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

Iron ore exploration in the Central Eastern Desert of Egypt: Insights from remote Sensing, Geophysical, and geochemical data 埃及中东部沙漠的铁矿勘探：来自遥感、地球物理和地球化学数据的见解

IF 4.1 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2025-11-11 DOI: 10.1016/j.ejrs.2025.11.001

Mahmoud Abd El-Rahman Hegab , Salem Mohamed Salem , Nehal Mohamed Soliman , Kareem Hamed Abd El Wahid , Soha Hassan , Alaa Nayef , Mohamed Anwar Ahmed

The novelty of this study lies in applying an integrated workflow that combines geological mapping, aeromagnetic analysis, remote sensing, and XRF analysis to delineate extensions of known iron ore deposits and identify previously unrecognized occurrences, while simultaneously providing new insights into the tectono-magmatic controls of iron mineralization in the Central Eastern Desert. The findings provide critical data on the spatial organization, mineral characteristics, and geological controls of iron ore in this complex tectonic setting, enabling more efficient exploration plans in the Eastern Desert. Metamorphosed banded iron formations (BIFs) prevail at several localities, e.g., Gabal El Hadid, Umm Nar, Umm Ghamis El Zarqa, El Sibai, El Dabbah, and Wadi Kareem. These BIFs occur within a metavolcano-sedimentary environment, with thicknesses of up to 5 m, in the form of bands and lenses composed of magnetite, hematite, and silica. Magnetic spectral analysis enabled clear discrimination among lithological units, definition of structural controls, and demarcation of alteration zones associated with iron mineralization.

这项研究的新颖之处在于，它将地质测绘、航磁分析、遥感和XRF分析相结合，应用了一个集成的工作流程，以描绘已知铁矿床的延伸，并识别以前未被识别的矿点，同时为中东部沙漠中铁矿化的构造-岩浆控制提供了新的见解。这一发现为研究这一复杂构造环境下的铁矿石空间组织、矿物特征和地质控制提供了关键数据，有助于制定更有效的东部沙漠勘探计划。变质带状铁地层（BIFs）在Gabal El Hadid、Umm Nar、Umm Ghamis El Zarqa、El Sibai、El Dabbah和Wadi Kareem等几个地方普遍存在。这些bif出现在变质火山-沉积环境中，厚度可达5米，以磁铁矿、赤铁矿和二氧化硅组成的带状和透镜状的形式存在。通过磁谱分析，可以清晰地区分岩性单元，明确构造控制，划分与铁矿成矿有关的蚀变带。

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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 : 2025-10-24 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

Deep multimodal unmixing of hyperspectral images using Convolutional Block Attention Module (CBAM) and LiDAR features 基于卷积块注意模块（CBAM）和激光雷达特征的高光谱图像深度多模态解混

IF 4.1 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2025-10-21 DOI: 10.1016/j.ejrs.2025.10.002

M Sreejam, L Agilandeeswari

Hyperspectral image unmixing has garnered considerable attention across various application domains, particularly remote sensing applications. However, relying solely on one modality to distinguish objects with similar spectral information presents several shortcomings. Enhanced performance can be achieved by integrating geographical information from Light Detection and Ranging (LiDAR) data into Unmixing. This paper introduces a new unmixing model that combines hyperspectral and LiDAR data. Impressive data representation and feature extraction using deep learning technology have been employed to develop the Multimodal Hyperspectral Unmixing Model using CBAM (Convolutional Block Attention Module) attention (MHUCBAM). The model exemplifies a sophisticated approach to multimodal unmixing, incorporating Spectral Spatial attention alongside the CBAM. Channel Attention improved the model’s capability to analyze complex spatial and spectral relationships. Our model achieves accurate unmixing of complex environments with effective multimodal data representation and deep feature extraction. Two real-world multimodal unmixing datasets, namely, Houston and Muffle, are used for the performance evaluation. A rigorous ablation analysis was performed to validate the performance of the proposed model. The comparative study with existing unmixing models demonstrated that utilizing latent features from LiDAR data resulted in better unmixing outcomes in terms of both Root Mean Square Error (RMSE) and Spectral Angular Distance (SAD).

