Classification of Hainan island natural forest based on multi-source remote sensing data

Abstract

Widely distributed in the vicinity of the equator, tropical forest is one type of forest with the most abundant species worldwide which has a profound effect on global climate change. Therefore, it is of great significance for a country to develop the forest resources inventory and perform dynamic monitoring. Research on the classification of natural forests not only supports the investigation of tropical forests, but also provides the basis for the study of forest species diversity. The dual-polarized SAR data from Sentinel-1A sensor and the optical remote sensing data from Landsat-8 sensor were used for classification of Hainan island tropical natural forest. First, we analyzed the single-band, multi-band, normalized difference vegetation index (NDVI) characteristics of optical data, and the single-phase, multi-temporal backscattering characteristics of SAR data. Then, optical and backscattering characteristics were selected for natural forest classification whereby the natural forest range of Hainan Island was extracted by using support vector machine (SVM). The natural tropical forest was classified into five types: tropical rain forest, tropical monsoon forest, evergreen coniferous forest, deciduous broad-leaved mixed forest and coastal forest. The accuracy of classification results was verified and evaluated based on a combination of field survey data and Hainan forestry survey data. The overall accuracy of the classification exceeded 80%. The results provide a reliable remote sensing classification method for Hainan island tropical forest classification. This dataset also has some reference value for the study of tropical natural forest classification in other areas.