Remote Sensing Applications-Society and Environment最新文献

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IF 4.5 Q2 ENVIRONMENTAL SCIENCES

Remote Sensing Applications-Society and Environment

Pub Date : 2026-01-01

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IF 4.5 Q2 ENVIRONMENTAL SCIENCES

Remote Sensing Applications-Society and Environment

Pub Date : 2026-01-01

引用次数: 0

Spatiotemporal dynamics of carbon, water, and energy balance in Bangladesh using multi-source remote sensing and climate data 基于多源遥感和气候数据的孟加拉国碳、水和能量平衡时空动态

IF 4.5 Q2 ENVIRONMENTAL SCIENCES

Remote Sensing Applications-Society and Environment

Pub Date : 2026-01-01 DOI: 10.1016/j.rsase.2025.101847

Nur Hussain , Md Saifuzzaman , Didar Islam , S.M. Shahriar Ahmed , Md Shamim Ahamed , Dara Shamsuddin

Exploring the complex interactions between climate variables and ecosystem processes is crucial for understanding long-term environmental changes. This study examines the spatiotemporal dynamics of carbon, water and energy fluxes and their impacts on ecosystem processes in Bangladesh from 2005 to 2022 utilizing multi-source remote sensing and ground-based meteorological data. Carbon dynamics are estimated through gross primary productivity (GPP), net primary production (NPP), and ecosystem respiration (RE). Water and energy balances are derived from evapotranspiration (ET), water use efficiency (WUE), net radiation (Rn), and latent heat (LE). Our estimates indicate that GPP varied from 2351.29 g C m⁻² y⁻¹ in 2009–2178.45 g C m⁻² y⁻¹ in 2020, while NPP ranged from 1248.13 g C m⁻² y⁻¹ in 2012 to 929.46 g C m⁻² y⁻¹ in 2020, reflecting temporal variations in photosynthetic efficiency and carbon storage. The ratio of LE/Rn was found to vary from 0.72 to 1.01, with an average of 83 %, indicating that a significant portion of the radiative energy was transferred to the atmosphere as turbulent flux. Validation of LUE-based GPP compared to FLUXCOM-GPP showed a moderate correlation (R² = 0.61, p < 0.005), supporting the reliability of the estimates. We also conducted multivariate regression analysis to assess the relationships between climate variables and carbon, water, and energy balance. The results indicate that photosynthetically active radiation (PAR) is the primary and dominant driver of GPP (R² = 0.97), while temperature and precipitation are key factors significantly influencing carbon uptake. This study presents a comprehensive, integrated assessment of carbon, water, and energy fluxes at the national scale across Bangladesh, emphasizing the crucial role of climate variables in shaping these fluxes and offering valuable insights for climate-resilient land management and sustainable carbon strategies in monsoon-dominated regions.

探索气候变量和生态系统过程之间复杂的相互作用对于理解长期环境变化至关重要。本研究利用多源遥感和地面气象数据，研究了2005 - 2022年孟加拉国碳、水和能量通量的时空动态及其对生态系统过程的影响。碳动态通过总初级生产力（GPP）、净初级生产力（NPP）和生态系统呼吸（RE）来估算。水分和能量平衡来源于蒸散发（ET）、水分利用效率（WUE）、净辐射（Rn）和潜热（LE）。我们的估计表明，GPP在2009年至2020年的2351.29 g C m−2 y−1之间变化，而NPP在2012年的1248.13 g C m−2 y−1到2020年的929.46 g C m−2 y−1之间变化，反映了光合效率和碳储量的时间变化。LE/Rn的比值在0.72 ~ 1.01之间变化，平均为83%，表明有很大一部分辐射能量以湍流通量的形式转移到大气中。与FLUXCOM-GPP相比，基于lue的GPP验证显示有中等相关性（R2 = 0.61, p < 0.005），支持估计的可靠性。我们还进行了多变量回归分析，以评估气候变量与碳、水和能量平衡之间的关系。结果表明，光合有效辐射（PAR）是GPP的主要和主导驱动因子（R2 = 0.97），而温度和降水是影响碳吸收的关键因素。本研究对孟加拉国全国范围内的碳、水和能源通量进行了全面、综合的评估，强调了气候变量在形成这些通量方面的关键作用，并为季风主导地区的气候适应型土地管理和可持续碳战略提供了有价值的见解。

