[Spatio-temporal dynamics of gross primary productivity in China from 1982 to 2017 based on different datasets].

Understanding the spatio-temporal variations of gross primary productivity (GPP) of terrestrial ecosystem and its relationship with climatic factors can provide important basis for vegetation restoration and protection. Based on meteorological data and three public GPP datasets (EC-LUE GPP, GLASS GPP, and NIRv GPP), we syste-matically analyzed the spatial-temporal variations of GPP and its response to climate change in China during 1982-2017. All the results based on the three GPP datasets showed that the annual and seasonal GPP in China increased annually from 1982 to 2017, with that in 1998 and 2002 significantly being higher than the average level during the study period, and that in 1989 and 1992 significantly being lower than the average annual GPP.

[Detection of Influencing Factors of Spatial Variability of Soil Respiration in Pangquangou Nature Reserve].

Soil respiration is an important process in maintaining global carbon balance. Taking the Pangquangou Nature Reserve as the research area, based on the field measurement of soil respiration () data combined with altitude (ELE), soil temperature (), soil moisture (SWC), normalized vegetation index (NDVI), slope (slope), soil total carbon (C), total nitrogen (N), and soil bulk density (BD), we analyzed the main driving forces and interactions of spatial differentiation by using the geographic detector model. The results showed that:① the spatial variation of and its influencing factors in the study area was moderate.

Spatio-temporal pattern and driving forces of ecosystem services in coalfields of Shanxi Pro-vince, China.

Although coal has made a huge contribution to the development of the economy and socie-ty and its economic benefits have often attracted much attention, little research has focused on the ecosystem services of coalfields. Based on remote sensing data, meteorological data, and soil data in Shanxi coalfields during 1986, 2000, and 2015, we estimated soil conservation and water yield using the InVEST model, assessed the net primary productivity of vegetation using the CASA mo-del, and estimated sand fixation using the RWEQ model. Further, we simulated the spatial patterns of ecosystem services (ESs) using the k-means cluster analysis method and analyzed the influence factors of ESs using the Geodetector model in Shanxi coalfield areas.

[Variation in fractional vegetation cover and its attribution analysis of different regions of Beijing-Tianjin Sand Source Region, China].

Based on the MODIS NDVI data from 2000 to 2018, we estimated the fractional vegetation cover (FVC) using the dimidiate pixel model and analyzed the spatiotemporal characteristics of FVC in the Beijing-Tianjin sand source region (BTSSR). The geographical detector model was used to estimate the impacts of natural and human factors on FVC spatial distribution at the regional scale. The results showed that the FVC of the BBTSR showed an increasing trend from 2000 to 2018, with an annual growth rate of 0.

[Quantitative analysis of the effects of human activities on vegetation in the Beijing-Tianjin sandstorm source region under the climate change].

It is of great practical significance for regional ecological management to understand the quantitative impacts of human activities on vegetation under climate change. Based on GIMMS NDVI3g data, meteorological data (temperature, precipitation) and standardized precipitation evapotranspiration index (SPEI), we used correlation analysis and trend analysis to examine the spatio-temporal variation of vegetation and its driving factors in different periods from 1982 to 2014 in the Beijing-Tianjin sandstorm source region. Regression analysis and residual analysis were used to quantify the impacts of human activities on vegetation changes in different sub-regions.

[Responses of vegetation growth to temperature during 1982-2015 in Xinjiang, China.].

Combined with the normalized difference vegetation index (NDVI) dataset, vegetation type data, and meteorological data, we revealed the variation of vegetation growth responses to air temperature in the growing-season during 1982-2015 in Xinjiang, using the moving-windows based partial correlation analysis, the unitary linear regression analysis and GIS spatial analysis. Results showed that, in the whole growing-seasons of study period, there was a significant downturn trend in the responses of vegetation growth to temperature. At the seasonal scale, the downturn trend was obvious especially in summer and autumn, while it was in adverse in spring.