Egyptian Journal of Remote Sensing and Space Sciences最新文献

Landslide Susceptibility Assessment Using an Explainable Stacking Learning Framework (ESLF) in Xinyuan County, Xinjiang, China 基于可解释叠加学习框架的新疆新源县滑坡易感性评价

IF 4.1 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2026-02-11 DOI: 10.1016/j.ejrs.2026.02.002

Yong Dai , Shenglong Gu , Qingkai Meng , Shilong Chen , Qiuhui Wang , Ying Meng , Han Wu , Qing Li

To address the lack of transparency in machine learning methods for landslide susceptibility assessment (LSA), this study proposes an Explainable Stacking Learning Framework (ESLF), taking Xinyuan County, Xinjiang, China as the study area. The framework effectively integrates Deep Neural Networks (DNN), Random Forests (RF), and Support Vector Machines (SVM), leveraging their respective strengths in pattern recognition, decision analysis, and hyperplane-based classification. A comprehensive landslide inventory and 11 predisposing factors were compiled to generate susceptibility maps through the application of Stacking, alongside individual DNN, RF, and SVM models. The results indicate that the stacking model outperforms single models, achieving AUC of 0.907, accuracy of 0.930, F1-score of 0.897, and a Kappa coefficient of 0.866. Very high and high susceptibility zones are mainly distributed in southern Talede Town, Biesituode Township, Xinyuan Town, Alemale Town, western Nalati Town, and northeastern parts of Zeketai, Areletuobie, Kansu Towns, and Tuergen Township. SHAP (SHapley Additive exPlanations) permutation importance analysis identifies elevation (995–2,253 m), distance to rivers (<836 m), land use type (shrubs/woodlands or other types), and engineering geological lithology (clastic rocks) as dominant controlling factors. These findings highlight the ESLF’s advantage in improving both accuracy and transparency, providing civil protection agencies with a reliable tool for understanding landslide susceptibility and implementing effective mitigation measures.

为了解决滑坡易感性评价（LSA）机器学习方法缺乏透明度的问题，本研究以新疆新源县为研究区，提出了一个可解释的叠加学习框架（ESLF）。该框架有效地集成了深度神经网络（DNN）、随机森林（RF）和支持向量机（SVM），利用它们各自在模式识别、决策分析和基于超平面的分类方面的优势。编制了一个全面的滑坡清单和11个诱发因素，通过应用Stacking以及单个DNN、RF和SVM模型生成敏感性图。结果表明，叠加模型的AUC为0.907，准确率为0.930，f1得分为0.897，Kappa系数为0.866，优于单一模型。高易感区和高易感区主要分布在塔勒德镇南部、别斯特奥多镇、新源镇、阿勒马莱镇、那拉提镇西部和泽克泰镇东北部、阿雷特奥多比镇、甘肃镇、图尔根镇。SHAP （SHapley Additive exPlanations）排列重要性分析确定海拔（995 - 2253米）、到河流的距离（<；836米）、土地利用类型（灌木/林地或其他类型）和工程地质岩性（碎屑岩）是主要的控制因素。这些发现突出了ESLF在提高准确性和透明度方面的优势，为民防机构提供了了解滑坡易感性和实施有效缓解措施的可靠工具。

{"title":"Landslide Susceptibility Assessment Using an Explainable Stacking Learning Framework (ESLF) in Xinyuan County, Xinjiang, China","authors":"Yong Dai , Shenglong Gu , Qingkai Meng , Shilong Chen , Qiuhui Wang , Ying Meng , Han Wu , Qing Li","doi":"10.1016/j.ejrs.2026.02.002","DOIUrl":"10.1016/j.ejrs.2026.02.002","url":null,"abstract":"<div><div>To address the lack of transparency in machine learning methods for landslide susceptibility assessment (LSA), this study proposes an Explainable Stacking Learning Framework (ESLF), taking Xinyuan County, Xinjiang, China as the study area. The framework effectively integrates Deep Neural Networks (DNN), Random Forests (RF), and Support Vector Machines (SVM), leveraging their respective strengths in pattern recognition, decision analysis, and hyperplane-based classification. A comprehensive landslide inventory and 11 predisposing factors were compiled to generate susceptibility maps through the application of Stacking, alongside individual DNN, RF, and SVM models. The results indicate that the stacking model outperforms single models, achieving AUC of 0.907, accuracy of 0.930, F1-score of 0.897, and a Kappa coefficient of 0.866. Very high and high susceptibility zones are mainly distributed in southern Talede Town, Biesituode Township, Xinyuan Town, Alemale Town, western Nalati Town, and northeastern parts of Zeketai, Areletuobie, Kansu Towns, and Tuergen Township. SHAP (SHapley Additive exPlanations) permutation importance analysis identifies elevation (995–2,253 m), distance to rivers (<836 m), land use type (shrubs/woodlands or other types), and engineering geological lithology (clastic rocks) as dominant controlling factors. These findings highlight the ESLF’s advantage in improving both accuracy and transparency, providing civil protection agencies with a reliable tool for understanding landslide susceptibility and implementing effective mitigation measures.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"29 1","pages":"Pages 196-208"},"PeriodicalIF":4.1,"publicationDate":"2026-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146188113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

GIS and google earth engine cloud platform to assess social resilience in flood-prone urban areas: The under-researched Bni Makada district (Tangier, Morocco) GIS和谷歌地球引擎云平台评估洪水易发城市地区的社会复原力：摩洛哥丹吉尔Bni Makada地区研究

IF 4.1 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2026-02-09 DOI: 10.1016/j.ejrs.2026.01.007

Yousra El Yacoubi, Miriam Wahbi, Mustapha Maatouk, Omar El Kharki, Hakim Boulaassal, Otmane Yazidi Alaoui

Climate change and socio-economic development increase the frequency and gravity of flooding. Accordingly, flood prevention has become an urgent challenge, and the development of robust risk management strategies is now paramount to cope with even more complex flooding patterns and mitigate their harmful consequences. Given this, enhancing overall resilience to flooding is a key priority, so that communities can effectively and efficiently tackle the potential repercussions. This study aims to develop a composite index SRI (Social Resilience Index) for assessing and mapping social resilience in Bni Makada urban district (Tangier, Morocco). SRI is computed by assigning equal weights −a transparent method favored for its lack of subjective bias in complex multidimensional assessments- and mapped using GIS (Geographic Information System), while recognizing that such weighting scheme, identified as the default setting in index construction and applied in 44 index methodologies (Beccari, 2016), represents one legitimate option among several, with no universally best choice existing (Tate, 2012). To derive this index, a collection of social and economic data must be gathered, including: − number of vulnerable persons, − potential flood-prone areas and – RSPI (Relative Spatial Poverty Index) based on a range of indicators associated with spatial economic concentration, spatial distribution of built-up areas, POIs (Points of Interest), as well as air pollution. For this, the Google Earth Engine platform was leveraged, providing a wide range of recent data to be exploited. The findings reveal a spatial disparity in terms of social resilience to flooding. The SRI empowers smart decisions, ensuring safer futures.

