Quantifying spatiotemporal dynamics of vegetation and its differentiation mechanism based on geographical detector

The influence of factors on vegetation changes in different regions is still largely unknown. We applied the geographic detector, a new spatial statistical method, to study the interactive effects of factors on the spatial patterns of normalised vegetation index (NDVI) changes and determine the optimal characteristics of key impact factors beneficial to vegetation growth. Our results show that from 2000 to 2015, the vegetation cover for the upper reaches of the Minjiang River, western China was in good condition. Furthermore, more than 80% of the areas had NDVI values ranging from 0.6 to 0.8 and NDVI > 0.8, and the spatial-temporal changes of vegetation cover were significant. The vegetation cover changes showed a significant transformation in the regions with NDVI > 0.6. Our study uniquely illustrated that elevation, annual average temperature and soil type can explain vegetation changes quite well. We propose that interactive effects exist among impact factors on vegetation NDVI, and the synergistic effects of the impact factors show mutual and nonlinear enhancements. The interactions among impact factors significantly enhance the impact of a single factor on vegetation changes. The most suitable characteristics of the main impact factors that promote vegetation growth were revealed by this study and will help improve our understanding of factors that impact NDVI and its driving mechanisms. Our findings suggest that the established favourable value range or the most suitable characteristics of impact factors will help management plans to intervene and promote vegetation change for vegetation restoration and alleviate environmental degradation.