Dynamic change, driving mechanism and spatiotemporal prediction of the normalized vegetation index: a case study from Yunnan Province, China - Han, Yang; Lin, Yilin; Zhou, Peng; Duan, Jinjiang; Cao, Zhaoxiang (Frontiers in Ecology and Evolution, 2023)
Vegetation indexes have been widely used to qualitatively and quantitatively evaluate vegetation cover and its growth vigor. To further extend the study of vegetation index, this paper proposes to study the spatial and temporal distribution characteristics and specific driving mechanisms of vegetation indices based on the example of Yunnan Province, China, and also adds the study of spatial and temporal prediction methods of vegetation index. This paper used data on this region's normalized vegetation index (NDVI), three meteorological factors, and eight social factors from 1998-2019. The dynamic change and driving mechanism of NDVI are studied using mean value analysis, univariate linear trend regression analysis, and partial correlation analysis. In addition, the Fourier function model and CA-Markov model are also used to predict the NDVI of Yunnan Province from 2020 to 2030 in time and space. The results show that: (1) The NDVI value in Yunnan Province is high, showing a significant growth trend. The increased vegetation coverage area has proliferated in the past 22 years without substantial vegetation degradation. (2) The positive promotion of meteorological factors is greater than the negative inhibition. The partial correlation of relative humidity among meteorological factors is the highest, which is the main driving factor. (3) NDVI value was significantly positively correlated with population and economy and negatively correlated with pasture land and agricultural area. (4) The NDVI values were predicted well in time (R=0.64) and space (Kappa=0.8086 and 0.806), satisfying the accuracy requirements. This paper aims to enrich the theoretical and technical system of ecological environment research by studying the dynamic change, driving mechanism, and spatial-temporal prediction of normalized vegetation index. Its results can provide the necessary theoretical basis for the simulation and prediction of the vegetation index.