Egyptian Journal of Remote Sensing and Space Sciences最新文献_第2页

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 : 2026-03-01 Epub 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。

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引用次数: 0

Integration between spatial analysis with MCDA for best site selection of hospitals: A case study of Port Said Governorate, Egypt 将空间分析与MCDA相结合用于医院最佳选址：以埃及塞得港省为例

IF 4.1 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2026-03-01 Epub Date: 2025-12-11 DOI: 10.1016/j.ejrs.2025.11.005

Hisham Nabil, Mahmoud El-Mewafi, Mohamed Zhran

The recognition and prioritization of hospital locations plays an important role in shaping the healthcare framework of any nation. The process of selecting suitable sites is an intricate, multi-faceted decision-making endeavor involving various stakeholders with their unique interests. The main objective of this study is to ascertain the most appropriate approach for the selection of the best location for the new hospital. Multi-criteria decision analysis (MCDA) presents a promising solution due to the multitude of criteria integral to the decision-making process. Accordingly, the current research focuses on four criteria (population density, proximity to road, distance to existing hospitals, and slope). This study employs three MCDA techniques, namely, analytical hierarchy process (AHP), fuzzy AHP (FAHP), and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Additionally, MCDA techniques utilizing geographical information systems (GIS), applied to Port Said Governorate, Egypt, are introduced. The results obtained through the application of AHP, FAHP, and TOPSIS are carefully compared, encompassing criterion rankings and suggested hospital site locations. These results indicate that AHP, FAHP, and TOPSIS give the same result for the best site selection for the new hospital in the study area. The results of this study provide decision-makers and urban planners vital information that makes it easier to pinpoint the best site location for a new hospital.

医院位置的识别和优先排序在塑造任何国家的医疗保健框架方面都起着重要作用。选择合适地点的过程是一个复杂的、多方面的决策过程，涉及不同利益相关者的独特利益。本研究的主要目的是确定为新医院选择最佳位置的最合适方法。多准则决策分析（MCDA）提出了一个有前途的解决方案，由于众多的标准组成的决策过程。因此，目前的研究重点是四个标准（人口密度、靠近道路、与现有医院的距离和坡度）。本研究采用三种MCDA技术，即分析层次分析法（AHP）、模糊层次分析法（FAHP）和理想解相似偏好排序法（TOPSIS）。此外，还介绍了在埃及塞得港省应用的利用地理信息系统（GIS）的MCDA技术。通过应用AHP、FAHP和TOPSIS获得的结果进行了仔细的比较，包括标准排名和建议的医院选址。这些结果表明，AHP、FAHP和TOPSIS对研究区新医院的最佳选址给出了相同的结果。这项研究的结果为决策者和城市规划者提供了重要的信息，使他们更容易确定新医院的最佳选址。

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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 : 2026-03-01 Epub 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。

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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 : 2026-03-01 Epub 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。

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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 : 2026-03-01 Epub 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。

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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 : 2026-03-01 Epub 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。

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引用次数: 0

Advancing coastal land use mapping through deep multi-label classification and multi-sensor data fusion 通过深度多标签分类和多传感器数据融合推进沿海土地利用制图

IF 4.1 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2025-12-01 Epub Date: 2025-10-24 DOI: 10.1016/j.ejrs.2025.10.004

Alireza Sharifi , Mohammad Mahdi Safari , Bayan Alabdullah

Coastal environments change and are environmentally sensitive. Land use classification must be accurate and timely for sustainable development, environmental monitoring, and catastrophe risk management. This research introduces a deep learning framework for categorizing coastal land use with multiple labels using high-resolution satellite pictures from several sensors. We design and evaluate a deep convolutional neural network architecture that classifies photos with multiple labels optimally using the MLRSNet dataset, which comprises 60 semantic classes from Chinese coastal locations. Data fusion merges spectral, spatial, and textural characteristics from many remote sensing methods, making classification findings more trustworthy and relevant to more circumstances. Numerous studies have proven that our method accurately separates complex and visually similar coastal categories including wetlands, beaches, rivers, ships, and urban coastlines. Precision, recall, F1-score, and mAP are used to evaluate the model. We also analyze its performance and mistakes in each class. The results demonstrate how deep learning and data fusion may address coastal remote sensing issues such semantic ambiguity, class variability, and class imbalance. This study enhances geographic artificial intelligence (GeoAI) by showing how to create a high-resolution shoreline map using a framework that works from start to end, can be scaled up, and can be utilized elsewhere. The recommended strategy affects environmental monitoring, coastal zone management, and fact-based decision-making, notably with climate change and urbanization along the coastline. Deep learning and multi-sensor satellite technologies can improve operational coastal monitoring systems, according to our findings.

