Feasibility of Using Landsat Synthetic Images to Classify Tree Species in Southwest Sichuan, China

Forest tree species are an important factor in stand structure surveys, which are the basis for forest ecological planning and the development of related forest policies. Since the number and types of tree species are related to ecosystem parameters such as biodiversity and habitat quality. An accurate and detailed assessment of tree species is a prerequisite for effective ecosystem management. Complex mountainous areas are surrounded by clouds all year round, making it difficult to obtain clean and usable images. Change Continue Detection and Classification(CCDC) is a method for land cover change detection and is able to produce synthetic Landsat images at any moment based on the time series model. The purpose of this paper is to verify the feasibility of synthetic Landsat imagery for tree species classification. In this study, a harmonic function model was built by using 518 images of northern Sichuan province of China from 2000 to 2020. The harmonic function model was used to generate synthetic Landsat images, then, three ensemble classification models were used to classify the trees based on the synthetic images. The total accuracies of the three classifications were 85% (RF), 60% (AdaBoost), and 78% (GBDT), respectively. The results show that Landsat synthetic images can be well applied to tree species classification in complex environments.