Remote Sensing Applications-Society and Environment最新文献

{"title":"Integrating SMAP and CYGNSS data for daily soil moisture and agricultural drought monitoring in Nghe An province, Vietnam","authors":"Tich Phuc Hoang ,&nbsp;Minh Cuong Ha ,&nbsp;Phuong Lan Vu ,&nbsp;José Darrozes ,&nbsp;Phuong Bac Nguyen","doi":"10.1016/j.rsase.2025.101664","DOIUrl":"10.1016/j.rsase.2025.101664","url":null,"abstract":"<div><div>In the context of climate change, droughts are increasingly frequent and severe, affecting broader regions. Consequently, effective drought monitoring is crucial for risk management and understanding climate change impacts. Soil moisture estimation using satellite data is a pivotal metric for developing time-series agricultural drought monitoring maps. This study proposes a methodology for constructing soil moisture and agricultural drought maps for Nghe An Province, Vietnam, using the SMAP dataset along with soil moisture estimations from CYGNSS data and additional ancillary data. The Self-Attention-based Imputation for Time Series (SAITS) model, employing self-attention mechanisms to impute missing values in multivariate time series, is used to construct the soil moisture dataset from SMAP, resulting in complete datasets with a training loss RMSE_SAITS <span><math><mo>=</mo></math></span> 0.073 <span><math><mrow><msup><mrow><mi>cm</mi></mrow><mrow><mn>3</mn></mrow></msup><mo>/</mo><msup><mrow><mi>cm</mi></mrow><mrow><mn>3</mn></mrow></msup></mrow></math></span>. Additionally, leveraging a Random Forest Regression model, CYGNSS data combined with meteorological, topographic, and soil texture information enable the estimation of daily soil moisture values, exhibiting a strong correlation with R <span><math><mo>=</mo></math></span> 0.889. Subsequently, integration of the two soil moisture products from SMAP and CYGNSS yields a dataset with a spatial resolution of 1km and a temporal resolution of 1 day. The soil moisture results were compared with moisture data from ERA5 (R <span><math><mo>=</mo></math></span> 0.75, ubRMSE <span><math><mo>=</mo></math></span> 0.055 <span><math><mrow><msup><mrow><mi>cm</mi></mrow><mrow><mn>3</mn></mrow></msup><mo>/</mo><msup><mrow><mi>cm</mi></mrow><mrow><mn>3</mn></mrow></msup></mrow></math></span>) and in-situ data in Nghe An province (R <span><math><mo>=</mo></math></span> 0.709, ubRMSE <span><math><mo>=</mo></math></span> 0.017 <span><math><mrow><msup><mrow><mi>cm</mi></mrow><mrow><mn>3</mn></mrow></msup><mo>/</mo><msup><mrow><mi>cm</mi></mrow><mrow><mn>3</mn></mrow></msup></mrow></math></span>). Finally, the Standardized Soil Moisture Index is calculated to transform the time-series soil moisture data into a standardized normal distribution, generating agricultural drought maps with 9 different levels. This study represents a significant advancement in agricultural drought monitoring, highlighting the immense potential of machine learning techniques when combined with satellite-based soil moisture data. Our approach effectively monitors drought in Nghe An Province, Vietnam, with broader applicability to other regions worldwide.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"39 ","pages":"Article 101664"},"PeriodicalIF":4.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144757515","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":"Simulating vegetation potential and quantifying uncertainty for precision forestation in arid regions","authors":"Jia Qu ,&nbsp;Zirui Gai ,&nbsp;Qi Liu ,&nbsp;Dongwei Gui ,&nbsp;Xinlong Feng ,&nbsp;Jianping Zhao ,&nbsp;Tao Lin ,&nbsp;Yunfei Liu ,&nbsp;Qian Jin ,&nbsp;Zeeshan Ahmed","doi":"10.1016/j.rsase.2025.101670","DOIUrl":"10.1016/j.rsase.2025.101670","url":null,"abstract":"<div><div>Large-scale forestation in arid regions with excessive planting density often aggravates water scarcity and disrupts local ecosystems. The Potential Normalized Difference Vegetation Index (PNDVI) reflects the optimal density of natural vegetation in the absence of human intervention, and can guide the planting site, area and density in arid areas. However, its accurate simulation with uncertainty quantification remains understudied. We propose a method to quantify uncertainty in PNDVI prediction by integrating deep learning, variational inference, and multiple environmental variables to fill this gap. The model was applied to the lower Tarim River Basin (LTRB) in northwest China and achieved the best performance with an average accuracy of 88.58 %, which is 10.09 % higher than conventional machine learning models. The overall uncertainty is characterized by a mean value of 0.298, with a standard deviation of 0.142. In the LTRB, regions near the river channel in the central and southeastern areas with low uncertainties are ideal for high-density forestation. This approach can offer scientific decision-support for arid-region forestation planning and has great socio-economic benefits by reducing water consumption, increasing land productivity and reducing ecological restoration costs.