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引用次数: 0

Monitoring of the smouldering coal-waste dump in Chorzów (Poland) using spectral indices: A UAV- and satellite-based approach 使用光谱指数监测Chorzów（波兰）的闷烧煤矸石堆：基于无人机和卫星的方法

IF 4.5 Q2 ENVIRONMENTAL SCIENCES

Remote Sensing Applications-Society and Environment

Pub Date : 2026-01-01 DOI: 10.1016/j.rsase.2025.101865

Anna Abramowicz, Michał Laska, Ádám Nádudvari, Oimahmad Rahmonov

The study aimed to evaluate the applicability of environmental indices in the monitoring of smouldering coal-waste dumps. A dump located in the Upper Silesian Coal Basin served as the research site for a multi-method analysis combining remote sensing and field-based data. Two UAV survey campaigns were conducted, capturing RGB, infrared, and multispectral imagery. These were supplemented with direct ground measurements of subsurface temperature and detailed vegetation mapping. Additionally, publicly available satellite data from the Landsat and Sentinel missions were analysed. A range of vegetation and fire-related indices (NDVI, SAVI, EVI, BAI, among others) were calculated to identify thermally active zones and assess vegetation conditions within these degraded areas. The results revealed strong seasonal variability in vegetation indices on thermally active sites, with evidence of disrupted vegetation cycles, including winter greening in moderately heated root zones – a pattern indicative of stress and degradation processes. While open-access satellite data, such as Landsat and Sentinel-2, proved useful in reconstructing the fire history of the dump, their spatial resolution was insufficient for detailed monitoring of small-scale thermal anomalies. The study highlights the diagnostic potential of UAV-based remote sensing in post-industrial environments undergoing land degradation but emphasises the importance of field validation for accurate environmental assessment.

本研究旨在评价环境指标在阴燃煤矸石堆场监测中的适用性。位于上西里西亚煤盆地的一个垃圾场作为研究地点，进行了结合遥感和实地数据的多方法分析。进行了两次无人机调查运动，捕获RGB、红外和多光谱图像。这些数据还补充了地下温度的直接地面测量和详细的植被测绘。此外，还分析了陆地卫星和哨兵任务的公开卫星数据。计算了一系列植被和火灾相关指数（NDVI、SAVI、EVI、BAI等），以确定热活跃区并评估这些退化地区的植被状况。结果显示，在热活跃区，植被指数具有强烈的季节性变化，植被循环被破坏，包括在中等热的根区冬季绿化，这是一种指示应力和退化过程的模式。虽然开放获取的卫星数据，如Landsat和Sentinel-2，在重建垃圾场的火灾历史方面被证明是有用的，但它们的空间分辨率不足以详细监测小规模的热异常。该研究强调了基于无人机的遥感在经历土地退化的后工业环境中的诊断潜力，但强调了实地验证对准确环境评估的重要性。

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引用次数: 0

Precise 3D crustal displacement retrieval in GCP-free environments: A geodetic and deep learning–assisted integration of InSAR and optical stereo data near the Denali Fault 无gcp环境下精确三维地壳位移反演：迪纳里断裂带附近InSAR和光学立体数据的大地测量和深度学习辅助整合

