High-resolution deep-learning-based remote-sensing imagery analysis has been widely used in land-use and crop-classification mapping. However, the influence of composite feature bands, including complex feature indice...High-resolution deep-learning-based remote-sensing imagery analysis has been widely used in land-use and crop-classification mapping. However, the influence of composite feature bands, including complex feature indices arising from different sensors on the backbone, patch size, and predictions in transferable deep models require further testing. The experiments were conducted in six sites in Henan province from2019 to 2021. This study sought to enable the transfer of classification models across regions and years for Sentinel-2 A(10-m resolution) and Gaofen PMS(2-m resolution) imagery. With feature selection and up-sampling of small samples, the performance of UNet++ architecture on five backbones and four patch sizes was examined. Joint loss, mean Intersection over Union(m Io U), and epoch time were analyzed, and the optimal backbone and patch size for both sensors were Timm-Reg Net Y-320 and 256 × 256, respectively. The overall accuracy and Fscores of the Sentinel-2 A predictions ranged from 96.86% to 97.72%and 71.29% to 80.75%, respectively, compared to 75.34%–97.72% and 54.89%–73.25% for the Gaofen predictions. The accuracies of each site indicated that patch size exerted a greater influence on model performance than the backbone. The feature-selection-based predictions with UNet++ architecture and upsampling of minor classes demonstrated the capabilities of deep-learning generalization for classifying complex ground objects, offering improved performance compared to the UNet, Deeplab V3+, Random Forest, and Object-Oriented Classification models. In addition to the overall accuracy, confusion matrices,precision, recall, and F1 scores should be evaluated for minor land-cover types. This study contributes to large-scale, dynamic, and near-real-time land-use and crop mapping by integrating deep learning and multi-source remote-sensing imagery.展开更多
Land use and cover change(LUCC)is the most direct manifestation of the interaction between anthropological activities and the natural environment on Earth's surface,with significant impacts on the environment and ...Land use and cover change(LUCC)is the most direct manifestation of the interaction between anthropological activities and the natural environment on Earth's surface,with significant impacts on the environment and social economy.Rapid economic development and climate change have resulted in significant changes in land use and cover.The Shiyang River Basin,located in the eastern part of the Hexi Corridor in China,has undergone significant climate change and LUCC over the past few decades.In this study,we used the random forest classification to obtain the land use and cover datasets of the Shiyang River Basin in 1991,1995,2000,2005,2010,2015,and 2020 based on Landsat images.We validated the land use and cover data in 2015 from the random forest classification results(this study),the high-resolution dataset of annual global land cover from 2000 to 2015(AGLC-2000-2015),the global 30 m land cover classification with a fine classification system(GLC_FCS30),and the first Landsat-derived annual China Land Cover Dataset(CLCD)against ground-truth classification results to evaluate the accuracy of the classification results in this study.Furthermore,we explored and compared the spatiotemporal patterns of LUCC in the upper,middle,and lower reaches of the Shiyang River Basin over the past 30 years,and employed the random forest importance ranking method to analyze the influencing factors of LUCC based on natural(evapotranspiration,precipitation,temperature,and surface soil moisture)and anthropogenic(nighttime light,gross domestic product(GDP),and population)factors.The results indicated that the random forest classification results for land use and cover in the Shiyang River Basin in 2015 outperformed the AGLC-2000-2015,GLC_FCS30,and CLCD datasets in both overall and partial validations.Moreover,the classification results in this study exhibited a high level of agreement with the ground truth features.From 1991 to 2020,the area of bare land exhibited a decreasing trend,with changes primarily occurring in the middle and lower reaches of the basin.The area of grassland initially decreased and then increased,with changes occurring mainly in the upper and middle reaches of the basin.In contrast,the area of cropland initially increased and then decreased,with changes occurring in the middle and lower reaches.The LUCC was influenced by both natural and anthropogenic factors.Climatic factors and population contributed significantly to LUCC,and the importance values of evapotranspiration,precipitation,temperature,and population were 22.12%,32.41%,21.89%,and 19.65%,respectively.Moreover,policy interventions also played an important role.Land use and cover in the Shiyang River Basin exhibited fluctuating changes over the past 30 years,with the ecological environment improving in the last 10 years.This suggests that governance efforts in the study area have had some effects,and the government can continue to move in this direction in the future.The findings can provide crucial insights for related research and regional sustainable development in the Shiyang River Basin and other similar arid and semi-arid areas.展开更多
Land cover classification is one of the main components of the modern weather research and forecasting models, which can influence the meteorological variable, and in turn the concentration of air pollutants. In this ...Land cover classification is one of the main components of the modern weather research and forecasting models, which can influence the meteorological variable, and in turn the concentration of air pollutants. In this study the impact of using two traditional land use classifications, the United States Geological Survey (USGS) and the Moderate-resolution Imaging Spectroradiometer (MODIS), were evaluated. The Weather Research and Forecasting model (WRF, version 3.2.1) was run for the period 18 - 22 August, 2014 (dry season) at a grid spacing of 3 km centered on the city of Manaus. The comparison between simulated and ground-based observed data revealed significant differences in the meteorological fields, for instance, the temperature. Compared to USGS, MODIS classification showed better skill in representing observed temperature for urban areas of Manaus, while the two files showed similar results for nearby areas. The analysis of the files suggests that the better quality of the simulations favorable to the MODIS file is straightly related to its better representation of urban class of land use, which is observed to be not adequately represented by USGS.展开更多