气候变化和社会经济发展增加了洪水发生的频率和严重性。因此，防洪已成为一项紧迫的挑战，制定强有力的风险管理策略对于应对更复杂的洪水模式和减轻其有害后果至关重要。鉴于此，加强对洪水的整体抵御能力是一个关键的优先事项，以便社区能够有效和高效地应对潜在的影响。本研究旨在建立一个综合指数SRI（社会弹性指数）来评估和绘制Bni Makada市区（丹吉尔，摩洛哥）的社会弹性。SRI是通过分配均等权重来计算的——这是一种透明的方法，因为它在复杂的多维评估中缺乏主观偏见——并使用GIS（地理信息系统）进行映射，同时认识到这种权重方案，被确定为指数构建中的默认设置，并应用于44种指数方法（Beccari, 2016），代表了几种合理选择中的一种，没有普遍存在的最佳选择（Tate, 2012）。为了得出这个指数，必须收集一系列社会和经济数据，包括：弱势人口数量、潜在洪涝易发地区和相对空间贫困指数（RSPI）， RSPI基于一系列与空间经济集中度、建成区的空间分布、兴趣点（poi）以及空气污染相关的指标。为此，谷歌Earth Engine平台发挥了作用，提供了大量可供利用的最新数据。研究结果揭示了社会对洪水恢复能力方面的空间差异。SRI赋予明智的决策，确保更安全的未来。

{"title":"GIS and google earth engine cloud platform to assess social resilience in flood-prone urban areas: The under-researched Bni Makada district (Tangier, Morocco)","authors":"Yousra El Yacoubi, Miriam Wahbi, Mustapha Maatouk, Omar El Kharki, Hakim Boulaassal, Otmane Yazidi Alaoui","doi":"10.1016/j.ejrs.2026.01.007","DOIUrl":"10.1016/j.ejrs.2026.01.007","url":null,"abstract":"<div><div>Climate change and socio-economic development increase the frequency and gravity of flooding. Accordingly, flood prevention has become an urgent challenge, and the development of robust risk management strategies is now paramount to cope with even more complex flooding patterns and mitigate their harmful consequences. Given this, enhancing overall resilience to flooding is a key priority, so that communities can effectively and efficiently tackle the potential repercussions. This study aims to develop a composite index SRI (Social Resilience Index) for assessing and mapping social resilience in Bni Makada urban district (Tangier, Morocco). SRI is computed by assigning equal weights −a transparent method favored for its lack of subjective bias in complex multidimensional assessments- and mapped using GIS (Geographic Information System), while recognizing that such weighting scheme, identified as the default setting in index construction and applied in 44 index methodologies (<span><span>Beccari, 2016</span></span>), represents one legitimate option among several, with no universally best choice existing (<span><span>Tate, 2012</span></span>). To derive this index, a collection of social and economic data must be gathered, including: − number of vulnerable persons, − potential flood-prone areas and – RSPI (Relative Spatial Poverty Index) based on a range of indicators associated with spatial economic concentration, spatial distribution of built-up areas, POIs (Points of Interest), as well as air pollution. For this, the Google Earth Engine platform was leveraged, providing a wide range of recent data to be exploited. The findings reveal a spatial disparity in terms of social resilience to flooding. The SRI empowers smart decisions, ensuring safer futures.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"29 1","pages":"Pages 179-195"},"PeriodicalIF":4.1,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146188114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SMF2Net: Spectral-assisted multi-receptive field fusion network for hyperspectral and multispectral image fusion SMF2Net：用于高光谱和多光谱图像融合的光谱辅助多接受场融合网络

IF 4.1 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2026-02-05 DOI: 10.1016/j.ejrs.2026.02.001

Siyuan Liu , Huanru Yue , Ruixia Cai , Bing Li , Yudong Zhang , Shuaiqi Liu

Hyperspectral image (HSI) is extensively used in classification, detection, tracking, and other tasks attributed to its capacity to capture extensive spectral information and comprehensively characterize the spectral signatures unique to distinct materials. Nevertheless, the acquisition of high-resolution HSI (HR-HSI) is fundamentally challenged by the intrinsic technical constraints of HSI sensors. Therefore, the fusion of low-resolution HSI and high-resolution multispectral image (MSI) to obtain HR-HSI has become a research hotspot. Existing fusion algorithms often do not make full use of the correlation between spatial and spectral information, which makes the fusion model lack interpretability. Therefore, this paper builds a spectral-assisted multi-receptive field fusion network (SMF2Net) for HSI and MSI fusion. Specifically, for spatial information, this paper designs a spectral feature-based spatial partition convolution block (SFSPB) and a multi-receptive field interaction fusion block (MFIFB) to capture the spatial information in the source images. Among them, the SFSPB extracts spatial information in different regions according to the spectral features of the image; meanwhile, the MFIFB extracts the spatial information of different receptive fields and performs feature fusion so that SMF2Net can obtain rich semantic information. For spectral information, this paper constructs a HybridFormer block based on spectral multi-head self-attention. It models the long-distance dependence of the spectrum through multi-head self-attention and enhances the spectral information in the fusion result through channel attention. In this paper, experiments are carried out on one real dataset and two simulated datasets. The experimental results indicate that the proposed algorithm can achieve advanced fusion effectiveness subjectively and objectively. The source code is available at: https://github.com/cvmdsp/SMF2Net.

