Remote Sensing Applications-Society and Environment最新文献

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Integrating radar and multi-spectral data to detect cocoa crops: a deep learning approach 整合雷达和多光谱数据来检测可可作物:一种深度学习方法
IF 3.8
Remote Sensing Applications-Society and Environment Pub Date : 2025-07-01 DOI: 10.1016/j.rsase.2025.101652
Adele Therias , Azarakhsh Rafiee , Stef Lhermitte , Philip van der Lugt , Roderik Lindenbergh
{"title":"Integrating radar and multi-spectral data to detect cocoa crops: a deep learning approach","authors":"Adele Therias ,&nbsp;Azarakhsh Rafiee ,&nbsp;Stef Lhermitte ,&nbsp;Philip van der Lugt ,&nbsp;Roderik Lindenbergh","doi":"10.1016/j.rsase.2025.101652","DOIUrl":"10.1016/j.rsase.2025.101652","url":null,"abstract":"<div><div>The production of cocoa beans contributes to 7.5 % of European Union (EU) driven deforestation. As a result, the recent European Union Deforestation-free Regulation (EUDR) mandates producers to track cocoa farm extents comprehensively. While Remote Sensing has enormous capacity in dynamic crop monitoring, cocoa crop detection shows challenges due to cocoa complex canopy structure, spectral similarity to forest, variable farming methods, and location in frequently cloudy regions. Previous research on cocoa crop detection has mainly focused on pixel-based classification, disregarding spatial context. In this research we have performed a semantic segmentation approach to incorporate spatial configuration and enhance cocoa crop detection. We have applied Convolutional Neural Network (CNN) for the to semantic segmentation of cocoa parcels, considering both spectral and spatial characteristics. Additionally, we have evaluated the impact of combining Synthetic Aperture RADAR (SAR) and MSI (Multi-Spectral Imagery) data in the training of a CNN to demonstrate the importance of texture, moisture, and canopy characteristics in identifying cocoa canopies. The impact of MSI dataset stack with different SAR polarizations, seasons and temporality has been evaluated. The methodology is tested on Sentinel 1 and 2 data over an area of 100 × 100 km in Ghana for which an extensive ground truth data set of almost 90,000 polygons was available for training and validation. The results show that the addition of single-day and temporal SAR to a single-day MSI image can improve the predictions, reaching an F1 score of 86.62 %. This research demonstrates the influence of SAR measurements, seasons, polarization, and ground truth classes on the semantic segmentation of cocoa.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"39 ","pages":"Article 101652"},"PeriodicalIF":3.8,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144534763","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}
引用次数: 0
Integration of recurrent neural network models and flood inundation extent libraries for flood forecasting 循环神经网络模型与洪水淹没范围库的集成用于洪水预报
IF 3.8
Remote Sensing Applications-Society and Environment Pub Date : 2025-06-30 DOI: 10.1016/j.rsase.2025.101649
Vinod Kr. Sharma , Abhinav Kr. Shukla , V.M. Chowdary , Sameer Saran , S. Kr. Srivastav
{"title":"Integration of recurrent neural network models and flood inundation extent libraries for flood forecasting","authors":"Vinod Kr. Sharma ,&nbsp;Abhinav Kr. Shukla ,&nbsp;V.M. Chowdary ,&nbsp;Sameer Saran ,&nbsp;S. Kr. Srivastav","doi":"10.1016/j.rsase.2025.101649","DOIUrl":"10.1016/j.rsase.2025.101649","url":null,"abstract":"<div><div>High rainfall events have increased the frequency of floods worldwide, resulting in significant loss of life and property. Developing countries like India face severe flood situations across various states during the monsoon season. Timely and accurate flood forecasting can help disaster management authorities save lives through timely evacuation. The utilisation of deep learning models can aid in accurate flood water level prediction. Recurrent Neural Network like Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) models have potential to analyze sequential data. There is a need to compare available deep learning models, including GRU and LSTM models, to identify the most efficient model for river water level forecasting. This study applies GRU and LSTM models to satellite-derived rainfall, soil moisture, and temperature data, as well as ground-based river water level measurements in the upper river basin, to forecast river water levels in the