A  B  C  D  E  F  G  H  I  J  K  L  M  N  O  P  Q  R  S  T  U  V  W  X  Y  Z  

  Subjects -> GEOGRAPHY (Total: 493 journals)
The end of the list has been reached or no journals were found for your choice.
Similar Journals
Journal Cover
PFG : Journal of Photogrammetry, Remote Sensing and Geoinformation Science
Number of Followers: 5  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 2512-2789 - ISSN (Online) 2512-2819
Published by Springer-Verlag Homepage  [2468 journals]
  • Editorial for a Special Issue: Assessment of Coastal Vulnerability to Sea
           Level Rise Using Remote Sensing

    • Free pre-print version: Loading...

      PubDate: 2024-07-04
       
  • Interactive Mixed Reality Methods for Visualization of Underground
           Utilities

    • Free pre-print version: Loading...

      Abstract: Abstract This research aims to overcome the difficulties associated with visualizing underground utilities by proposing six interactive visualization methods that utilize Mixed Reality (MR) technology. By leveraging MR technology, which enables the seamless integration of virtual and real-world content, a more immersive and authentic experience is possible. The study evaluates the proposed visualization methods based on scene complexity, parallax effect, real-world occlusion, depth perception, and overall effectiveness, aiming to identify the most effective methods for addressing visual perceptual challenges in the context of underground utilities. The findings suggest that certain MR visualization methods are more effective than others in mitigating the challenges of visualizing underground utilities. The research highlights the potential of these methods, and feedback from industry professionals suggests that each method can be valuable in specific contexts.
      PubDate: 2024-07-03
       
  • Satellite-based Bathymetry Supported by Extracted Coastlines

    • Free pre-print version: Loading...

      Abstract: Abstract Bathymetry is the measurement of ocean depths using a variety of techniques. Available techniques include sonar systems, light detection and ranging (LIDAR), and remote sensing systems. Acoustic systems, also known as LIDAR, are inefficient in terms of both time and money. This study applied remote sensing techniques to reduce both time and cost. The objective of this study is to use freely accessible Sentinel‑2 multispectral images to extract the depth information. Temporal variation was minimized by comparing the histograms of satellite images obtained over four consecutive months. The sea topography is determined using regression analysis, utilizing samples from reference data. The reference data is adjusted with the changes in shorelines, as the alteration of shorelines serves as a parameter for these modifications. Using the regression coefficients, analyses were conducted in regions with undetermined depths. The bathymetry maps were evaluated against a reference dataset and improved by incorporating shorelines. The analyses were carried out individually over four months, and the derived bathymetric data showed significant monthly average and monthly shoreline changes. The employed methodology offers an alternative approach for bathymetry studies that require temporal resolution when the available reference bathymetric data is insufficient.
      PubDate: 2024-07-02
       
  • Correction to: Exploring the Potential of Thermal Remote Sensing for
           Marine Freshwater Springs Identification in the United Arab Emirates

    • Free pre-print version: Loading...

      PubDate: 2024-06-28
       
  • Monitoring Creeping Landslides with InSAR in a Loess-covered Mountainous
           Area in the Ili Valley, Central Asia

    • Free pre-print version: Loading...

      Abstract: Abstract Loess landslides in mountainous regions of the Ili Valley have resulted in numerous casualties as well as huge economic losses. However, the characteristics and driving mechanisms of surface deformation related to loess landslides in mountainous areas remain unclear, thus limiting our ability to identify, monitor, and warn populations of potential catastrophic events. This study was conducted in a typical mountainous area of the Ili Valley, where landslides have been documented by field investigations, unmanned aerial vehicle images, and light detection and ranging data. With ascending and descending Sentinel‑1 time series synthetic aperture radar images, acquired using the small baselines subset method, surface deformation was observed for the period from October 2014 to October 2021, and loess landslides were concurrently mapped to delineate hazardous areas. Using the methods of this study, we were able to identify 74.4% of previously documented landslides. Additionally, we observed a seasonal time-series of deformation that had a time delay of less than one month and was responsive to rainfall. Our analysis of the characteristics and driving mechanisms of creeping landslides in the Ili Valley led to the compilation of a new inventory of active slopes that will offer valuable guidance for land managers tasked with implementing disaster prevention measures.
      PubDate: 2024-06-06
       
  • Evaluation of InSAR Tropospheric Correction Methods over North-West Iran

    • Free pre-print version: Loading...

