Spatial differentiation of the NPP and NDVI and its influencing factors vary with grassland type on the Qinghai-Tibet Plateau

Environ Monit Assess. 2021 Jan 7;193(1):48. doi: 10.1007/s10661-020-08824-y.

Abstract

Grasslands are the dominant ecosystem of the Qinghai-Tibet Plateau (QTP), and they play an important role in climate regulation and represent an important ecological barrier in China. However, the spatial differentiation characteristics of net primary productivity (NPP) and normalized differential vegetation index (NDVI) and the main influencing factors that vary with grassland type on the QTP are not clear. In this study, standardized precipitation evapotranspiration index (SPEI), digital elevation model (DEM), precipitation, temperature, slope, photosynthetically active radiation (PAR) and grazing intensity were considered the driving factors. First, a grey relational degree analysis was performed to test for the quantitative relationships between NPP, NDVI and factors. Then, the geographical detector method was applied to analyze the interaction relationships of the factors. Finally, based on the geographically weighted regression (GWR) model, the influence of factors varied with grassland type on the NPP and NDVI was revealed from the perspective of spatial differentiation. The results were as follows: (1) The NPP and NDVI had roughly the same degrees of correlation with each impact factor by the grey relational degree analysis, each factor was closely related to the NPP and NDVI, and the relational degree between grazing intensity and NPP was greater than that between grazing intensity and NDVI. (2) The interaction relationships between influencing factors and NPP and NDVI varied with the grassland type and presented bivariate enhancement and nonlinear enhancement, and the interaction effects between grazing intensity and any factor on each grassland type had a greater impact on NPP. (3) The main influencing factors of the spatial heterogeneity of NPP were grazing intensity and PAR, which were "high from northeast to southwest, low from northwest to southeast" and "low in the middle and high around". The main influencing factors on the NDVI were precipitation and PAR, which were "low in the middle and high around" and "high in the north, low in the south".

Keywords: Geographically weighted regression; Interaction relationship; Spatial heterogeneity effect; Vegetation.

MeSH terms

  • China
  • Climate Change
  • Ecosystem*
  • Environmental Monitoring
  • Grassland*
  • Tibet