lower region of the Bagmati river basin. The LSTM models, particularly with the Swish activation function, outperform GRU models in terms of accuracy (89 %), Mean Square Error (MSE, 0.0163), Mean Absolute Error (MAE, 0.0864), and R-squared (0.9630), demonstrating superior predictive capabilities. This work is further enhanced by integrating historical flood extent libraries, derived from remote sensing satellite data and water levels at gauge stations, to simulate probable flood inundation at specific water levels. Comparative analysis of different deep learning models and the integration of flood extent libraries significantly improves the reliability and accuracy of flood forecasting.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"39 ","pages":"Article 101649"},"PeriodicalIF":3.8,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144524120","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}
引用次数: 0
Multi-model convolutional neural network architectures for coastal forest extent and aboveground biomass estimation 沿海森林面积和地上生物量估算的多模型卷积神经网络结构
IF 3.8
Remote Sensing Applications-Society and Environment Pub Date : 2025-06-27 DOI: 10.1016/j.rsase.2025.101647
Angelo R. Agduma , Richard Dein D. Altarez
{"title":"Multi-model convolutional neural network architectures for coastal forest extent and aboveground biomass estimation","authors":"Angelo R. Agduma ,&nbsp;Richard Dein D. Altarez","doi":"10.1016/j.rsase.2025.101647","DOIUrl":"10.1016/j.rsase.2025.101647","url":null,"abstract":"<div><div>Mapping coastal forests through large-scale remote sensing remains challenging, despite extensive local, national, and global efforts. In particular, Sarangani Bay Protected Seascape (SBPS) in the Philippines has been largely overlooked in both national and global coastal forest mapping initiatives. To address this gap, we evaluated the performance of three convolutional neural network (CNN) models, U-Net, DeepLabV3, and PSPNet, in identifying coastal forests within SBPS. These forested areas detected were subsequently analyzed for leaf area index (LAI), which was then used to estimate aboveground biomass (AGB). Among the models tested, U-Net demonstrated the highest accuracy, achieving an overall accuracy of 92.66 %. In contrast, DeepLabV3, while the fastest to train, yielded lower accuracy. AGB estimates revealed that the municipalities of <em>Glan</em> and <em>Maasim</em> had the highest AGB, with 2582.43 Mg ha<sup>−1</sup> and 1260.57 Mg ha<sup>−1</sup>, respectively, while <em>Alabel</em> recorded the lowest at 27.27 Mg ha<sup>−1</sup>. Although distinguishing true mangroves from non-true mangrove classes in coastal forests remains a limitation, the integration of remote sensing and deep learning offers strong potential for enhancing the accuracy and efficiency for land use and land cover classification, as well as AGB estimation.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"39 ","pages":"Article 101647"},"PeriodicalIF":3.8,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144524291","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}
引用次数: 0
Combining multisource remote sensing images using machine learning methods (RF and SVM) for improved cotton field mapping 结合多源遥感图像,利用机器学习方法(RF和SVM)改进棉田制图
IF 3.8
Remote Sensing Applications-Society and Environment Pub Date : 2025-06-26 DOI: 10.1016/j.rsase.2025.101645
Arash ZandKarimi , Ali Shamsoddini , Omid Ebrahimi
{"title":"Combining multisource remote sensing images using machine learning methods (RF and SVM) for improved cotton field mapping","authors":"Arash ZandKarimi ,&nbsp;Ali Shamsoddini ,&nbsp;Omid Ebrahimi","doi":"10.1016/j.rsase.2025.101645","DOIUrl":"10.1016/j.rsase.2025.101645","url":null,"abstract":"<div><div>Large-scale crop mapping serves as a crucial data source for both cropland management and agricultural monitoring. This paper introduces an Improved Cotton White Index (ICWI) specifically developed to enhance the accuracy of cotton identification at the county level. To assess its efficacy, ICWI has been applied in five counties—Pars Abad, Arzuiyeh, Jafarabad, Behshahr in Iran and Moree Plains in Australia. These regions exhibit diverse climates and varying environmental conditions. Utilizing Sentinel 1 and Sentinel 2 time series data, Random Forest (RF) and Support Vector Machine (SVM) models, both with and without incorporating ICWI, were applied for identifying cotton farm