Remote Sensing Applications-Society and Environment最新文献

{"title":"Advancing climate change Research: Robust methodology for precise mapping of sea level rise using satellite-derived bathymetry and the google Earth Engine API","authors":"Mohammad Ashphaq ,&nbsp;Pankaj K. Srivastava ,&nbsp;D. Mitra","doi":"10.1016/j.rsase.2025.101557","DOIUrl":"10.1016/j.rsase.2025.101557","url":null,"abstract":"<div><div>Sea level rise (SLR), linked to climate change, poses risks to coastal areas and requires urgent action. Traditional methods to measure SLR, such as tide gauges, satellite altimetry, and GNSS-based techniques, have limitations in coverage, accuracy, and data continuity. This study applies Random Forest regression in Google Earth Engine (GEE) to automate satellite-derived bathymetry (SDB) prediction for accurate SLR mapping and time-series analysis. The SDB has been predicted using Landsat series satellite data and derived products, including Chlorophyll, Total Suspended Material, and Turbidity, for the years 1993, 2003, 2013, and 2023. The results demonstrated high accuracy, strong correlation coefficients between in-situ bathymetry and SDB, and low error measures. The correlation coefficients with in-situ bathymetry were 0.8924 in 1993, 0.9386 in 2003, 0.9638 in 2013, and 0.9444 in 2023. Tidal correction was applied to the SDB maps to calculate SLR changes between 1993 and 2023. The analysis delineated a consistent rise in mean SDB values, suggesting a potential increase in sea level over the past four decades. A robust methodology for SLR time-series analysis has been proposed, with all codes accessible for deployment through Landsat collections and temporal parameters.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"38 ","pages":"Article 101557"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143850538","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":"Ensemble machine learning models for monitoring riparian vegetation dynamics using historical aerial orthophotos","authors":"Afzali Hamid ,&nbsp;Rusnák Miloš","doi":"10.1016/j.rsase.2025.101545","DOIUrl":"10.1016/j.rsase.2025.101545","url":null,"abstract":"<div><div>Historical aerial photographs are well-known as a reliable source of information on historical land cover and land use. However, extracting this information can be challenging due to the limited spectral characteristics in black-and-white images. In this study, we evaluate a textural-based approach using Machine Learning (ML) models to detect the spatial pattern of the braided-wandering multichannel system from historical aerial images with an emphasis on riparian vegetation.</div><div>Five aerial datasets (1949–1992) were used to extract textural information through Gray level Co-occurrence Matrix (GLCM) and geomorphological operations on High-resolution, preprocessed, and normalized orthophotos. We used Random Forest (RF), Light gradient boosting machines (LightGBM), and Extreme Gradient Boosting (XGBoost) ML methods through two classification schemes to classify images into five main classes. GridSearchCV hyperparameter optimization tool were utilized to optimize models and Sequential Feature Selection (SFS) algorithm to reduce the dimensionality of the data cube. The results indicated the efficacy of Morphological operations (Gradient, Eroded, and Dilated) and GLCM features (contrast, entropy) in the final classified map. The RF model demonstrated greater stability and higher median accuracy across datasets. While there was no significant difference between LightGBM and XGBoost in terms of accuracy metrics, XGBoost's performance was notably more variable but significantly faster. In our study, the shadow effects, distortion, and radiometric differences within the orthophotos remain challenging. Despite limitations, the proposed approach addresses key challenges in extracting information from historical orthophotos and can be extended to broader ecological and environmental applications.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"38 ","pages":"Article 101545"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143835132","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":"Satellite-based remote sensing analysis for the exploration of MVT Pb-Zn mineralization using an integrated approach of minimum distance classification, deep autoencoder and fuzzy logic modeling","authors":"Soran Qaderi ,&nbsp;Abbas Maghsoudi ,&nbsp;Amin Beiranvand Pour ,&nbsp;Mahyar Yousefi","doi":"10.1016/j.rsase.2025.101561","DOIUrl":"10.1016/j.rsase.2025.101561","url":null,"abstract":"<div><div>Mississippi Valley-type (MVT) Pb-Zn mineralization is a key economic resource, yet its exploration is challenging