The DInSAR technique is used for monitoring the desert height changes to study sandstorms. Hunshandake Sandy Land, as the test area, is one of the main sources of sandstorms in Beijing. In order to study the sandstorm...The DInSAR technique is used for monitoring the desert height changes to study sandstorms. Hunshandake Sandy Land, as the test area, is one of the main sources of sandstorms in Beijing. In order to study the sandstorm source and its impact, a pair of EnviSat ASAR images of Oct. 11, 2005, and Oct. 26, 2004, is processed on the basis of analysis of six ERS-2 and EnviSat radar images. After the image configuration, flat earth effect correction, data filtering, phase unwrapping, and geo-coding, a deformation model over Hunshandake desert is built. According to the results, the height decreased in most areas and increased in a few areas, which basically coincides with the strong sandstorm appearing in Beijing in the Spring of 2005. The results show DInSAR has an important role in monitoring of desert surface deformation.展开更多
Water scarcity is a challenge in many arid and semi-arid regions; this may lead to a series of environmental problems and could be stressed even further by the effects from climate change. This study focused on the wa...Water scarcity is a challenge in many arid and semi-arid regions; this may lead to a series of environmental problems and could be stressed even further by the effects from climate change. This study focused on the water resource management in Shanshan County, an inland arid region located in northwestern China with a long history of groundwater overexploitation. A model of the supply and demand system in the study area from 2006 to2030, including effects from global climate change,was developed using a system dynamics(SD)modeling tool. This SD model was used to 1) explore the best water-resource management options by testing system responses under various scenarios and2) identify the principal factors affecting the responses, aiming for a balance of the groundwater system and sustainable socio-economic development.Three causes were identified as primarily responsible for water issues in Shanshan: low water-use efficiency low water reuse, and increase in industrial waterdemand. To address these causes, a combined scenario was designed and simulated, which was able to keep the water deficiency under 5% by 2030. The model provided some insights into the dynamic interrelations that generate system behavior and the key factors in the system that govern water demand and supply. The model as well as the study results may be useful in water resources management in Shanshan and may be applied, with appropriate modifications, to other regions facing similar water management challenges.展开更多
The United Nations 2030 Agenda for Sustainable Development provides an important framework for economic,social,and environmental action.A comprehensive indicator system to aid in the systematic implementation and moni...The United Nations 2030 Agenda for Sustainable Development provides an important framework for economic,social,and environmental action.A comprehensive indicator system to aid in the systematic implementation and monitoring of progress toward the Sustainable Development Goals(SDGs)is unfortunately limited in many countries due to lack of data.The availability of a growing amount of multi-source data and rapid advancements in big data methods and infrastructure provide unique opportunities to mitigate these data shortages and develop innovative methodologies for comparatively monitoring SDGs.Big Earth Data,a special class of big data with spatial attributes,holds tremendous potential to facilitate science,technology,and innovation toward implementing SDGs around the world.Several programs and initiatives in China have invested in Big Earth Data infrastructure and capabilities,and have successfully carried out case studies to demonstrate their utility in sustainability science.This paper presents implementations of Big Earth Data in evaluating SDG indicators,including the development of new algorithms,indicator expansion(for SDG 11.4.1)and indicator extension(for SDG 11.3.1),introduction of a biodiversity risk index as a more effective analysis method for SDG 15.5.1,and several new high-quality data products,such as global net ecosystem productivity,high-resolution global mountain green cover index,and endangered species richness.These innovations are used to present a comprehensive analysis of SDGs 2,6,11,13,14,and 15 from 2010 to 2020 in China utilizing Big Earth Data,concluding that all six SDGs are on schedule to be achieved by 2030.展开更多
基金supported partially by the National Natural Science Foundation of China （Nos.40774009 and 40974016） the National Hi-tech R＆D Program of China （No. 2009AA121402）+1 种基金 the Special Project Fund of Taishan Scholars of Shandong Province China （No. TSXZ0502） the Research ＆ Innovation Team Support Program of SDUST China
文摘The DInSAR technique is used for monitoring the desert height changes to study sandstorms. Hunshandake Sandy Land, as the test area, is one of the main sources of sandstorms in Beijing. In order to study the sandstorm source and its impact, a pair of EnviSat ASAR images of Oct. 11, 2005, and Oct. 26, 2004, is processed on the basis of analysis of six ERS-2 and EnviSat radar images. After the image configuration, flat earth effect correction, data filtering, phase unwrapping, and geo-coding, a deformation model over Hunshandake desert is built. According to the results, the height decreased in most areas and increased in a few areas, which basically coincides with the strong sandstorm appearing in Beijing in the Spring of 2005. The results show DInSAR has an important role in monitoring of desert surface deformation.
文摘Water scarcity is a challenge in many arid and semi-arid regions; this may lead to a series of environmental problems and could be stressed even further by the effects from climate change. This study focused on the water resource management in Shanshan County, an inland arid region located in northwestern China with a long history of groundwater overexploitation. A model of the supply and demand system in the study area from 2006 to2030, including effects from global climate change,was developed using a system dynamics(SD)modeling tool. This SD model was used to 1) explore the best water-resource management options by testing system responses under various scenarios and2) identify the principal factors affecting the responses, aiming for a balance of the groundwater system and sustainable socio-economic development.Three causes were identified as primarily responsible for water issues in Shanshan: low water-use efficiency low water reuse, and increase in industrial waterdemand. To address these causes, a combined scenario was designed and simulated, which was able to keep the water deficiency under 5% by 2030. The model provided some insights into the dynamic interrelations that generate system behavior and the key factors in the system that govern water demand and supply. The model as well as the study results may be useful in water resources management in Shanshan and may be applied, with appropriate modifications, to other regions facing similar water management challenges.
基金supported by the Big Earth Data Science Engineering Program of the Chinese Academy of Sciences Strategic Priority Research Program(XDA19090000 and XDA19030000)。
文摘The United Nations 2030 Agenda for Sustainable Development provides an important framework for economic,social,and environmental action.A comprehensive indicator system to aid in the systematic implementation and monitoring of progress toward the Sustainable Development Goals(SDGs)is unfortunately limited in many countries due to lack of data.The availability of a growing amount of multi-source data and rapid advancements in big data methods and infrastructure provide unique opportunities to mitigate these data shortages and develop innovative methodologies for comparatively monitoring SDGs.Big Earth Data,a special class of big data with spatial attributes,holds tremendous potential to facilitate science,technology,and innovation toward implementing SDGs around the world.Several programs and initiatives in China have invested in Big Earth Data infrastructure and capabilities,and have successfully carried out case studies to demonstrate their utility in sustainability science.This paper presents implementations of Big Earth Data in evaluating SDG indicators,including the development of new algorithms,indicator expansion(for SDG 11.4.1)and indicator extension(for SDG 11.3.1),introduction of a biodiversity risk index as a more effective analysis method for SDG 15.5.1,and several new high-quality data products,such as global net ecosystem productivity,high-resolution global mountain green cover index,and endangered species richness.These innovations are used to present a comprehensive analysis of SDGs 2,6,11,13,14,and 15 from 2010 to 2020 in China utilizing Big Earth Data,concluding that all six SDGs are on schedule to be achieved by 2030.
