorithms applying compressive sensing (CS). SAS imaging algorithms applying CS were formulated for a single sensor and uniform line array and were verified through simulation and experimental data. The simulation showed better resolution than the ω-k algorithms, one of the representative conventional SAS algorithms, with minimal performance degradation by side lobes. The experimental data confirmed that the proposed method is superior and robust with respect to sensor loss.
      Citation: Remote Sensing
      PubDate: 2021-05-14
      DOI: 10.3390/rs13101924
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1925: Subtask Attention Based Object
           Detection in Remote Sensing Images

    • Authors: Shengzhou Xiong, Yihua Tan, Yansheng Li, Cai Wen, Pei Yan
      First page: 1925
      Abstract: Object detection in remote sensing images (RSIs) is one of the basic tasks in the field of remote sensing image automatic interpretation. In recent years, the deep object detection frameworks of natural scene images (NSIs) have been introduced into object detection on RSIs, and the detection performance has improved significantly because of the powerful feature representation. However, there are still many challenges concerning the particularities of remote sensing objects. One of the main challenges is the missed detection of small objects which have less than five percent of the pixels of the big objects. Generally, the existing algorithms choose to deal with this problem by multi-scale feature fusion based on a feature pyramid. However, the benefits of this strategy are limited, considering that the location of small objects in the feature map will disappear when the detection task is processed at the end of the network. In this study, we propose a subtask attention network (StAN), which handles the detection task directly on the shallow layer of the network. First, StAN contains one shared feature branch and two subtask attention branches of a semantic auxiliary subtask and a detection subtask based on the multi-task attention network (MTAN). Second, the detection branch uses only low-level features considering small objects. Third, the attention map guidance mechanism is put forward to optimize the network for keeping the identification ability. Fourth, the multi-dimensional sampling module (MdS), global multi-view channel weights (GMulW) and target-guided pixel attention (TPA) are designed for further improvement of the detection accuracy in complex scenes. The experimental results on the NWPU VHR-10 dataset and DOTA dataset demonstrated that the proposed algorithm achieved the SOTA performance, and the missed detection of small objects decreased. On the other hand, ablation experiments also proved the effects of MdS, GMulW and TPA.
      Citation: Remote Sensing
      PubDate: 2021-05-14
      DOI: 10.3390/rs13101925
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1926: Evaluation of CFOSAT Scatterometer
           Wind Data in Global Oceans

    • Authors: Ye, Li, Li, Liu, Tang, Chen, Yang, Zhou, Zhang, Wang, Tang
      First page: 1926
      Abstract: The China-France Oceanography SATellite (CFOSAT), launched on 29 October 2018, is a joint mission developed by China and France. To evaluate the CFOSAT wind product, L2B swath data with a spatial resolution of 25 × 25 km were compared with in situ measurements between December 2018 and December 2020. The in situ measurements were collected from 217 buoys. All buoy winds were adjusted to 10 m height using a simple logarithmic correction method. The temporal and spatial separations between the CFOSAT and in situ measurements were restricted to less than 30 min and 0.25°. The results indicate that the CFOSAT wind retrievals agree well with the buoy measurements. The root mean square errors (RMSEs) of wind vectors were 1.39 m s–1 and 34.32° and negligible biases were found. In the near shore under rain-free conditions, the RMSEs were enhanced to 1.42 m s–1 and 33.43°. Similarly, the RMSEs were reduced to 1.16 m s–1 and 30.41°offshore after the rain effect was removed. After winds less than 4 m s–1 were removed, the RMSE of wind directions was reduced to 19.69°. The effects of significant wave height, air-sea temperature difference, sea surface temperature, atmospheric pressure and ocean surface current on the wind residuals were assessed. The performance of wind retrievals under the passage of tropical cyclones was evaluated. The evaluation results show that the CFOSAT wind retrievals satisfy the accuracy requirements of scientific research, although some improvements are needed to enhance the performance.
      Citation: Remote Sensing
      PubDate: 2021-05-14
      DOI: 10.3390/rs13101926
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1927: Assessing an Atmospheric Correction
           Algorithm for Time Series of Satellite-Based Water-Leaving Reflectance
           Using Match-Up Sites in Australian Coastal Waters