沿海环境不断变化，对环境十分敏感。土地利用分类必须准确、及时地用于可持续发展、环境监测和巨灾风险管理。本研究引入了一个深度学习框架，利用来自多个传感器的高分辨率卫星图片，对沿海土地使用进行多标签分类。我们设计并评估了一个深度卷积神经网络架构，该架构使用MLRSNet数据集对具有多个标签的照片进行最佳分类，该数据集包含来自中国沿海地区的60个语义类。数据融合融合了许多遥感方法的光谱、空间和纹理特征，使分类结果更加可信，适用于更多的情况。许多研究已经证明，我们的方法可以准确地分离复杂的和视觉上相似的海岸类别，包括湿地、海滩、河流、船舶和城市海岸线。使用Precision， recall， F1-score和mAP来评估模型。我们还分析了它在每节课上的表现和错误。研究结果表明，深度学习和数据融合可以解决沿海遥感问题，如语义模糊、类别可变性和类别不平衡。这项研究通过展示如何使用从头到尾工作的框架创建高分辨率海岸线地图来增强地理人工智能（GeoAI），该框架可以按比例放大，并可以在其他地方使用。建议的策略影响环境监测、海岸带管理和基于事实的决策，特别是沿海地区的气候变化和城市化。根据我们的研究结果，深度学习和多传感器卫星技术可以改善沿海监测系统的运行。

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引用次数: 0

Comparative assessment of GIS-based multi-criteria decision analysis (AHP) and machine learning (MaxEnt) approaches for wildfire susceptibility modeling in Montenegro 基于gis的多准则决策分析（AHP）和机器学习（MaxEnt）方法在黑山野火易感性建模中的比较评估

IF 4.1 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2025-12-01 Epub Date: 2025-11-24 DOI: 10.1016/j.ejrs.2025.11.003

Filip Vujović , Goran Grozdanić , Radovan Đurović , Aleksandar Valjarević , Ivica Milevski

This study provides a national-scale assessment of wildfire susceptibility in Montenegro by comparing two modeling frameworks: a Geographic Information System–based Multi-Criteria Decision Analysis (GIS-MCDA) using fuzzy standardization with the Analytic Hierarchy Process (AHP), and a machine-learning approach based on the Maximum Entropy model (MaxEnt). Historical wildfire occurrences from the Moderate Resolution Imaging Spectroradiometer (MODIS) archive (2001–2024) were analyzed together with initial thirteen geoenvironmental initial causal criteria spanning vegetation, climatic, topographic and anthropogenic factors. After the Variance Inflation Factor (VIF) analysis, eleven causal criteria were retained for further modeling. Results of validation using receiver operating characteristic (ROC) curves showed clear performance differences: GIS-MCDA with fuzzy standardization and AHP weighting achieved low predictive accuracy (area under the curve, AUC = 0.51), whereas MaxEnt performed strongly (AUC = 0.81). These findings highlight the ability of MaxEnt to capture nonlinear relationships and complex interactions among geoenvironmental causal criteria, whereas GIS-MCDA with fuzzy standardization and AHP proved inadequate for reliable wildfire susceptibility assessment in this context. These results confirm the findings of previous studies, while showing an even weaker performance of GIS-MCDA with fuzzy standardization and AHP compared to earlier studies. Importantly, this is the first application of machine learning through MaxEnt for wildfire susceptibility assessment in Montenegro, providing a spatial basis for wildfire management and a foundation for national-scale wildfire risk assessment.

