#Kumaun Lesser Himalaya

利用多尺度事件同步等方法分析喜马拉雅山麓2001-2022年气候参数变化及其关联。

Abstract Accurately land use and land cover (LULC) mapping in mountainous regions is inherently challenging due to the complex topography, terrain-induced spectral distortions, and heterogeneous landscape characteristics. Overcoming these challenges requires the integration of advanced remote sensing techniques, GIS, terrain correction algorithms, and careful field-based validation. This study systematically evaluated and compared multiple pixel-based and object-based classification approaches using Sentinel-2 imagery (2020) to identify the most suitable method for accurate LULC mapping in the Kumaun Lesser Himalaya, India. Six land cover classes: water, barren land, settlement, agriculture, forest, and grassland were classified and assessed. Among the assessed approaches, the object-based Random Tree classifier produced the highest classification accuracy, demonstrating its effectiveness for mapping in discriminating heterogenous land cover types within complex mountainous landscapes. The optimal classifier model was subsequently employed to analyse long-term LULC dynamics from 1972 to 2020. The analysis showed a decline in forest (11.08%), agriculture land (17.04%), and water bodies (0.95%), while barren land, settlements, and grassland increased by 1.76%, 0.93%, and 26.40%, respectively. The marked expansion of grassland is likely associated with agricultural land abandonment, forest degradation, and rural-to-urban migration. These long-term landscape transformations indicate increasing anthropogenic pressures and environmental change in the fragile Himalayan ecosystem, with potential consequences for biodiversity conservation, ecosystem stability, and watershed functions. The findings provide a scientific basis for long-term environmental monitoring, biodiversity conservation, sustainable land-use planning, ecological restoration, and informed environmental management in the Kumaun Lesser Himalaya.

整合遥感与多准则决策，评估西喜马拉雅五流域冰湖溃决洪水易发性。

用SVM、RF、XGBoost、CatBoost及堆叠集成预测印度Kangra区土壤侵蚀易发性。

融合RUSLE、遥感与随机森林，评估并制图喜马拉雅Bhagirathi流域土壤侵蚀热点。

用AHP-GIS多准则模型评估喜马拉雅Soar谷地地下水潜力区并做精度验证。

提出滑坡优先级评分排序系统，识别喜马偕尔邦金瑙尔县需优先治理的高风险区域。

用遥感和水动力模型评估拉达克上印度河盆地四个冰湖的溃决洪水风险。