    • Authors: Li, Jupp, Schroeder, Sagar, Sixsmith, Dorji
      First page: 1927
      Abstract: An atmospheric correction algorithm for medium-resolution satellite data over general water surfaces (open/coastal, estuarine and inland waters) has been assessed in Australian coastal waters. In situ measurements at four match-up sites were used with 21 Landsat 8 images acquired between 2014 and 2017. Three aerosol sources (AERONET, MODIS ocean aerosol and climatology) were used to test the impact of the selection of aerosol optical depth (AOD) and Ångström coefficient on the retrieved accuracy. The initial results showed that the satellite-derived water-leaving reflectance can have good agreement with the in situ measurements, provided that the sun glint is handled effectively. Although the AERONET aerosol data performed best, the contemporary satellite-derived aerosol information from MODIS or an aerosol climatology could also be as effective, and should be assessed with further in situ measurements. Two sun glint correction strategies were assessed for their ability to remove the glint bias. The most successful one used the average of two shortwave infrared (SWIR) bands to represent sun glint and subtracted it from each band. Using this sun glint correction method, the mean all-band error of the retrieved water-leaving reflectance at the Lucinda Jetty Coastal Observatory (LJCO) in north east Australia was close to 4% and unbiased over 14 acquisitions. A persistent bias in the other strategy was likely due to the sky radiance being non-uniform for the selected images. In regard to future options for an operational sun glint correction, the simple method may be sufficient for clear skies until a physically based method has been established.
      Citation: Remote Sensing
      PubDate: 2021-05-14
      DOI: 10.3390/rs13101927
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1928: Measuring Alpha and Beta Diversity by
           Field and Remote-Sensing Data: A Challenge for Coastal Dunes Biodiversity
           Monitoring

    • Authors: Flavio Marzialetti, Silvia Cascone, Ludovico Frate, Mirko Di Febbraro, Alicia Teresa Rosario Acosta, Maria Laura Carranza
      First page: 1928
      Abstract: Combining field collected and remotely sensed (RS) data represents one of the most promising approaches for an extensive and up-to-date ecosystem assessment. We investigated the potential of the so called spectral variability hypothesis (SVH) in linking field-collected and remote-sensed data in Mediterranean coastal dunes and explored if spectral diversity provides reliable information to monitor floristic diversity, as well as the consistency of such information in altered ecosystems due to plant invasions. We analyzed alpha diversity and beta diversity, integrating floristic field and Remote-Sensing PlanetScope data in the Tyrrhenian coast (Central Italy). We explored the relationship among alpha field diversity (species richness, Shannon index, inverse Simpson index) and spectral variability (distance from the spectral centroid index) through linear regressions. For beta diversity, we implemented a distance decay model (DDM) relating field pairwise (Jaccard similarities index, Bray–Curtis similarities index) and spectral pairwise (Euclidean distance) measures. We observed a positive relationship between alpha diversity and spectral heterogeneity with richness reporting the higher R score. As for DDM, we found a significant relationship between Bray–Curtis floristic similarity and Euclidean spectral distance. We provided a first assessment of the relationship between floristic and spectral RS diversity in Mediterranean coastal dune habitats (i.e., natural or invaded). SVH provided evidence about the potential of RS for estimating diversity in complex and dynamic landscapes.
      Citation: Remote Sensing
      PubDate: 2021-05-15
      DOI: 10.3390/rs13101928
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1929: Ka-Band Radar Cross-Section of
           Breaking Wind Waves

    • Authors: Yury Yu. Yurovsky, Vladimir N. Kudryavtsev, Semyon A. Grodsky, Bertrand Chapron
      First page: 1929
      Abstract: The effective normalized radar cross section (NRCS) of breaking waves, σwb, is empirically derived based on joint synchronized Ka-band radar and video records of the sea surface from a research tower. The σwb is a key parameter that, along with the breaker footprint fraction, Q, defines the contribution of non-polarized backscattering, NP =σwbQ, to the total sea surface NRCS. Combined with the right representation of the regular Bragg and specular backscattering components, the NP component is fundamental to model and interpret sea surface radar measurements. As the first step, the difference between NRCS values for breaking and non-breaking conditions is scaled with the optically-observed Q and compared with the geometric optics model of breaker backscattering. Optically-derived Q might not be optimal to represent the effect of breaking waves on the radar measurements. Alternatively, we rely on the breaking crest length that is firmly detected by the video technique and the empirically estimated breaker decay (inverse wavelength) scale in the direction of breaking wave propagation. A simplified model of breaker NRCS is then proposed using the geometric optics approach. This semi-analytical model parameterizes the along-wave breaker decay from reported breaker roughness spectra, obtained in laboratory experiments with mechanically-generated breakers. These proposed empirical breaker NRCS estimates agree satisfactorily with observations.
      Citation: Remote Sensing
      PubDate: 2021-05-15
      DOI: 10.3390/rs13101929
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1930: Emergency Landing Spot Detection
           Algorithm for Unmanned Aerial Vehicles