高光谱图像（HSI）被广泛应用于分类、检测、跟踪和其他任务，因为它能够捕获大量的光谱信息，并全面表征不同材料的独特光谱特征。然而，高分辨率HSI （HR-HSI）的获取从根本上受到HSI传感器固有技术限制的挑战。因此，融合低分辨率HSI与高分辨率多光谱图像（MSI）获取HR-HSI已成为研究热点。现有的融合算法往往没有充分利用空间信息和光谱信息之间的相关性，使得融合模型缺乏可解释性。为此，本文构建了一个频谱辅助的多感受场融合网络（SMF2Net），用于HSI和MSI融合。具体而言，对于空间信息，本文设计了基于光谱特征的空间分割卷积块（SFSPB）和多感受场交互融合块（MFIFB）来捕获源图像中的空间信息。其中，SFSPB根据图像的光谱特征提取不同区域的空间信息；同时，MFIFB提取不同感受野的空间信息并进行特征融合，使SMF2Net能够获得丰富的语义信息。对于光谱信息，本文构建了一个基于光谱多头自关注的HybridFormer块。它通过多头自关注来模拟光谱的远距离依赖，并通过信道关注来增强融合结果中的光谱信息。本文在一个真实数据集和两个模拟数据集上进行了实验。实验结果表明，该算法在主观上和客观上都能达到较好的融合效果。源代码可从https://github.com/cvmdsp/SMF2Net获得。

{"title":"SMF2Net: Spectral-assisted multi-receptive field fusion network for hyperspectral and multispectral image fusion","authors":"Siyuan Liu , Huanru Yue , Ruixia Cai , Bing Li , Yudong Zhang , Shuaiqi Liu","doi":"10.1016/j.ejrs.2026.02.001","DOIUrl":"10.1016/j.ejrs.2026.02.001","url":null,"abstract":"<div><div>Hyperspectral image (HSI) is extensively used in classification, detection, tracking, and other tasks attributed to its capacity to capture extensive spectral information and comprehensively characterize the spectral signatures unique to distinct materials. Nevertheless, the acquisition of high-resolution HSI (HR-HSI) is fundamentally challenged by the intrinsic technical constraints of HSI sensors. Therefore, the fusion of low-resolution HSI and high-resolution multispectral image (MSI) to obtain HR-HSI has become a research hotspot. Existing fusion algorithms often do not make full use of the correlation between spatial and spectral information, which makes the fusion model lack interpretability. Therefore, this paper builds a spectral-assisted multi-receptive field fusion network (SMF2Net) for HSI and MSI fusion. Specifically, for spatial information, this paper designs a spectral feature-based spatial partition convolution block (SFSPB) and a multi-receptive field interaction fusion block (MFIFB) to capture the spatial information in the source images. Among them, the SFSPB extracts spatial information in different regions according to the spectral features of the image; meanwhile, the MFIFB extracts the spatial information of different receptive fields and performs feature fusion so that SMF2Net can obtain rich semantic information. For spectral information, this paper constructs a HybridFormer block based on spectral multi-head self-attention. It models the long-distance dependence of the spectrum through multi-head self-attention and enhances the spectral information in the fusion result through channel attention. In this paper, experiments are carried out on one real dataset and two simulated datasets. The experimental results indicate that the proposed algorithm can achieve advanced fusion effectiveness subjectively and objectively. The source code is available at: <span><span>https://github.com/cvmdsp/SMF2Net</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"29 1","pages":"Pages 147-157"},"PeriodicalIF":4.1,"publicationDate":"2026-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146188081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Air pollution mapping and monitoring using Sentinel-5P data and Google Earth Engine 使用Sentinel-5P数据和谷歌地球引擎绘制空气污染地图和监测

IF 4.1 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2026-02-05 DOI: 10.1016/j.ejrs.2026.01.008

Sara Sameh , Ahmed Zaki , Basem Elsaka , Ashraf A.A. Beshr , Ashraf G. Shehata

Urbanization and industrialization have significantly altered land surface temperature (LST) and air quality (AQ), particularly in rapidly developing regions such as Sharkia Governorate, Egypt. This research presents a comprehensive spatio-temporal distribution of LST and air pollutants (APTs) interactions in Sharkia Governorate, Egypt, for 2024. LST data were derived from the MOD11A1 product, while PM2.5 and PM10 concentrations were obtained as AOD-derived estimates using empirical models calibrated in other regions, which are considered invalidated approximations pending local calibration. Sentinel-5P (S5P) data were used for APTs, and all datasets were processed through the Google Earth Engine (GEE). The southern industrial zones, particularly 10th of Ramadan and Belbies, exhibit significantly higher LST values, whereas northern rural areas maintain lower temperatures due to vegetation cover and agricultural activity. Correlation analysis reveals strong positive relationships between LST and key pollutants such as Methane (CH₄, r = 0.8589) and UVAI (r = 0.8874), indicating that higher temperatures enhance pollutant concentration and dispersion. A moderately positive correlation was found with Formaldehyde (HCHO, r = 0.4282). Conversely, Ozone (O₃) exhibited a moderately negative correlation (r = -0.7854) with LST. Particulate matter (PM2.5, PM10; r = 0.286, 0.413) showed weak correlations, suggesting that their emissions are primarily driven by anthropogenic sources rather than temperature fluctuations. Principal Component Analysis (PCA) was incorporated to explore multivariate relationships among pollutants and identify dominant underlying factors influencing air quality patterns. Geographically weighted regression (GWR) identified pollution hotspots in the southwest, particularly high-density urban and industrial areas, where CH₄ and UVAI showed stronger impacts on LST (local R² > 0.8). All PM results are presented as invalidated estimates, and their interpretation is approached cautiously. The findings underscore the implications of urbanization and industrial activities on local climate, air quality, and public health, emphasizing the need for ground-based validation and mitigation policies.

城市化和工业化已经显著改变了地表温度和空气质量，特别是在快速发展的地区，如埃及的Sharkia省。本文研究了2024年埃及Sharkia省地表温度与空气污染物相互作用的综合时空分布。LST数据来自MOD11A1产品，而PM2.5和PM10浓度则是使用在其他地区校准的经验模型获得的aod估算值，这些估计值被认为是无效的近似值，有待当地校准。APTs使用Sentinel-5P （S5P）数据，所有数据集都通过谷歌地球引擎（GEE）进行处理。南部工业区，特别是斋月10日和贝尔比斯，地表温度值明显较高，而北部农村地区由于植被覆盖和农业活动而保持较低的温度。相关分析显示，地表温度与甲烷（CH4, r = 0.8589）、UVAI （r = 0.8874）等主要污染物呈显著正相关，表明温度升高会增强污染物浓度和扩散。与甲醛呈中度正相关（HCHO, r = 0.4282）。相反，臭氧（O3）与地表温度呈中等负相关（r = -0.7854）。颗粒物（PM2.5、PM10; r = 0.286、0.413）呈弱相关性，表明它们的排放主要受人为源驱动，而非温度波动。采用主成分分析（PCA）探讨污染物之间的多元关系，并确定影响空气质量格局的主要潜在因素。地理加权回归（GWR）发现西南污染热点地区，特别是高密度城市和工业区，CH4和UVAI对地表温度的影响更强（当地R2 >； 0.8）。所有的PM结果都是无效的估计，并且它们的解释是谨慎的。研究结果强调了城市化和工业活动对当地气候、空气质量和公共卫生的影响，强调需要制定基于地面的验证和减缓政策。