in all five study areas. Additionally, the performance of the ICWI-based models was compared with that of models using the White Boll Index (WBI) to evaluate accuracy and robustness across different regions. The ICWI not only improves the accuracy of cotton identification but also contributes to comprehending the crop's phenology. Spectral analysis of the index's output enables the differentiation of various vegetative stages, from initial growth to full flowering. The analysis of results reveals that integrating the ICWI index into SVM and RF models markedly improves cotton identification accuracy across all regions. The ICWI index demonstrates a noteworthy 4 % overall accuracy boost, and an average increase of 9 % in Kappa. Importantly, in all study areas, our method achieved higher accuracy compared to the White Boll Index (WBI). The study's findings indicated that the proposed index has the potential to enhance the accuracy of cotton mapping using satellite time series images.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"39 ","pages":"Article 101645"},"PeriodicalIF":3.8,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144518172","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}
引用次数: 0
Machine learning models for subtropical forest aboveground biomass mapping using combined SAR and optical satellite imagery 基于SAR和光学卫星影像的亚热带森林地上生物量制图的机器学习模型
IF 3.8
Remote Sensing Applications-Society and Environment Pub Date : 2025-06-23 DOI: 10.1016/j.rsase.2025.101640
Abraham Aidoo Borsah , Man Sing Wong , Majid Nazeer , Guoqiang Shi
{"title":"Machine learning models for subtropical forest aboveground biomass mapping using combined SAR and optical satellite imagery","authors":"Abraham Aidoo Borsah ,&nbsp;Man Sing Wong ,&nbsp;Majid Nazeer ,&nbsp;Guoqiang Shi","doi":"10.1016/j.rsase.2025.101640","DOIUrl":"10.1016/j.rsase.2025.101640","url":null,"abstract":"<div><div>Forest biomass assessment is a critical element influencing the decisions of stakeholders involved in forest management. In tropical and subtropical biodiversity hotspots, accurately measuring aboveground biomass (AGB) is crucial for ecosystem sustainability. However, estimating AGB in these forests is challenging due to the complex vegetation species, necessitating the integration of data from various sources. Therefore, this study aims to investigate the feasibility of integrating ground-based measurements with SAR and optical remote sensing data for estimating AGB in the subtropical forest of Hong Kong and compare various modeling approaches - Stepwise linear regression (SLR), K-nearest neighbors' regression (KNN), and Gradient boosted regression trees (GBRT) - in terms of their effectiveness for AGB mapping. Extensive field data were collected and then converted into biomass values per plot using a locally developed allometric model, designed to facilitate aboveground biomass (AGB) mapping. From the results, we observed that the combination of Sentinel-1 and Sentinel-2 datasets significantly enhanced our model's performance with the GBRT model (R<sup>2</sup> = 0.84, RMSE = 26.50 tons/ha), outperforming the KNN (R<sup>2</sup> = 0.67, RMSE = 38.33 tons/ha) and SLR (R<sup>2</sup> = 0.57, RMSE = 43.88 tons/ha). Furthermore, the GBRT modelling approach demonstrated fewer deviations, with residuals exhibiting less variability in the AGB predictions from the combined dataset, followed by the Sentinel-2 dataset and then the Sentinel-1 dataset. Seasonal analysis revealed a strong correlation between AGB and NDVI, with band ratios involving Sentinel-2 vegetation red-edge bands (SR74, SR85) serving as influential predictors for biomass estimation. In contrast, Sentinel-1 radar backscatter predictors demonstrated a weaker impact on biomass estimation. This research highlights the potential of machine learning approaches in conjunction with satellite remote sensing for accurate AGB mapping in subtropical forests, providing valuable insights for forest management and conservation. The findings not only contribute to the growing field of remote sensing applications but also align with Sustainable Development Goals (SDG) 13 by addressing climate change and SDG 11 by promoting urban sustainability and mitigating environmental risks.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"39 ","pages":"Article 101640"},"PeriodicalIF":3.8,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144491318","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}
