Remote Sensing Applications-Society and Environment最新文献

{"title":"Investigation of atmospheric clouds and boundary layer dynamics during a dust storm in the Western-Indian region","authors":"Dharmendra Kumar Kamat ,&nbsp;Som Kumar Sharma ,&nbsp;Prashant Kumar ,&nbsp;Kondapalli Niranjan Kumar ,&nbsp;Aniket ,&nbsp;Sourita Saha ,&nbsp;Hassan Bencherif","doi":"10.1016/j.rsase.2024.101442","DOIUrl":"10.1016/j.rsase.2024.101442","url":null,"abstract":"<div><div>This study investigates the dynamics of atmospheric clouds and boundary layer due to a sudden dust storm over Ahmedabad (23.02° N, 72.57° E), a Western-Indian region, during the pre-monsoon season on May 13, 2024. The storm was triggered by the outflow from convective systems originating in southwest Gujarat and southeast Rajasthan, combined with the significant deepening of the thermal low core over Ahmedabad, which generated strong near-surface winds and initiated the dust storm. These systems and the dust storm were captured by the INSAT-3D satellite and MODIS instrument on NASA's Aqua and Terra satellites. The ground-based Ceilometer Lidar backscatter profile showed an abrupt change in the mixed layer height (MLH) from ∼2.5 km to about 250 m during the storm due to attenuation of the signal by heavy dust load. The MLH, ∼2 km on 12 May (previous day), shallowed to ∼800 m on 14 May (post dust storm day), with increased backscatter indicating high dust concentration. Vertical visibility dropped to 340–660 m during the dust storm. During the storm, relative humidity near the surface increased from 29% to 48% due to moisture transport by frontal system along the density current pathway, while near-surface wind speeds peaked at around 6–10 m/s. After the storm, deep convective clouds formed with a vertical extent of ∼11 km, resulting in approximately 19 mm of rainfall with nearly 15 mm falling within just 1 h indicating the dust-cloud interaction. This study highlights the impact of moist convection and subsequent dust storm on clouds and boundary layer dynamics, emphasizing the importance of ground-based instruments, satellites, and reanalysis datasets in atmospheric monitoring. Understanding the causes, mechanisms, and consequences of dust storms is critical for mitigating their effects and adapting to the changing climate patterns that may influence their frequency and intensity.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101442"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143128310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MAPunet: High-resolution snow depth mapping through U-Net pixel-wise regression","authors":"Alejandro Betato ,&nbsp;Hernán Díaz Rodríguez ,&nbsp;Niamh French ,&nbsp;Thomas James ,&nbsp;Beatriz Remeseiro","doi":"10.1016/j.rsase.2025.101477","DOIUrl":"10.1016/j.rsase.2025.101477","url":null,"abstract":"<div><div>Accurate snow depth prediction is essential for hydrological risk assessment, flood prediction, water resource management, and weather forecasting. While previous studies have successfully applied deep learning techniques to generate snow depth maps, many have been constrained by geographical coverage or low data resolution. This work addresses these limitations by integrating high-resolution LiDAR maps, satellite imagery, digital elevation models, and three novel time-dependent variables. Additionally, the well-known U-Net architecture has been customized to perform pixel-wise regression and accurately predict snow depth over large geographic areas. The proposed method, called MAPunet, effectively models snow depth in the mountainous region of Davos, achieving an average error of 0.62 m at a 5 m resolution. The experimental results demonstrate the potential of combining high-resolution data with advanced deep learning techniques for enhanced snow depth mapping.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101477"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143346715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating shoreline prediction accuracy with the Kalman filter model: A case study of Nijhum Dwip, Bay of Bengal","authors":"Anamika Das Kona ,&nbsp;Md Enamul Hoque ,&nbsp;Md Atiqur Rahman","doi":"10.1016/j.rsase.2025.101469","DOIUrl":"10.1016/j.rsase.2025.101469","url":null,"abstract":"<div><div>Shoreline dynamics play a critical role in coastal zone management and environmental conservation. This study investigates shoreline changes and predictions for Nijhum Dwip, located in the Meghna estuary, over the period from 1980 to 2020, with a forecast for 2030. Utilizing multi-temporal Landsat imagery, Digital Shoreline Analysis System (DSAS), and the Kalman Filter Model, the study analyzes spatial and temporal shoreline variations. Results indicate a significant accretion trend, particularly in Segment B, which exhibits a net shoreline movement of 1322.85 m and an average rate of 31.96 m/yr. Segment A shows moderate accretion, with an average rate of 7.79 m/yr. The Kalman Filter Model predicts a mean accretion of 1601.23 m by 2030, aligning with historical accretion patterns. Model validation through Root Mean Square Error (RMSE) analysis yields a value of 95 m, highlighting discrepancies between predicted and observed shoreline positions. This comprehensive study underscores the utility of advanced geospatial and statistical methods in coastal change monitoring and provides actionable insights for sustainable coastal management.