Both tillage erosion and water erosion are severe erosional forms that occur widely on sloping agricultural land.However,previous studies have rarely considered the process of landform change due to continuous simulat...Both tillage erosion and water erosion are severe erosional forms that occur widely on sloping agricultural land.However,previous studies have rarely considered the process of landform change due to continuous simulation experiments of alternating tillage erosion and water erosion.To identify such changes,we applied a scouring experiment(at a 60 L min-1 water discharge rate based on precipitation data from the local meteorological station and the catchment area in the Yuanmou County,Yunnan Province,China)and a series of simulated tillage experiments where plots were consecutively tilled 5,10,and 15 times in rotation(representing 5 yr,10 yr,and 15 yr of tillage)at slope gradients of 5°,10°,and 20°.Close-range photogrammetry(CRP)employing an unmanned aerial vehicle(UAV)and a real-time kinematic global positioning system(RTK-GPS)was used to measure landform changes,and highresolution digital elevation models(DEMs)were generated to calculate net soil loss volumes.Additionally,the CRP was determined to be accurate and applicable through the use of erosion pins.The average tillage erosion rates were 69.85,131.45,and 155.34 t·hm-2·tillage pass-1,and the average water erosion rates were 1892.52,2961.76,and 4405.93 t·hm-2·h-1 for the 5°,10°,and 20°sloping farmland plots,respectively.The water erosion rates increased as tillage intensity increased,indicating that tillage erosion accelerates water erosion.Following these intensive tillage treatments,slope gradients gradually decreased,while the trend in slope gradients increased in runoff plots at the conclusion of the scouring experiment.Compared to the original plots(prior to our experiments),interactions between tillage and water erosion caused no obvious change in the landform structure of the runoff plots,while the height of all the runoff plots decreased.Our findings showed that both tillage erosion and water erosion caused a pseudo-steady-state landform evolutionary mechanism and resulted in thin soil layers on cultivated land composed of purple soil in China.展开更多
The evolution of land use patterns and the emergence of urban heat islands(UHI)over time are critical issues in city development strategies.This study aims to establish a model that maps the correlation between change...The evolution of land use patterns and the emergence of urban heat islands(UHI)over time are critical issues in city development strategies.This study aims to establish a model that maps the correlation between changes in land use and land surface temperature(LST)in the Mashhad City,northeastern Iran.Employing the Google Earth Engine(GEE)platform,we calculated the LST and extracted land use maps from 1985 to 2020.The convolutional neural network(CNN)approach was utilized to deeply explore the relationship between the LST and land use.The obtained results were compared with the standard machine learning(ML)methods such as support vector machine(SVM),random forest(RF),and linear regression.The results revealed a 1.00°C–2.00°C increase in the LST across various land use categories.This variation in temperature increases across different land use types suggested that,in addition to global warming and climatic changes,temperature rise was strongly influenced by land use changes.The LST surge in built-up lands in the Mashhad City was estimated to be 1.75°C,while forest lands experienced the smallest increase of 1.19°C.The developed CNN demonstrated an overall prediction accuracy of 91.60%,significantly outperforming linear regression and standard ML methods,due to the ability to extract higher level features.Furthermore,the deep neural network(DNN)modeling indicated that the urban lands,comprising 69.57%and 71.34%of the studied area,were projected to experience extreme temperatures above 41.00°C and 42.00°C in the years 2025 and 2030,respectively.In conclusion,the LST predictioin framework,combining the GEE platform and CNN method,provided an effective approach to inform urban planning and to mitigate the impacts of UHI.展开更多
