Spatiotemporal evolution of tropical forest degradation and its impact on ecological sensitivity: A case study in Jinghong, Xishuangbanna, China

Abstract

Due to rapid urbanization and a growing population, the tropical forest in southwestern China has experienced a dramatic shrinkage, which threatens its biodiversity and imposes limitations to sustainable development. Spatiotemporal change analysis and ecological sensitivity assessment are the important prerequisites for investigating the relationship between eco-environmental quality and human activities. In this study, the tropical forest and other land cover types in Jinghong, China were firstly classified by a machine learning classification algorithm (support vector machine, SVM) with 7 pairs of remote sensing (RS) data (from 1989 to 2018). Then the spatiotemporal change patterns were analyzed. The ecological sensitivity was evaluated based on an index system method (ISM) in which a weighted combination of eleven indicators were produced using an analytic hierarchy process (AHP) method and GIS. Meanwhile four individual sensitivity indicators, including biodiversity sensitivity (BS), water resources sensitivity (WRS), geological hazard sensitivity (GHS) and soil erosion sensitivity (SES) were assessed respectively to create a multi-perspective understanding of the entire ecological sensitivity. The results suggest that the tropical forest experienced a continual decrease from 5631.78 km2 in 1999 to 4216.23 km2 in 2018 with an average change rate of −1.49%. The decreased area was mainly encroached on by human settlements and agriculture, particularly in the south of Jinghong. Furthermore, it could be seen that urbanization is the key driver for the changes to ecological sensitivity with both positive and negative impacts. In Jinghong, the region covered by a tropical forest has a relatively higher comprehensive ecological sensitivity (CES) than that of an urban area. This work shows RS and GIS to be powerful tools providing profound insights to researchers with regard to the spatiotemporal evolution of tropical forests and ecological sensitivity. The results are significant for improving policies in order to keep a sustainable balance in regional ecosystem management.