      Abstract: Abstract Synthetic Aperture Radar (SAR) interferometric measurements are highly affected by tropospheric delay caused by different troposphere conditions between SAR acquisitions. Tropospheric correction is even more critical in measuring the small surface deformation such as interseismic slips over tectonic faults. Various approaches from which some of them are based on auxiliary data and weather models and the other ones are relied on only phase measurements have been already proposed for tropospheric correction. The capability of these methods, however, has not been investigated over areas such as Iran which are suffering from insufficient data and inaccurate performance of weather models. Hence, as the main goal in this study, a comprehensive statistical comparison between tropospheric correction methods is performed over the North-West Iran which is subject to high tectonic activity. The correction methods based on spectrometer data (MERIS), weather models (MERRA-2 and ERA-Interim), topography-dependent functions (linear and power-law relations) and a combination of low- and high-pass (L/H) filters are applied on single-master interferometric phase. The persistent scatterer time series analysis using 12 Envisat ASAR raw data in two different frames spanning between 2004 and 2008 is applied to estimate the interseismic deformation. The results show that the L/H filters which are based on only phase measurements is more able to reduce the phase spatial variations. None of the other tropospheric correction methods consistently reduced tropospheric signals over different times due to lack of accurate weather model and auxiliary data in Iran. The superiority of the combined filters over other approaches is that no auxiliary data is required. More importantly, the deformation due to the interseismic slip over the active faults in the study area is considerably preserved after the tropospheric correction using L/H filters while this is not the case for other correction approaches.
      PubDate: 2024-06-01
       
  • Reports

    • Free pre-print version: Loading...

      PubDate: 2024-06-01
       
  • German and European Ground Motion Service: a Comparison

    • Free pre-print version: Loading...

      Abstract: Abstract Since the end of 2022, two ground motion services that cover the complete area of Germany are available as web services: the German Ground Motion Service (Bodenbewegungsdienst Deutschland, BBD) provided by the Federal Institute for Geosciences and Natural Resources (BGR), and the first release of the European Ground Motion Service (EGMS) as part of the Copernicus Land Monitoring Service. Both services are based on InSAR displacement estimations generated from Sentinel‑1 data. It would seem relevant to compare the products of the two services against one another, assess the data coverage they provide, and investigate how well they perform compared to other geodetic techniques. For a study commissioned by the surveying authority of the state of Baden-Württemberg (Landesamt für Geoinformation und Landentwicklung Baden-Württemberg, LGL), BBD and EGMS data from different locations in Baden-Württemberg, Saarland, and North Rhine-Westphalia (NRW) were investigated and validated against levelling and GNSS data. We found that both services provide good data quality. BBD shows slightly better calibration precision than EGMS. The coverage provided by EGMS is better than that of BBD on motorways, federal roads, and train tracks of the Deutsche Bahn. As an example, where both services have difficulties in determining the correct displacements, as they cannot be described well by the displacement models used for processing, we present the test case of the cavern field at Epe (NRW). Finally, we discuss the implications of our findings for the use of the products of BBD and EGMS for monitoring tasks.
      PubDate: 2024-06-01
       
  • The Influence of SAR Image Resolution, Wavelength and Land Cover Type on
           Characteristics of Persistent Scatterer

    • Free pre-print version: Loading...

      Abstract: Abstract Persistent Scatterers (PS) are points selected by Persistent Scatterer for Synthetic Aperture Radar Interferometry (PS-InSAR) technology. PS density and quality determine the accuracy of deformation monitoring results. A comprehension of PS and its influencing factors could provide suggestions for data selection and parameter setting in the time series of InSAR, and it can also provide the decision basis for radar satellite engineers to select imaging modes for different application requirements. PS characteristics are mainly affected by SAR image resolution, wavelength and land cover type, etc. However, these influencing factors are coupled together, so it is difficult to study the relationship between the single factor and PS characteristics. Therefore, this paper adopted the Split-Spectrum to TerraSAR datasets to construct a series of simulated SAR datasets with different resolutions while keeping the other imaging parameters the same. We found that the PS density presents a declining linear trend as the bandwidth (resolution) decreases, while the deformation patterns of PS obtained from different bandwidth datasets are consistent. In addition, we proposed a simplified model to estimate the PS density obtained from 1/k bandwidth datasets. Then, we compared the PS results obtained from X-band TerraSAR datasets and C-band Sentinel-1A datasets and analyzed the reason for the difference from the perspective of spatiotemporal decorrelation. Finally, combined with the land cover map and Bayesian estimation, we obtained the distribution probability of PS on land cover types.
      PubDate: 2024-06-01
       
