Spatiotemporal Variation of Vegetation Net Primary Productivity and Its Responses to Climate Change in the Huainan Coal Mining Area

Abstract

The health status of the ecosystem in the Huainan coal mining area was determined based on the net primary productivity (NPP) of vegetation which was simulated by using the GLOPEM–CEVSA coupled model. The characteristics of time-series changes in the NPP of vegetation were analyzed using a univariate linear regression method, and the characteristics of spatial changes were analyzed using the spatial analysis techniques of the geographic information system and spatial autocorrelation method. In addition, the correlations between the NPP and major climatic factors (temperature, precipitation and sunshine duration) and their significance were quantitatively analyzed using statistical analysis methods. The results are as follows: (1) the pixel-by-pixel regression analysis shows that within the study period, 30.52% of the area showed a significant increase, 35.74% showed no changes, and 2.36% showed a significant decrease in the NPP. Overall, the NPP in the study area increased slowly. (2) The spatial distribution of the areas with and without changes in the NPP was also determined. The spatial distribution data show that the NPP is high in the southern and northern areas; however, it is low in the central study area. These results are in agreement with the intensity of human activity (mining area construction and urban development). (3) From a vegetation perspective, high NPP values occurred mostly in croplands, followed by forestlands and grasslands. (4) The pixel-by-pixel correlation analysis of the NPP and the climatic factors of the same period showed some correlations between the annual NPP, the annual precipitation and the annual mean temperature. The data showed significant positive and negative correlations in few areas, and a relatively weak correlation between the annual NPP and annual sunshine duration. From the coupling view of the meteorological factors, it can be found that many pixels were jointly affected by the meteorological factors.