    • Authors: Gabriel Loureiro, André Dias, Alfredo Martins, José Almeida
      First page: 1930
      Abstract: The use and research of Unmanned Aerial Vehicle (UAV) have been increasing over the years due to the applicability in several operations such as search and rescue, delivery, surveillance, and others. Considering the increased presence of these vehicles in the airspace, it becomes necessary to reflect on the safety issues or failures that the UAVs may have and the appropriate action. Moreover, in many missions, the vehicle will not return to its original location. If it fails to arrive at the landing spot, it needs to have the onboard capability to estimate the best area to safely land. This paper addresses the scenario of detecting a safe landing spot during operation. The algorithm classifies the incoming Light Detection and Ranging (LiDAR) data and store the location of suitable areas. The developed method analyses geometric features on point cloud data and detects potential right spots. The algorithm uses the Principal Component Analysis (PCA) to find planes in point cloud clusters. The areas that have a slope less than a threshold are considered potential landing spots. These spots are evaluated regarding ground and vehicle conditions such as the distance to the UAV, the presence of obstacles, the area’s roughness, and the spot’s slope. Finally, the output of the algorithm is the optimum spot to land and can vary during operation. The proposed approach evaluates the algorithm in simulated scenarios and an experimental dataset presenting suitability to be applied in real-time operations.
      Citation: Remote Sensing
      PubDate: 2021-05-15
      DOI: 10.3390/rs13101930
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1931: Centroid Error Analysis of Beacon
           Tracking under Atmospheric Turbulence for Optical Communication Links

    • Authors: Hyung-Chul Lim, Chul-Sung Choi, Ki-Pyoung Sung, Jong-Uk Park, Mansoo Choi
      First page: 1931
      Abstract: Optical satellite communication has received considerable attention as a promising alternative to radio frequency communication because of its potential advantages including higher data rates and license free spectrum. Many studies have conducted performance analyses of optical communication channels, but few have investigated beacon tracking channels under atmospheric turbulence. The centroid accuracy of beacon tracking channels is limited by not only noise sources, but also a finite delay time, which also fluctuates due to atmospheric turbulence. Consequently, the centroid error is an important figure of merit when evaluating the performance of a beacon tracking system. In this study, the closed-form expressions were derived for average centroid error and fade probability, based on received photoelectron counts depending on exposure time, taking into account the log-normal tracking channels. We analyzed the angular positioning performance of beacon tracking detectors onboard small satellites in the presence of atmospheric turbulence, in terms of centroid error and fade probability. We found that an optimal exposure time exists, which minimizes the centroid error, and that fade probability is inversely proportional to the exposure time. These are significant properties to consider in the design of beacon tracking systems.
      Citation: Remote Sensing
      PubDate: 2021-05-15
      DOI: 10.3390/rs13101931
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1932: Ocean–Atmosphere Interactions
           during Hurricanes Marco and Laura (2020)