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101469"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143101045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unveiling subsidence patterns: Time series analysis for land deformation investigation in the west-Qurna oil field, Iraq","authors":"Ali Alkhazraji ,&nbsp;Jadunandan Dash","doi":"10.1016/j.rsase.2024.101411","DOIUrl":"10.1016/j.rsase.2024.101411","url":null,"abstract":"<div><div>Land subsidence is a worldwide geological and environmental risk caused by natural occurrences and human actions. Its effects include a range of socio-economic, environmental, and hydrogeological consequences, such as damage to infrastructure like buildings, roads, bridges, and pipelines, as well as increased flooding and reduced groundwater storage capacity. Due to these diverse impacts, it is crucial to monitor the spatial and temporal scope of land subsidence. This study presents an investigation into land subsidence within the West-Qurna oil field, a large oil reservoir situated in Iraq's Basrah governorate. The study employs Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) analysis from the European Space Agency Sentinel 1A over six years, from June 2017 to May 2023. The Stanford Method for Persistent Scatterers (StaMPS) has been utilised to assess the scale and magnitude of land deformations in this region. Results revealed a notable subsidence within the central urban area of the oil field, forming an ellipsoidal subsidence bowl spanning 86 km². The peak subsidence rate is identified at −13.2 ± 0.4 mm/yr within this bowl, with a cumulative vertical displacement of 75 mm throughout the six-year observation period. Furthermore, uplifting phenomena are also detected at the study area's peripheries, reaching a maximum rate of 12 ± 0.4 mm/yr and a cumulative shift of 54 mm. Temporal analysis showcases a significant alteration in subsidence rates, with rates of −18 mm/yr observed between 2017 and 2020, followed by −5 mm/yr post-2020. This change is attributed to COVID-19-related oil production reductions enacted by the government to boost prices. Our analysis points toward oil extraction as a probable primary driver of subsidence in the studied area, although a deeper probe into the impact of groundwater extraction for reservoir injection remains essential.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101411"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143092296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Post-tornado roadway debris detection from satellite images: An integrated GIS and image processing approach","authors":"Richard Boadu Antwi,&nbsp;Prince Lartey Lawson,&nbsp;Eren Erman Ozguven,&nbsp;Ren Moses","doi":"10.1016/j.rsase.2024.101439","DOIUrl":"10.1016/j.rsase.2024.101439","url":null,"abstract":"<div><div>Southeastern United States frequently experience tornadoes, necessitating rapid response and recovery efforts by state and federal agencies. Accurate information about the extent and severity of tornado-induced damage, especially debris volume and locations, is crucial for these efforts. This study, therefore, focuses on post-tornado debris assessment in Leon County, Florida, which was hit by two EF-2 and an EF-1 tornadoes in May 2024. Using satellite imagery from the Planetscope satellite and Geographic Information Systems (GIS), a macro-level evaluation of tornado debris impact was conducted, particularly on roadways and impacted communities. The proposed approach includes an evaluation of the overall post-tornado debris impact across the entire county and its population, and a detailed analysis of debris impact on roadways and its effect on accessibility. Spectral indices from satellite images, specifically the Normalized Difference Vegetation Index (NDVI), were utilized to derive assessment parameters. By comparing NDVI values from pre- and post-tornado images, we analyzed changes in vegetation and debris accumulation along roadway segments leading to possible roadway closures. This integrated method provides critical insights for enhancing disaster response and recovery operations in tornado-prone regions. Findings indicate that high volumes of vegetative debris were present in the south-central parts of the county, which is occupied by the highest population of county residents. The roadway segments in this region also recorded highest debris volumes, which is a critical information for agencies that need to know highly impacted locations. Comparing the results to ground truth damage data, the accuracy recorded was 74%.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101439"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143092542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hedgerow map of Bavaria, Germany, based on orthophotos and convolutional neural networks","authors":"Verena Huber-García ,&nbsp;Jennifer Kriese ,&nbsp;Sarah Asam ,&nbsp;Mariel Dirscherl ,&nbsp;Michael Stellmach ,&nbsp;Johanna Buchner ,&nbsp;Kristel Kerler ,&nbsp;Ursula Gessner","doi":"10.1016/j.rsase.2025.101451","DOIUrl":"10.1016/j.rsase.2025.101451","url":null,"abstract":"<div><div>Hedgerows play a significant role in biodiversity preservation, carbon sequestration, soil stability and the ecological integrity of rural landscapes. Understanding their current condition and future development is therefore crucial for a range of stakeholders such as municipalities, state agencies or environmental organizations. The wall-to-wall mapping and characterization of hedgerows in-situ is, however, very labour-, time- and cost-intensive. This impedes a regular monitoring at adequate intervals. In the Federal State of Bavaria, Germany, the hedgerow biotope mapping is repeated every 20–30 years for each district. State-wide consistent and up-to-date data are hence not available. In this study we present an approach for mapping all hedgerows in Bavaria using orthophotos