Spatiotemporal Distribution Pattern and Driving Factors Analysis of GPP in Beijing-Tianjin-Hebei Region by Long-Term MODIS Data

Abstract

Gross primary productivity (GPP) is an important parameter that represents the productivity of vegetation and responses to various ecological environments. Using the Mann–Kendall methods, Pearson correlation, and the Geodetector, this study investigated the spatiotemporal variation and driving factors of GPP from 2000 to 2020. The results showed that (1) in terms of spatial distribution, GPP showed a trend of “low-high-low” regions, with low values for grassland and arable land and a high value for forest land. The growth trend is fast in forest areas, while the growth trend is not obvious in cultivated areas. The regions with significant growth accounted for 68.73% of the whole region. (2) The whole region shows a growth rate of 2.07 g C∙m−2∙yr−1, showing obvious seasonality, with a slow growth trend in spring and autumn and a fast growth trend in summer. (3) The driving factors of GPP spatial differentiation in the Beijing-Tianjin-Hebei region were land surface temperature, land use type, and nighttime light data, while precipitation and downward surface shortwave radiation show no strong explanatory power for the spatial differentiation of GPP, which means that these two factors have less driving force on the spatial differentiation of GPP. The interaction of LUCC with the other factors presents two-factor enhancement, while the LST interaction with the other three factors presents non-linear enhancement. This study could provide a theoretical basis for the sustainable development of the Beijing-Tianjin-Hebei Region.