引用次数: 0
High-precision population estimates by remote sensing big data and advanced transformer deep learning model 基于遥感大数据和先进变压器深度学习模型的高精度人口估计
IF 3.8
Remote Sensing Applications-Society and Environment Pub Date : 2025-06-23 DOI: 10.1016/j.rsase.2025.101638
Ziyun Yan , Lei Ma , Xuan Wang , Yongil Kim , Liqiang Zhang
{"title":"High-precision population estimates by remote sensing big data and advanced transformer deep learning model","authors":"Ziyun Yan ,&nbsp;Lei Ma ,&nbsp;Xuan Wang ,&nbsp;Yongil Kim ,&nbsp;Liqiang Zhang","doi":"10.1016/j.rsase.2025.101638","DOIUrl":"10.1016/j.rsase.2025.101638","url":null,"abstract":"<div><div>High-precision population estimation is crucial for sensing where and how people live, which consequently supports sustainable development goals. Yet, there isn't a systematic theory that explains how geospatial big data works in population estimation studies, and deep learning models are eagerly applied in such fields as social sciences (e.g., population estimates) due to the recent prosperity of artificial intelligence (AI). The Shapley Additive Explanations (SHAP) tool was used in this study to check how well machine learning models and geospatial big data could be interpreted quantitatively for the population estimation process. The results show significant disparities among artificial intelligence models for population estimates, not only in estimate accuracy but also in dependencies on geospatial data. It was found that the classic Random Forest model relies too much on derived urban morphological features. The advanced transformer deep learning model, which can understand scenes, does much better and can directly get population-related semantics from satellite imagery. Subsequently, the high-precision population estimates were promised by integrating CNN's local and Transformer's global interpretation abilities. This study firstly implements the advanced Transformer model in population estimates and provides interpretability evidence within the deep learning framework. It was expected to become a typical application demonstration of AI in the social sciences.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"39 ","pages":"Article 101638"},"PeriodicalIF":3.8,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144491319","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}
引用次数: 0
Land use and land cover fraction estimation for Sentinel-2 RGB images: A new LULC mapping task 基于Sentinel-2 RGB影像的土地利用和土地覆盖比例估算:一种新的LULC制图任务
IF 3.8
Remote Sensing Applications-Society and Environment Pub Date : 2025-06-21 DOI: 10.1016/j.rsase.2025.101626
José Rodríguez-Ortega , Siham Tabik , Yassir Benhammou , Rohaifa Khaldi , Domingo Alcaraz-Segura
{"title":"Land use and land cover fraction estimation for Sentinel-2 RGB images: A new LULC mapping task","authors":"José Rodríguez-Ortega ,&nbsp;Siham Tabik ,&nbsp;Yassir Benhammou ,&nbsp;Rohaifa Khaldi ,&nbsp;Domingo Alcaraz-Segura","doi":"10.1016/j.rsase.2025.101626","DOIUrl":"10.1016/j.rsase.2025.101626","url":null,"abstract":"<div><div>Governmental institutions provide regional Land Use Land Cover (LULC) maps,<span><span><sup>1</sup></span></span> but their complex formats, varied resolutions and diverse annotations limit usability. Traditionally, LULC mapping is framed as multiclass classification (assigning one dominant label per image) or multi-label classification (identifying coexisting classes). Alternative approaches remain unexplored due to challenges in leveraging existing LULC products for new tasks. This work presents a novel reformulation — LULC fraction estimation per public Sentinel-2 RGB image — that predicts both the presence and the fractional abundance of coexisting LULC categories within each image. Our contributions include: (1) Land-1.0,<span><span><sup>2</sup></span></span> the first open source dataset with LULC fractions, climatological, and topographic data for 21,489 tiles; (2) a systematic method to build such datasets from existing LULC products; and (3) three deep learning solutions, where multitask models outperform single-task approaches. Future remote sensing foundation models could further improve the results by expanding the representation beyond supervised CNNs. This scalable and cost-effective method will help practitioners in environmental science and many other fields establish better monitoring of natural resources and biodiversity conservation using affordable RGB imagery and environmental data without the need to obtain and process expensive and complex hyperspectral or multispectral data.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"39 ","pages":"Article 101626"},"PeriodicalIF":3.8,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144490886","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}