本研究通过比较两种建模框架：基于地理信息系统的多标准决策分析（GIS-MCDA）和基于最大熵模型（MaxEnt）的机器学习方法，对黑山的野火易感性进行了全国范围的评估。利用2001-2024年MODIS档案资料，结合植被、气候、地形和人为因素等13个地质环境初始因果准则，对历史野火发生情况进行了分析。在方差膨胀因子（VIF）分析后，保留了11个因果标准以进一步建模。使用受试者工作特征（ROC）曲线进行验证的结果显示出明显的性能差异：采用模糊标准化和AHP加权的GIS-MCDA的预测精度较低（曲线下面积，AUC = 0.51），而MaxEnt的预测精度较高（AUC = 0.81）。这些发现强调了MaxEnt能够捕捉地质环境因果标准之间的非线性关系和复杂相互作用，而具有模糊标准化和层次分析法的GIS-MCDA在这种情况下不足以进行可靠的野火易感性评估。这些结果证实了之前的研究结果，但与之前的研究相比，模糊标准化和层次分析法的GIS-MCDA的表现更弱。重要的是，这是通过MaxEnt首次将机器学习应用于黑山的野火易感性评估，为野火管理提供了空间基础，为全国范围的野火风险评估奠定了基础。

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引用次数: 0

SpecSpatMamba: an efficient hyperspectral image classification method integrating spectral-spatial dual-path and state space model SpecSpatMamba：一种结合光谱空间双路径和状态空间模型的高效高光谱图像分类方法

IF 4.1 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2025-12-01 Epub Date: 2025-10-08 DOI: 10.1016/j.ejrs.2025.10.001

Jianshang Liao , Liguo Wang

Current hyperspectral image classification methods face three critical limitations: (1) traditional CNNs suffer from the curse of dimensionality when processing high-dimensional spectral data, leading to overfitting and poor generalization; (2) existing approaches fail to effectively address spectral band redundancy, resulting in computational inefficiency and suboptimal feature representation; (3) conventional methods lack synergistic utilization of spatial-spectral information, treating spectral and spatial dimensions uniformly rather than exploiting their distinct characteristics. To address these gaps, this paper proposes SpecSpatMamba, a novel hyperspectral image classification method integrating spectral-spatial dual-path feature extraction with state space models. SpecSpatMamba introduces three core innovations: (1) Dual-path feature extraction with spectral-spatial separation, where 1 × 1 convolutions extract spectral features and 3 × 3 convolutions capture spatial features; (2) Hybrid architecture combining state space models with convolutional operations for balanced long-range dependency and local feature capture; (3) Computational efficiency breakthrough achieving O(L·d) linear complexity compared to Transformer’s O(L2·d) complexity. Experiments on four benchmark datasets—Indian Pines, Pavia University, Salinas Valley, and Houston2013—demonstrate competitive performance compared to state-of-the-art methods. SpecSpatMamba achieves overall accuracies of 95.11 %, 98.61 %, 96.97 %, and 91.48 %, respectively. Notably, SpecSpatMamba demonstrates superior cross-dataset consistency and robust performance across diverse geographic environments, with particularly strong improvements in complex urban scenarios (+0.39 % on Houston2013) and agricultural settings (+0.57 % on Salinas Valley), confirming the method’s effectiveness in addressing high-dimensional hyperspectral data challenges.

目前的高光谱图像分类方法面临三个关键的局限性：(1)传统cnn在处理高维光谱数据时存在维数诅咒，导致过拟合和泛化差；(2)现有方法无法有效处理频谱冗余，导致计算效率低下，特征表示不理想；(3)传统方法缺乏对空间光谱信息的协同利用，对光谱和空间维度进行统一处理，未能充分挖掘其各自的特征。为了解决这些问题，本文提出了SpecSpatMamba，一种将光谱-空间双路径特征提取与状态空间模型相结合的新型高光谱图像分类方法。SpecSpatMamba引入了三个核心创新：(1)光谱-空间分离双路径特征提取，其中1 × 1卷积提取光谱特征，3 × 3卷积捕获空间特征；(2)结合状态空间模型和卷积运算的混合架构，平衡远程依赖和局部特征捕获；(3)与Transformer的O（L2·d）复杂度相比，计算效率突破，实现了O（L·d）线性复杂度。在四个基准数据集（indian Pines、Pavia University、Salinas Valley和houston 2013）上进行的实验表明，与最先进的方法相比，它们的性能具有竞争力。SpecSpatMamba的总体准确率分别为95.11%、98.61%、96.97%和91.48%。值得注意的是，SpecSpatMamba在不同地理环境中表现出卓越的跨数据集一致性和稳健的性能，在复杂的城市场景（休斯顿2013年+ 0.39%）和农业环境（萨利纳斯山谷+ 0.57%）中表现出特别强的改进，证实了该方法在解决高维高光谱数据挑战方面的有效性。

