Quantifying the nonlinear response of vegetation greening to driving factors in Longnan of China based on machine learning algorithm

Abstract

The main influencing factors and their nonlinear effects on the changes of vegetation in China’s mountainous areas under the interaction of different factors are not yet clear, and comprehending the evolutionary trends and driving mechanisms of vegetation is crucial to reveal the changes in ecosystem structure and function. In this study, trend analysis (M−K, T-S and EEMD) combined with machine learning algorithm, namely Boosted Regression Tree model (BRT), were used to quantify the trends of nonlinear responses and thresholds for bioclimatic variables, topography, soil properties and anthropogenic factors for vegetation changes in Longnan. The results showed that the trend analysis clearly confirm the increasing trend of vegetation at multiple spatio-temporal scales. The BRT indicated that total precipitation (bio12, 15.22%), land use (LUCC, 12.68%), elevation (DEM, 11.20%), and population density (Pd, 9.20%) were the more important factors of dominant vegetation greening. Bioclimatic variables were found to revealed the effects of climate with vegetation more clearly. In addition, the BRT revealed that the selected factors have the different nonlinear response relationships to vegetation greening trend and specific thresholds. Among them, increasing of cropland, grassland and forestland can promote vegetation greening. However, GDP, Pd, DEM, bio12, mean diurnal range and temperature seasonality (bio2, bio4) exceed the threshold can significantly inhibit vegetation growth. The BRT combined with trend analysis revealed the nonlinear response relationships and thresholds of the drivers behind the vegetation change patterns, which have obvious effects in exploring the driving mechanisms of vegetation changes in mountainous areas. This study provided an important reference for better revealing the interaction mechanisms between vegetation changes and drivers in the semi-humid zone in East Asia even globally.

Keywords

• Bioclimatic variables
• Boosted regression tree model
• Nonlinear trend
• Spatio-temporal variation of vegetation