Assessment on vegetation dynamics under climate change for energy saving with satellite data and geographically weighted regression

The sensitivity of net primary productivity (NPP) to future climate change is critical for carbon dynamics and energy saving. Geographically weighted regression, which allows the use of remotely sensed NPP to establish spatial correlations with leaf area index (LAI) and topographically-based climate factors (temperature, precipitation and solar radiation), is introduced in this paper. For most area of North China, the effect of precipitation and LAI on NPP is positive, while that of temperature is negative. Grassland is most sensitive to climate change. LAI will decrease by -21.96%. Similarly, climate change may reduce NPP by -6.29%.