  • Editorial for Special Issue: RADAR REMOTE SENSING

    • Free pre-print version: Loading...

      PubDate: 2024-05-29
       
  • Combining Spatial Downscaling Techniques and Diurnal Temperature Cycle
           Modelling to Estimate Diurnal Patterns of Land Surface Temperature at
           Field Scale

    • Free pre-print version: Loading...

      Abstract: Abstract Land surface Temperature (LST) at high spatial resolution and at sub-daily scale is highly useful for monitoring evaporative stress in plants, heatwave events, and droughts. Spatial downscaling methods are often used to improve the spatial resolution of LST and Diurnal Temperature Cycle (DTC) models are available to estimate the diurnal variation in LST using limited multi-temporal satellite observations. In this paper, we propose a simple approach to estimate DTC at field scale combining spatial downscaling and DTC modelling. For downscaling the LST from medium-resolution sensors, we have compared three spatial downscaling techniques: Principal Component Regression based disaggregation, DisTrad disaggregation model and a Spatio Temporal Integrated Temperature Fusion Model (STITFM). The PCR-based disaggregation technique uses multiple fine-resolution auxiliary datasets such as vegetation indices, radar backscattering coefficient, etc. The downscaled LSTs from PCR and DisTrad were compared with the original fine-resolution LST from ECOSTRESS and Landsat. The spatially downscaled LST observations from all the three models were then used in the GOT01‑ts DTC model to estimate the corresponding diurnal temperature cycle at fine resolution. The DTC estimated from the downscaled LSTs from all the three methods were compared with in situ DTC obtained from ground observations over four sites. The PCR technique using multiple indices captured the spatial and diurnal patterns of LST across four different sites, yielding a combined Root Mean Square Error (RMSE) of 2.48 K and 0.95 coefficient of determination (R2). The proposed approach can be potentially used to model the diurnal variability of land surface fluxes over different landscapes with finer spatial resolution.
      PubDate: 2024-05-21
       
  • Exploring the Potential of Thermal Remote Sensing for Marine Freshwater
           Springs Identification in the Arabian Gulf

    • Free pre-print version: Loading...

      Abstract: Abstract The initial objective of this research is to understand if thermal remote sensing is a viable source to detect or identify submarine freshwater in the United Arab Emirates (UAE). It was established that the discharged freshwater was at least 0.5 °C cooler than the surrounding seawater but the influence of the surrounding temperatures depends upon the time of year of observations. These findings are dependent upon the discharge volume of freshwater. Analysis of imagery acquired over the UAE began with two study areas: the first from Dubai to the Musandam peninsula, and the second from Abu Dhabi city north to Sir Abu Nu’Ayr. These two areas have been investigated using a time series of Landsat 8 thermal satellites to identify consistently appearing thermal anomalies on the sea surface. A trial area was chosen for ground truthing to validate the results using drop-down video and probe measurements. Several points were selected around each anomaly due to the large 100-meter pixel size of the thermal band. It is recommended to conduct the ground truthing focusing on anomalies in winter, which is a period of higher water table than in May. However, while submarine freshwater springs were not identified, there does appear to be some correlation with observations made from the remote sensing and features identified in the field. Therefore, the anomalies detected from the Landsat 8 imagery should not be discounted since it is highly probable that at least one of them may be identified because of groundwater discharge.
      PubDate: 2024-05-21
       
  • Bundle Adjustment of Aerial Linear Pushbroom Hyperspectral Images with
           Sub-Pixel Accuracy

    • Free pre-print version: Loading...