due to complex alteration patterns and high costs. This study integrates ASTER satellite imagery with deep learning to enhance prospectivity mapping. We applied image processing techniques, including Principal Component Analysis (PCA), Band Ratios (BR), Band Math (BM), and Spectral Angle Mapper (SAM), to identify alteration zones. The Minimum Distance Classification (MDC) method classified these zones, extracting key evidence layers. These layers—dolomitization (MDC-PCA, SAM) and carbonate-iron oxide (MDC-BR, MDC-BM)—were integrated using Deep Autoencoder (DAE) and Fuzzy Logic Modeling (GFO) to generate prospectivity maps. Prediction-area (P-A) plots showed the DAE model outperformed GFO, achieving a normalized density (N_d) of 4.1 compared to 3.61 for GFO, indicating a more precise delineation of high-potential mineralization zones. Field validation confirmed strong alignment with known Pb-Zn occurrences. This study highlights the effectiveness of remote sensing and deep learning in cost-effective mineral exploration and provides a scalable framework for similar metallogenic provinces.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"38 ","pages":"Article 101561"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143876581","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":"A novel pixel-based deep neural network in posterior probability space for the detection of agriculture changes using remote sensing data","authors":"Gurwinder Singh ,&nbsp;Narayan Vyas ,&nbsp;Neelam Dahiya ,&nbsp;Sartajvir Singh ,&nbsp;Neha Bhati ,&nbsp;Vishakha Sood ,&nbsp;Dileep Kumar Gupta","doi":"10.1016/j.rsase.2025.101591","DOIUrl":"10.1016/j.rsase.2025.101591","url":null,"abstract":"<div><div>Agricultural land classification is a crucial and demanding task, essential for managing resources and tracking changes in farming activities. Remote sensing (RS) is an excellent technology for monitoring agricultural land and detecting seasonal fluctuations globally. Deep learning models offer promising prospects for crop monitoring. While traditional machine learning methods struggle to capture temporal variations in agricultural land efficiently. This study addresses the challenge of accurately classifying land cover and detecting year-to-year changes using a deep learning (DL)-based approach. The novelty of this research is an integration using a pixel-based deep neural network (PDNN) classifier, which will advance the classification abilities for identifying land cover classes. By comparing images taken over time, the PDNN can help identify different land cover efficiently. The thematic images using the PDNN were derived, and change detection was carried out by adopting a posterior probability space (PPS)-based change detection. The application of the proposed model is demonstrated using the Landsat-9 dataset over Moga District, Punjab, India. Compared to the random forest (RF) and support vector machine (SVM), the PDNN model achieved great performance. While PDNN had an accuracy ranging from 90.6 % to 93.6 %, RF and SVM had lower accuracies, with RF ranging between 86.8 % and 92.2 % and SVM between 88 % and 92.4 %. The PDNN model also excelled in detecting changes in land cover, showing an accuracy between 87.4 % and 90 %, while RF achieved 82.9 %–86.2 % and SVM ranged from 79 % to 83.9 %. The proposed model was adept at capturing changes in agricultural land cover, such as year-to-year variations. The PDNN model demonstrated superior proficiency in capturing seasonal and year-to-year variations in agricultural land cover, effectively identifying subtle transitions in crop cycles. This highlights its potential for long-term agricultural monitoring and precision farming applications. This approach would serve as a key to sustainable agriculture, which guides farmers and policymakers to make better choices.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"38 ","pages":"Article 101591"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144069197","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":"Optical and radar remote sensing for burn scar mapping in the grassland biome","authors":"Talya R. Molema ,&nbsp;Solomon G. Tesfamichael ,&nbsp;Emmanuel Fundisi","doi":"10.1016/j.rsase.2025.101548","DOIUrl":"10.1016/j.rsase.2025.101548","url":null,"abstract":"<div><div>Wildfires remain a major ongoing threat to the integrity of the environment and therefore emphasis is placed on employing efficient assessment techniques, such as remote sensing. Grassland fires received lesser attention compared to forest fires, despite their significant contribution to global wildfire occurrences. This study, conducted in South Africa, utilized Sentinel-1 