Remote Sensing Applications-Society and Environment最新文献

{"title":"Enhancing grassland cut detection using Sentinel-2 time series through integration of Sentinel-1 SAR and weather data","authors":"Aleksandar Dujakovic ,&nbsp;Cody Watzig ,&nbsp;Andreas Schaumberger ,&nbsp;Andreas Klingler ,&nbsp;Clement Atzberger ,&nbsp;Francesco Vuolo","doi":"10.1016/j.rsase.2025.101453","DOIUrl":"10.1016/j.rsase.2025.101453","url":null,"abstract":"<div><div>The detection of grassland cuts is relevant for modelling grassland yield and quality because information on cut dates and cut intensity aids in the modelling of the nutrient biomass ratio of fodder. This research improves an existing grassland cut detection methodology developed for Austria based on Sentinel-2 (S2) optical time series. To further improve the detection accuracy, the new method incorporates Sentinel-1 (S1) Synthetic Aperture Radar (SAR) and daily weather data utilizing a machine learning-based model (Catboost). Cuts are first identified through a threshold-based comparison between a fitted idealized grassland growth curve and the observed NDVI values. The Catboost model subsequently addresses limitations in S2 data caused by cloud cover and other sub-optimum observation conditions. The Catboost model (1) identifies missing cuts in periods with no S2 data, and (2) eliminates false positive cuts. Weather data is utilized to identify the start of the cutting season and to define the (minimum required) time span between two consecutive cuts. Results demonstrate an improvement in cut date f-score (from 0.77 to 0.81), a reduced false detection rate (from 0.21 to 0.16), and a slight decrease in mean absolute error between true and estimated cut dates (from 4.6 to 4.1). The improvement in the accuracy was more evident for plots with high mowing frequency, while some remaining false detections were evident for extensively managed grasslands. The incorporation of S1 SAR and weather data enables the cut detection for the entire calendar year and eliminates the need for fixed growing season start/end dates. However, S1 SAR data alone did not provide reliable detection accuracy, showing its limitations in depicting vegetation dynamics for grassland. Overall, the improvements in accuracy and flexibility demonstrate the efficacy of the enhanced methodology, emphasizing the potential of combining S1 and S2 with weather data in large scale and cost-efficient grassland monitoring.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101453"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143091978","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":"Evaluation of soybean plants affected by Aphelenchoides besseyi using remote sensing and machine learning techniques","authors":"João Lucas Della-Silva ,&nbsp;Valeria de Oliveira Faleiro ,&nbsp;Tatiane Deoti Pelissari ,&nbsp;Amanda Ferreira ,&nbsp;Neurienny Ferreira Dias ,&nbsp;Daniel Henrique dos Santos ,&nbsp;Thaís Lourençoni ,&nbsp;Joelma Nayara ,&nbsp;Wendel Bueno Morinigo ,&nbsp;Larissa Pereira Ribeiro Teodoro ,&nbsp;Paulo Eduardo Teodoro ,&nbsp;Dthenifer Cordeiro Santana ,&nbsp;Izabela Cristina de Oliveira ,&nbsp;Ester Cristina Schwingel ,&nbsp;Renan de Almeida Silva ,&nbsp;Carlos Antonio da Silva Junior","doi":"10.1016/j.rsase.2025.101461","DOIUrl":"10.1016/j.rsase.2025.101461","url":null,"abstract":"<div><div>Soybeans (<em>Glycine max</em> (L.) Merrill) are a major player in food security, and pest loss control is a major focus of research and technological development by the agricultural sector. Among these pests, <em>Aphelenchoides besseyi</em> contaminates the aerial part of the plant, which can be detected in the leaf's spectral response, based on in situ hyperspectral sensors with the adoption of remote sensing techniques, such as spectral models. Assessing such data using machine learning allows the identification of optimal computational conditions to evaluate different levels of infection by the green stem nematode in soybeans. Thus, this research aimed to (i) discriminate the spectral bands most sensitive to nematode infection, (ii) identify the spectral model with the greatest accuracy for distinguishing different levels of nematode infection according to reflectance, and (iii) verify the resilience to the impact of <em>A. besseyi</em> on soybeans. From this approach, the near and short-wave infrared spectral portions contributed most to discriminating different amounts of nematodes in the plant, in a scenario in which the logistic regression algorithm had greater performance. Finally, this evaluation suggests that the best discrimination conditions occur with data obtained in the final half of the soybean cultivation cycle.