Land Cover Classification Using ICESat-2 Photon Counting Data and Landsat 8 OLI Data: A Case Study in Yunnan Province, China

Land cover classification is important for effectively protecting and developing land resources. This study investigates the joint use of the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) data and Landsat 8 Operational Land Imager (OLI) data in land cover classification with random forest (RF) in Yunnan province, China, to explore the application potential of photon counting light detection and ranging (LiDAR) data in land cover classification. The contributions of this letter are: 1) the joint use of ICESat-2 and Landsat 8 image datasets can provide better land cover classification accuracy, achieving 10% and 3% accuracy gains for five types (forest/low-vegetation/water/construction-land/barren) and four types (vegetation/water/construction-land/barren)of land cover, respectively; 2) the proposed feature selection improves the overall accuracy by 1.5% and 1% for five and four land cover types, respectively; 3) the accuracy of the land cover classification reached 82% and 98% for five and four types of land cover; and 4) the terrain factors, the number of canopy photons, and solar conditions significantly impact land cover classification for a complex terrain area.