{"title":"Air pollution mapping and monitoring using Sentinel-5P data and Google Earth Engine","authors":"Sara Sameh , Ahmed Zaki , Basem Elsaka , Ashraf A.A. Beshr , Ashraf G. Shehata","doi":"10.1016/j.ejrs.2026.01.008","DOIUrl":"10.1016/j.ejrs.2026.01.008","url":null,"abstract":"<div><div>Urbanization and industrialization have significantly altered land surface temperature (LST) and air quality (AQ), particularly in rapidly developing regions such as Sharkia Governorate, Egypt. This research presents a comprehensive spatio-temporal distribution of LST and air pollutants (APTs) interactions in Sharkia Governorate, Egypt, for 2024. LST data were derived from the MOD11A1 product, while PM2.5 and PM10 concentrations were obtained as AOD-derived estimates using empirical models calibrated in other regions, which are considered invalidated approximations pending local calibration. Sentinel-5P (S5P) data were used for APTs, and all datasets were processed through the Google Earth Engine (GEE). The southern industrial zones, particularly 10th of Ramadan and Belbies, exhibit significantly higher LST values, whereas northern rural areas maintain lower temperatures due to vegetation cover and agricultural activity. Correlation analysis reveals strong positive relationships between LST and key pollutants such as Methane (CH<sub>4</sub>, r = 0.8589) and UVAI (r = 0.8874), indicating that higher temperatures enhance pollutant concentration and dispersion. A moderately positive correlation was found with Formaldehyde (HCHO, r = 0.4282). Conversely, Ozone (O<sub>3</sub>) exhibited a moderately negative correlation (r = -0.7854) with LST. Particulate matter (PM2.5, PM10; r = 0.286, 0.413) showed weak correlations, suggesting that their emissions are primarily driven by anthropogenic sources rather than temperature fluctuations. Principal Component Analysis (PCA) was incorporated to explore multivariate relationships among pollutants and identify dominant underlying factors influencing air quality patterns. Geographically weighted regression (GWR) identified pollution hotspots in the southwest, particularly high-density urban and industrial areas, where CH<sub>4</sub> and UVAI showed stronger impacts on LST (local R<sup>2</sup> > 0.8). All PM results are presented as invalidated estimates, and their interpretation is approached cautiously. The findings underscore the implications of urbanization and industrial activities on local climate, air quality, and public health, emphasizing the need for ground-based validation and mitigation policies.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"29 1","pages":"Pages 158-178"},"PeriodicalIF":4.1,"publicationDate":"2026-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146188082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Topographic deformation and geomorphic instability of mine dump slopes: A UAV-based multi-scale analysis framework 矿山排土场边坡地形变形与地貌失稳：基于无人机的多尺度分析框架

IF 4.1 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2026-02-05 DOI: 10.1016/j.ejrs.2026.02.003

Amit Kumar Mankar, Radhakanta Koner

The structural integrity of mine dump slopes within open-pit excavations constitutes a multifaceted geomechanical phenomenon, wherein dynamic interactions between anthropogenic perturbations and intrinsic geomorphological parameters dictate stability thresholds. Catastrophic slope failures have profound repercussions, encompassing occupational hazards, substantial capital losses, and perturbations in extraction continuity. This study employs a hybridized, multimodal analytical paradigm to investigate the spatiotemporal evolution of dump slope stability by integrating unmanned aerial vehicle (UAV)-facilitated photogrammetric reconstruction, multi-scale model-to-model (M3C2) topographic displacement approach, and high-fidelity numerical simulations grounded in the Limit Equilibrium Method (LEM) within a Multi-Modal Optimization (MMO) framework. High-resolution UAV-derived orthophotography, acquired in January 2023, has enabled the generation of dense three-dimensional topographic models. Computational modelling delineated geotechnically compromised zones 1 (FOS = 1.17) and 2 (FOS = 1.11), exhibiting subcritical stability indices. M3C2 differential analysis elucidated pronounced mass displacement phenomena, registering peak translational vectors of 23.77 m and 18.08 m within Zones 1 and 2, respectively, over a four-month temporal window. These slope deformations are predominantly attributable to progressive overburden deposition-induced loading, which is compounded by the destabilizing effects of haul road modifications and decommissioning sequences. The results substantiate the profound interdependence between engineered interventions and slope deformation mechanics, reinforcing the need for real-time geotechnical surveillance and adaptive mitigation strategies. This study advances the discourse on UAV-integrated geospatial analytics as a pivotal instrument for enhancing predictive stability assessments, facilitating pre-emptive risk abatement, and augmenting the resilience of extractive operations within open-pit mining environments.

露天开挖矿山排土场边坡的结构完整性是一个多方面的地质力学现象，其中人为扰动和固有地貌参数之间的动态相互作用决定了稳定性阈值。灾难性的边坡破坏具有深远的影响，包括职业危害、大量资本损失和开采连续性的扰动。本研究采用一种混合、多模态分析范式，通过集成无人机（UAV）辅助的摄影测量重建、多尺度模型对模型（M3C2）地形位移方法以及基于多模态优化（MMO）框架下极限平衡法（LEM）的高保真数值模拟，研究排土场边坡稳定性的时空演变。2023年1月获得的高分辨率无人机衍生正射影摄影技术，已经能够生成密集的三维地形模型。计算模型描绘了地质技术受损区1 （FOS = 1.17）和2 (FOS = 1.11)，表现出亚临界稳定性指数。M3C2差分分析揭示了明显的质量位移现象，在4个月的时间窗口内，在1区和2区分别记录了23.77 m和18.08 m的峰值平移矢量。这些边坡变形主要是由于覆盖层沉积引起的累进加载，再加上运输道路改造和退役顺序的不稳定影响。结果证实了工程干预与边坡变形力学之间的深刻相互依存关系，加强了对实时岩土监测和适应性缓解策略的需求。该研究推进了无人机集成地理空间分析作为增强预测稳定性评估、促进先发制人风险降低和增强露天采矿环境中采掘作业弹性的关键工具的论述。