radar and Sentinel-2 optical data to map burn scars in grasslands, in a biome representative of grasslands found elsewhere. Employing the Random Forest (RF) and Support Vector Machine (SVM) algorithms within the Google Earth Engine (GEE) platform to classify the data, the study achieved high producer's and user's accuracies in identifying burn scars using optical data (&gt;90 %). Comparison of variable importance showed the infrared as well as vegetation and fuel moisture indices being the most influential variables to the classification. However, radar data produced lower accuracies (&lt;50 %) owing to significant confusion in distinguishing grass, bare land and water bodies since these features have structural compositions similar to burnt areas. Nonetheless, radar data proved effective in differentiating burn scars from shadows. Combining optical and radar data yielded comparable accuracies to the optical-alone data but improved the discrimination between burnt areas and shadows. This discrimination capability also agrees with the importance of radar data that ranked better than the visible bands of the optical data. The benefit of merging optical and radar data underscores the importance of radar data, which remains unaffected by atmospheric interference like smoke, haze and clouds, enabling continuous monitoring even during fire events.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"38 ","pages":"Article 101548"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143886240","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":"Multi-frequency SAR polarimetry and Ground Penetrating Radar for paleochannel identification in the Thar Desert, India","authors":"Sashikanta Sahoo ,&nbsp;Ajanta Goswami ,&nbsp;Shubham Awasthi ,&nbsp;Mahesh Thakur ,&nbsp;Anup Das ,&nbsp;Brijendra Pateriya","doi":"10.1016/j.rsase.2025.101533","DOIUrl":"10.1016/j.rsase.2025.101533","url":null,"abstract":"<div><div>Paleochannels are essential sources of groundwater in the arid region. To effectively use these resources, they must be precisely located and understood through remote sensing, drilling, and hydrogeological investigations. The information gathered from these studies can be used to assess the potential for sustainable development of groundwater resources in the paleochannels. According to several recent studies, Synthetic Aperture Radar (SAR) data provides better insight into subsurface features, such as buried channels, than optical sensor data due to the ability of microwave signals to penetrate dry sand. In this paper, we investigated the ability of multi-frequency L and C band SAR satellite datasets to delineate the paleochannels in the Thar desert region of Western Rajasthan, India. This study used images from ALOS-2 and Sentinel-1 for subsurface understanding in the desert area. Adaptive filter image enhancement was used on both L-band and C-band SAR datasets. It was observed that cross-polarization channels in the L-band ALOS-PALSAR-2 were more sensitive to the buried channel enhancement compared to the C-band. This was because of the more penetration capability of the L band datasets in the Paleochannels. The study found higher radar backscatter coefficient (σo) values along the probably buried channel compared to surrounding regions, indicating higher soil moisture content in the channel. The results also revealed that post-monsoon data is more effective for delineating the Paleochannels because they have greater moisture levels along their old courses. Additionally, results were validated through field observations and comparison with prior literature. Further, validation was done through Ground Penetrating Radar (GPR) imaging and sediment analysis. These results provide a fresh understanding for identifying buried channels in the ancient environment of the Thar desert region. The findings showed that longer wavelength SAR data, like L-band SAR, can help find shallow buried channels in dry areas. However, both L and C band SAR data can be more effective for getting precise information about near-surface paleochannels in the desert area, which are very crucial for mapping groundwater potential and managing the water resources in this region.