高光谱图像解混已经在各个应用领域引起了相当大的关注，特别是遥感应用。然而，仅仅依靠一种模态来区分具有相似光谱信息的物体存在一些缺点。通过将来自光探测和测距（LiDAR）数据的地理信息集成到Unmixing中，可以提高性能。本文介绍了一种结合高光谱和激光雷达数据的新解混模型。使用深度学习技术的令人印象深刻的数据表示和特征提取被用于开发使用CBAM（卷积块注意模块）注意（MHUCBAM）的多模态高光谱解混模型。该模型体现了一种复杂的多模态解混方法，将频谱空间关注与CBAM结合在一起。通道注意提高了模型分析复杂空间和光谱关系的能力。该模型通过有效的多模态数据表示和深度特征提取实现了复杂环境的精确解混。两个真实世界的多模态解混数据集，即Houston和Muffle，用于性能评估。进行了严格的烧蚀分析，以验证所提出模型的性能。与现有解混模型的对比研究表明，利用LiDAR数据的潜在特征在均方根误差（RMSE）和光谱角距离（SAD）方面都可以获得更好的解混结果。

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引用次数: 0

Efficient monitoring of groundwater level changes using compressive remote sensing 压缩遥感对地下水位变化的有效监测

IF 4.1 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2025-10-17 DOI: 10.1016/j.ejrs.2025.10.003

Cihan Bayındır , Ali Rıza Alan

In this paper, we propose and discuss the applicability of compressive sensing (CS) for the remote measurement and analysis of groundwater level changes. For this purpose, we consider three watersheds in Turkey and utilize the data acquired by the Gravity Recovery and Climate Experiment (GRACE) satellite at these watersheds. These watersheds are Fırat (Euphrates), Kızılırmak, and Büyük Menderes (Greater Menderes). The data collected by the GRACE satellite have a temporal resolution on the order of months, however, due to operation and maintenance considerations it is known that some of the GRACE data may be missing. Using the time series data collected between 2002 and 2019 at these three watersheds we show that the time series of the groundwater table (GWT) can be reconstructed using CS which utilizes fewer samples than the classical Shannon’s theorem states. Thus, when the CS technique is utilized, measurement times and hardware storage requirements of groundwater sensing systems can be significantly reduced where some errors can be observed in the reconstruction of the GWT level. In some cases, such parameters can be exactly reconstructed by CS even in the presence of missing data if certain sparsity and sampling conditions are satisfied. The CS-based GWT reconstruction technique proposed in this paper can also be extended to measure and analyze other types of data such as in situ groundwater levels, groundwater velocities, and groundwater volume flux data in hydrology and hydraulics.

本文提出并讨论了压缩感知（CS）技术在地下水位变化遥感测量与分析中的适用性。为此，我们考虑了土耳其的三个流域，并利用了重力恢复和气候实验（GRACE）卫星在这些流域获得的数据。这些流域分别是Fırat（幼发拉底河）、Kızılırmak和b y k Menderes（大Menderes）。GRACE卫星收集的数据具有月级的时间分辨率，但是，由于操作和维护方面的考虑，已知一些GRACE数据可能会丢失。利用2002年至2019年在这三个流域收集的时间序列数据，我们表明使用CS可以重建地下水位（GWT）的时间序列，该方法比经典香农定理使用的样本更少。因此，当使用CS技术时，地下水传感系统的测量次数和硬件存储要求可以大大减少，但在重建GWT水位时可以观察到一些误差。在某些情况下，如果满足一定的稀疏性和采样条件，即使存在缺失数据，CS也可以精确地重建这些参数。本文提出的基于cs的GWT重建技术还可以扩展到其他类型的数据，如水文水力学中的地下水位、地下流速、地下水体积通量等数据的测量和分析。

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引用次数: 0

Advanced time series forecasting of vegetation health using deep learning models: A remote sensing approach to analyzing climate change impact 使用深度学习模型的植被健康高级时间序列预测：一种分析气候变化影响的遥感方法

IF 4.1 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2025-10-16 DOI: 10.1016/j.ejrs.2025.09.005