Mountains are undergoing widespread changes caused by human activities and climate change.Given the importance of mountains,the protection and sustainable development of mountain ecosys-tems have been listed as the go...Mountains are undergoing widespread changes caused by human activities and climate change.Given the importance of mountains,the protection and sustainable development of mountain ecosys-tems have been listed as the goals of the United Nations 2030 Sustainable Development Agenda.As one of the indicators,the Mountain Green Cover Index(MGCI)datasets can provide consis-tent and comparable status of green vegetation in mountainous areas,which can support the mapping of heterogeneous mountain ecosystem health and monitoring changes over time.The produc-tion of explicitly high-spatial-resolution MGCI datasets is therefore urgently needed to support the protection measures at subnational and multitemporal scales.In this paper,the MGCI datasets with 500-meter spatial resolutions,covering the economic corridors of the Belt and Road Initiative(BRI),were developed for 2010 to 2019 based on all available Landsat-8 data and the Google Earth Engine cloud computing platform.The validation of green vegeta-tion cover with the ground-truth samples indicated that the data-sets can achieve an overall accuracy of 94.06%,with well-detailed spatial and temporal variations.The archived datasets include the MGCI of each BRI economic corridor,matched to a geospatial layer denoting the economic corridor boundaries.The essential informa-tion of the datasets and their limitations,along with the production flow,were described in this paper.展开更多
Land use reflects human activities on land.Urban land use is the highest level human alteration on Earth,and it is rapidly changing due to population increase and urbanization.Urban areas have widespread effects on lo...Land use reflects human activities on land.Urban land use is the highest level human alteration on Earth,and it is rapidly changing due to population increase and urbanization.Urban areas have widespread effects on local hydrology,climate,biodiversity,and food production[1,2].However,maps,that contain knowledge on the distribution,pattern and composition of various land use types in urban areas,are limited to city level.The mapping standard on data sources,methods,land use classification schemes varies from city to city,due to differences in financial input and skills of mapping personnel.To address various national and global environmental challenges caused by urbanization,it is important to have urban land uses at the national and global scales that are derived from the same or consistent data sources with the same or compatible classification systems and mapping methods.This is because,only with urban land use maps produced with similar criteria,consistent environmental policies can be made,and action efforts can be compared and assessed for large scale environmental administration.However,despite of the fact that a number of urban-extent maps exist at global scales[3,4],more detailed urban land use maps do not exist at the same scale.Even at big country or regional levels such as for the United States,China and European Union,consistent land use mapping efforts are rare[5,6](e.g.,https://sdi4apps.eu/open_land_use/).展开更多
Large-scale projects,such as the construction of railways and highways,usually cause an extensive Land Use Land Cover Change(LULCC).The China-Central Asia-West Asia Economic Corridor(CCAWAEC),one key large-scale proje...Large-scale projects,such as the construction of railways and highways,usually cause an extensive Land Use Land Cover Change(LULCC).The China-Central Asia-West Asia Economic Corridor(CCAWAEC),one key large-scale project of the Belt and Road Initiative(BRI),covers a region that is home to more than 1.6 billion people.Although numerous studies have been conducted on strategies and the economic potential of the Economic Corridor,reviewing LULCC mapping studies in this area has not been studied.This study provides a comprehensive review of the recent research progress and discusses the challenges in LULCC monitoring and driving factors identifying in the study area.The review will be helpful for the decision-making of sustainable development and construction in the Economic Corridor.To this end,350 peer-reviewed journal and conference papers,as well as book chapters were analyzed based on 17 attributes,such as main driving factors of LULCC,data collection methods,classification algorithms,and accuracy assessment methods.It was observed that:(1)rapid urbanization,industrialization,population growth,and climate change have been recognized as major causes of LULCC in the study area;(2)LULCC has,directly and indirectly,caused several environmental issues,such as biodiversity loss,air pollution,water pollution,desertification,and land degradation;(3)there is a lack of well-annotated national land use data in the region;(4)there is a lack of reliable training and reference datasets to accurately study the long-term LULCC in most parts of the study area;and(5)several technical issues still require more attention from the scientific community.Finally,several recommendations were proposed to address the identified issues.展开更多