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"39 ","pages":"Article 101670"},"PeriodicalIF":4.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144750406","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":"Remotely sensed mapping of plant diversity in Earth's largest mangrove forests: Developing a spectral diversity metric with DESIS hyperspectral data and the ‘spectral species’ concept","authors":"Subham Banerjee ,&nbsp;Swapan Kumar Sarker ,&nbsp;Bryan Pijanowski","doi":"10.1016/j.rsase.2025.101676","DOIUrl":"10.1016/j.rsase.2025.101676","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Global biodiversity monitoring faces significant challenges, yet recent advancements in spaceborne remote sensing, particularly through hyperspectral sensors, are opening new avenues for cost-effective and scalable plant diversity mapping. The high spectral resolution of these sensors enables precise identification of plant traits and community compositions. Employing the “Spectral Species” concept, which categorizes spectral imagery pixels into distinct spectral types, we have developed a novel semi-discrete “Spectral Species Diversity” (SSD) metric. This metric has proven effective in modeling plant diversity, as demonstrated by our study in the mangrove forests of Bangladesh's Sundarbans using DESIS (DLR Earth Sensing Imaging Spectrometer) hyperspectral imagery.&lt;/div&gt;&lt;div&gt;In this study, we analyzed data from 110 Permanent Sampling Plots in the Sundarbans, calculated traditional plant diversity indices (Species Richness, Shannon and Simpson Diversity), and compared these with our spectral diversity metric. The comparison revealed robust correlations between field-measured plant diversity and our DESIS-derived SSD (&lt;em&gt;R&lt;/em&gt;&lt;sup&gt;&lt;em&gt;2&lt;/em&gt;&lt;/sup&gt; = 0.473 for Shannon diversity and &lt;em&gt;R&lt;/em&gt;&lt;sup&gt;&lt;em&gt;2&lt;/em&gt;&lt;/sup&gt; = 0.468 for Simpson diversity). However, species richness showed poor correlation with the newly developed SSD metric. Conversely, the continuous conventional Coefficient of Variation (CV) spectral diversity metric, also computed using the same hyperspectral dataset, underperformed relative to the SSD metric. Furthermore, when assessing the performance of our SSD metric using multispectral imagery from Sentinel-2 and Landsat 8, the metrics derived from Sentinel-2 exhibited weaker relationships with plant diversity (&lt;em&gt;R&lt;/em&gt;&lt;sup&gt;&lt;em&gt;2&lt;/em&gt;&lt;/sup&gt; = 0.152 for Shannon Diversity and &lt;em&gt;R&lt;/em&gt;&lt;sup&gt;&lt;em&gt;2&lt;/em&gt;&lt;/sup&gt; = 0.144 for Simpson Diversity), and those from Landsat 8 were less effective.&lt;/div&gt;&lt;div&gt;Upon examining different spectral space sizes, we determined that the optimal size for computing spectral diversity metrics was 150 m × 150 m. This size most effectively captured plant diversity in the vegetation survey plots. While the SSD metric within these spectral spaces mirrored the plant diversity trend across the three salinity zones of the Sundarbans, the observed differences were not statistically significant. Nonetheless, the alignment in pattern highlights the ecological relevance of the SSD metric.&lt;/div&gt;&lt;div&gt;This study underscores that the newly developed SSD metric, utilizing hyperspectral imaging and adapting the Spectral Species concept, can accurately map plant diversity in ecologically diverse ecosystems like the Sundarbans mangroves. Future enhancements, such as aligning spectral space with vegetation survey plot dimensions and incorporating data from SWIR sensors, SAR, or LiDAR, could further refine the metric's robustness and global applicability. These improvements will provide crucial insights for biodiversity co","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"39 ","pages":"Article 101676"},"PeriodicalIF":4.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144738669","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":"Spatial representativeness of NO2 monitoring stations with respect to Sentinel-5P satellite based estimates","authors":"Patryk Tadeusz Grzybowski ,&nbsp;Jan Paweł Musiał ,&nbsp;Krzysztof Mirosław Markowicz","doi":"10.1016/j.rsase.2025.101682","DOIUrl":"10.1016/j.rsase.2025.101682","url":null,"abstract":"<div><div>Nitrogen dioxide (NO₂) pollution is one of the most significant environmental threats to human health. To mitigate the negative effects of NO₂ and other air pollutants, it is essential to monitor pollution through a wide and reliable network. This study aimed to demonstrate the feasibility of using estimated NO₂ concentrations derived from Sentinel-5P, which is a mission that is part of the European Earth Observation Programme Copernicus.satellite data, combined with meteorological factors, to support NO₂ pollution monitoring. Unlike point ground measurements, this approach provides data for the entire area of interest. The main objective of this work is to determine what fraction of Poland is covered by spatially representative (SR) surface NO₂ concentrations measured