Forest Type Mapping at a Regional Scale Based Using Multitemporal Sentinel-2 Imagery

Abstract

This study used multispectral satellite imagery (Sentinel-2 MSI) to evaluate forest type mapping capabilities over a mountainous area (Shangri-La, Yunnan Province, China) at regional level. Coupled with the cloud computing platform of Google Earth Engine, the sentinel-2 satellite images were used to extract multi-temporal and spectral information, and then combined with terrain information. The random forest algorithm was adopted to identify the typical forest types. Firstly, the area is classified into forest and non-forest types. Secondly, the forest cover was sub-classified into coniferous forest and broad-leaved forest. In the end, eight types of coniferous forests (Cupressus funebris forest, Abies forest, Pinus densata forest, Picea forest, Pinus yunnanensis forest, Larix forest, Pinus armandi forest, Tsuga dumosa forest) were identified within the cover of coniferous forest. As for the whole area, the overall accuracy of forest and non-forest was 95.76%, and the Kappa coefficient was 91.34%. Within the forest coverage, the overall accuracy of the coniferous forest and the broadleaf forest was 89.74%, and the Kappa coefficient was 79.26%. And within the coniferous forest, the overall accuracy of the eight types of coniferous forest was 91.59%, and its Kappa coefficient was 90.33%. The classification results indicated that topographic information is beneficial to the extraction of forest type information, and multi-temporal Sentinel-2 imagery has great potential to accurately identify forest type at regional level.