      Abstract: Abstract Linear pushbroom (LP) cameras are often used in airborne hyperspectral imaging (HSI). Orthoimages are generated for HSI analyses but these require accurate camera orientations from bundle adjustment. However, the limited image overlap leads to an over-parameterization in the bundle adjustment with six degrees of freedom per image exposure, i.e., LP image line. Feature-based matching based on salient key points in small image neighborhoods cannot be readily applied to LP image lines, since each LP image line is only a single pixel wide in one of the image dimensions. A naive mosaicing of consecutive LP image lines leads to unacceptably large errors owing to the relative camera motion within these mosaics, even for a stabilized camera. Thus, a new method that allows the use of established methods for feature-based matching from aerial LP image lines is presented, and observations are retrieved and used in the bundle adjustment. The examination of the spatial misregistration between spectral bands in HSI cameras, i.e., the chromatic aberration, is also examined. The method assumes a stabilized camera system with a processed global navigation satellite system aided inertial navigation system solution. The bundle adjustment is done by estimating trajectory corrections in time intervals and retrieving discrete trajectory estimates from cubic spline interpolation. An experiment was conducted to demonstrate the method. The chromatic aberration is shown to be of sub-pixel level in the LP HSI camera, and the resulting planimetric accuracy (normalized median absolute deviation) from the bundle adjustment is \(\sim 1/4\) of the ground sampling distance in each of the north and east components. The accurate estimates from the bundle adjustment are shown to be suitable for high-quality orthoimage generation.
      PubDate: 2024-05-16
       
  • Coastal Shoreline Change in Eastern Indian Metropolises

    • Free pre-print version: Loading...

      Abstract: Abstract The coastal regions of India have a high population density and are ecologically productive. However, they are also susceptible to both human activity and natural calamities, which can lead to erosion and accretion. As part of the sustainable management of coastal zones, these threats have taken precedence in evaluating shoreline dynamicity. This study demonstrated the effectiveness of integrating remote sensing and geographic information systems for comprehensive long-term coastal change analyses. The analysis reveals that the mean erosion rate along the Chennai coast ranges from −0.2 to −2.5 m/year. Accretion is also recorded along certain parts of the Chennai coast, with rates ranging from 1 to 4.6 m/year. The Vishakhapatnam shoreline has a consistent pattern of both erosion and accretion, with erosion rates ranging from −0.1 to −6.8 m/year and accretion from 0.2 to 5 m/year. However, most of the Puri coast exhibits an accretion pattern, with values ranging from approximately 0.1 to 3.22 m/year. The fluctuations in shorelines of these three metropolises are a matter of great concern, given that these coastal cities play a substantial part in India’s economic and cultural endeavors. The ongoing occurrence of climate change and global warming has led to an elevation in the worldwide sea level, along with a heightened intensity and frequency of extreme occurrences like tropical cyclones in the Bay of Bengal, where these three coasts are situated. The coastlines of these urban areas may experience alterations due to natural phenomena like rising sea levels and tropical cyclones, as well as a diverse array of human activity. This study may help to facilitate the formulation of suitable management strategies and regulations for the coastal areas of Vishakhapatnam, Puri, Chennai, and other Indian coastal places that have similar physical attributes.
      PubDate: 2024-04-29
      DOI: 10.1007/s41064-024-00286-y
       
  • Deformation Monitoring and Primary Driving Factor Analysis in the Coastal
           Area of Liaohe Oilfield Utilizing MT-InSAR and PCA

    • Free pre-print version: Loading...