IF 4.5 Q2 ENVIRONMENTAL SCIENCES

Remote Sensing Applications-Society and Environment

Pub Date : 2026-01-01 DOI: 10.1016/j.rsase.2026.101894

Zahra Alizadeh Zakaria , Farshid Farnood Ahmadi , Hamid Ebadi

Retrieving accurate 3D deformation fields from Interferometric Synthetic Aperture Radar)InSAR(line-of-sight (LOS) measurements is challenging because LOS data provide only one-dimensional motion, and the absence of ground control points (GCPs) in remote regions makes reliable 3D reconstruction even more difficult. This study introduces a deep learning-assisted approach to map 3D deformation fields without GCPs, integrating Pleiades stereo imagery with InSAR techniques over a 20 km × 20 km segment of the Denali Fault, Alaska. Initial 3D displacements from Pleiades, derived through DEM differencing and COSI-Corr, eliminate GCPs and reveal vertical displacements of ±10 mm (uplift/subsidence), with horizontal displacements of ±6.4 mm (east-west) and ±5.9 mm (north-south), consistent with the fault's right-lateral strike-slip kinematics and local GNSS IGS14/NNR velocities (2019–2024). These Pleiades displacements, combined with PS-InSAR rates and geological features like slope and Terrain Ruggedness Index (TRI), serve as inputs to a U-Net model that transforms LOS data into 3D fields, expanding displacement ranges to ±20 mm across all components. We enhance U-Net estimates with geodetic optimization and use Monte Carlo Dropout (10 samples) to quantify uncertainties of 0.1–0.3 mm for east-west and 0.5–0.8 mm for north-south and vertical displacements. Validation of the model against Pleiades test data yields RMSEs of 1.17 mm (east-west), 1.46 mm (north-south), and 1.52 mm (vertical), with an RMSE of 1.98 mm against the vertical component of three local GNSS stations (6–50 km distance, IGS14/NNR frame, 2019–2024). This InSAR-Pleiades-deep learning method offers a scalable solution for 3D deformation monitoring, advancing seismic hazard assessment in GCP-free environments.

从干涉合成孔径雷达（InSAR）视距（LOS）测量中获取准确的3D变形场是具有挑战性的，因为LOS数据仅提供一维运动，并且在偏远地区缺乏地面控制点（gcp），使得可靠的3D重建变得更加困难。该研究引入了一种深度学习辅助方法来绘制没有gcp的3D变形场，将Pleiades立体图像与InSAR技术集成在阿拉斯加Denali断层的20公里× 20公里段上。Pleiades的初始三维位移通过DEM差分和cos - corr得到，消除了gcp，显示垂直位移为±10 mm（隆起/沉降），水平位移为±6.4 mm（东西方向）和±5.9 mm（南北方向），与断层的右侧走滑运动学和当地GNSS IGS14/NNR速度（2019-2024）一致。这些Pleiades位移，结合PS-InSAR速率和地质特征（如坡度和地形崎岖指数（TRI）），作为U-Net模型的输入，将LOS数据转换为3D场，将所有组件的位移范围扩展到±20毫米。我们通过大地测量优化来增强U-Net估计，并使用蒙特卡罗Dropout（10个样本）来量化东西位移0.1-0.3 mm，南北和垂直位移0.5-0.8 mm的不确定性。根据Pleiades测试数据验证模型的RMSE为1.17 mm（东西方向），1.46 mm（南北方向）和1.52 mm（垂直方向），其中三个本地GNSS站（6-50 km距离，IGS14/NNR框架，2019-2024）的垂直分量RMSE为1.98 mm。这种insar - pleades -深度学习方法为3D变形监测提供了可扩展的解决方案，推进了无gcp环境下的地震危害评估。

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IF 4.5 Q2 ENVIRONMENTAL SCIENCES

Remote Sensing Applications-Society and Environment

Pub Date : 2026-01-01

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IF 4.5 Q2 ENVIRONMENTAL SCIENCES

Remote Sensing Applications-Society and Environment

Pub Date : 2026-01-01

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IF 4.5 Q2 ENVIRONMENTAL SCIENCES

Remote Sensing Applications-Society and Environment

Pub Date : 2026-01-01

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IF 4.5 Q2 ENVIRONMENTAL SCIENCES

Remote Sensing Applications-Society and Environment

Pub Date : 2026-01-01

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IF 4.5 Q2 ENVIRONMENTAL SCIENCES

Remote Sensing Applications-Society and Environment

Pub Date : 2026-01-01

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