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101461"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143091979","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":"Exploring the link between spectra, inherent optical properties in the water column, and sea surface temperature and salinity","authors":"Solomon White ,&nbsp;Encarni Medina Lopez ,&nbsp;Tiago Silva ,&nbsp;Evangelos Spyrakos ,&nbsp;Adrien Martin ,&nbsp;Laurent Amoudry","doi":"10.1016/j.rsase.2025.101454","DOIUrl":"10.1016/j.rsase.2025.101454","url":null,"abstract":"<div><div>Sea surface salinity and temperature are important measures of ocean health. They provide information about ocean warming, atmospheric interactions, and acidification, with further effects on the global thermohaline circulation and as a consequence the global water cycle. In coastal waters they provide information about sub mesoscale circulations and tidal currents, riverine discharge and upwelling effects. This paper explores the methodology to extract sea surface salinity (SSS) and temperature (SST) from ground based hyperspectral ocean radiance. Water leaving radiance is linked to the inherent optical properties of the water column, effected by the constituent parts. Hyperspectral data at ground level is then used as input to train a linear regression model against temporally and spatially matched water data of SSS and SST. Furthermore, a neural network model to be able to estimate the SST and SSS with the hyperspectral data averaged to multispectral bands to emulate the satellite use case. The neural network model is able to learn the relationship between the multispectral radiance to both SSS and SST values, and can predict these with a root mean square error (RMSE) of 0.2PSU and 0.1 degree respectively. This demonstrates the feasibility of similar algorithms applied to multispectral ocean colour satellites with enhanced coverage and spatial resolution.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101454"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143091898","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":"UAV visual imagery-based evaluation of blue carbon as seagrass beds on a tidal flat scale","authors":"Takuya Akinaga ,&nbsp;Mitsuyo Saito ,&nbsp;Shin-ichi Onodera ,&nbsp;Fujio Hyodo","doi":"10.1016/j.rsase.2024.101430","DOIUrl":"10.1016/j.rsase.2024.101430","url":null,"abstract":"<div><div>Seagrass and seaweed beds (SSBs) have a high carbon sequestration function (blue carbon) in shallow coastal waters. Unmanned aerial vehicles (UAVs) are a highly useful tool for monitoring SSBs because of their ease of use and ability to acquire high-resolution photographs. In many previous studies using UAV, surveys of SSBs have been based on area alone, but it is insufficient to properly assess the habitat and carbon fixation of SSBs.</div><div>In this study, we estimated above-ground biomass and carbon of eelgrass in shallow coastal waters by combining aerial photography of visible images, quadrat surveys, and sampling of eelgrass. The analysis area was a tidal flat on an island located in the Seto Inland Sea in western Japan. Aerial photography was conducted by UAV to acquire high-resolution RGB visual images of the area. The quadrat survey and sampling were used to develop regression formulas for estimating biomass and carbon of eelgrass. The former was conducted to investigate the relationship between the coverage and Leaf Area Index (LAI), and the latter was conducted to investigate the relationship between leaf area and biomass, carbon of eelgrass. Those showed clear relationship between coverage and LAI (R² = 0.97) and between leaf area and biomass, carbon (biomass: R² = 0.98, carbon: R² = 0.98).</div><div>To identify eelgrass beds, the maximum likelihood classification was adapted. After calculating the coverage from the distribution, biomass and carbon were estimated by adapting regression formulas developed by quadrat survey and sampling.</div><div>The proposed method can be easily adapted from visible images taken by UAVs and robust to the effects of water, which provides high adaptability regarding the estimation for biomass and carbon of eelgrass on the tidal flat.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101430"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143092326","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":"An improved radiative transfer inversion of physical temperatures in Antarctic ice sheet using SMOS observations","authors":"Yi Zhou ,&nbsp;Yongjiu Feng ,&nbsp;Yuze Cao ,&nbsp;Shurui Chen ,&nbsp;Zhenkun Lei ,&nbsp;Mengrong Xi ,&nbsp;Jingbo Sun ,&nbsp;Yuhao Wang ,&nbsp;Tong Hao ,&nbsp;Xiaohua Tong","doi":"10.1016/j.rsase.2025.101487","DOIUrl":"10.1016/j.rsase.2025.101487","url":null,"abstract":"<div><div>The internal temperature plays a pivotal role in dictating the dynamics and thermal processes of the Antarctic ice sheet. Low-frequency microwave remote sensing methods show promise for effectively gauging the ice sheet's deep glaciological properties. Our study leverages brightness temperature data at L-band (1.4 GHz) from the Soil Moisture and Ocean Salinity (SMOS) satellite, integrating it with glaciological thermodynamic and radiative transfer models to infer the ice sheet's internal temperature. We fine-tune the geothermal heat flux and snow accumulation rate parameters using the Generalized Simulated Annealing (GSA) algorithm. Our findings reveal that this methodology, compared to estimations grounded on prior knowledge, diminishes the Root Mean Square Error (RMSE) for brightness temperature inversion by roughly 3 K. Further, the RMSE for the physically inverted temperature profile, when benchmarked against ice core drilling data from Dome C and Dome Fuji, stands at 1.55 K and 1.36 K, respectively. This approach narrows the uncertainty in assessing the Antarctic ice sheet's temperature profile, particularly within the upper 2000 m. Accurately determined physical temperatures within the ice sheet enhance our comprehension of its intricate thermal structure. We anticipate that these insights should provide valuable scientific input for addressing concerns related to the ice sheet's mass balance and evolutionary processes.