Classification of Alpine Grasslands in Cold and High Altitudes Based on Multispectral Landsat-8 Images: A Case Study in Sanjiangyuan National Park, China

Land-use–cover change (LUCC)/vegetation cover plays a critical role in Earth system science and is reflection of human activities environmental changes. LUCC will affect the structure function ecosystems series other terrestrial surface processes, such as energy exchange, water circulation, biogeochemical vegetation productivity. Therefore, accurate mapping monitoring are bases for simulating global carbon hydrological cycles, studying interactions land climate, assessing degradation. Based on field GPS surveys UAV data, with cloud-free snow/glacier algorithms SVM classifier to train model alpine grassland, grassland were extracted by using Landsat-8 OLI satellite images Sanjiangyuan National Park this paper. The latest datasets types 30 m × spatial resolution three parks prepared formed. classification results show that could better distinguish major land-use types, overall accuracy was very high. However, subcategories, accuracies four typical grasslands relatively low, especially between desert steppes meadows, steppes. It manifests limitations multispectral remote sensing imageries finer-resolution classifications high-altitude mountains. method can be utilized same band matching, Landsat 7, 9, Sentinel-2, etc. described paper rapidly efficiently process annual maps source areas Yellow River, Yangtze Lancang River. provide timely high-spatial-resolution supporting scientific decisions sustainable management Park.