A Framework for Subregion Ensemble Learning Mapping of Land Use/Land Cover at the Watershed Scale - Runxiang Li; Xiaohong Gao; Feifei Shi (Remote Sensing, 2024)
Land use/land cover (LULC) data are essential for Earth science research. Due to the high fragmentation and heterogeneity of landscapes, machine learning-based LULC classification frequently emphasizes results such as classification accuracy, efficiency, and variable importance analysis. However, this approach often overlooks the intermediate processes, and LULC mapping that relies on a single classifier typically does not yield satisfactory results. In this paper, to obtain refined LULC classification products at the watershed scale and improve the accuracy and efficiency of watershed-scale mapping, we propose a subregion ensemble learning classification framework. The Huangshui River watershed, located in the transition belts between the Qinghai-Tibet Plateau and Loess Plateau, is chosen as the case study area, and Sentinel-2A/B multi-temporal data are selected for ensemble learning classification. Using the proposed method, the block classification scale is analyzed and illustrated at the watershed, and the classification accuracy and efficiency of the new method are compared and analyzed against three ensemble learning methods using several variables. The proposed watershed-scale ensemble learning framework has better accuracy and efficiency for LULC mapping and has certain advantages over the other methods. The method proposed in this study provides new ideas for watershed-scale LULC mapping technology.