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"38 ","pages":"Article 101533"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143906027","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":"The impact of industrial activities on the surrounding environment based on hybrid filter and machine learning","authors":"Agus Suprijanto ,&nbsp;Yumin Tan ,&nbsp;Rodolfo Domingo Moreno Santillan ,&nbsp;Syed Mohammad Masum","doi":"10.1016/j.rsase.2025.101599","DOIUrl":"10.1016/j.rsase.2025.101599","url":null,"abstract":"<div><div>Industrial development has emerged as a significant driver of environmental degradation and urban heat island (UHI) formation. However, studies explicitly addressing the long-term spatial impact of heavy industries—particularly in tropical, cloud-prone regions—remain limited due to persistent data gaps and noise in satellite observations. This study addresses that research gap by analyzing the environmental effects of industrial activities in Cilegon City, Indonesia—one of the nation's largest industrial zones—using monthly Landsat-8 time series data from 2014 to 2022. A hybrid filtering approach was applied to reconstruct high-quality data by removing cloud and cloud shadow interference. The reconstructed NDVI and LST were then used as multivariate input features to model Land Surface Temperature (LST) using the XGBoost algorithm, with 30-m spatial resolution. The predicted LST was subsequently analyzed alongside NDVI to examine spatio-temporal trends and quantify industrial heat island (IHI) effects. Results show that industrial heat extends up to 1.5 km from core industrial zones, with IHI intensity reaching 5.58 °C in 2022. Vegetation health showed severe decline, with NDVI values dropping by 81.36 % in industrial cores and 29.25 % in adjacent areas. LST exhibited a positive trend of 0.23 °C/month in highly industrialized subdistricts and maintained a strong negative correlation with NDVI (r = −0.95). These findings highlight the amplified environmental impact of industrial activities in cloud-prone tropical cities and emphasize the urgent need for sustainable land management and the implementation of green infrastructure to mitigate local warming and protect surrounding ecosystems.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"38 ","pages":"Article 101599"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144130960","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":"Leveraging temporal, textural, and socio-environmental features for accurate detection of abandoned farmland","authors":"Seungjoo Baek ,&nbsp;Heeyeun Yoon","doi":"10.1016/j.rsase.2025.101598","DOIUrl":"10.1016/j.rsase.2025.101598","url":null,"abstract":"<div><div>Detecting abandoned farmland is crucial for effective agricultural land management. By leveraging satellite imagery, and using temporal and textural features, machine learning studies have identified such lands. However, demographic and locational factors, though recognized as key drivers of farmland abandonment, have not been used as classification attributes. Our research employs Support Vector Machines and Random Forests, integrating these socio-environmental factors, achieves accuracies of 89.2 % and 88.5 % for overall and user classifications, respectively. We discovered that factors such as parcel size, slope, and road proximity are more critical than other conventional features in identifying abandoned farmland, recommending further investigation into these socio-environmental factors to uncover more impactful classification attributes. Additionally, our findings contrast with other studies by demonstrating that both higher and lower harmonic components of NDVI and NDWI enhance classification accuracy. This research could greatly assist in agricultural land surveys and in developing policies for sustainable farmland preservation.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"38 ","pages":"Article 101598"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144178502","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":"Multitemporal satellite images for monitoring the volcanic activities and geothermal potential of Ternate Island's Gamalama Volcano, Indonesia's densest active volcanic island","authors":"Bondan Galih Dewanto ,&nbsp;Danang Sri Hadmoko ,&nbsp;Nurul Fitrah Ramadhani ,&nbsp;Admiral Musa Julius","doi":"10.1016/j.rsase.2025.101555","DOIUrl":"10.1016/j.rsase.2025.101555","url":null,"abstract":"<div><div>Throughout history, Ternate, a diminutive volcanic island located in the North Maluku Province of Indonesia, has functioned as a significant center of the nation's social and economic activity. The Gamalama Volcano constituted a significant element of Ternate Island's topography, and its eruption resulted in substantial disruption. The aims of this current contribution are: to monitor the historical activities of the Gamalama volcano and understanding the geothermal potential to support the energy needs in Ternate Island. The multi-temporal analysis was conducted to monitor the activity of Gamalama Volcano, utilizing satellite imagery spanning a period of 50 years. The imagery sources included Landsat 