引用次数: 0
Tracking coastal changes in the central-eastern margin of Tyrrhenian Sea through integrated NDWI-derived shorelines from multi-sensor satellite time series 基于多传感器卫星时间序列的综合ndwi海岸线追踪第勒尼安海中东部边缘海岸变化
IF 3.8
Remote Sensing Applications-Society and Environment Pub Date : 2025-06-19 DOI: 10.1016/j.rsase.2025.101636
G. Iacobucci, D. Piacentini, F. Troiani
{"title":"Tracking coastal changes in the central-eastern margin of Tyrrhenian Sea through integrated NDWI-derived shorelines from multi-sensor satellite time series","authors":"G. Iacobucci,&nbsp;D. Piacentini,&nbsp;F. Troiani","doi":"10.1016/j.rsase.2025.101636","DOIUrl":"10.1016/j.rsase.2025.101636","url":null,"abstract":"<div><div>Coastal zones are dynamic and vulnerable geomorphological systems where erosion and sedimentation interact with climatic variability and anthropogenic pressures. Monitoring shoreline dynamics is crucial for managing coastal risks, preserving ecosystems, and protecting communities, as nearly half of the global population lives near the coast.</div><div>Although long-term satellite archives are available, few studies have effectively integrated multi-sensor imagery to investigate the combined role of human and natural drivers on decadal shoreline evolution. This study addresses this gap by reconstructing 40 years (1984–2024) of shoreline changes along a 20 km stretch between Torvaianica and the Tor Caldara Natural Reserve (Lazio, Italy), an area historically affected by intense anthropogenic impacts.</div><div>Shorelines were extracted from Landsat 5, Landsat 8, and Sentinel 2 imagery using the Normalized Difference Water Index (NDWI), which enhances the land–water boundary and allows for consistent shoreline detection across sensors. Extracted shorelines were analyzed using the Digital Shoreline Analysis System (DSAS) to calculate End Point Rate (EPR), Net Shoreline Movement (NSM), Linear Regression Rate (LRR), and Weighted Linear Regression (WLR).</div><div>Results show a maximum LRR of −1.07 myr and a mean erosion rate of 0.44–0.55 myr, with 86.9 % of the coastline undergoing erosion. Storm events between 2012 and 2024 were identified using hydrometric levels and wind speed above the 95th percentile, revealing links between storm clusters and short-term shoreline change. Accuracy assessment highlights Sentinel 2 as the most reliable dataset.</div><div>This research demonstrates the value of combining NDWI-based shoreline extraction with long-term datasets to support sustainable coastal management.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"39 ","pages":"Article 101636"},"PeriodicalIF":3.8,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144366454","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}
引用次数: 0
Remote sensing for aboveground biomass monitoring in terrestrial ecosystems: A systematic review 陆地生态系统地上生物量遥感监测系统综述
IF 3.8
Remote Sensing Applications-Society and Environment Pub Date : 2025-06-18 DOI: 10.1016/j.rsase.2025.101635
Sana Ullah , Majid Nazeer , Man Sing Wong , Gomal Amin
{"title":"Remote sensing for aboveground biomass monitoring in terrestrial ecosystems: A systematic review","authors":"Sana Ullah ,&nbsp;Majid Nazeer ,&nbsp;Man Sing Wong ,&nbsp;Gomal Amin","doi":"10.1016/j.rsase.2025.101635","DOIUrl":"10.1016/j.rsase.2025.101635","url":null,"abstract":"<div><div>Monitoring aboveground biomass (AGB) in terrestrial ecosystems is crucial for understanding carbon dynamics, assessing ecosystem health, and informing climate change mitigation strategies. Over the past two decades, advancements in remote sensing (RS) technologies, including optical, synthetic aperture radar (SAR), and light detection and ranging (LiDAR), combined with integrated approaches, have significantly improved AGB estimation accuracy. This systematic review examines spatiotemporal trends in AGB research, evaluates the scale of studies and field plots, and assesses the effectiveness of RS types and their synergies globally. It