Dynamic change, driving mechanism and spatiotemporal prediction of the normalized vegetation index: a case study from Yunnan Province, China

Abstract

Vegetation indexes have been widely used to qualitatively and quantitatively evaluate vegetation cover and its growth vigor. To further extend the study of vegetation indexes, this paper proposes to study the spatial and temporal distribution characteristics and specific driving mechanisms of vegetation indexes based on the example of Yunnan Province, China, and also adds the study of spatial and temporal prediction methods of vegetation indexes. This paper used data on this region’s normalized vegetation index (NDVI), three meteorological factors, and eight social factors from 1998 to 2019. The dynamic change in and driving mechanism of the NDVI were studied using mean value analysis, univariate linear trend regression analysis, and partial correlation analysis. In addition, the Fourier function model and the CA–Markov model were 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 increased 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) The NDVI value is significantly positively correlated with population and economy and negatively correlated with pasture land and agricultural area. (4) The NDVI values are 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 spatiotemporal prediction of the normalized vegetation index. Its results can provide the necessary theoretical basis for the simulation and prediction of vegetation indexes.

Characteristics of Vegetation Change and Its Climatic and Anthropogenic Driven Pattern in the Qilian Mountains

“…Partial correlation analysis can reveal the impact intensity of one factor on another while other factors remain unchanged [11,13]. Multiple correlation analysis can analyze the correlation between two or more factors and a certain factor, so as to identify the comprehensive influences of multiple factors on this factor [50].…”

“…In terms of identifying VC change, researchers mostly adopt linear regression trend analysis [10][11][12][13][14], and a few adopt the ordinary least squares method [15]. At present the, GeoDetector model is one of the most commonly used methods to detect the drivers of vegetation change and has received extensive attention from researchers [16][17][18].…”

“…However, it can only analyze the influence intensity of driving factors statistically and is unable to display the spatial heterogeneity of driving factors' influence on a map [12,21]. Hence, studies have usually adopted correlation analysis (or partial correlation analysis) to spatially clarify the driving effect of climate variables on vegetation change [11,13]. In addition, the geographic weighted regression model is also frequently used to spatially identify the comprehensive driving effects of multiple factors [15,22].…”

Impact of late winter sea surface temperature seesaw between equatorial central Pacific and Philippine Sea on early spring vegetation over the low-latitude highlands of China

“…However, Yan et al (2021) demonstrated that extreme precipitation has remarkable impact on net primary productivity of vegetation and its spatial distribution. Han et al (2023) pointed that relative humidity is the main driving factor of NDVI, compared to other meteorological factors. In the eastern part of the CLLH, vegetation growth is thought to be governed by temperature (Hou et al 2015;Xue et al 2023).…”