    • Authors: Emily N. Eley, Bulusu Subrahmanyam, Corinne B. Trott
      First page: 1932
      Abstract: During August of the 2020 Atlantic Hurricane Season, the Gulf of Mexico (GoM) was affected by two subsequent storms, Hurricanes Marco and Laura. Hurricane Marco entered the GoM first (22 August) and was briefly promoted to a Category 1 storm. Hurricane Laura followed Marco closely (25 August) and attained Category 4 status after a period of rapid intensification. Typically, hurricanes do not form this close together; this study aims to explain the existence of both hurricanes through the analysis of air-sea fluxes, local thermodynamics, and upper-level circulation. The GoM and its quality of warm, high ocean heat content waters proved to be a resilient and powerful reservoir of heat and moisture fuel for both hurricanes; however, an area of lower ocean heat content due to circulation dynamics was crucial in the evolution of both Marco and Laura. An analysis of wind shear further explained the evolution of both hurricanes. Furthermore, a suite of satellite observations and ocean model outputs were used to evaluate the biophysical modulations in the GoM. The cold core eddy (CCE) and Mississippi River surface plume had the greatest biophysical oceanic responses; the oceanic modulations were initialized by Marco and extended temporally and spatially by Laura. Reduced sea surface temperatures (SST), changes in sea surface salinity (SSS), and changes in Chlorophyll-a (Chl-a) concentrations are related to translation speeds, and respective contributions of hurricane winds and precipitation are evaluated in this work.
      Citation: Remote Sensing
      PubDate: 2021-05-15
      DOI: 10.3390/rs13101932
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1933: Automatic Creation of Storm Impact
           Database Based on Video Monitoring and Convolutional Neural Networks

    • Authors: Aurelien Callens, Denis Morichon, Pedro Liria, Irati Epelde, Benoit Liquet
      First page: 1933
      Abstract: Data about storm impacts are essential for the disaster risk reduction process, but unlike data about storm characteristics, they are not routinely collected. In this paper, we demonstrate the high potential of convolutional neural networks to automatically constitute storm impact database using timestacks images provided by coastal video monitoring stations. Several convolutional neural network architectures and methods to deal with class imbalance were tested on two sites (Biarritz and Zarautz) to find the best practices for this classification task. This study shows that convolutional neural networks are well adapted for the classification of timestacks images into storm impact regimes. Overall, the most complex and deepest architectures yield better results. Indeed, the best performances are obtained with the VGG16 architecture for both sites with F-scores of 0.866 for Biarritz and 0.858 for Zarautz. For the class imbalance problem, the method of oversampling shows best classification accuracy with F-scores on average 30% higher than the ones obtained with cost sensitive learning. The transferability of the learning method between sites is also investigated and shows conclusive results. This study highlights the high potential of convolutional neural networks to enhance the value of coastal video monitoring data that are routinely recorded on many coastal sites. Furthermore, it shows that this type of deep neural network can significantly contribute to the setting up of risk databases necessary for the determination of storm risk indicators and, more broadly, for the optimization of risk-mitigation measures.
      Citation: Remote Sensing
      PubDate: 2021-05-15
      DOI: 10.3390/rs13101933
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1934: Crustal Structure of the Nile Delta:
           Interpretation of Seismic-Constrained Satellite-Based Gravity Data

    • Authors: Soha Hassan, Mohamed Sultan, Mohamed Sobh, Mohamed S. Elhebiry, Khaled Zahran, Abdelaziz Abdeldayem, Elsayed Issawy, Samir Kamh
      First page: 1934
      Abstract: Interpretations of the tectonic setting of the Nile Delta of Egypt and its offshore extension are challenged by the thick sedimentary cover that conceals the underlying structures and by the paucity of deep seismic data and boreholes. A crustal thickness model, constrained by available seismic and geological data, was constructed for the Nile Delta by inversion of satellite gravity data (GOCO06s), and a two-dimensional (2D) forward density model was generated along the Delta’s entire length. Modelling results reveal the following: (1) the Nile Delta is formed of two distinctive crustal units: the Southern Delta Block (SDB) and the Northern Delta Basin (NDB) separated by a hinge zone, a feature widely reported from passive margin settings; (2) the SDB is characterized by an east–west-trending low-gravity (⁓−40 mGal) anomaly indicative of continental crust characteristics (depth to Moho (DTM): 36–38 km); (3) the NDB and its offshore extension are characterized by high gravity anomalies (hinge zone: ⁓10 mGal; Delta shore line: >40 mGal; south Herodotus Basin: ⁓140 mGal) that are here attributed to crustal thinning and stretching and decrease in DTM, which is ⁓35 km at the hinge zone, 30–32 km at the shoreline, and 22–20 km south of the Herodotus Basin; and (4) an apparent continuation of the east-northeast–west-southwest transitional crust of the Nile Delta towards the north-northeast–south-southwest-trending Levant margin in the east. These observations together with the reported extensional tectonics along the hinge zone, NDB and its offshore, the low to moderate seismic activity, and the absence of volcanic eruptions in the Nile Delta are all consistent with the NDB being a non-volcanic passive margin transition zone between the North African continental crust (SDB) and the Mediterranean oceanic crust (Herodotus Basin), with the NDB representing a westward extension of the Levant margin extensional transition zone.
      Citation: Remote Sensing
      PubDate: 2021-05-15
      DOI: 10.3390/rs13101934
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1935: Using Landsat Imagery to Assess Burn
           Severity of National Forest Inventory Plots