and deep learning. We used hedgerow polygons of the federal in-situ biotope mapping from 5 focus districts as well as additional manually digitized polygons as training and test data and orthophotos as input in a DeepLabV3 Convolutional Neural Network (CNN). The CNN has a Resnet50 Backbone and was optimized using the Dice loss as cost function. The orthophotos were acquired in 2019–2021. They have a spatial resolution of 20 cm and were fed to the CNN at tiles of 125 × 125 m. The generated hedgerow probability tiles were post-processed through merging and averaging the overlapping tile boarders, shape simplification and filtering. The resulting hedgerow vector data set achieved medium overall accuracies (precision = 0.43, recall = 0.53, F1-score = 0.48). The model generally overestimated the number of hedgerows, and hedgerows were often confused with riparian as well as urban vegetation. Looking at each hedgerow polygon individually, the mapping accuracy varied considerably, with a median F1-score of 0.51 for all detected objects. In addition, we found differences in accuracies among districts in different landscapes. For example, the Hassberge district, a landscape rich of hedgerows, reached a F1-score of 0.61. A comprehensive comparison with the Copernicus High Resolution Layer (HRL) Small Woody Features (SWF) revealed significant differences between the datasets. About 43 % of the hedgerows in our data set were not present in the SWF layer. Especially narrow, elongated vegetated structures are not captured in the SWF product. This highlights the potential to use our state-wide hedgerow map of Bavaria in combination with the SWF dataset, but also by itself, for a range of administrative, statistical and nature conservation applications.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101451"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143091975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison between machine learning classification and trajectory-based change detection for identifying eucalyptus areas in Landsat time series","authors":"Debora da Paz Gomes Brandão Ferraz,&nbsp;Raúl Sánchez Vicens","doi":"10.1016/j.rsase.2024.101444","DOIUrl":"10.1016/j.rsase.2024.101444","url":null,"abstract":"<div><div>In forestry, where species management has inter-annual durations, time series longer than one year have been used to map planted areas, estimate biophysical parameters, and determine planting age. Eucalyptus plantations have characteristics that enhance the use of time series in their classification, such as clear-cutting before planting or previous rotation and the rapid growth of large vegetation cover, which remains stable throughout the rotation period. This article compares the performance of two classification methods for mapping Eucalyptus areas: an object-oriented classification (GEOBIA) using products generated by the LandTrendr change detection algorithm based on trajectories and a classification using machine learning (random forest). Using a Landsat time series from 1985 to 2020, tests were conducted in a pilot area in Rio de Janeiro, Brazil. Both methods showed high accuracy in detecting Eucalyptus areas. However, the trajectory-based classification proved slightly superior, achieving a global accuracy of 0.988 and an F-Score of 0.975, while the classification using the random forest algorithm achieved a global accuracy of 0.954 and an F-score of 0.849. Regarding identifying the initial year of planting, both methods proved effective without showing significant differences (p-value = 0.1003). However, detecting the initial year using the LandTrendr algorithm proved more assertive. Both methods revealed periods of increase and stabilization in Eucalyptus planting throughout the time series, proving promising for determining the location and age of each stand and, thus, obtaining information about the time of use of that area for Eucalyptus cultivation.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101444"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143101042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ViT-ChangeFormer: A deep learning approach for cropland abandonment detection in lahore, Pakistan using Landsat-8 and Sentinel-2 data","authors":"Mannan Karim ,&nbsp;Haiyan Guan ,&nbsp;Jiahua Zhang ,&nbsp;Muhammad Ayoub","doi":"10.1016/j.rsase.2025.101468","DOIUrl":"10.1016/j.rsase.2025.101468","url":null,"abstract":"<div><div>Cropland abandonment poses significant environmental, economic, and social challenges globally. As urbanization encroaches on agricultural areas, understanding the dynamics of abandoned croplands and accurately classifying and detecting them are essential for informed sustainable land use and effective policy development. However, traditional methods struggle to identify abandoned croplands due to temporal variability, limited spectral data and challenges in land cover variations. To address these challenges, we introduced an innovative deep learning approach that combines a Vision Transformer (ViT) with ChangeFormer for the classification and change detection of cropland abandonment using Landsat-8 and Sentinel-2 datasets in Lahore, Pakistan. We employed ViT for image classification, enhancing its efficacy through the incorporation of Vegetation Indices (VIs). This integration led to notable improvements in F1 score and Overall Accuracy (OA), elevating them from 86% and 88%to 92% and 95% respectively. Subsequently, ViT-generated classified rasters facilitated in identification of abandoned lands using ChangeFormer model. The direct comparison showcased a significant enhancement in ChangeFormer's performance, with F1 score and OA escalating from 91% and 90% to 97.5% and 96%, respectively. The improvment was particularly evident when testing ChangeFormer with ViT-generated rasters compared to raw imagery for binary change detection. The study identified 32,043 ha of abandoned cropland (14,613 in 2019 and 17,430 in 2024), with 16.35% converted to built-up areas in 2024. Urbanization was the primary driver, followed by conversions to barren land and water bodies. While our approach improves cropland abandonment detection, addressing unavailability of high-resolution imagery, computational costs, and integrating socio-economic and climate factors could enhance its accuracy and effectiveness.