Land surface temperature(LST) directly affects the energy balance of terrestrial surface systems and impacts regional resources, ecosystem evolution, and ecosystem structures. Xinjiang Uygur Autonomous Region is locat...Land surface temperature(LST) directly affects the energy balance of terrestrial surface systems and impacts regional resources, ecosystem evolution, and ecosystem structures. Xinjiang Uygur Autonomous Region is located at the arid Northwest China and is extremely sensitive to climate change. There is an urgent need to understand the distribution patterns of LST in this area and quantitatively measure the nature and intensity of the impacts of the major driving factors from a spatial perspective, as well as elucidate the formation mechanisms. In this study, we used the MOD11C3 LST product developed on the basis of Moderate Resolution Imaging Spectroradiometer(MODIS) to conduct regression analysis and determine the spatiotemporal variation and differentiation pattern of LST in Xinjiang from 2000 to 2020. We analyzed the driving mechanisms of spatial heterogeneity of LST in Xinjiang and the six geomorphic zones(the Altay Mountains, Junggar Basin, Tianshan Mountains, Tarim Basin, Turpan-Hami(Tuha) Basin, and Pakakuna Mountain Group) using geographical detector(Geodetector) and geographically weighted regression(GWR) models. The warming rate of LST in Xinjiang during the study period was 0.24℃/10a, and the spatial distribution pattern of LST had obvious topographic imprints, with 87.20% of the warming zone located in the Gobi desert and areas with frequent human activities, and the cooling zone mainly located in the mountainous areas. The seasonal LST in Xinjiang was at a cooling rate of 0.09℃/10a in autumn, and showed a warming trend in other seasons. Digital elevation model(DEM), latitude, wind speed, precipitation, normalized difference vegetation index(NDVI), and sunshine duration in the single-factor and interactive detections were the key factors driving the LST changes. The direction and intensity of each major driving factor on the spatial variations of LST in the study area were heterogeneous. The negative feedback effect of DEM on the spatial differentiation of LST was the strongest. Lower latitudes, lo展开更多
China’s urbanization has attracted a lot of attention due to its unprecedented pace and intensity in terms of land,population,and economic impact.However,due to the lack of consistent and harmonized data,little is kn...China’s urbanization has attracted a lot of attention due to its unprecedented pace and intensity in terms of land,population,and economic impact.However,due to the lack of consistent and harmonized data,little is known about the patterns and dynamics of the interaction between these different aspects over the past few decades.Along with the implementation of the 2030 Agenda for Sustainable Development,a standardized dataset for assessing the sustainability of urbanization in China is needed.In this paper,we used remote sensing data from multiple sources(time-series of Landsat and Sentinel images)to map the impervious surface area(ISA)at five-year intervals from 1990 to 2015 and then converted the results into a standardized dataset of the built-up area for 433 Chinese cities with 300,000 inhabitants or more.This dataset was produced following the well-established rules adopted by the United Nations(UN).Validation of the ISA maps in urban areas based on the visual interpretation of Google Earth images showed that the average overall accuracy(OA),producer’s accuracy(PA)and user’s accuracy(UA)were 91.24%,92.58%and 89.65%,respec-tively.Comparisons with other existing urban built-up area datasets derived from the National Bureau of Statistics of China,the World Bank and UN-habitat indicated that our dataset,namely the stan-dardized urban built-up area dataset for China(SUBAD-China),provides an improved description of the spatiotemporal character-istics of the urbanization process and is especially applicable to a combined analysis of the spatial and socio-economic domains in urban areas.Potential applications of this dataset include combin-ing the spatial expansion and demographic information provided by UN to calculate sustainable development indicators such as SDG 11.3.1.The dataset could also be used in other multidimensional syntheses related to the study of urbanization in China.展开更多
基金supported by the Third Xinjiang Scientific Expedition Program(2021xjkk0801).
文摘Land surface temperature(LST) directly affects the energy balance of terrestrial surface systems and impacts regional resources, ecosystem evolution, and ecosystem structures. Xinjiang Uygur Autonomous Region is located at the arid Northwest China and is extremely sensitive to climate change. There is an urgent need to understand the distribution patterns of LST in this area and quantitatively measure the nature and intensity of the impacts of the major driving factors from a spatial perspective, as well as elucidate the formation mechanisms. In this study, we used the MOD11C3 LST product developed on the basis of Moderate Resolution Imaging Spectroradiometer(MODIS) to conduct regression analysis and determine the spatiotemporal variation and differentiation pattern of LST in Xinjiang from 2000 to 2020. We analyzed the driving mechanisms of spatial heterogeneity of LST in Xinjiang and the six geomorphic zones(the Altay Mountains, Junggar Basin, Tianshan Mountains, Tarim Basin, Turpan-Hami(Tuha) Basin, and Pakakuna Mountain Group) using geographical detector(Geodetector) and geographically weighted regression(GWR) models. The warming rate of LST in Xinjiang during the study period was 0.24℃/10a, and the spatial distribution pattern of LST had obvious topographic imprints, with 87.20% of the warming zone located in the Gobi desert and areas with frequent human activities, and the cooling zone mainly located in the mountainous areas. The seasonal LST in Xinjiang was at a cooling rate of 0.09℃/10a in autumn, and showed a warming trend in other seasons. Digital elevation model(DEM), latitude, wind speed, precipitation, normalized difference vegetation index(NDVI), and sunshine duration in the single-factor and interactive detections were the key factors driving the LST changes. The direction and intensity of each major driving factor on the spatial variations of LST in the study area were heterogeneous. The negative feedback effect of DEM on the spatial differentiation of LST was the strongest. Lower latitudes, lo
基金funded by the Strategic Priority Research Program of the Chinese Academy of Sciences[XDA19030104,XDA19090121]the Key Research and Development Projects of Hainan Province[ZDYF2019008].
文摘China’s urbanization has attracted a lot of attention due to its unprecedented pace and intensity in terms of land,population,and economic impact.However,due to the lack of consistent and harmonized data,little is known about the patterns and dynamics of the interaction between these different aspects over the past few decades.Along with the implementation of the 2030 Agenda for Sustainable Development,a standardized dataset for assessing the sustainability of urbanization in China is needed.In this paper,we used remote sensing data from multiple sources(time-series of Landsat and Sentinel images)to map the impervious surface area(ISA)at five-year intervals from 1990 to 2015 and then converted the results into a standardized dataset of the built-up area for 433 Chinese cities with 300,000 inhabitants or more.This dataset was produced following the well-established rules adopted by the United Nations(UN).Validation of the ISA maps in urban areas based on the visual interpretation of Google Earth images showed that the average overall accuracy(OA),producer’s accuracy(PA)and user’s accuracy(UA)were 91.24%,92.58%and 89.65%,respec-tively.Comparisons with other existing urban built-up area datasets derived from the National Bureau of Statistics of China,the World Bank and UN-habitat indicated that our dataset,namely the stan-dardized urban built-up area dataset for China(SUBAD-China),provides an improved description of the spatiotemporal character-istics of the urbanization process and is especially applicable to a combined analysis of the spatial and socio-economic domains in urban areas.Potential applications of this dataset include combin-ing the spatial expansion and demographic information provided by UN to calculate sustainable development indicators such as SDG 11.3.1.The dataset could also be used in other multidimensional syntheses related to the study of urbanization in China.
