Monitoring the dynamic changes in vegetation cover and driving factors from 2000 to 2020 in the Maoershan Forest Farm region, China, using satellite remote sensing data

Abstract

Aim of study: Natural climate change is a central driver of global ecosystem and forest change. Climate change and topographical factors have had the greatest impact on different types of forests around the world. We used remote sensing technology to detect and analyze the temporal and spatial changes of forest vegetation to provide reference for regional management. Area of study: Maoershan Forest Farm, China. Material and methods: The Landsat images were preprocessed using ArcGIS and ENVI software. The normalized difference vegetation index (NDVI) was calculated to identify vegetation changes from 2000 to 2020. In addition, the vegetation fraction cover (VFC) was calculated using the pixel binary model. The driving factors and their influences on vegetation changes in this region were determined using the random forest algorithm and Pearson correlation analysis method. Main results: From 2000 to 2020, the NDVI showed an overall increasing trend. The results indicated that compared with the climatic factors, topographic factors were more important to vegetation growth in the study area. Among the topographic factors, elevation was the most important factor affecting vegetation growth and both showed a negative correlation. Among the climatic factors, relative humidity was the primary driving factor affecting vegetation growth and both showed a positive correlation. Research highlights: Accurate and timely assessment of vegetation change and its relationship to climate and topographical changes can provide very useful information for policy makers, governments and planners in formulating management policies.