By analyzing the applicability of the new Code for Classification of Urban Land Use and Planning Standards of Development Land from the angle of planning management,this paper points out the conflicts between the plan...By analyzing the applicability of the new Code for Classification of Urban Land Use and Planning Standards of Development Land from the angle of planning management,this paper points out the conflicts between the planning and land use management institutions.Referring to the experience of land use control in the US and the UK through zoning and case law respectively,this paper puts forward that the urban land use classification should take into consideration the characteristics of the actual urban planning system and the possibility of mixed land use due to the uncertainty of urban development,and be linked to the institutions of planning and land supply management.展开更多
To link regional land use/cover changes with environmental effects,land cover changes are required to reflect vegetation successions,whereas the land cover classification systems commonly used nowadays cannot serve th...To link regional land use/cover changes with environmental effects,land cover changes are required to reflect vegetation successions,whereas the land cover classification systems commonly used nowadays cannot serve this purpose.In this paper,a new land cover classification system is established in which land covers are classified by the vegetation succes-sions,taking Zamtang County,Barkam County and Jinchuan County in the upper Dadu River watershed as a study area.Using multi-temporal remote sensing images,the land cover data of 1967,1986 and 2000 are obtained by means of integration of unsupervised classification and visual interpretation methods.The database facilitates the study of land use/cover changes,en-vironmental effects and ecological construction.Land cover changes reflect the main ecological processes in the upper Dadu River watershed.The landscape composed mainly of grasslands,wildwoods and alpine scrubs in 1967 was changed to that of grasslands,secondary forests,al-pine scrubs,fragmentary wildwoods,artificial forests,secondary scrubs in 2000,meanwhile,the landscape got more fragmentized.The total area of the forests decreased by 9.43%.Study results have shown the process of restoration of logged areas in forest centers.From 1967 to 2000,only 6.86 percents of logged areas were converted to shrubs,meadows or crop-lands,and the rest were converted into artificial forests or secondary forests.So the ecological shelter functions will be restored,stage by stage.Firewood collection,charcoal production and overgrazing are the three major triggers for the extensive degradation of alpine oak forests,Sa-bina tibetica forests and meadows.The arid valley grasslands expanded too.The degradation of vegetation in the southern slopes impairs ecological shelter functions and affects livelihood of local residents,so it is essential to find effective measures for ecological restoration and recon-struction.Field investigations have found that the current measures have not concerned with how to keep the livelihood of local farmers and herders.The most important measure for ecological protection and restoration is to help the farmers and herders to raise the living standard,which means that they will never need to rely only on the colonizing of croplands,the logging of forests and the grazing of livestock to make a living.展开更多
基金supported by the National Science and Technology Platform Construction (2005DKA32300)the Major Research Projects of the Ministry of Education (16JJD770019)the Open Program of Collaborative Innovation Center of Geo-Information Technology for Smart Central Plains Henan Province (G202006)。
文摘High-resolution deep-learning-based remote-sensing imagery analysis has been widely used in land-use and crop-classification mapping. However, the influence of composite feature bands, including complex feature indices arising from different sensors on the backbone, patch size, and predictions in transferable deep models require further testing. The experiments were conducted in six sites in Henan province from2019 to 2021. This study sought to enable the transfer of classification models across regions and years for Sentinel-2 A(10-m resolution) and Gaofen PMS(2-m resolution) imagery. With feature selection and up-sampling of small samples, the performance of UNet++ architecture on five backbones and four patch sizes was examined. Joint loss, mean Intersection over Union(m Io U), and epoch time were analyzed, and the optimal backbone and patch size for both sensors were Timm-Reg Net Y-320 and 256 × 256, respectively. The overall accuracy and Fscores of the Sentinel-2 A predictions ranged from 96.86% to 97.72%and 71.29% to 80.75%, respectively, compared to 75.34%–97.72% and 54.89%–73.25% for the Gaofen predictions. The accuracies of each site indicated that patch size exerted a greater influence on model performance than the backbone. The feature-selection-based predictions with UNet++ architecture and upsampling of minor classes demonstrated the capabilities of deep-learning generalization for classifying complex ground objects, offering improved performance compared to the UNet, Deeplab V3+, Random Forest, and Object-Oriented Classification models. In addition to the overall accuracy, confusion matrices,precision, recall, and F1 scores should be evaluated for minor land-cover types. This study contributes to large-scale, dynamic, and near-real-time land-use and crop mapping by integrating deep learning and multi-source remote-sensing imagery.