{"title":"Topographic deformation and geomorphic instability of mine dump slopes: A UAV-based multi-scale analysis framework","authors":"Amit Kumar Mankar, Radhakanta Koner","doi":"10.1016/j.ejrs.2026.02.003","DOIUrl":"10.1016/j.ejrs.2026.02.003","url":null,"abstract":"<div><div>The structural integrity of mine dump slopes within open-pit excavations constitutes a multifaceted geomechanical phenomenon, wherein dynamic interactions between anthropogenic perturbations and intrinsic geomorphological parameters dictate stability thresholds. Catastrophic slope failures have profound repercussions, encompassing occupational hazards, substantial capital losses, and perturbations in extraction continuity. This study employs a hybridized, multimodal analytical paradigm to investigate the spatiotemporal evolution of dump slope stability by integrating unmanned aerial vehicle (UAV)-facilitated photogrammetric reconstruction, multi-scale model-to-model (M3C2) topographic displacement approach, and high-fidelity numerical simulations grounded in the Limit Equilibrium Method (LEM) within a Multi-Modal Optimization (MMO) framework. High-resolution UAV-derived orthophotography, acquired in January 2023, has enabled the generation of dense three-dimensional topographic models. Computational modelling delineated geotechnically compromised zones 1 (FOS = 1.17) and 2 (FOS = 1.11), exhibiting subcritical stability indices. M3C2 differential analysis elucidated pronounced mass displacement phenomena, registering peak translational vectors of 23.77 m and 18.08 m within Zones 1 and 2, respectively, over a four-month temporal window. These slope deformations are predominantly attributable to progressive overburden deposition-induced loading, which is compounded by the destabilizing effects of haul road modifications and decommissioning sequences. The results substantiate the profound interdependence between engineered interventions and slope deformation mechanics, reinforcing the need for real-time geotechnical surveillance and adaptive mitigation strategies. This study advances the discourse on UAV-integrated geospatial analytics as a pivotal instrument for enhancing predictive stability assessments, facilitating pre-emptive risk abatement, and augmenting the resilience of extractive operations within open-pit mining environments.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"29 1","pages":"Pages 133-146"},"PeriodicalIF":4.1,"publicationDate":"2026-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146188115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Risks assessment in the karstic environment of the limestone plateau, Ghanayim city, Asyut government, Egypt 埃及阿斯尤特政府加纳伊姆市石灰岩高原岩溶环境风险评价

IF 4.1 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2026-02-03 DOI: 10.1016/j.ejrs.2026.01.009

Hassan Ibrahim Diab , Ahmed Mohammed El-Sayed

The karstic phenomenon is a challenging environment for geotechnical engineers and construction companies due to its risky characteristics, such as weakness, fragility, and cavernous areas. A simple, untraditional, and more accurate integrated approach involving remote sensing, geographic information systems, and ground penetrating radar technologies was used to hold the risky karstic features and evaluate the soil stability before any construction process at the construction site of the train station of Al Ghanayim city, Asyut, Egypt. The remote sensing approach focused on evaluating the topographic features of wadi Sarga watershed, hydro-morphometric parameters of its sub-watersheds, and its drainage network, to assess the rainwater harvesting potential. And then the associated hazard level has been calculated, indicating the construction area is of high hazard degree, so the ground penetrating radar technology has been applied to illustrate the shallow subsurface layers and their expected risky karstic characteristics. As a result, a final potential map of the expected caverns has been illustrated with their positions and depths, providing valuable information to help in making a suitable decision in the treatment process. The applied approach can be followed in similar karstic environments to illustrate and imagine any environmental hazards (ex., caves), and to avoid any future disaster that may put people’s lives in danger.

岩溶现象对岩土工程师和建筑公司来说是一个具有挑战性的环境，因为它的风险特征，如软弱、脆弱和洞穴区域。在埃及阿斯尤特市Al Ghanayim火车站施工现场，采用了一种简单、非传统、更精确的综合方法，包括遥感、地理信息系统和探地雷达技术，以掌握危险岩溶特征，并在施工前评估土壤稳定性。遥感方法侧重于评价萨尔加河流域的地形特征、子流域的水文形态参数及其排水网络，以评估其雨水收集潜力。在此基础上，计算了相应的危险性等级，表明施工区域属于高危险性区域，并应用探地雷达技术对浅层次表层及其预期危险岩溶特征进行了描述。因此，最终的潜在洞穴地图已经显示出它们的位置和深度，为在处理过程中做出合适的决策提供了有价值的信息。应用的方法可以在类似的岩溶环境中遵循，以说明和想象任何环境危害（例如，洞穴），并避免任何可能危及人们生命的未来灾难。

{"title":"Risks assessment in the karstic environment of the limestone plateau, Ghanayim city, Asyut government, Egypt","authors":"Hassan Ibrahim Diab , Ahmed Mohammed El-Sayed","doi":"10.1016/j.ejrs.2026.01.009","DOIUrl":"10.1016/j.ejrs.2026.01.009","url":null,"abstract":"<div><div>The karstic phenomenon is a challenging environment for geotechnical engineers and construction companies due to its risky characteristics, such as weakness, fragility, and cavernous areas. A simple, untraditional, and more accurate integrated approach involving remote sensing, geographic information systems, and ground penetrating radar technologies was used to hold the risky karstic features and evaluate the soil stability before any construction process at the construction site of the train station of Al Ghanayim city, Asyut, Egypt. The remote sensing approach focused on evaluating the topographic features of wadi Sarga watershed, hydro-morphometric parameters of its sub-watersheds, and its drainage network, to assess the rainwater harvesting potential. And then the associated hazard level has been calculated, indicating the construction area is of high hazard degree, so the ground penetrating radar technology has been applied to illustrate the shallow subsurface layers and their expected risky karstic characteristics. As a result, a final potential map of the expected caverns has been illustrated with their positions and depths, providing valuable information to help in making a suitable decision in the treatment process. The applied approach can be followed in similar karstic environments to illustrate and imagine any environmental hazards (ex., caves), and to avoid any future disaster that may put people’s lives in danger.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"29 1","pages":"Pages 120-132"},"PeriodicalIF":4.1,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146188111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimizing shipborne gravity data around Egypt for enhanced geomatics applications 优化埃及周围的船载重力数据，以增强地理信息应用

IF 4.1 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2026-02-03 DOI: 10.1016/j.ejrs.2026.01.010

Mahmoud Abulkhair , Ahmed Zaki , Ahmed Elhadary , Basem Elsaka , Mostafa Rabah

This study presents a comprehensive crossover adjustment of shipborne gravity measurements around Egypt. The dataset, from 17 marine surveys, displayed inconsistencies attributable to differences in data collecting sources, instruments, and processing techniques. To validate and enhance data quality, a systematic adjustment was carried out using the x2sys package from GMT based on least-squares estimation. Three adjustment approaches were executed and assessed: institution-based, equal-weight, and weighted analyses. The institution-based approach categorized ship surveys according to data source to overcome some of the inconsistency, and the crossover analysis was applied to each group separately. This technique yielded inadequate improvements owing to a minimal number and/or an uneven distribution of COs. To overcome this flaw, an equal-weight approach was implemented for all surveys, supposing identical observational conditions and accuracy. This procedure showed better performance, especially when incorporating both internal and external COEs. However, the weighted adjustment, which accounted for track-specific accuracy, yielded the most significant improvement, achieving an optimal agreement of 5.10 mGal after adjustment. Additionally, a conditional analysis eliminating COEs beyond ±50 mGal led to further refinement, decreasing the discrepancies at intersections to 4.62 mGal with a 9.41% gain in internal agreement. Validation against altimetric gravity models and GGMs clarified improvement in external alignment, with residual SD reductions of up to 15.99% and 16.63% corresponding to external COs and all COs adjustment, respectively. The optimized shipborne gravity data consequently supports a wide range of geomatics applications, including precise marine geoid modeling and better determination of dynamic ocean topography and ocean circulation.