Sarhad Baez Hasan , Shahab Wahhab Kareem

The growing consequences of climate change on vegetation ecosystems require advanced predictive tools for environmental monitoring and adaptive management. This research explored a new application of hybrid deep learning models to forecast the Normalized Difference Vegetation Index (NDVI) time series, using Sentinel-2 high-resolution satellite images. Specifically, this research investigated vegetation dynamics in four climatically different regions of Northern Iraq from 2016 to 2024, developing and comparing eight deep learning models, including traditional recurrent networks (Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BiLSTM), and Gated Recurrent Unit (GRU)) and Convolutional Neural Networks (CNN), resulting in unique hybrid models that combine spatial and temporal feature extraction mechanisms. The study utilized a large dataset of 43,200 images with a spatial resolution of 10 m, employing systematic data preparation that included NDVI processing (NDVI calculations, normalization, and time-series sequence construction) necessary for model training and learning. The model performance was rigorously evaluated, where hybrid models were demonstrated to outperform other models, with BiLSTM-GRU appearing to deliver high accuracy (coefficient of determination scores R² of up to 0.851) and low prediction errors (Mean Squared Error (MSE) as low as 6.04 × 10⁻⁴). In terms of ecological region, model performance was assessed across regions, as well as across different regions, finding general trends in performance, particularly in regions with homogeneous vegetation cover at each time sampling period. The Monte Carlo dropout method offered the opportunity to infer uncertainty, which in turn helped build confidence in predictions. The predictions for the future periods of 2025–2028 show promising seasonal patterns and long-term trends, which are important with respect to climate-adjusted planning.

气候变化对植被生态系统的影响日益严重，需要先进的预测工具来进行环境监测和适应性管理。本研究探索了混合深度学习模型的新应用，利用Sentinel-2高分辨率卫星图像预测归一化植被指数（NDVI）时间序列。具体而言，本研究以2016 - 2024年伊拉克北部4个气候不同地区的植被动态为研究对象，开发并比较了8种深度学习模型，包括传统递归网络（长短期记忆（LSTM）、双向长短期记忆（BiLSTM）和门控递归单元（GRU））和卷积神经网络（CNN），形成了独特的结合时空特征提取机制的混合模型。本研究利用空间分辨率为10 m的43,200幅图像的大型数据集，采用系统的数据准备，包括NDVI处理（NDVI计算、归一化和时间序列序列构建），这是模型训练和学习所必需的。对模型的性能进行了严格的评估，混合模型被证明优于其他模型，BiLSTM-GRU似乎提供了高精度（决定系数R2高达0.851）和低预测误差（均方误差（MSE）低至6.04 × 10−4）。就生态区域而言，对模型的性能进行了跨区域和不同区域的评估，发现了性能的总体趋势，特别是在每个采样期植被覆盖均匀的区域。蒙特卡洛辍学法提供了推断不确定性的机会，这反过来又有助于建立对预测的信心。对2025-2028年未来时期的预测显示出有希望的季节模式和长期趋势，这对气候调整规划很重要。

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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 : 2025-10-08 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

UAV-based agricultural spraying: A study on spiral movements and pesticide optimization 基于无人机的农业喷洒：螺旋运动与农药优化研究

IF 4.1 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2025-09-17 DOI: 10.1016/j.ejrs.2025.09.001

Mevlüt İnan , Ali Karci

Unmanned aerial vehicles (UAVs) have become an essential component of precision agriculture, providing enhanced accuracy and operational efficiency in pesticide application. This study presents an innovative spraying protocol that integrates spiral flight trajectories with volumetric classification of olive trees, enhancing operational performance while reducing environmental impact. Using high-resolution UAV imagery in conjunction with advanced image processing, trees were categorized into small, medium, and large canopy-volume classes. For each group, optimized spiral patterns with predefined turn counts and flight altitudes were assigned to achieve uniform droplet deposition across complex canopy structures. Field experiments conducted in the Hekimhan district of Malatya, Türkiye, demonstrated an 85 % improvement in spraying efficiency, a 15 % reduction in chemical usage, and a 20 % decrease in operational time compared with conventional methods. The proposed approach significantly improved targeting precision and minimized off-target drift. These results clearly indicate that the proposed protocol is scalable, environmentally sustainable, and operationally efficient for pesticide application in orchards and other tree-based agricultural systems.This approach demonstrates considerable potential for widespread adoption in precision agriculture, offering a replicable and adaptable framework for enhancing the efficiency and sustainability of pesticide application in diverse orchard systems.

无人机（uav）已成为精准农业的重要组成部分，为农药施用提供了更高的准确性和操作效率。本研究提出了一种创新的喷雾方案，将螺旋飞行轨迹与橄榄树的体积分类相结合，提高了操作性能，同时减少了对环境的影响。利用高分辨率无人机图像结合先进的图像处理，将树木分为小、中、大树冠体积类。对于每一组，优化的螺旋模式与预定义的转弯数和飞行高度分配，以实现均匀的液滴沉积在复杂的冠层结构。在吉尔吉斯斯坦共和国马拉提亚的Hekimhan地区进行的实地试验表明，与传统方法相比，喷洒效率提高了85%，化学品使用量减少了15%，作业时间减少了20%。该方法显著提高了瞄准精度，减小了偏离目标漂移。这些结果清楚地表明，所提出的协议具有可扩展性、环境可持续性和操作效率，适用于果园和其他基于树木的农业系统的农药施用。这种方法在精准农业中具有广泛应用的巨大潜力，为提高不同果园系统中农药施用的效率和可持续性提供了可复制和适应性的框架。