      Abstract: Abstract Water injection and oil production often lead to significant surface deformations in oilfields. Such geological hazards are substantial potential threats to both the oilfield and its surrounding infrastructure. Liaohe oilfield is China’s largest production base for heavy and high-viscosity oil. It has a long history of surface deformation in its core production area near the Bohai Bay. Multitemporal synthetic aperture radar interferometry (MT-InSAR) offers long-term monitoring capabilities and has previously been used to monitor deformations in Liaohe oilfield. However, quantitative analysis of deformation-driving factors is less common in previous studies, and investigations are limited to data up until 2021. Continuous monitoring and analysis of deformation patterns and influencing factors in this region are of crucial practical importance. In this study, we acquired the latest long-term deformation data and the driving factors in Liaohe oilfield via MT-InSAR. For the first time, a quantitative analysis of deformation-driving factors was performed, and the ultra-long-term (from 1958 to 2023) deformation data with respect to Liaohe oilfield were collected and analyzed to interpret the deformation characteristics, variation tendency, and influencing factors. We employed 217 Sentinel-1A/B SAR images from 2019 to 2023 to calculate the deformation rates and cumulative deformations in this area. The results revealed three subsidence funnels and one uplift area, with deformation rates ranging from [−152, 41] mm/year and [−182, 51] mm/year (negative values indicating subsidence and positive values indicating uplift) for the ascending and descending datasets, respectively. Principal component analysis (PCA) was applied to the cumulative deformation sequence for extracting the primary components. The PCA results, groundwater data, and precipitation data were utilized for a quantitative analysis of primary deformation-driving factors. The analysis results indicate that the surface deformations exhibit a strongly linear trend mainly due to oilfield exploitation, coupled with slight variations related to precipitation and groundwater extraction. By integrating historical deformation information with the monitored results, the development process of the surface deformation was investigated and divided into six stages. This process was related to the oil energy demand, extraction techniques, and reservoir compaction phases. This study contributes to understanding the spatiotemporal evolution of surface deformation in the Liaohe oilfield and the driving factors, and it provides valuable insights for similar research in other oilfields.
      PubDate: 2024-04-22
      DOI: 10.1007/s41064-024-00283-1
       
  • Evaluating Sea Level Rise Impacts on the Southeastern Türkiye Coastline:
           a Coastal Vulnerability Perspective

    • Free pre-print version: Loading...

      Abstract: Abstract Coastal areas are inherently sensitive and dynamic, susceptible to natural forces like waves, winds, currents, and tides. Human activities further accelerate coastal changes, while climate change and global sea level rise add to the challenges. Recognizing and safeguarding these coasts, vital for both socioeconomic and environmental reasons, becomes imperative. The objective of this study is to categorize the coasts of the Mersin and İskenderun bays along the southeastern coast of Türkiye based on their vulnerability to natural forces and human-induced factors using the coastal vulnerability index (CVI) method. The study area encompasses approximately 520 km of coastline. The coastal vulnerability analysis reveals that the coastal zone comprises various levels of vulnerability along the total coastline: 24.7% (128 km) is categorized as very high vulnerability, 27.4% (142 km) as high vulnerability, 23.7% (123 km) as moderate vulnerability, and 24.3% (126 km) as low vulnerability. Key parameters influencing vulnerability include coastal slope, land use, and population density. High and very high vulnerability are particularly prominent in coastal plains characterized by gentle slopes, weak geological and geomorphological features, and significant socioeconomic value.
      PubDate: 2024-04-19
      DOI: 10.1007/s41064-024-00284-0
       
  • Building a Fully-Automatized Active Learning Framework for the Semantic
           Segmentation of Geospatial 3D Point Clouds

    • Free pre-print version: Loading...

      Abstract: Abstract In recent years, significant progress has been made in developing supervised Machine Learning (ML) systems like Convolutional Neural Networks. However, it’s crucial to recognize that the performance of these systems heavily relies on the quality of labeled training data. To address this, we propose a shift in focus towards developing sustainable methods of acquiring such data instead of solely building new classifiers in the ever-evolving ML field. Specifically, in the geospatial domain, the process of generating training data for ML systems has been largely neglected in research. Traditionally, experts have been burdened with the laborious task of labeling, which is not only time-consuming but also inefficient. In our system for the semantic interpretation of Airborne Laser Scanning point clouds, we break with this convention and completely remove labeling obligations from domain experts who have completed special training in geosciences and instead adopt a hybrid intelligence approach. This involves active and iterative collaboration between the ML model and humans through Active Learning, which identifies the most critical samples justifying manual inspection. Only these samples (typically \(\ll 1{\%}\) of Passive Learning training points) are subject to human annotation. To carry out this annotation, we choose to outsource the task to a large group of non-specialists, referred to as the crowd, which comes with the inherent challenge of guiding those inexperienced annotators (i.e., “short-term employees”) to still produce labels of sufficient quality. However, we acknowledge that attracting enough volunteers for crowdsourcing campaigns can be challenging due to the tedious nature of labeling tasks. To address this, we propose employing paid crowdsourcing and providing monetary incentives to crowdworkers. This approach ensures access to a vast pool of prospective workers through respective platforms, ensuring timely completion of jobs. Effectively, crowdworkers become human processing units in our hybrid intelligence system mirroring the functionality of electronic processing units.
      PubDate: 2024-04-03
      DOI: 10.1007/s41064-024-00281-3
       
  • Reports

    • Free pre-print version: Loading...

