A prominent contradiction between supply and demand of water resources has restricted local development in social and economic aspects of Zhangye City,located in a typical arid region of China.Our study quantified the...A prominent contradiction between supply and demand of water resources has restricted local development in social and economic aspects of Zhangye City,located in a typical arid region of China.Our study quantified the Water Resource Stress Index(WRSI)from 2003 to 2017 and examined the factors of population,urbanization level,GDP per capita,Engel coefficient,and water consumption per unit of GDP by using the extended stochastic impact by regression on population,affluence and technology(STIRPAT)model to find the key factors that impact WRSI of Zhangye City to relieve the pressure on water resources.The ridge regression method is applied to improve this model to eliminate multicollinearity problems.The WRSI system was developed from the following three aspects:water resources utilization(WR),regional economic development water use(WU),and water environment stress(WE).Results show that the WRSI index has fallen from 0.81(2003)to 0.17(2017),with an average annual decreased rate of 9.8%.Moreover,the absolute values of normalized coefficients demonstrate that the Engel coefficient has the largest positive contribution to increase WRSI with an elastic coefficient of 0.2709,followed by water consumption per unit of GDP and population with elastic coefficients of 0.0971 and 0.0387,respectively.In contrast,the urbanization level and GDP per capita can decrease WRSI by−0.2449 and−0.089,respectively.The decline of WRSI was attributed to water-saving society construction which included the improvement of water saving technology and the adjustment of agricultural planting structures.Furthermore,this study demonstrated the feasibility of evaluating the driving forces affecting WRSI by using the STIRPAT model and ridge regression analysis.展开更多
Analyzing spatiotemporal dynamics of land use and land cover over time is widely recognized as important to better understand and provide solutions for social, economic, and environmental problems, especially in ecolo...Analyzing spatiotemporal dynamics of land use and land cover over time is widely recognized as important to better understand and provide solutions for social, economic, and environmental problems, especially in ecologically fragile region. In this paper, a case study was taken in Zhenlai County, which is a part of farming-pastoral ecotone of Northeast China. This study seeks to use multi-temporal satellite images and other data from various sources to analyze spatiotemporal changes from 1932 to 2005, and applied a quantitative methodology named intensity analysis in the time scale of decades at three levels: time interval, category, and transition. The findings of the case study are as follows: 1) the interval level of intensity analysis revealed that the annual rate of overall change was relatively fast in 1932–1954 and 1954–1976 time intervals. 2) The category level showed that arable land experienced less intensively gains and losses if the overall change was to have been distributed uniformly across the landscape while the gains and losses of forest land, grassland, water, settlement, wetland and other unused land were not consistent and stationary across the four time intervals. 3) The transition level illustrated that arable land expanded at the expense of grassland before 2000 while it gained intensively from wetland from 2000 to 2005. Settlement targets arable land and avoids grassland, water, wetland and other unused land. Besides, the loss of grassland was intensively targeted by arable land, forest land and wetland in the study period while the loss of wetland was targeted by water except for the time interval of 1976–2000. 4) During the early reclamation period, land use change of the study area was mainly affected by the policy, institutional and political factors, followed by the natural disasters.展开更多
Altay Prefecture plays a vital role as an ecological barrier in Northwest China.Studying the ecosystem service value is of great significance for promoting regional green high-quality development and maintaining ecolo...Altay Prefecture plays a vital role as an ecological barrier in Northwest China.Studying the ecosystem service value is of great significance for promoting regional green high-quality development and maintaining ecological security.Based on Global ESA land cover data from 2000 to 2015,the trade-off and synergy relationships and driving force factors between ecosystem services in Altay Prefecture were analyzed in this study.The analysis produced four main results.(1)The ecosystem service value in Altay Prefecture continued to increase from 113.521×10^(9) yuan in 2000 to 115.777×10^(9) yuan in 2015,for an increase of about 1.98%.(2)The distribution of ecosystem service value had obvious spatial agglomeration characteristics,with hot spot areas mainly concentrated in the"two rivers and one lake"and the mountainous areas in the northwest,while the cold spot areas were mainly the forest and grass-covered areas in the northern mountainous areas and within Jimunai County.(3)The trade-off and synergy relationship among ecosystem services was mainly synergistic,with a total of 77.78% of ecosystem service relative relationships showing a significant positive correlation at the 0.01 level.(4)Economic factors and industrial structure are important factors affecting ecosystem service value in Altay Prefecture.Ecosystem service value is positively correlated with per capita GDP and the output value of the tertiary industry,but negatively correlated with the output value of the secondary industry.展开更多
The distribution and dynamic changes of regional or national population data with long time series are very important for regional planning,resource allocation,government decision-making,disaster assessment,ecological...The distribution and dynamic changes of regional or national population data with long time series are very important for regional planning,resource allocation,government decision-making,disaster assessment,ecological protection,and other sustainability research.However,the existing population datasets such as LandScan and WorldPop all provide data from 2000 with limited time series,while GHS-POP only utilizes land use data with limited accuracy.In view of the limited remote sensing images of long time series,it is necessary to combine existing multi-source remote sensing data for population spatialization research.In this research,we developed a nighttime light desaturation index(NTLDI).Through the cross-sensor calibration model based on an autoencoder convolutional neural network,the NTLDl was calibrated with the same period Visible Infrared Imaging Radiometer Suite Day/Night Band(VIRS-DNB)data.Then,the geographically weighted regression method is used to determine the population density of China from 1990 to 2020 based on the long time series NTL.Furthermore,the change characteristics and the driving factors of China's population spatial distribution are analyzed.The large-scale,long-term population spatialization results obtained in this study are of great significance in government planning and decision-making,disaster assessment,resource allocation,and other aspects.展开更多