基金supported by the National Key Research and Development Program of China(2017YFC0505102)the National Natural Science Foundation of China(No.41401313)+2 种基金the Major Science and Technology Program for Water Pollution Control and Treatment(2017ZX07101001)the Applied Basic Research Program of Sichuan(2018JY0034)the Major Science and Technology Projects in Sichuan Province(2018SZDZX0034)。
文摘Both tillage erosion and water erosion are severe erosional forms that occur widely on sloping agricultural land.However,previous studies have rarely considered the process of landform change due to continuous simulation experiments of alternating tillage erosion and water erosion.To identify such changes,we applied a scouring experiment(at a 60 L min-1 water discharge rate based on precipitation data from the local meteorological station and the catchment area in the Yuanmou County,Yunnan Province,China)and a series of simulated tillage experiments where plots were consecutively tilled 5,10,and 15 times in rotation(representing 5 yr,10 yr,and 15 yr of tillage)at slope gradients of 5°,10°,and 20°.Close-range photogrammetry(CRP)employing an unmanned aerial vehicle(UAV)and a real-time kinematic global positioning system(RTK-GPS)was used to measure landform changes,and highresolution digital elevation models(DEMs)were generated to calculate net soil loss volumes.Additionally,the CRP was determined to be accurate and applicable through the use of erosion pins.The average tillage erosion rates were 69.85,131.45,and 155.34 t·hm-2·tillage pass-1,and the average water erosion rates were 1892.52,2961.76,and 4405.93 t·hm-2·h-1 for the 5°,10°,and 20°sloping farmland plots,respectively.The water erosion rates increased as tillage intensity increased,indicating that tillage erosion accelerates water erosion.Following these intensive tillage treatments,slope gradients gradually decreased,while the trend in slope gradients increased in runoff plots at the conclusion of the scouring experiment.Compared to the original plots(prior to our experiments),interactions between tillage and water erosion caused no obvious change in the landform structure of the runoff plots,while the height of all the runoff plots decreased.Our findings showed that both tillage erosion and water erosion caused a pseudo-steady-state landform evolutionary mechanism and resulted in thin soil layers on cultivated land composed of purple soil in China.
文摘The evolution of land use patterns and the emergence of urban heat islands(UHI)over time are critical issues in city development strategies.This study aims to establish a model that maps the correlation between changes in land use and land surface temperature(LST)in the Mashhad City,northeastern Iran.Employing the Google Earth Engine(GEE)platform,we calculated the LST and extracted land use maps from 1985 to 2020.The convolutional neural network(CNN)approach was utilized to deeply explore the relationship between the LST and land use.The obtained results were compared with the standard machine learning(ML)methods such as support vector machine(SVM),random forest(RF),and linear regression.The results revealed a 1.00°C–2.00°C increase in the LST across various land use categories.This variation in temperature increases across different land use types suggested that,in addition to global warming and climatic changes,temperature rise was strongly influenced by land use changes.The LST surge in built-up lands in the Mashhad City was estimated to be 1.75°C,while forest lands experienced the smallest increase of 1.19°C.The developed CNN demonstrated an overall prediction accuracy of 91.60%,significantly outperforming linear regression and standard ML methods,due to the ability to extract higher level features.Furthermore,the deep neural network(DNN)modeling indicated that the urban lands,comprising 69.57%and 71.34%of the studied area,were projected to experience extreme temperatures above 41.00°C and 42.00°C in the years 2025 and 2030,respectively.In conclusion,the LST predictioin framework,combining the GEE platform and CNN method,provided an effective approach to inform urban planning and to mitigate the impacts of UHI.
基金was funded by the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant number XDA19030303)the National Key Research and Development Program of China(No.2020YFA0608700)the Youth Innovation Promotion Association of CAS(Grant 2019365).