      PubDate: 2024-04-01
      DOI: 10.1007/s41064-024-00289-9
       
  • Building Detection from SkySat Images with Transfer Learning: a Case
           Study over Ankara

    • Free pre-print version: Loading...

      Abstract: Abstract The detection and continuous updating of buildings in geodatabases has long been a major research area in geographic information science and is an important theme for national mapping agencies. Advancements in machine learning techniques, particularly state-of-the-art deep learning (DL) models, offer promising solutions for extracting and modeling building rooftops from images. However, tasks such as automatic labelling of learning data and the generalizability of models remain challenging. In this study, we assessed the sensor and geographic area adaptation capabilities of a pretrained DL model implemented in the ArcGIS environment using very-high-resolution (50 cm) SkySat imagery. The model was trained for digitizing building footprints via Mask R‑CNN with a ResNet50 backbone using aerial and satellite images from parts of the USA. Here, we utilized images from three different SkySat satellites with various acquisition dates and off-nadir angles and refined the pretrained model using small numbers of buildings as training data (5–53 buildings) over Ankara. We evaluated the buildings in areas with different characteristics, such as urban transformation, slums, regular, and obtained high accuracies with F‑1 scores of 0.92, 0.94, and 0.96 from SkySat 4, 7, and 17, respectively. The study findings showed that the DL model has high transfer learning capability for Ankara using only a few buildings and that the recent SkySat satellites demonstrate superior image quality.
      PubDate: 2024-03-18
      DOI: 10.1007/s41064-024-00279-x
       
  • A Geospatial Approach to Mapping and Monitoring Real Estate-Induced Urban
           Expansion in the National Capital Region of Delhi

    • Free pre-print version: Loading...

      Abstract: Abstract Monitoring of real estate growth is essential with the increasing demand for housing and working space in cities. In this study, a new methodological framework is proposed to map the area under real estate using geospatial techniques. In this framework, the built-up area and open land at successive stages of development are used to map the area under real estate. Three machine learning algorithms were used, namely random forest (RF), support vector machine (SVM), and feedforward neural networks (FFNN), to classify the land use and land cover (LULC) map of Delhi NCR during 1990–2018, which is the basic input for real estate mapping. The results of the study show that optimized RF performed better than SVM and FFNN in LULC classification. The real estate land increased by 279% in Delhi NCR during 1990–2018. The area under real estate increased by 33%, 47%, 29%, 21%, and 22% during 1990–1996, 1996–2003, 2003–2008, 2008–2014, and 2014–2018, respectively. Among the cities surrounding Delhi, Gurgaon, Rohtak, Noida, and Faridabad have witnessed maximum real estate growth. The approach used in this study could be used for real estate mapping in other cities across the world.
      PubDate: 2024-03-18
      DOI: 10.1007/s41064-024-00278-y
       
 
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: 44.200.140.218
 
Home (Search)
API
About JournalTOCs
News (blog, publications)
JournalTOCs on Twitter   JournalTOCs on Facebook

JournalTOCs © 2009-
JournalTOCs
 
 

 A  B  C  D  E  F  G  H  I  J  K  L  M  N  O  P  Q  R  S  T  U  V  W  X  Y  Z  

  Subjects -> GEOGRAPHY (Total: 493 journals)
The end of the list has been reached or no journals were found for your choice.
Similar Journals
Similar Journals
HOME > Browse the 73 Subjects covered by JournalTOCs  
SubjectTotal Journals
 
 
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: 44.200.140.218
 
Home (Search)
API
About JournalTOCs
News (blog, publications)
JournalTOCs on Twitter   JournalTOCs on Facebook

JournalTOCs © 2009-