at ground-based stations. Additionally, the study investigated how many people live in areas not covered by SR NO₂ measurements and identified potential locations for new stations to improve the spatial representativeness of the NO₂ monitoring network across Poland. Four methods for determining SR were tested: Global Moran's I, variability of the correlation coefficient with distance from the station, variability of semivariance with distance from the station, and similarity threshold. It was revealed that approximately 74–94 % of the urban population and 10–30 % of the rural population, where the yearly NO₂ limit was exceeded (&gt;10 μg/m³), are covered by the representative NO₂ measurement network, depending on the method used. Finally, it was proposed to add 10–17 new urban stations and 0–5 new rural stations. This would ensure that 91–98 % of the population is covered by the SR monitoring network.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"39 ","pages":"Article 101682"},"PeriodicalIF":4.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144738671","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":"An evaluation of pansharpening algorithms on Worldview-4 satellite imagery over Western Himalaya","authors":"Dhiraj Kumar Singh ,&nbsp;George P. Petropoulos ,&nbsp;Dileep Kumar Gupta ,&nbsp;Sartajvir Singh ,&nbsp;Vishakha Sood ,&nbsp;Spyridon E. Detsikas","doi":"10.1016/j.rsase.2025.101677","DOIUrl":"10.1016/j.rsase.2025.101677","url":null,"abstract":"<div><div>This study compares component substitution (CS) and multiresolution analysis (MRA) pansharpening algorithms applied to high-resolution WorldView-4 imagery over the Indian Western Himalaya. The performance of these methods was evaluated using quantitative (i.e., visual assessment) and qualitative metrics (such as Relative Average Spectral Error (RASE), Root Mean Square Error (RMSE), Error Relative Global Dimensionless Synthesis (ERGAS), Bias, and the Fidelity-Deformation (FD) metric). The FD metric captures both spectral fidelity and spatial structure preservation by integrating localized and global error measures. The results indicated that MRA-based approaches (i.e., ATWT_M2, M3, and MTF_GLP) exhibit reduced spectral distortions, as reflected by lower Bias and RASE values, making them suitable for applications that demand high spectral fidelity. In contrast, CS-based approaches, such as HCS and BDSD, achieved lower ERGAS and RMSE values, suggesting improved spatial detail preservation. Overall, although pansharpened imagery may be advantageous for developing fine-resolution applications, the choice of the pansharpening algorithm should be made carefully, considering the specific application.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"39 ","pages":"Article 101677"},"PeriodicalIF":4.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144750407","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":"Applying the science impact framework to understand real-world applications and impacts of ICESat and ICESat-2 data on decision-making","authors":"Siobhan L. Light ,&nbsp;Molly E. Brown ,&nbsp;Aimee R. Neeley ,&nbsp;Thomas A. Neumann","doi":"10.1016/j.rsase.2025.101669","DOIUrl":"10.1016/j.rsase.2025.101669","url":null,"abstract":"<div><div>Assessing the societal impact of satellite remote sensing datasets is essential to understanding how these data influence decision-making and to identifying opportunities for further engagement. However, measuring such impacts remains challenging for missions serving diverse stakeholder communities. In this study, we evaluate the broader impact of NASA's Ice, Cloud, and land Elevation Satellite (ICESat) and its successor mission, ICESat-2 by adapting the scientific impact framework (SIF), originally developed to assess public health research, into an Earth science-specific version (e-SIF). This framework captures data dissemination, community awareness, data-driven actions, measurable changes, and future influence, moving beyond traditional academic metrics to assess the missions' reach and effectiveness. Our findings reveal extensive global usage of ICESat and ICESat-2 data, with applications including, but not limited to, shallow water bathymetry, climate mitigation strategies, and forest management. By comparing the prevalence of topical areas in academic literature to real-world applications, we found that althoughthe cryosphere is the most frequently studied domain, differences between research focus and practical use highlight potential areas where further research could better support stakeholders. We found that ICESat and ICESat-2 data are widely employed by national and international governmental and non-governmental organizations but found only limited use by private sector and local governments. We recommend that the ICESat-2 Applications Team expand outreach efforts to these sectors to enhance dissemination of mission data. Furthermore, numerous ICESat-2 applications benefit from long-term data continuity, reinforcing the need for a successor mission. This study demonstrates the feasibility to use e-SIF to evaluate the impact of Earth science missions.