1, Landsat 4, Landsat 5, Landsat 7, ASTER, and Landsat 8. The present study employed the single-channel algorithm to derive the land surface temperature (LST). The band combination and ratio were utilized to infer the geological context and geothermal capacity of the Gamalama Volcano. The analysis of normalized differential vegetation index (NDVI) utilized in the calculation of LST has revealed that vegetation growth has occurred subsequent to certain volcanic eruptions. As per the LST data, the average temperature of the surface within the crater escalated to 38.472 °C during the eruption of 1997, thereby establishing it as the maximum temperature recorded in the past half-century. The volcanic activity of Gamalama Volcano was elucidated through the utilization of the LST technique, which has the capacity to cover various temporal intervals. The congruence between the LST data derived from Landsat and ASTER data substantiates the dependability of the LST methodology. The geothermal potential of approximately 16 °C has been observed in the crater and sand region of the volcano, along with the identification of supplementary hot spots in the north-eastern and western regions of the volcano's primary structure. The utilization of Landsat 8 band combinations and band ratios has substantiated the presence of an area exhibiting elevated geothermal potential within the andesite and basaltic andesite geological formations. The practicality of utilizing multi-source optical satellites for monitoring volcanic activity has been exemplified by the multiple eruptions that have occurred at Gamalama Volcano. Furthermore, this technology could potentially be employed for conducting exploratory research into the geothermal potential of the region.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"38 ","pages":"Article 101555"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143844315","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":"Development of topo-bathymetric continuum profiles for coastal barriers with global open-access data","authors":"Valeria Fanti ,&nbsp;Carlos Loureiro ,&nbsp;Óscar Ferreira","doi":"10.1016/j.rsase.2025.101528","DOIUrl":"10.1016/j.rsase.2025.101528","url":null,"abstract":"<div><div>Coastal barriers are dynamic and vulnerable coastal environments exposed to storms and rising sea levels, requiring a thorough understanding of their physical and geomorphological characteristics. Despite this, high-resolution topo-bathymetric data are not openly available for most of the world’s coastal areas, preventing accurate estimation of the exposure to storms and associated risks. Global models of topography and bathymetry, derived from remote sensing techniques, are available worldwide as an open-source solution to characterise coastal morphology. However, their coarse resolution, limited vertical and horizontal accuracy, alongside inconsistencies in the transition from land to the shallow nearshore zone, make their use in coastal areas challenging, requiring careful evaluation. This study investigates the potential and limitations of four recent open-access satellite-derived topographic models (Copernicus GLO-30 DEM, AW3D30, TanDEM-X, Euro-Maps 3D) and three bathymetric models (GEBCO_2023, SRTM15+, ETOPO 2022) in five coastal barriers. It proposes a new approach to integrate global models to provide a consistent representation of the topo-bathymetric continuum profile in coastal areas characterised by a barrier morphology. Coastal barrier profiles, representative of natural sectors and characterized by morphological homogeneity, were derived by merging global topographic and bathymetric digital elevation models and implementing an equilibrium profile in the transition zone. The profiles obtained from the global models were compared with higher resolution local or regional topo-bathymetry. The global topographies tend to underestimate the dune top, with TanDEM-X giving the best results in terms of dune crest height and beach slope. The barrier continuum profiles that merged TanDEM-x and ETOPO 2022 global models were found to have the lowest error, with a vertical RMSE of 0.76 m. Based on integration of these remotely sensed models, it is possible to determine average representative coastal barrier profiles suitable for use in global to regional coastal studies or in data-poor areas, potentially serving as a cost-effective tool for preliminary coastal hazard assessments and early warning systems at wide spatial scales.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"38 ","pages":"Article 101528"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143839627","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}