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101487"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143428054","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":"First thermographic survey within the Euganean thermal district (Italy) with an unmanned aerial vehicle","authors":"Luca Peruzzo ,&nbsp;Andrea Berton ,&nbsp;Michele Crivellaro ,&nbsp;Cristina Da Lio ,&nbsp;Sandra Donnici ,&nbsp;Paolo Fabbri ,&nbsp;Gian Marco Scarpa ,&nbsp;Fabio Tateo ,&nbsp;Luca Zaggia ,&nbsp;Andrea Fasson","doi":"10.1016/j.rsase.2024.101431","DOIUrl":"10.1016/j.rsase.2024.101431","url":null,"abstract":"<div><div>The territory of the Euganean Hills is known worldwide for the occurrence of thermal springs known since ancient times. Currently, local authorities greatly enhance the natural capital of the Hills, including the thermal waters. Despite all this, remote thermal sensing has never been performed before and the present work aims to fill this gap. For this purpose, an UAV survey was conducted in a selected area, accompanied by ground measurements of water temperature and conductivity. The thermographic survey identified known and unknown thermal springs, as well as water leaks from greenhouses and abandoned wells. Since the thermal water is both hot and salty, the UAV system is able to detect the points where salt water is introduced into the fresh water network used for irrigation purposes. Ground controls have made it possible to trace the mixing process between these two types of water, salty (of thermal origin) and fresh (suitable for agriculture) over wider distances and with greater precision. Climate changes and the variable exploitation of water resources cause the continuous change in the balance between salt thermal and fresh water. Therefore, a strong salinization of the water in the surface network can occur as has been documented within the area under examination, also causing severe damage to agriculture. The thermographic survey, accompanied by in situ measurements, proved to be a very effective system for the management of a highly vulnerable territory as observed in the Euganean Hills.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101431"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143092430","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":"What time is the tide? The importance of tides for ocean colour applications to estuaries","authors":"Giulia Sent ,&nbsp;Carlos Antunes ,&nbsp;Evangelos Spyrakos ,&nbsp;Thomas Jackson ,&nbsp;Elizabeth C. Atwood ,&nbsp;Ana C. Brito","doi":"10.1016/j.rsase.2024.101425","DOIUrl":"10.1016/j.rsase.2024.101425","url":null,"abstract":"<div><div>Tides can play a major role in transitional water dynamics, being the primary driver of fluctuations in water parameters. In the last decade, remote sensing methods have become a popular tool for cost-effective systematic observations, at relatively high spatial and temporal scales. However, the presence of tides introduces complexities, given that Sun-synchronous satellites will observe a different tidal condition at each overpass, effectively aliasing the daily signal. This can create non-obvious biases when using remote sensing data for monitoring tidally-dominated systems, potentially leading to misinterpretation of patterns and incorrect estimates of periodicities. In this work, we used a six-year Sentinel-2-derived turbidity dataset to evaluate the impact of tidal aliasing on the applicability of a Sun-synchronous satellite to a tidally-dominated system (Tagus estuary, Portugal). Each satellite observation was classified according to tidal phase. Results indicate that tidal processes dominated over seasonal variability, with significant differences observed between turbidity levels of different tidal phases (p &lt; 0.0001). Climatology analyses also revealed significant changes between all-data and per-tidal-phase data (p &lt; 0.001), highlighting the importance of classifying satellite data by tidal condition. Additionally, tidal condition labelling at each Sentinel-2 overpass revealed that not all tidal conditions are observed by a Sun-synchronous satellite, as Low tide and Floods are always observed during Spring tides and High tide and Ebbs observed under Neap tides. Spring Low tides are overrepresented compared to all other tidal conditions. This result is particularly relevant for water quality monitoring based on remote sensing data in tidally-dominated systems.