Mapping Forest Type with Multi-Seasonal Landsat Data and Multiple Environmental Factors in Yunnan Province Based on Google Earth Engine

Abstract

An accurate forest type map is important for the forestry resources monitor and management. Forest type mapping over a large and complicated mountain area is full of challenge due to complex forest type compositions, similar spectral characteristics between various forest types, lack of high quality images caused by clouds or cloud shadows, and the difficulties in managing and processing large amount data. This study aims to explore forest-type mapping methods over a mountain region with strong geographic and climate heterogeneity characteristic landscape (Yunnan Province, China). Based on Landsat OLI dense time series data, four median seasonal composites consist of 7 spectral bands and 5 vegetation indexes were derived on Google Earth Engine cloud platform. The Random Forest classifier was used in two-level classification, which includes the classification of forest/non-forest and the classification of forest-type. In two-level classification, three types of feature combination, single-seasonal composite in four seasons, multi-seasonal composite and the combination of multi-seasonal composite and three environmental factors, were set, respectively. We also compared our method with two commonly used methods. The resultant forest map was evaluated by using overall accuracy and Kappa and compared to the ground survey data and four public forest products. The results show that multi-seasonal multispectral variables and the combination with environment factors improved accuracy of forest type classification. Our result is also superior to stat-of-the-art result, FCS2020, in the research area.