With the rapid development of the society and the economy, people are paying more attention to the value of natural resources and the benefits of the ecological environment. Evaluating the value of eco-assets has beco...With the rapid development of the society and the economy, people are paying more attention to the value of natural resources and the benefits of the ecological environment. Evaluating the value of eco-assets has become a focus of concern. Quantitative remote sensing measurements, land data and other auxiliary data were used to measure the eco-assets in 46 regions of the Wanjiang Demonstration Area from 1990 to 2013. This paper analyzes temporal and spatial variations of eco-assets’ distribution, composition, change patterns and the factors driving variations. The results show that the distribution of eco-assets in the regions is very uneven, the central region has higher ecological assets than other regions, and it declined first and then rose during the period 1990-2013. The total amount of eco-assets increased by 3.05%. The change in the amount of ecological assets was not large, but it is important that the amount of assets was basically stable, and increases in the proportion of degraded areas was small. Grassland and water body eco-assets decreased by 11.19% and 0.66%, respectively, and that of cultivated land decreased by 15.54%, but forest land increased by 6.42%. As for the change pattern of ecological assets, the per capita assets of Hefei had the largest reduction, and those of Xuancheng the second largest. The spatial and temporal changes of ecological assets in the Wanjiang Demonstration Area include natural factors and human factors. The government’s macro-control and economic policies are the main driving factors for the spatial and temporal changes of the ecological assets pattern.展开更多
基金the Natural Science Foundation of Gansu Province,China(Grant No.18JR3RA385)the National Natural Science Foundation of China(Grant No.41801079)The authors would like to thank the editors and anonymous reviewers for their detailed and constructive comments,which helped to significantly improve the manuscript.
文摘A prominent contradiction between supply and demand of water resources has restricted local development in social and economic aspects of Zhangye City,located in a typical arid region of China.Our study quantified the Water Resource Stress Index(WRSI)from 2003 to 2017 and examined the factors of population,urbanization level,GDP per capita,Engel coefficient,and water consumption per unit of GDP by using the extended stochastic impact by regression on population,affluence and technology(STIRPAT)model to find the key factors that impact WRSI of Zhangye City to relieve the pressure on water resources.The ridge regression method is applied to improve this model to eliminate multicollinearity problems.The WRSI system was developed from the following three aspects:water resources utilization(WR),regional economic development water use(WU),and water environment stress(WE).Results show that the WRSI index has fallen from 0.81(2003)to 0.17(2017),with an average annual decreased rate of 9.8%.Moreover,the absolute values of normalized coefficients demonstrate that the Engel coefficient has the largest positive contribution to increase WRSI with an elastic coefficient of 0.2709,followed by water consumption per unit of GDP and population with elastic coefficients of 0.0971 and 0.0387,respectively.In contrast,the urbanization level and GDP per capita can decrease WRSI by−0.2449 and−0.089,respectively.The decline of WRSI was attributed to water-saving society construction which included the improvement of water saving technology and the adjustment of agricultural planting structures.Furthermore,this study demonstrated the feasibility of evaluating the driving forces affecting WRSI by using the STIRPAT model and ridge regression analysis.
基金Under the auspices of National Youth Science Foundation of China(No.41601173)China Postdoctoral Science Foundation(No.2016M600954)
文摘Analyzing spatiotemporal dynamics of land use and land cover over time is widely recognized as important to better understand and provide solutions for social, economic, and environmental problems, especially in ecologically fragile region. In this paper, a case study was taken in Zhenlai County, which is a part of farming-pastoral ecotone of Northeast China. This study seeks to use multi-temporal satellite images and other data from various sources to analyze spatiotemporal changes from 1932 to 2005, and applied a quantitative methodology named intensity analysis in the time scale of decades at three levels: time interval, category, and transition. The findings of the case study are as follows: 1) the interval level of intensity analysis revealed that the annual rate of overall change was relatively fast in 1932–1954 and 1954–1976 time intervals. 2) The category level showed that arable land experienced less intensively gains and losses if the overall change was to have been distributed uniformly across the landscape while the gains and losses of forest land, grassland, water, settlement, wetland and other unused land were not consistent and stationary across the four time intervals. 3) The transition level illustrated that arable land expanded at the expense of grassland before 2000 while it gained intensively from wetland from 2000 to 2005. Settlement targets arable land and avoids grassland, water, wetland and other unused land. Besides, the loss of grassland was intensively targeted by arable land, forest land and wetland in the study period while the loss of wetland was targeted by water except for the time interval of 1976–2000. 4) During the early reclamation period, land use change of the study area was mainly affected by the policy, institutional and political factors, followed by the natural disasters.