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引用次数: 0

Sentinel-2 and Planet-Scope as reliable tools for water quality monitoring of small reservoirs Sentinel-2和Planet-Scope是小型水库水质监测的可靠工具

IF 4.1 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2025-12-01 Epub Date: 2025-11-17 DOI: 10.1016/j.ejrs.2025.11.002

Silvia Di Francesco , Francesco Biondi , Barbara Casentini , Stefano Fazi , Stefano Amalfitano , Marco D’Eugenio , Francesca Todisco , Stefano Casadei , Francesca Giannone

In an era marked by increasingly frequent extreme weather events, small inland reservoirs are emerging as crucial yet often overlooked water resources. This study investigates the potential of remote sensing techniques to efficiently monitor the water quality of those reservoirs and improve their management. Although many works in literature have tried to derive water quality parameters from different satellite platforms, micro satellites constellations like PlanetScope have never been investigated: they can be a promising tool for investigation of SRs thanks to their high spatial and temporal resolution. Focusing on Spina Reservoir, a small lake in the province of Perugia where a water quality survey has been conducted, the research combines on-site biochemical analyses with satellite imagery from Sentinel-2, well known and explored free Platform, and PlanetScope. The performances of images corrected with the default atmospheric correction and with a specific pre-processor (ACOLITE) for inland and coastal water are discussed.

Water Quality Semi-empirical algorithms (indices) based on one or more spectral bands at different wavelengths are used to build correlation curves respect to in-situ measurements (e.g Chlorophyll-a, turbidity, Cyanobacteria), enabling the evaluation and comparison of the performance.

PlanetScope images displayed higher reliability with respect to Sentinel-2 data and correction with ACOLITE lead to more accurate interpolations, except for chlorophyll-a, even if satellite images with lower spatial resolution (Sentinel-2) can also provide a well-distributed dataset.

The findings underscore the significant potential of PlanetScope microsatellite constellation for real-time, cost-effective water quality assessment that could be easily applied on a larger scale, as regional assessment.

在一个极端天气事件日益频繁的时代，小型内陆水库正成为至关重要但往往被忽视的水资源。本研究探讨了遥感技术在有效监测水库水质和改善水库管理方面的潜力。尽管文献中的许多作品都试图从不同的卫星平台上获得水质参数，但像PlanetScope这样的微型卫星星座从未被研究过：由于它们具有高时空分辨率，它们可以成为研究SRs的有前途的工具。该研究以佩鲁贾省的斯皮纳水库（Spina Reservoir）为研究对象，将现场生化分析与Sentinel-2卫星图像结合起来，Sentinel-2是著名的免费平台，PlanetScope也进行了水质调查。讨论了使用默认大气校正和特定预处理（ACOLITE）对内陆和沿海水域进行校正的图像的性能。基于不同波长的一个或多个光谱波段的半经验算法（指数）用于建立与原位测量（例如叶绿素-a，浊度，蓝藻）相关的曲线，从而对性能进行评估和比较。PlanetScope图像相对于Sentinel-2数据显示出更高的可靠性，即使空间分辨率较低的卫星图像（Sentinel-2）也可以提供分布良好的数据集，但ACOLITE校正导致更准确的插值（叶绿素-a除外）。这些发现强调了PlanetScope微卫星星座在实时、具有成本效益的水质评估方面的巨大潜力，可以很容易地应用于更大规模的区域评估。

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引用次数: 0