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101468"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143101043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integration of multi-method ASTER data analysis and geometric average modeling for hydrothermal alteration mapping and mineral prospectivity assessment of copper deposits, Anti-Atlas, Morocco","authors":"Lahsen Achkouch ,&nbsp;Ahmed Attou ,&nbsp;Hafid Mezougane ,&nbsp;Mohammed Ouchchen ,&nbsp;Younesse El Cheikh ,&nbsp;Younes Mamouch ,&nbsp;Abdelhamid Bajadi ,&nbsp;Bouchra Dadi ,&nbsp;Rachid Ahmed ,&nbsp;Behnam Sadeghi","doi":"10.1016/j.rsase.2025.101467","DOIUrl":"10.1016/j.rsase.2025.101467","url":null,"abstract":"<div><div>Satellite imagery is a crucial tool for mineral exploration, as it enables the identification of mineralization systems through hydrothermal alteration mapping. This study focuses on the Tikirt region within the Anti-Atlas belt, well-documented for its copper mineralization, to produce a predictive mineral prospectivity map. Advanced processing of ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) satellite imagery was conducted to detect key alteration minerals, including argillic, phyllic, propylitic, and iron oxide. Spectral analysis techniques such as Spectral Feature Fitting (SFF), Matched Filtering (MF), Least-Squares Fitting (LS-Fit), and Constrained Energy Minimization (CEM) were utilized alongside the references spectra from the USGS library to delineate alteration zones. These were integrated using the geometric average model to generate a comprehensive prospectivity map highlighting areas with high mineralization potential. Field validation and laboratory analyses, confirmed the accuracy of the predictive map, demonstrating a strong alignment between high-potential zones, mapped faults, and mineralization occurrences. Assessment of the accuracy of alteration mapping indicates a satisfactory level of consistency with reference data (overall accuracy = 80%) and a high level of agreement (Kappa coefficient = 73.3%). Notably, the study identified several new areas with significant potential for copper mineralization, particularly associated with Neoproterozoic formations located in the northeast, northwest, south, and southeast of the study area. The results revealed a strong correlation between ASTER image processing and field/laboratory data, underscoring the effectiveness of integrating ASTER imagery with advanced spectral analysis for regional-scale mineral prospectivity. This integrated approach offers significant potential for guiding future exploration projects.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101467"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143101044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Remote sensing and gravity investigations for barite detection in Neoproterozoic rocks in the Ariab area, Red Sea Hills, Sudan","authors":"Abazar M.A. Daoud ,&nbsp;Ali Shebl ,&nbsp;Mohamed M. Abdelkader ,&nbsp;Ali Ahmed Mohieldain ,&nbsp;Árpád Csámer ,&nbsp;Albarra M.N. Satti ,&nbsp;Péter Rózsa","doi":"10.1016/j.rsase.2024.101416","DOIUrl":"10.1016/j.rsase.2024.101416","url":null,"abstract":"<div><div>The increasing global demand for barite, driven by its geological importance and various industrial applications, advises the scientific community to improve attempts to identify and explore its deposits in different geological settings. This boost in interest aims to ensure sustainable supply by locating new sources and better understanding the conditions in which barite forms. This study presents an integrated approach using multispectral (Landsat 8 &amp; 9, Sentinel-2, and ASTER) and hyperspectral (PRISMA) remote sensing data, along with geophysical gravity data, to improve the localization of barite deposits. Several image processing methods, including false colour composites, principal component analysis, band ratios, minimum noise fraction, and spectral analysis, were employed for the discrimination of barite deposits, revealing their association with felsic rocks (referred to as group C). Additionally, lineament extraction was performed using the recent and advanced different filters like Tilt Angle Horizontal Gradient (TAHG) and Enhanced Horizontal Gradient Amplitude (EHGA) on Bouguer anomalies, highlighting the structural control of barite deposits by the D3 deformation phase. Field investigations were conducted to validate our findings. Based on these field observations, the integrated methodology successfully mapped the distribution of barite and its host rocks, resulting in an updated geological map for barite distribution that can be used in further exploration phases. We strongly recommend the adopted approach and the newly proposed image combinations for preliminary explorations of barite in similar arid terrains.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101416"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143092290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}