基金The National Natural Science Foundation of China(41871196)The Scientific Research Project in Altay Prefecture,Xinjiang Uygur Autonomous Region of China(2019-529)。
文摘Altay Prefecture plays a vital role as an ecological barrier in Northwest China.Studying the ecosystem service value is of great significance for promoting regional green high-quality development and maintaining ecological security.Based on Global ESA land cover data from 2000 to 2015,the trade-off and synergy relationships and driving force factors between ecosystem services in Altay Prefecture were analyzed in this study.The analysis produced four main results.(1)The ecosystem service value in Altay Prefecture continued to increase from 113.521×10^(9) yuan in 2000 to 115.777×10^(9) yuan in 2015,for an increase of about 1.98%.(2)The distribution of ecosystem service value had obvious spatial agglomeration characteristics,with hot spot areas mainly concentrated in the"two rivers and one lake"and the mountainous areas in the northwest,while the cold spot areas were mainly the forest and grass-covered areas in the northern mountainous areas and within Jimunai County.(3)The trade-off and synergy relationship among ecosystem services was mainly synergistic,with a total of 77.78% of ecosystem service relative relationships showing a significant positive correlation at the 0.01 level.(4)Economic factors and industrial structure are important factors affecting ecosystem service value in Altay Prefecture.Ecosystem service value is positively correlated with per capita GDP and the output value of the tertiary industry,but negatively correlated with the output value of the secondary industry.
基金supported by National Natural Science Foundation of China[Grant Number 41930650]Ningxia Hui Autonomous Region Key Research and Development Project[Grant Number 2022BEG03064]State Key Laboratory INTERNATIONAL JOURNAL OF DIGITAL EARTH 2719 of Geo-Information Engineering and Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of MNR,CASM[Grant Number 2021-03-04].
文摘The distribution and dynamic changes of regional or national population data with long time series are very important for regional planning,resource allocation,government decision-making,disaster assessment,ecological protection,and other sustainability research.However,the existing population datasets such as LandScan and WorldPop all provide data from 2000 with limited time series,while GHS-POP only utilizes land use data with limited accuracy.In view of the limited remote sensing images of long time series,it is necessary to combine existing multi-source remote sensing data for population spatialization research.In this research,we developed a nighttime light desaturation index(NTLDI).Through the cross-sensor calibration model based on an autoencoder convolutional neural network,the NTLDl was calibrated with the same period Visible Infrared Imaging Radiometer Suite Day/Night Band(VIRS-DNB)data.Then,the geographically weighted regression method is used to determine the population density of China from 1990 to 2020 based on the long time series NTL.Furthermore,the change characteristics and the driving factors of China's population spatial distribution are analyzed.The large-scale,long-term population spatialization results obtained in this study are of great significance in government planning and decision-making,disaster assessment,resource allocation,and other aspects.
基金National Natural Science Foundation of China(41571124)
文摘With the rapid development of the society and the economy, people are paying more attention to the value of natural resources and the benefits of the ecological environment. Evaluating the value of eco-assets has become a focus of concern. Quantitative remote sensing measurements, land data and other auxiliary data were used to measure the eco-assets in 46 regions of the Wanjiang Demonstration Area from 1990 to 2013. This paper analyzes temporal and spatial variations of eco-assets’ distribution, composition, change patterns and the factors driving variations. The results show that the distribution of eco-assets in the regions is very uneven, the central region has higher ecological assets than other regions, and it declined first and then rose during the period 1990-2013. The total amount of eco-assets increased by 3.05%. The change in the amount of ecological assets was not large, but it is important that the amount of assets was basically stable, and increases in the proportion of degraded areas was small. Grassland and water body eco-assets decreased by 11.19% and 0.66%, respectively, and that of cultivated land decreased by 15.54%, but forest land increased by 6.42%. As for the change pattern of ecological assets, the per capita assets of Hefei had the largest reduction, and those of Xuancheng the second largest. The spatial and temporal changes of ecological assets in the Wanjiang Demonstration Area include natural factors and human factors. The government’s macro-control and economic policies are the main driving factors for the spatial and temporal changes of the ecological assets pattern.