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101425"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143092433","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":"SimPoolFormer: A two-stream vision transformer for hyperspectral image classification","authors":"Swalpa Kumar Roy ,&nbsp;Ali Jamali ,&nbsp;Jocelyn Chanussot ,&nbsp;Pedram Ghamisi ,&nbsp;Ebrahim Ghaderpour ,&nbsp;Himan Shahabi","doi":"10.1016/j.rsase.2025.101478","DOIUrl":"10.1016/j.rsase.2025.101478","url":null,"abstract":"<div><div>The ability of vision transformers (ViTs) to accurately model global dependencies has completely changed the field of vision research. However, because of their drawbacks, such as their high computational costs, dependence on significant labeled datasets, and restricted capacity to capture essential local features, efforts are being made to create more effective alternatives. On the other hand, vision multilayer perceptron (MLP) architectures have shown excellent capability in image classification tasks, performing equivalent to or even better than the widely used state-of-the-art ViTs and convolutional neural networks (CNNs). Vision MLPs have linear computational complexity, require less training data, and can attain long-range data dependencies through advanced mechanisms similar to transformers at much lower computational costs. Thus, in this paper, a novel deep learning architecture is developed, namely, SimPoolFormer, to address current shortcomings imposed by vision transformers. SimPoolFormer is a two-stream attention-in-attention vision transformer architecture based on two computationally efficient networks. The developed architecture replaces the computationally intensive multi-headed self-attention in ViT with SimPool for efficiency, while ResMLP is adopted in a second stream to enhance hyperspectral image (HSI) classification, leveraging its linear attention-based design. Results illustrate that SimPoolFormer is significantly superior to several other deep learning models, including 1D-CNN, 2D-CNN, RNN, VGG-16, EfficientNet, ResNet-50, and ViT on three complex HSI datasets: QUH-Tangdaowan, QUH-Qingyun, and QUH-Pingan. For example, in terms of average accuracy, SimPoolFormer improved the HSI classification accuracy over 2D-CNN, VGG-16, EfficientNet, ViT, ResNet-50, RNN, and 1D-CNN by 0.98%, 3.81%, 4.16%, 7.94%, 9.45%, 12.25%, and 13.95%, respectively, on the QUH-Qingyun dataset.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101478"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143128314","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":"Analysis of asbestos-cement roof classification in urban areas: Supervised and unsupervised methods with multispectral and hyperspectral remote sensing","authors":"Manuel Saba ,&nbsp;Carlos Castrillón-Ortíz ,&nbsp;David Valdelamar-Martínez ,&nbsp;Oscar E. Coronado-Hernández ,&nbsp;Ciro Bustillo-LeCompte","doi":"10.1016/j.rsase.2025.101464","DOIUrl":"10.1016/j.rsase.2025.101464","url":null,"abstract":"<div><div>Asbestos-cement roofs, commonly found in urban areas, pose environmental and health risks as they deteriorate, releasing asbestos fibres into the atmosphere. Accurate identification and classification of these roofs are essential for assessing potential hazards and implementing appropriate remediation measures. This study presents a comprehensive analysis of supervised and unsupervised classification methods for the identification of asbestos-cement roofs in an urban area using both multispectral and hyperspectral remote sensing data. Six well-established supervised classification methods and two unsupervised classification methods were employed to analyse multispectral (WorldView 3 satellite) and hyperspectral data (overflight), offering ground pixel resolutions of 3.7 m and 1.2 m for both images. ENVI® was utilized for classification purposes. The supervised methods included in the study were Parallelepiped (PP), Minimum Distance (MiD), Mahalanobis Distance (MhD), Spectral Angle Mapper (SAM), Support Vector Machine (SVM) and Spectral Information Divergence (SID). In contrast, unsupervised methods were K-Means and ISO-Data. The classification performance of each method was assessed based on several metrics. The novelty of this study lies in the first-ever comparison of six supervised and two unsupervised methods applied to hyperspectral imagery captured via aerial survey and satellite imagery over the same urban area. Results indicate that hyperspectral data outperformed multispectral data in terms of asbestos-cement roof classification, demonstrating the potential of hyperspectral imagery for more precise identification. Additionally, the supervised classifiers consistently outperformed the unsupervised methods, highlighting the importance of a priori knowledge for accurate classification. In contrast, the cost-benefit analysis reveals that multispectral imagery is significantly more cost-efficient, being up to 6.5 times less expensive and requiring approximately 32 times fewer computational resources than hyperspectral imagery. This study provides important insights for urban planning, environmental assessment, and public health management by enabling accurate and efficient identification of asbestos-cement roofs in urban areas. The findings highlight the critical role of selecting appropriate remote sensing data and classification techniques for such applications. The methodology and results offer valuable guidance to local authorities, researchers, and policymakers in addressing asbestos-related risks, particularly in developing countries confronting these challenges.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101464"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143349348","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":"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}