    • Authors: Flavie Pelletier, Bianca N.I. Eskelson, Vicente J. Monleon, Yi-Chin Tseng
      First page: 1935
      Abstract: As the frequency and size of wildfires increase, accurate assessment of burn severity is essential for understanding fire effects and evaluating post-fire vegetation impacts. Remotely-sensed imagery allows for rapid assessment of burn severity, but it also needs to be field validated. Permanent forest inventory plots can provide burn severity information for the field validation of remotely-sensed burn severity metrics, although there is often a mismatch between the size and shape of the inventory plot and the resolution of the rasterized images. For this study, we used two distinct datasets: (1) ground-based inventory data from the United States national forest inventory to calculate ground-based burn severity; and (2) remotely-sensed data from the Monitoring Trends in Burn Severity (MTBS) database to calculate different remotely-sensed burn severity metrics based on six weighting scenarios. Our goals were to test which MTBS metric would best align with the burn severity of national inventory plots observed on the ground, and to identify the superior weighting scenarios to extract pixel values from a raster image in order to match burn severity of the national inventory plots. We fitted logistic and ordinal regression models to predict the ground-based burn severity from the remotely-sensed burn severity averaged from six weighting scenarios. Among the weighting scenarios, two scenarios assigned weights to pixels based on the area of a pixel that intersected any parts of a national inventory plot. Based on our analysis, 9-pixel weighted averages of the Relative differenced Normalized Burn Ratio (RdNBR) values best predicted the ground-based burn severity of national inventory plots. Finally, the pixel specific weights that we present can be used to link other Landsat-derived remote sensing metrics with United States forest inventory plots.
      Citation: Remote Sensing
      PubDate: 2021-05-15
      DOI: 10.3390/rs13101935
      Issue No: Vol. 13, No. 10 (2021)
       
  • Remote Sensing, Vol. 13, Pages 1936: CscGAN: Conditional Scale-Consistent
           Generation Network for Multi-Level Remote Sensing Image to Map Translation
           

    • Authors: Yuanyuan Liu, Wenbin Wang, Fang Fang, Lin Zhou, Chenxing Sun, Ying Zheng, Zhanlong Chen
      First page: 1936
      Abstract: Automatic remote sensing (RS) image to map translation is a crucial technology for intelligent tile map generation. Although existing methods based on a generative network (GAN) generated unannotated maps at a single level, they have limited capacity in handling multi-resolution map generation at different levels. To address the problem, we proposed a novel conditional scale-consistent generation network (CscGAN) to simultaneously generate multi-level tile maps from multi-scale RS images, using only a single and unified model. Specifically, the CscGAN first uses the level labels and map annotations as prior conditions to guide hierarchical feature learning with different scales. Then, a multi-scale discriminator and two multi-scale generators are introduced to describe both high-resolution and low-resolution representations, aiming to improve the similarity of generated maps and thus produce high-quality multi-level tile maps. Meanwhile, a level classifier is designed for further exploring the characteristics of tile maps at different levels. Moreover, the CscGAN is optimized by jointly multi-scale adversarial loss, level classification loss, and scale-consistent loss in an end-to-end manner. Extensive experiments on multiple datasets and study areas demonstrate that the CscGAN outperforms the state-of-the-art methods in multi-level map translation, with great robustness and efficiency.
      Citation: Remote Sensing
      PubDate: 2021-05-15
      DOI: 10.3390/rs13101936
      Issue No: Vol. 13, No. 10 (2021)
       
 
JournalTOCs
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Email: journaltocs@hw.ac.uk
Tel: +00 44 (0)131 4513762
 


Your IP address: 3.210.184.142
 
Home (Search)
API
About JournalTOCs
News (blog, publications)
JournalTOCs on Twitter   JournalTOCs on Facebook

JournalTOCs © 2009-