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"39 ","pages":"Article 101669"},"PeriodicalIF":4.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144738670","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":"Investigation of evolutionary fuzzy MPCM machine learning and probabilistic SVM models for Butea monosperma species mapping","authors":"Payel Mani ,&nbsp;Dipanwita Dutta ,&nbsp;Anil Kumar","doi":"10.1016/j.rsase.2025.101667","DOIUrl":"10.1016/j.rsase.2025.101667","url":null,"abstract":"<div><div>Accurate identification of plant species at an optimal level of precision remains a major challenge in ecological observations when using conventional classification methods. This study explores the potentiality of multi-temporal datasets with machine learning classifiers for the identification and distribution of Butea monosperma tree species, a native floral species grown in many countries of South and Southeast Asia. For identifying optimum combinations of temporal images the Euclidian distance-based separability analysis was employed on the multi-temporal GCI, MSAVI2 indices database (24 toatal temporal dates). This study uses the fuzzy Modified Possibilistic <em>c-</em>Mean (MPCM) classification method combined with the green chlorophyll (GCl), MSAVI2 temporal index to handle the complexity and uncertainty inherent in the phenological data. Owing to its lesser variance on the testing target species over the other classes, the 21 optimum temporal combinations of GCl images were chosen as a benchmark for comparison of the output with the Probabilistic Support Vector Machine (PSVM) with Radial Basis Function (RBF) kernel approach machine learning classifier which is well known for its ability to handle probabilistic information and high-dimensional data. In this study, a diverse dataset of tree species phenological observations has been employed to evaluate the performance of both classifiers. Key metrices such as overall accuracy and F1-score were utilized for the comparison of different models. The MPCM classifier achieved notable performance, with 92 % overall accuracy and an F1-score of 0.93 when utilizing the 21-temporal GCI database. In contrast, a single-date output resulted in only 65% overall accuracy and an F1-score of 0.74. When compared to PSVM model, which exhibits an F-score of 0.88 and an overall accuracy of 82 %, the utilization of MPCM with combined 21 temporal GCI indices demonstrated superior classification performance. Additionally, this research provides insights into how various evolutionary strategies and algorithms can enhance the classifiers’ adaptability to changing data distributions.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"39 ","pages":"Article 101667"},"PeriodicalIF":4.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144750405","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":"Where are the fences? Large-scale fence detection using deep learning and multimodal aerial imagery","authors":"Romain Wenger ,&nbsp;Eric Maire ,&nbsp;Caryl Buton ,&nbsp;Sylvain Moulherat ,&nbsp;Cybill Staentzel","doi":"10.1016/j.rsase.2025.101658","DOIUrl":"10.1016/j.rsase.2025.101658","url":null,"abstract":"<div><div>Fences play a crucial yet often overlooked role in land use management, biodiversity preservation, and ecological connectivity. However, their fine-scale linear nature poses significant challenges for automated detection using traditional remote sensing approaches. In this study, we propose a deep learning-based method for large-scale fence detection using freely available multimodal remote sensing data. We leverage high-resolution orthophotographs combined with Digital Surface Models (DSM) to enhance the fences identification across diverse landscapes. This work makes two major contributions: the development and open release of a dedicated dataset for fence semantic segmentation, and a comprehensive ablation study evaluating multiple deep learning configurations on multimodal RGB and DSM imagery. Our findings indicate that fusing DSM with RGB data leads to improved segmentation accuracy, particularly in complex and vegetated areas. Additionally, the use of Binary Cross-Entropy (BCE) loss provides marginal performance gains over other loss functions, reinforcing its effectiveness for fine-scale object detection. However, these improvements remain relatively small when considering the significant computational cost associated with processing LiDAR-derived elevation data. Our results suggest that while DSM data can enhance fence detection, its use should be carefully evaluated based on the study area’s characteristics and available resources. In many cases, high-resolution orthophotographs alone provide a viable and scalable alternative for detecting fences at a national scale. We systematically evaluate the impact of different experimental parameters, including sampling strategies, data normalization techniques, and loss functions, highlighting the importance of methodological choices in optimizing model performance. Future work should explore the classification of LiDAR point clouds or high-resolution drone imagery to further enhance fence detection capabilities while optimizing computational efficiency. The code and the dataset are freely available on Zenodo (<span><span>https://zenodo.org/records/13902550</span><svg><path></path></svg></span>) and Github (<span><span>https://github.com/r-wenger/MultiFranceFences</span><svg><path></path></svg></span>).