本研究提出了埃及周围船载重力测量的综合交叉调整。来自17个海洋调查的数据集显示了由于数据收集来源、仪器和处理技术的差异而导致的不一致性。为了验证和提高数据质量，使用基于最小二乘估计的GMT的x2sys包进行了系统调整。执行和评估了三种调整方法：基于机构的、等权重的和加权的分析。基于制度的方法根据数据来源对船舶调查进行分类，以克服一些不一致性，并对每组分别进行交叉分析。由于COs的数量很少和/或分布不均匀，这种技术的改进不足。为了克服这一缺陷，在假设相同的观测条件和精度的情况下，对所有调查实施了等权方法。该程序表现出更好的性能，特别是在合并内部和外部coe时。然而，考虑到赛道特定精度的加权调整产生了最显著的改善，调整后达到了5.10 mGal的最佳一致性。此外，通过条件分析消除了COEs超过±50 mGal的差异，进一步改进，将交叉处的差异减少到4.62 mGal，内部一致性增加9.41%。对高程重力模型和GGMs的验证表明，外部COs和所有COs调整后的剩余SD分别减少了15.99%和16.63%。优化的船载重力数据因此支持广泛的地理信息学应用，包括精确的海洋大地水准面建模和更好地确定动态海洋地形和海洋环流。

{"title":"Optimizing shipborne gravity data around Egypt for enhanced geomatics applications","authors":"Mahmoud Abulkhair , Ahmed Zaki , Ahmed Elhadary , Basem Elsaka , Mostafa Rabah","doi":"10.1016/j.ejrs.2026.01.010","DOIUrl":"10.1016/j.ejrs.2026.01.010","url":null,"abstract":"<div><div>This study presents a comprehensive crossover adjustment of shipborne gravity measurements around Egypt. The dataset, from 17 marine surveys, displayed inconsistencies attributable to differences in data collecting sources, instruments, and processing techniques. To validate and enhance data quality, a systematic adjustment was carried out using the x2sys package from GMT based on least-squares estimation. Three adjustment approaches were executed and assessed: institution-based, equal-weight, and weighted analyses. The institution-based approach categorized ship surveys according to data source to overcome some of the inconsistency, and the crossover analysis was applied to each group separately. This technique yielded inadequate improvements owing to a minimal number and/or an uneven distribution of COs. To overcome this flaw, an equal-weight approach was implemented for all surveys, supposing identical observational conditions and accuracy. This procedure showed better performance, especially when incorporating both internal and external COEs. However, the weighted adjustment, which accounted for track-specific accuracy, yielded the most significant improvement, achieving an optimal agreement of 5.10 mGal after adjustment. Additionally, a conditional analysis eliminating COEs beyond ±50 mGal led to further refinement, decreasing the discrepancies at intersections to 4.62 mGal with a 9.41% gain in internal agreement. Validation against altimetric gravity models and GGMs clarified improvement in external alignment, with residual SD reductions of up to 15.99% and 16.63% corresponding to external COs and all COs adjustment, respectively. The optimized shipborne gravity data consequently supports a wide range of geomatics applications, including precise marine geoid modeling and better determination of dynamic ocean topography and ocean circulation.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"29 1","pages":"Pages 104-119"},"PeriodicalIF":4.1,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146188112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bitumen zoning in Khuzestan Province, southwestern Iran: an integrated approach using remote sensing, spectroscopy, and analytical hierarchy process (AHP) 伊朗西南部胡齐斯坦省沥青分区：利用遥感、光谱和层次分析法（AHP）的综合方法

IF 4.1 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2026-01-28 DOI: 10.1016/j.ejrs.2026.01.002

Danya Karimi , Hajar Ghafleh Maramazi , Mohsen Rezaei , Abbas Charchi

This research explores the spatial distribution of natural bitumen in Khuzestan Province through a novel integrated approach that combines Sentinel-2 satellite imagery, spectral data from the FieldSpec3 spectroradiometer, and the Analytic Hierarchy Process (AHP). The study addresses the existing gap in integrating spectral analysis with multi-criteria spatial modeling to enhance the precision of bitumen mapping. This combined methodology represents the first-time application of such an integrated approach in this particular region. Field data were gathered from three mines: Ahangaran, Ghaland, and Dodangeh. Twelve bitumen samples were collected and analyzed spectrally. Results obtained using TSG and PRISM software showed that the Ghaland mine samples had the most pronounced absorption features in the 1.69 to 1.79 μm range, suggesting a higher concentration of heavy hydrocarbons. Across all samples, heavy hydrocarbons consistently exceeded light hydrocarbons. Classification using the Spectral Angle Mapper (SAM) method achieved 91 % accuracy and closely aligned with field observations. In the spatial modeling phase, five information layers were incorporated: oil fields, bitumen mines, faults, anticlines, and key geological formations. These layers were weighted and integrated within a GIS environment to produce a bitumen probability map. The map was compared with the SAM classification results, showing significant overlap in high-potential zones, which supports the validity of the applied approach. In both methods, the eastern region of the study area exhibited the highest bitumen occurrence potential. Out of the total 64,057 km² area of Khuzestan Province, about 0.011 km² (roughly 11,000 m²) was identified as zones with high potential for bitumen occurrence.

本研究通过结合Sentinel-2卫星图像、FieldSpec3光谱辐射计的光谱数据和层次分析法（AHP）的新颖综合方法，探索了胡齐斯坦省天然沥青的空间分布。该研究解决了目前将光谱分析与多准则空间建模相结合以提高沥青测绘精度的不足。这一综合方法是首次在这一特定区域应用这种综合方法。现场数据收集自三个矿区：Ahangaran、Ghaland和Dodangeh。对12份沥青样品进行了光谱分析。TSG和PRISM软件的结果表明，Ghaland矿样品在1.69 ~ 1.79 μm范围内的吸收特征最为明显，表明其含重烃浓度较高。在所有样本中，重碳氢化合物一直超过轻烃。利用光谱角成像仪（SAM）方法进行分类，准确率达到91%，与野外观测结果非常接近。在空间建模阶段，纳入了油田、沥青矿、断层、背斜和关键地质构造五个信息层。这些层被加权并在GIS环境中集成，以生成沥青概率图。将该地图与SAM分类结果进行比较，发现在高电位区有明显的重叠，这支持了应用方法的有效性。两种方法均显示研究区东部地区沥青赋存潜力最大。在Khuzestan省64,057平方公里的总面积中，约0.011平方公里（约11,000平方米）被确定为高沥青产区。