文摘Mountains are undergoing widespread changes caused by human activities and climate change.Given the importance of mountains,the protection and sustainable development of mountain ecosys-tems have been listed as the goals of the United Nations 2030 Sustainable Development Agenda.As one of the indicators,the Mountain Green Cover Index(MGCI)datasets can provide consis-tent and comparable status of green vegetation in mountainous areas,which can support the mapping of heterogeneous mountain ecosystem health and monitoring changes over time.The produc-tion of explicitly high-spatial-resolution MGCI datasets is therefore urgently needed to support the protection measures at subnational and multitemporal scales.In this paper,the MGCI datasets with 500-meter spatial resolutions,covering the economic corridors of the Belt and Road Initiative(BRI),were developed for 2010 to 2019 based on all available Landsat-8 data and the Google Earth Engine cloud computing platform.The validation of green vegeta-tion cover with the ground-truth samples indicated that the data-sets can achieve an overall accuracy of 94.06%,with well-detailed spatial and temporal variations.The archived datasets include the MGCI of each BRI economic corridor,matched to a geospatial layer denoting the economic corridor boundaries.The essential informa-tion of the datasets and their limitations,along with the production flow,were described in this paper.
基金partially supported by the National Key Research and Development Program of China(2016YFA0600104)supported by donations made by Delos Living LLC,and the Cyrus Tang Foundation+2 种基金supported by the National Natural Science Foundation of China(41471419)Beijing Institute of Urban Planningsupported by the Fundamental Research Funds for the Central Universities(CCNU19TD002).
文摘Land use reflects human activities on land.Urban land use is the highest level human alteration on Earth,and it is rapidly changing due to population increase and urbanization.Urban areas have widespread effects on local hydrology,climate,biodiversity,and food production[1,2].However,maps,that contain knowledge on the distribution,pattern and composition of various land use types in urban areas,are limited to city level.The mapping standard on data sources,methods,land use classification schemes varies from city to city,due to differences in financial input and skills of mapping personnel.To address various national and global environmental challenges caused by urbanization,it is important to have urban land uses at the national and global scales that are derived from the same or consistent data sources with the same or compatible classification systems and mapping methods.This is because,only with urban land use maps produced with similar criteria,consistent environmental policies can be made,and action efforts can be compared and assessed for large scale environmental administration.However,despite of the fact that a number of urban-extent maps exist at global scales[3,4],more detailed urban land use maps do not exist at the same scale.Even at big country or regional levels such as for the United States,China and European Union,consistent land use mapping efforts are rare[5,6](e.g.,https://sdi4apps.eu/open_land_use/).
基金This research was jointly funded by the Strategic Priority Research Program of the Chinese Academy of Science(CAS)(XDA19030303)the National Natural Science Foundation of China(41631180,41701432,41571373)+1 种基金the Youth Innovation Promotion Association CAS(grant 2019365)the CAS-TWAS President’s Fellowship for International Doctoral Students.
文摘Large-scale projects,such as the construction of railways and highways,usually cause an extensive Land Use Land Cover Change(LULCC).The China-Central Asia-West Asia Economic Corridor(CCAWAEC),one key large-scale project of the Belt and Road Initiative(BRI),covers a region that is home to more than 1.6 billion people.Although numerous studies have been conducted on strategies and the economic potential of the Economic Corridor,reviewing LULCC mapping studies in this area has not been studied.This study provides a comprehensive review of the recent research progress and discusses the challenges in LULCC monitoring and driving factors identifying in the study area.The review will be helpful for the decision-making of sustainable development and construction in the Economic Corridor.To this end,350 peer-reviewed journal and conference papers,as well as book chapters were analyzed based on 17 attributes,such as main driving factors of LULCC,data collection methods,classification algorithms,and accuracy assessment methods.It was observed that:(1)rapid urbanization,industrialization,population growth,and climate change have been recognized as major causes of LULCC in the study area;(2)LULCC has,directly and indirectly,caused several environmental issues,such as biodiversity loss,air pollution,water pollution,desertification,and land degradation;(3)there is a lack of well-annotated national land use data in the region;(4)there is a lack of reliable training and reference datasets to accurately study the long-term LULCC in most parts of the study area;and(5)several technical issues still require more attention from the scientific community.Finally,several recommendations were proposed to address the identified issues.