</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"39 ","pages":"Article 101658"},"PeriodicalIF":3.8,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144714210","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":"Design of spectral indices for the detection of soil pollutants associated with the disappearance of persons: The case of Mexico","authors":"J.L. Silván-Cárdenas,&nbsp;A.J. Alegre-Mondragón,&nbsp;J.M. Madrigal-Gómez,&nbsp;C. Silva-Arias","doi":"10.1016/j.rsase.2025.101675","DOIUrl":"10.1016/j.rsase.2025.101675","url":null,"abstract":"<div><div>Studies on soil contamination detection through remote sensing have so far focused on pollutants from agricultural, mining and industrial activities. However, the extended practice of using chemical substances for the disappearance of people and/or evidence of crimes by criminal organizations can cause soils disturbance and contamination that may be detected through remote sensing methods. This article describes an experiment that simulated soil contamination with substances related to criminal activities. The visible-infrared spectral reflectance of contaminated and non-contaminated areas was measured for six months and measurements were analyzed to design spectral indices involving one, two or three wavebands. The analyzes showed that nine of twelve polluted soils could be detected at least once with at least one index, of which those contaminated with diesel and chlorhydric acid required a hyperspectral resolution (less than 24 nm). Furthermore, by limiting the wavebands to those from 15 commercial satellites and one unmanned aerial vehicle (UAV) camera, we showed that only four substances could be detected using one of the 15 indices with different detection rates, and only the WorldView-3 (WV3) satellite contained the required wavebands to detect these four substances. Some of these multispectral indices were further demonstrated in a couple real-world forensic search areas, indicating a great potential for forensic searches.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"39 ","pages":"Article 101675"},"PeriodicalIF":3.8,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144714208","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":"Assessing the impact of subsurface conditions and aging infrastructure on urban land subsidence","authors":"Zhoobin Rahimi ,&nbsp;George Korfiatis ,&nbsp;Valentina Prigiobbe ,&nbsp;Rita Sousa","doi":"10.1016/j.rsase.2025.101665","DOIUrl":"10.1016/j.rsase.2025.101665","url":null,"abstract":"<div><div>Land subsidence is a critical issue in urban coastal areas, driven by both natural geological processes and human activities such as groundwater extraction, infrastructure degradation, and urbanization. This study examines land subsidence patterns in Hoboken, New Jersey, using an integrated modeling framework that combines the Land Subsidence Severity Index (LSSI) and the Risk of Infiltration Index (RI), the latter focusing on sewer network deterioration. A multi-criteria analysis employing the Analytical Hierarchy Process (AHP) was used to assess the relative importance of hydrogeological variables, while a weighted overlay analysis enabled the integration of LSSI and RI layers for predictive subsidence mapping.</div><div>Sentinel-1 SAR data were processed using the Small Baseline Subset (SBAS) technique to derive InSAR-based subsidence rates at spatial resolutions of 20 m, 40 m, and 80 m. Nine LSSI-RI weight combinations were tested and evaluated using precision and recall metrics across four subsidence severity levels. The optimal model, assigning 70 % weight to LSSI and 30 % to RI, achieved 96.00 % precision and 51.49 % recall in the very high severity zone, which significantly outperform lower LSSI-weighted configurations. This result underscores the importance of hydrogeological conditions in severe subsidence prediction and highlights the value of integrating satellite remote sensing with infrastructure and geotechnical data to enhance urban risk assessment. The findings provide a transferable framework to support proactive urban planning, infrastructure maintenance, and subsidence risk mitigation, which is particularly important in vulnerable coastal cities facing aging underground infrastructure and shallow groundwater conditions.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"39 ","pages":"Article 101665"},"PeriodicalIF":4.5,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144721350","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}