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引用次数: 0

Climate impact on spatial patterns of Aedes aegypti abundance in Al-Quseer with distribution maps 气候对Al-Quseer地区埃及伊蚊丰度空间格局的影响及分布图

IF 4.1 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2025-09-16 DOI: 10.1016/j.ejrs.2025.09.002

M.H. Rady , Areej A. Al-Khalaf , M.S. Salama , Islam Abou El-Magd , M. Emam , Shaimaa A.A. Moʼmen , Shaimaa M. Farag , M.S. Yones , Abdelwahab Khalil

The invasion of new mosquito disease vectors can alter the abundance of resident mosquito populations, leading to new vector distribution patterns and associated disease risks. A notable example is the re-invasion of the Red Sea region by Aedes aegypti since 2017, facilitated by the area’s hot and humid conditions. In this study, Ae. aegypti larvae were collected from indoors and outdoors habitats and entomological indices were calculated. To assess the influence of climate on spatial distribution, we utilized Landsat-8 satellite-derived maps of Al Quseer (Red Sea Governorate, Egypt), incorporating key climatic and environmental abiotic factors to develop a cartographic model. This model classified areas into different risk levels for Aedes breeding and prevalence. Our results indicate that the primary climatic and environmental factors affecting Ae. aegypti distribution and abundance were temperature, moisture, and vegetation cover—the latter of which indirectly influences microclimates by providing shade and maintaining humidity, thereby affecting mosquito resting sites and survival. The study identified three major risk levels based on breeding suitability: high-risk areas (0.15 km2), moderate-risk areas (0.47 km2), and limited-risk areas (7.24 km2). Of the total study area (4,659 km2), mosquito activity was detected across 655.62 km2, while 4,003.78 km2 remained unaffected. Urban areas within high-risk zones covered 9.11 km2, whereas only 0.25 km2 of urban districts in Al Quseer fell outside the mosquito’s range. Understanding the ecological drivers of Ae. aegypti abundance and predicting its future distribution provides critical insights into vector biology and potential expansion, offering valuable guidance for integrated dengue control strategies.

新的蚊子病媒的入侵可以改变蚊子种群的丰富程度，导致新的病媒分布模式和相关的疾病风险。一个值得注意的例子是，自2017年以来，埃及伊蚊（Aedes aegypti）在红海地区炎热潮湿的条件下再次入侵。在这项研究中，Ae。在室内和室外生境采集埃及伊蚊幼虫，计算昆虫学指数。为了评估气候对空间分布的影响，我们利用Landsat-8卫星衍生的Al Quseer（红海省，埃及）地图，结合关键的气候和环境非生物因子建立了制图模型。该模型将伊蚊孳生和流行的风险等级划分为不同的地区。研究结果表明，主要的气候和环境因素影响了白蛉的生长。埃及伊蚊的分布和数量取决于温度、湿度和植被覆盖——后者通过提供荫凉和保持湿度间接影响小气候，从而影响蚊子的休息地点和生存。该研究根据育种适宜性确定了3个主要风险等级：高风险区（0.15 km2）、中等风险区（0.47 km2）和有限风险区（7.24 km2）。在总研究面积（4659 km2）中，655.62 km2有蚊虫活动，4003.78 km2未受影响。高风险区的城市面积为9.11平方公里，而Al Quseer的城市地区只有0.25平方公里不在蚊子的活动范围内。了解Ae的生态驱动因素。埃及伊蚊的丰度和对其未来分布的预测为了解媒介生物学和潜在的扩展提供了重要的见解，为登革热综合控制战略提供了有价值的指导。

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引用次数: 0

GPS and LiDAR optimizing transformation parameters for localization in autonomous vehicles GPS和LiDAR优化自动驾驶汽车定位变换参数

IF 4.1 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2025-09-16 DOI: 10.1016/j.ejrs.2025.09.004