{"title":"Bitumen zoning in Khuzestan Province, southwestern Iran: an integrated approach using remote sensing, spectroscopy, and analytical hierarchy process (AHP)","authors":"Danya Karimi , Hajar Ghafleh Maramazi , Mohsen Rezaei , Abbas Charchi","doi":"10.1016/j.ejrs.2026.01.002","DOIUrl":"10.1016/j.ejrs.2026.01.002","url":null,"abstract":"<div><div>This research explores the spatial distribution of natural bitumen in Khuzestan Province through a novel integrated approach that combines Sentinel-2 satellite imagery, spectral data from the FieldSpec3 spectroradiometer, and the Analytic Hierarchy Process (AHP). The study addresses the existing gap in integrating spectral analysis with multi-criteria spatial modeling to enhance the precision of bitumen mapping. This combined methodology represents the first-time application of such an integrated approach in this particular region. Field data were gathered from three mines: Ahangaran, Ghaland, and Dodangeh. Twelve bitumen samples were collected and analyzed spectrally. Results obtained using TSG and PRISM software showed that the Ghaland mine samples had the most pronounced absorption features in the 1.69 to 1.79 μm range, suggesting a higher concentration of heavy hydrocarbons. Across all samples, heavy hydrocarbons consistently exceeded light hydrocarbons. Classification using the Spectral Angle Mapper (SAM) method achieved 91 % accuracy and closely aligned with field observations. In the spatial modeling phase, five information layers were incorporated: oil fields, bitumen mines, faults, anticlines, and key geological formations. These layers were weighted and integrated within a GIS environment to produce a bitumen probability map. The map was compared with the SAM classification results, showing significant overlap in high-potential zones, which supports the validity of the applied approach. In both methods, the eastern region of the study area exhibited the highest bitumen occurrence potential. Out of the total 64,057 km<sup>2</sup> area of Khuzestan Province, about 0.011 km<sup>2</sup> (roughly 11,000 m<sup>2</sup>) was identified as zones with high potential for bitumen occurrence.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"29 1","pages":"Pages 86-103"},"PeriodicalIF":4.1,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146078363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A geospatial approach to assessing shoreline change dynamics along the eThekwini coast of South Africa 评估南非德班尼海岸海岸线变化动态的地理空间方法

IF 4.1 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2026-01-21 DOI: 10.1016/j.ejrs.2026.01.004

Ekang C. Amatebelle, Zachariah H. Mshelia, Abiodun A. Ogundeji, Solomon T. Owolabi

The eThekwini coast represents a critical hotspot of vulnerability to coastal hazards, particularly erosion and flooding. However, studies assessing historical shoreline change in eThekwini over the past four decades remain limited. The objective of this study was to assess and quantify shoreline change dynamics in eThekwini using geospatial techniques. Satellite acquired imagery from 1984 to 2024 were process in ArcGIS 10.8 using the Modified Normalised Difference Water Index (MNDWI) and the Digital Shoreline Analysis System (DSAS v6.0). The study quantify shoreline change using established statistical metrics i.e. Net Shoreline Movement (NSM), End Point Rate (EPR), Linear Regression Rate (LRR), and Weighted Linear Regression (WLR). The results show that 57.45 % of the shoreline was accretional while 42.55 % were erosional. However, the long-term accretional trend is likely to shift towards erosion, as the overall change rate of −1.03 m/yr consistently exceeds the ± 0.66 m/yr uncertainty threshold, reflecting the combined influence of urban development, climate change and sea level rise impacts on the coast. Accretion dominated Reg-1 and Reg-4, whereas Reg-2 and Reg-3 experienced statistically significant erosion beyond uncertainty. The erosional hotspots were largely concentrated near major rivers including Umgeni, uMdloti, and uMkhomazi river mouths. These patterns strongly correlate with the changes in land use, coastal infrastructures, damming, sand mining, and extreme weather events affecting the coastal city. These results provide actionable evidence for coastal risk management by identifying erosion hotspots, informing municipal setback lines, guiding risk-based land-use planning, and prioritising adaptation measures under South Africa’s Integrated Coastal Management framework (ICM Act No. 24 of 2008, amended Act No. 36 of 2014).Therefore, this study advocates a transformative approach that combine hybrid engineering and nature-based solutions to reduce human vulnerability and provide essential baseline data to support adaptive coastal management, and disaster preparedness to enhance coastal resilience in eThekwini.

德班尼海岸是易受海岸灾害，特别是侵蚀和洪水影响的关键热点。然而，评估过去四十年来德班岛海岸线历史变化的研究仍然有限。本研究的目的是利用地理空间技术评估和量化德班尼岛的海岸线变化动态。利用修正的归一化差水指数（MNDWI）和数字海岸线分析系统（DSAS v6.0），在ArcGIS 10.8中对1984 - 2024年的卫星影像进行处理。该研究使用既定的统计指标，即净海岸线移动（NSM）、终点率（EPR）、线性回归率（LRR）和加权线性回归（WLR）来量化海岸线变化。结果表明：57.45%的岸线为增生型，42.55%为侵蚀型。然而，由于−1.03 m/yr的总变化率持续超过±0.66 m/yr的不确定性阈值，长期的增积趋势可能转向侵蚀，这反映了城市发展、气候变化和海平面上升对海岸的综合影响。Reg-1和Reg-4以吸积为主，而Reg-2和Reg-3则经历了统计上显著的侵蚀。侵蚀热点主要集中在主要河流附近，包括Umgeni、uMdloti和uMkhomazi河口。这些模式与土地利用、沿海基础设施、筑坝、采砂和影响沿海城市的极端天气事件的变化密切相关。这些结果为沿海风险管理提供了可操作的证据，包括识别侵蚀热点、告知市政退坡线、指导基于风险的土地利用规划，以及根据南非沿海综合管理框架（2008年第24号ICM法案，2014年第36号法案修订）确定适应措施的优先次序。因此，本研究提倡采用一种变革性的方法，将混合工程和基于自然的解决方案结合起来，以减少人类的脆弱性，并提供必要的基线数据，以支持适应性沿海管理和备灾，以增强德班尼的沿海复原力。

