[Spatio-temporal dynamics of gross primary productivity in China from 1982 to 2017 based on different datasets]. / 不同数据集的1982­2017年中国总初级生产力的时空动态.

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. From 1982 to 2017, GPP showed a significant upward trend in most regions of China, with the regions with significant increases accounting for 75.7%, 73.0%, and 69.6% of the whole study area, respectively. There was a significant positive correlation between annual GPP and precipitation and temperature, but spatial heterogeneity was strong. Among them, the regions with positive correlation between GPP and temperature were mainly distributed in Northwest and Central China, while the regions with positive correlation between GPP and precipitation were mainly distributed in North China. There was obvious spatial-temporal heterogeneity in regions that GPP being affected by temperature and precipitation in different seasons. Temperature was the limiting factor of GPP in spring, autumn and winter, while summer GPP was mainly affected by precipitation.

[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 (Rs) data combined with altitude (ELE), soil temperature (T), 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 Rs spatial differentiation by using the geographic detector model. The results showed that:① the spatial variation of Rs and its influencing factors in the study area was moderate. The Rs was significantly positively correlated with NDVI, T, and N (P<0.01) and negatively with ELE, slope, and SWC (P<0.01). The Rs was significantly correlated with BD(P<0.05) but not with C(P>0.05). ② The multivariate linear model composed of NDVI and T explained 64.3% of Rs spatial variation. ③ ELE, T, and NDVI were the dominant driving forces of Rs spatial differentiation in the study area, which could explain 64%, 59%, and 48% of the spatial variability. ④ The interaction of the two factors enhanced the explanatory power of Rs spatial differentiation, and the maximum interaction factors were ELE∩BD (q=0.73), and T∩slope (q=0.74), respectively. Therefore, in the process of Rs estimation, combined with topographical and environmental conditions, the interaction between multiple factors should be considered.

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. The results showed that soil conservation service, water yield service, and sand fixation service increased continuously. The high-value area of soil conservation service was mainly concentrated in the north of Hedong coalfield and the northeast of Qinshui coalfield, while the low-value area was distributed in the southwestern edge of Datong coalfield. The high-value area of water yield service was mainly concentrated in the northeast of Qinshui coalfield, while the low-value area was distributed in the northeast of Qinshui coalfield, Xishan coalfield and northwestern Qinshui coalfield. The high-value area for vegetation production service was mainly concentrated in the southeast of Qinshui coalfield, while the low-value area was distributed in Datong coalfield, Ningwu coalfield, Xishan coalfield, and northern Hedong coalfield. The distribution of low- and high-value areas of sand fixation service was unfixed. Ecosystem service bundles could be divided into four categories. The first category belonged to soil conservation service bundle, mainly distributed in the northern Ningwu coalfield, the northern Hedong coalfield, and the northern Qinshui coalfield. The second was water yield service bundle, mainly distributed in Huoxi coalfield and southern Qinshui coalfield. The third category belonged to vegetation production service bundle, mainly distributed in parts of Qinshui coalfield. The fourth category belonged to sand fixation service bundle, mainly distributed in the southern part of Hedong coalfield and Qinshui coalfield. Soil conservation service was greatly affected by temperature, digital elevation model (DEM), and industrial output value, with q values of 0.5, 0.3, and 0.2, respectively. Water yield service was greatly affected by precipitation, temperature, and DEM, with q values of 0.8, 0.3, and 0.2, respectively. The industrial output value, precipitation, and temperature q values of vegetation production service were 0.7, 0.6, and 0.2, respectively. The main influen-cing factors of sand fixation service were precipitation, temperature, and DEM, while the q values were 0.7, 0.3, and 0.3, respectively. The spatial distribution of coalfields ESs and the relationship between multiple ESs were closely related to natural and human factors. Therefore, maintaining the coordination relationship between natural-human factors and ecological services would be helpful to the management of the land reclamation, ecological reconstruction, and the sustainable development of coalfields ecosystem.

[Variation in fractional vegetation cover and its attribution analysis of different regions of Beijing-Tianjin Sand Source Region, China]. / äº¬æ´¥é£Žæ²™æºåŒºä¸åŒåˆ†åŒºæ¤è¢«è¦†ç›–åº¦å˜åŒ–åŠå½’å› åˆ†æž.

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 0.013·(10 a)-1 and a vegetation increase rate of 8.2%. The area with high FVC was concentrated in the Yanshan Mountain water source protection area, followed by the pastoral transitional zone desertified land control area and the Otindag sandy land area. The area with poor FVC was concentrated in the northern arid grassland area. The explanatory power of driving factors to FVC varied across different regions. Among the natural factors, annual precipitation was the main driving factor for the spatial distribution of FVC in the northern arid grassland area, the Otindag sandy land area and the Yanshan Mountain water source protection area. Slope was the main driving factor for the spatial distribution of FVC in the pastoral transitional zone desertified land control area. Among different human activities, the number of large livestock at the year-end was the main driving factor controlling the spatial distribution of FVC in the northern arid grassland area and the pastoral transitional zone desertified land control area, while population density was the main driving factor controlling the spatial distribution of FVC in the Otindag sandy land area and the Yanshan Mountain water source protection area. There were regional differences in the influen-ce of other factors on FVC spatial distribution. The results of the interaction detector showed that the two-factor interactions were mainly the double-synergy and nonlinear synergy. The interaction of human activities with annual precipitation and slope could more fully explain the spatial variations of FVC. The range of suitable vegetation growth identified by the risk detector was the area with annual precipitation of 316.4-486.0 mm, average relative humidity of 48.4%-57.6%, and average annual temperature of 2.5-7.9 ℃, while other driving factors were different in different zones.

[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. The results showed that from 1982 to 2014, the degradation status in 77.1% of degraded vegetation was significantly improved and 64.1% of vegetation had an increasing trend in the study area, with mean annual NDVI decreasing from southeast to northwest. Vegetation coverage increased in 74.5% of the areas after the implementation of the Beijing-Tianjin sandstorm source control project, with mountains in northern Shanxi showing the most obvious increases. Among all the climate factors, rainfall had the strongest correlation with vegetation change. Human activities, such as ecological engineering, played an active role in most areas, especially in mountains of northern Shanxi, where the contribution of human activities reached 94.9%.

[Responses of vegetation growth to temperature during 1982-2015 in Xinjiang, China.] / 1982­2015年新疆地区植被生长对气温的响应.

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. During the whole gro-wing season, the responses of different vegetation types to air temperature change showed a decreasing trend. Seasonally, the responses of grassland and forest to temperature change showed a significant increase, while that of shrubland and desert were exactly the opposite in spring. The responses of all natural vegetation (grassland, shrubland, desert and forest) to temperature change showed a significant decreasing trend in summer, whereas their responses in autumn had no significant statistical characteristics. Spatially, the decreasing influence of temperature on the vegetation growth during the growing season in Xinjiang was universal, which might be due to the change in precipitation and solar radiation.