Tropical forests classification based on weighted separation index from multi-temporal sentinel-2 images in Hainan island

Abstract

Tropical forests play a vital role in biodiversity conservation and the maintenance of sustainability. Although different time-series spatial resolution satellite images have provided opportunities for tropical forests classification, the complexity and diversity of vegetation types still pose challenges, especially for distinguishing different vegetation types. In this paper, we proposed a Spectro-Temporal Feature Selection (STFS) method based on the Weighted Separation Index (WSI) using multi-temporal Sentinel-2 data for mapping tropical forests in Jianfengling area, Hainan Province. The results showed that the tropical forests were classified with an overall accuracy of 93% and an F1 measure of 0.92 with multi-temporal Sentinel-2 data. As our results also revealed, the WSI based STFS method could be efficient in tropical forests classification by using a fewer feature subset compared with Variable Selection Using Random Forest (14 features and all 40 features, respectively) to achieve the same accuracy. The analysis also showed it was not advisable to only pursue a higher WSI value while ignoring the heterogeneity and diversity of features. This study demonstrated that the WSI can provide a new feature selection method for multi-temporal remote sensing image classification.