{"title":"A geospatial approach to assessing shoreline change dynamics along the eThekwini coast of South Africa","authors":"Ekang C. Amatebelle, Zachariah H. Mshelia, Abiodun A. Ogundeji, Solomon T. Owolabi","doi":"10.1016/j.ejrs.2026.01.004","DOIUrl":"10.1016/j.ejrs.2026.01.004","url":null,"abstract":"<div><div>The eThekwini coast represents a critical hotspot of vulnerability to coastal hazards, particularly erosion and flooding. However, studies assessing historical shoreline change in eThekwini over the past four decades remain limited. The objective of this study was to assess and quantify shoreline change dynamics in eThekwini using geospatial techniques. Satellite acquired imagery from 1984 to 2024 were process in ArcGIS 10.8 using the Modified Normalised Difference Water Index (MNDWI) and the Digital Shoreline Analysis System (DSAS v6.0). The study quantify shoreline change using established statistical metrics i.e. Net Shoreline Movement (NSM), End Point Rate (EPR), Linear Regression Rate (LRR), and Weighted Linear Regression (WLR). The results show that 57.45 % of the shoreline was accretional while 42.55 % were erosional. However, the long-term accretional trend is likely to shift towards erosion, as the overall change rate of −1.03 m/yr consistently exceeds the ± 0.66 m/yr uncertainty threshold, reflecting the combined influence of urban development, climate change and sea level rise impacts on the coast. Accretion dominated Reg-1 and Reg-4, whereas Reg-2 and Reg-3 experienced statistically significant erosion beyond uncertainty. The erosional hotspots were largely concentrated near major rivers including Umgeni, uMdloti, and uMkhomazi river mouths. These patterns strongly correlate with the changes in land use, coastal infrastructures, damming, sand mining, and extreme weather events affecting the coastal city. These results provide actionable evidence for coastal risk management by identifying erosion hotspots, informing municipal setback lines, guiding risk-based land-use planning, and prioritising adaptation measures under South Africa’s Integrated Coastal Management framework (ICM Act No. 24 of 2008, amended Act No. 36 of 2014).Therefore, this study advocates a transformative approach that combine hybrid engineering and nature-based solutions to reduce human vulnerability and provide essential baseline data to support adaptive coastal management, and disaster preparedness to enhance coastal resilience in eThekwini.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"29 1","pages":"Pages 72-85"},"PeriodicalIF":4.1,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Stacked ensemble model for flood layer extraction using EOS-04 satellite in Fine Resolution Stripmap (FRS) mode EOS-04卫星精细分辨率条带图（FRS）模式下洪水层提取的叠加系综模型

IF 4.1 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2026-01-20 DOI: 10.1016/j.ejrs.2026.01.005

Y.V. Sai Bhageerath, A.V. Suresh Babu, K.H.V. Durga Rao

This study presents a novel methodology for enhancing flood detection in India using Fine Resolution Stripmap (FRS) mode imagery from the EOS-04 Synthetic Aperture Radar (SAR) satellite. The proposed approach integrates deep learning and classical machine learning techniques through a stacked ensemble framework designed for water body extraction. Five deep learning models are independently trained, and their outputs are combined to construct a new feature matrix, which is subsequently used as input for five traditional classifiers: Logistic Regression, XGBoost, Random Forest, K-Nearest Neighbors, and Support Vector Machine. Among these, the XGBoost classifier achieved the highest classification accuracy of 96.8 %. A Land-Use-Land-Cover (LULC) water mask, provided by the National Remote Sensing Centre (NRSC), was applied to delineate the flood layer. Model performance was validated through cross-comparison with flood maps derived from concurrent-date optical satellite imagery, yielding an overall accuracy of 96.5%. Furthermore, a pixel-wise uncertainty map was derived from the ensemble predictions to quantify model confidence, offering additional insight into spatial reliability of flood extent mapping. By leveraging the complementary strengths of deep and traditional learning approaches, this method addresses the inherent limitations of FRS-mode SAR data for flood detection. The proposed framework offers a robust and scalable solution for operational flood monitoring, with significant implications for disaster preparedness and response across flood-prone regions of India.

本研究提出了一种利用EOS-04合成孔径雷达（SAR）卫星的精细分辨率条带图（FRS）模式图像增强印度洪水探测的新方法。该方法通过设计用于水体提取的堆叠集成框架，将深度学习和经典机器学习技术相结合。五个深度学习模型被独立训练，它们的输出被组合成一个新的特征矩阵，该特征矩阵随后被用作五个传统分类器的输入：逻辑回归、XGBoost、随机森林、k近邻和支持向量机。其中，XGBoost分类器的分类准确率最高，达到96.8%。由国家遥感中心（NRSC）提供的土地利用-土地覆盖（LULC）水掩膜被用来划定洪水层。通过与来自同期光学卫星图像的洪水图进行交叉比较，验证了模型的性能，总体精度为96.5%。此外，从集合预测中导出了逐像素的不确定性图，以量化模型置信度，为洪水范围制图的空间可靠性提供了额外的见解。通过利用深度学习方法和传统学习方法的互补优势，该方法解决了frs模式SAR数据用于洪水探测的固有局限性。拟议的框架为可操作的洪水监测提供了一个强大和可扩展的解决方案，对印度洪水易发地区的备灾和响应具有重要意义。

{"title":"Stacked ensemble model for flood layer extraction using EOS-04 satellite in Fine Resolution Stripmap (FRS) mode","authors":"Y.V. Sai Bhageerath, A.V. Suresh Babu, K.H.V. Durga Rao","doi":"10.1016/j.ejrs.2026.01.005","DOIUrl":"10.1016/j.ejrs.2026.01.005","url":null,"abstract":"<div><div>This study presents a novel methodology for enhancing flood detection in India using Fine Resolution Stripmap (FRS) mode imagery from the EOS-04 Synthetic Aperture Radar (SAR) satellite. The proposed approach integrates deep learning and classical machine learning techniques through a stacked ensemble framework designed for water body extraction. Five deep learning models are independently trained, and their outputs are combined to construct a new feature matrix, which is subsequently used as input for five traditional classifiers: Logistic Regression, XGBoost, Random Forest, K-Nearest Neighbors, and Support Vector Machine. Among these, the XGBoost classifier achieved the highest classification accuracy of 96.8 %. A Land-Use-Land-Cover (LULC) water mask, provided by the National Remote Sensing Centre (NRSC), was applied to delineate the flood layer. Model performance was validated through cross-comparison with flood maps derived from concurrent-date optical satellite imagery, yielding an overall accuracy of 96.5%. Furthermore, a pixel-wise uncertainty map was derived from the ensemble predictions to quantify model confidence, offering additional insight into spatial reliability of flood extent mapping. By leveraging the complementary strengths of deep and traditional learning approaches, this method addresses the inherent limitations of FRS-mode SAR data for flood detection. The proposed framework offers a robust and scalable solution for operational flood monitoring, with significant implications for disaster preparedness and response across flood-prone regions of India.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"29 1","pages":"Pages 60-71"},"PeriodicalIF":4.1,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0