Long-term evolution of winter habitats in Poyang Lake derived from satellite imagery using machine learning methods

Abstract

Poyang Lake is a freshwater lake in China which is a vital winter habitat for many kinds of wildlife and a critical component of the regional ecology. Here, we use Landsat satellite imagery to systematically assess habitat characteristic changes from 1990 to 2021. Four machine learning methods including random forest (RF), gradient boosting tree (GBT), support vector machine (SVM) and classification and regression trees (CART) are analyzed by comparing the overall accuracy and Kappa coefficients. The results show that the accuracy of random forest is higher than that of the other three machine learning methods. The long-term characteristics of Poyang Lake winter habitat types are extracted from Landsat satellite images using the RF method. These results show that the mudflat area was larger than water surface and sand. After 2012, more mudflat area had been converted into grassland, which is related to the early onset of the winter dry season in the Poyang Lake area. The habitats were scattered and fragmented from 1990 to 1998; after 1997−1998, however, the degree of landscape patch density and interference decreased, indicating a decreased impact of human-related interference and natural factors on the evolution of habitats in and around Poyang Lake.

Graphical abstract

Keywords

Poyang Lake / habitats / machine learning / landscape index

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