also addresses uncertainties, prospects, and constraints in RS-based AGB estimation, identifying key research gaps for future investigations. The literature survey reveals a growing use of RS methods and synergies over the last decade. North America and Europe are leading in LiDAR applications, while Asia contributes significantly to optical applications. China and the United States have the highest number of field plots, reflecting extensive sampling efforts and large-scale execution. Research trends focus on temperate, tropical, and boreal forests, with tropical forests exhibiting the highest mean AGB (245 ton ha<sup>−1</sup>). Quantification of data sources shows 30 % of studies used LiDAR, followed by optical (27 %), optical-LiDAR (16 %), SAR-LiDAR (10 %), optical-SAR (7 %), SAR (6 %), and optical-SAR-LiDAR (5 %) combinations. Optical-SAR-LiDAR synergy demonstrated the highest efficiency (R<sup>2</sup> &gt; 0.60), highlighting its potential when integrated with climatic, topographic, and biophysical data using advanced modeling techniques and comprehensive methodologies. This review provides valuable insights for researchers and policymakers focused on carbon cycles, RS, and climate change.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"39 ","pages":"Article 101635"},"PeriodicalIF":3.8,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144471109","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}
引用次数: 0
Integrating remote sensing data and fully connected CNN for flood probability and risk assessment in the Port St Johns coastal town, South Africa 整合遥感数据和全连接CNN,对南非圣约翰港沿海城镇进行洪水概率和风险评估
IF 3.8
Remote Sensing Applications-Society and Environment Pub Date : 2025-06-16 DOI: 10.1016/j.rsase.2025.101630
Phila Sibandze , Ahmed Mukalazi Kalumba , Gbenga Abayomi Afuye , Mahlatse Kganyago
{"title":"Integrating remote sensing data and fully connected CNN for flood probability and risk assessment in the Port St Johns coastal town, South Africa","authors":"Phila Sibandze ,&nbsp;Ahmed Mukalazi Kalumba ,&nbsp;Gbenga Abayomi Afuye ,&nbsp;Mahlatse Kganyago","doi":"10.1016/j.rsase.2025.101630","DOIUrl":"10.1016/j.rsase.2025.101630","url":null,"abstract":"<div><div>The rising frequency and intensity of floods pose risks to human lives, infrastructure, and ecosystems, particularly in coastal regions, as traditional flood management systems struggle with uncertainties, complex environmental factors, and rapid urbanization, reducing decision-making accuracy. The study employs remote sensing data and a Convolutional Neural Network (CNN) to assess flood probability and risk in Port St Johns, South Africa, utilizing thirteen flood-influencing variables to minimize overfitting and extract robust features, addressing complex terrain and climate variability. The study uses data from ALOS DEM, CHIRPS, and Copernicus to analyze various factors such as Height Above the Nearest Drainage (HAND), TWI, MNDWI, TRI, distance to river, elevation, slope, aspect, curvature, flow accumulation and direction, precipitation, and land cover, using optimized kernel sizes, Rectified Linear Unit (ReLu), and regularization techniques. The results reveal significant correlations between terrain-related and hydrological factors, such as slope (3.98 %), HAND (3.07 %) and elevation (1.29 %), affecting water movement, accumulation, and drainage potential, with land cover (0.42 %) and precipitation (0.39 %) playing a secondary role. The CNN model for flood probability prediction reveals high accuracy and predictive performance, with a mean absolute error of 0.007 and a precision of 0.988 for flood-affected and unaffected areas. The InaSAFE analysis reveals that 26 % of Port St Johns’ population (870 people) and 34 % of structures (896 buildings) are directly affected by flooding, with high-risk zones affecting 420 people, 5.3 km of roads, and 479 buildings. The findings of the model enhance community safety and resilience to climate-induced flooding by improving flood risk prediction, optimizing evacuation, resource allocation, and disaster management through early warning systems and damage assessments.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"39 ","pages":"Article 101630"},"PeriodicalIF":3.8,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144330660","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}
引用次数: 0
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