Sundoss ALMahadeen

Accurate localization is necessary for autonomous vehicles, with the demand for the correct fusion of Global Navigation Satellite System (GNSS) and Light Detection and Ranging (LiDAR) data. Existing static transformation parameter optimization methods do not work well to address dynamic environmental conditions such as GNSS signal weakening in urban canyons and LiDAR inconsistencies in open or obstructed environments. This work presents an LSTM-based technique of real-time transformation parameter optimization, automatically adjusting translation, rotation, and scale factors. The LSTM network processes sequential GNSS and LiDAR data, leveraging temporal correlations to enhance accuracy. Exhaustive experiments on real and simulated data demonstrate that the presented model reduces localization error by 25% compared to traditional techniques. The architecture provides an improvement of robustness over flexibility in complex situations like urban, rural, and tunneling conditions, and hence it is a strong solution for autonomous vehicle navigation

准确的定位对自动驾驶汽车来说是必要的，需要正确融合全球导航卫星系统（GNSS）和光探测和测距（LiDAR）数据。现有的静态变换参数优化方法不能很好地解决城市峡谷中GNSS信号减弱、开放或受阻环境中LiDAR不一致等动态环境条件。本文提出了一种基于lstm的实时变换参数优化技术，自动调整平移、旋转和比例因子。LSTM网络处理连续的GNSS和LiDAR数据，利用时间相关性来提高准确性。在真实数据和仿真数据上的详尽实验表明，该模型与传统定位方法相比，定位误差降低了25%。该架构在城市、农村和隧道条件等复杂情况下提供了鲁棒性和灵活性的改进，因此它是自动车辆导航的强大解决方案

引用次数: 0

A multi-criteria GIS model for geohazard assessment in the Charvak reservoir area, Uzbekistan 乌兹别克斯坦Charvak库区地质灾害评价的多标准GIS模型

IF 4.1 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2025-09-01 DOI: 10.1016/j.ejrs.2025.09.003

Dilbarkhon Fazilova , Khasan Magdiev , Mirshodjon Makhmudov , Alisher Fazilov

Mountainous reservoir regions are particularly susceptible to geohazards due to steep topography, fractured lithologies, active faults, and seasonal hydrological fluctuations. The Charvak basin in northeastern Uzbekistan, designated as a Free Tourist Recreation Zone, is increasingly affected by expanding infrastructure and tourism, which increases exposure to natural hazards. This study presents the first integrated geohazard susceptibility map of the Charvak basin using remote sensing and multi-criteria GIS analysis. A GIS-based model was developed to evaluate slope-related hazards—landslides, debris flows, and rockfalls—based on six indicators: slope gradient, lithological strength, lineament density, Normalized Difference Water Index (NDWI), distance to active faults, and distance to the reservoir shoreline. The indicators were weighted using the Analytic Hierarchy Process (AHP), with slope gradient (0.28) and lineament density (0.24) identified as dominant factors. The resulting composite index was validated through comparison with landslide and debris flow inventories as well as seismicity data. The susceptibility map indicates that ∼19 % of the basin falls into high and very high hazard classes, while ∼48 % is classified as low to very low. High-susceptibility zones overlap substantially with infrastructure, including 21 % of villages and tourism facilities and 27 % of the road network. These findings provide a spatial basis for risk-informed land-use regulation, infrastructure planning, and disaster management in the Charvak region. More broadly, the study demonstrates the effectiveness of combining remote sensing and multi-criteria GIS methods for geohazard assessment in other mountainous and data-limited environments.

由于地形陡峭、岩性断裂、活动断层和季节性水文波动，山区库区特别容易受到地质灾害的影响。乌兹别克斯坦东北部的Charvak盆地被指定为自由旅游游乐区，受到基础设施和旅游业不断扩大的影响，这增加了自然灾害的风险。本文首次利用遥感和多准则GIS技术，绘制了沙尔瓦克盆地的综合地质灾害易感性图。开发了一个基于gis的模型来评估与斜坡相关的灾害——滑坡、泥石流和落石，该模型基于六个指标：坡度、岩性强度、线条密度、归一化差水指数（NDWI）、到活动断层的距离以及到水库岸线的距离。采用层次分析法（AHP）对各指标进行加权，确定坡度（0.28）和线条密度（0.24）为主导因素。通过与滑坡、泥石流清查和地震活动性数据的对比，验证了所得到的综合指数。易感性图表明，流域约19%的地区属于高和非常高的危险等级，而约48%的地区属于低到非常低的危险等级。高易感性地区与基础设施有很大的重叠，包括21%的村庄和旅游设施以及27%的道路网络。这些发现为Charvak地区基于风险的土地利用监管、基础设施规划和灾害管理提供了空间基础。更广泛地说，该研究表明，在其他山区和数据有限的环境中，将遥感和多准则GIS方法结合起来进行地质灾害评估是有效的。

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引用次数: 0