基金supported by the Central Government to Guide Local Technological Development(23ZYQH0298)the Science and Technology Project of Gansu Province(20JR10RA656,22JR5RA416)the Science and Technology Project of Wuwei City(WW2202YFS006).
文摘Land use and cover change(LUCC)is the most direct manifestation of the interaction between anthropological activities and the natural environment on Earth's surface,with significant impacts on the environment and social economy.Rapid economic development and climate change have resulted in significant changes in land use and cover.The Shiyang River Basin,located in the eastern part of the Hexi Corridor in China,has undergone significant climate change and LUCC over the past few decades.In this study,we used the random forest classification to obtain the land use and cover datasets of the Shiyang River Basin in 1991,1995,2000,2005,2010,2015,and 2020 based on Landsat images.We validated the land use and cover data in 2015 from the random forest classification results(this study),the high-resolution dataset of annual global land cover from 2000 to 2015(AGLC-2000-2015),the global 30 m land cover classification with a fine classification system(GLC_FCS30),and the first Landsat-derived annual China Land Cover Dataset(CLCD)against ground-truth classification results to evaluate the accuracy of the classification results in this study.Furthermore,we explored and compared the spatiotemporal patterns of LUCC in the upper,middle,and lower reaches of the Shiyang River Basin over the past 30 years,and employed the random forest importance ranking method to analyze the influencing factors of LUCC based on natural(evapotranspiration,precipitation,temperature,and surface soil moisture)and anthropogenic(nighttime light,gross domestic product(GDP),and population)factors.The results indicated that the random forest classification results for land use and cover in the Shiyang River Basin in 2015 outperformed the AGLC-2000-2015,GLC_FCS30,and CLCD datasets in both overall and partial validations.Moreover,the classification results in this study exhibited a high level of agreement with the ground truth features.From 1991 to 2020,the area of bare land exhibited a decreasing trend,with changes primarily occurring in the middle and lower reaches of the basin.The area of grassland initially decreased and then increased,with changes occurring mainly in the upper and middle reaches of the basin.In contrast,the area of cropland initially increased and then decreased,with changes occurring in the middle and lower reaches.The LUCC was influenced by both natural and anthropogenic factors.Climatic factors and population contributed significantly to LUCC,and the importance values of evapotranspiration,precipitation,temperature,and population were 22.12%,32.41%,21.89%,and 19.65%,respectively.Moreover,policy interventions also played an important role.Land use and cover in the Shiyang River Basin exhibited fluctuating changes over the past 30 years,with the ecological environment improving in the last 10 years.This suggests that governance efforts in the study area have had some effects,and the government can continue to move in this direction in the future.The findings can provide crucial insights for related research and regional sustainable development in the Shiyang River Basin and other similar arid and semi-arid areas.
基金This work received funding support from CNPq(National Counsel of Technological and Scientific Development,process 404104/2013-4)CAPES(Coordination for the Improvement of Higher Education Personnel)and Araucária Foundation
文摘Land cover classification is one of the main components of the modern weather research and forecasting models, which can influence the meteorological variable, and in turn the concentration of air pollutants. In this study the impact of using two traditional land use classifications, the United States Geological Survey (USGS) and the Moderate-resolution Imaging Spectroradiometer (MODIS), were evaluated. The Weather Research and Forecasting model (WRF, version 3.2.1) was run for the period 18 - 22 August, 2014 (dry season) at a grid spacing of 3 km centered on the city of Manaus. The comparison between simulated and ground-based observed data revealed significant differences in the meteorological fields, for instance, the temperature. Compared to USGS, MODIS classification showed better skill in representing observed temperature for urban areas of Manaus, while the two files showed similar results for nearby areas. The analysis of the files suggests that the better quality of the simulations favorable to the MODIS file is straightly related to its better representation of urban class of land use, which is observed to be not adequately represented by USGS.
基金supported by National Natural Science Foundation of China (Grant No.51078152)the Foundation of the Ministry of Education of China for Young Scholars in Hu-manities and Social Science Research (Grant No.12YJCZH167)the Special Fund for BasicScientific Research of China's Central Colleges(the South China University of Technology,No.x2jzD2118190)
文摘By analyzing the applicability of the new Code for Classification of Urban Land Use and Planning Standards of Development Land from the angle of planning management,this paper points out the conflicts between the planning and land use management institutions.Referring to the experience of land use control in the US and the UK through zoning and case law respectively,this paper puts forward that the urban land use classification should take into consideration the characteristics of the actual urban planning system and the possibility of mixed land use due to the uncertainty of urban development,and be linked to the institutions of planning and land supply management.
基金This work was supported by the National Natural Science Foundation of China(Grant No.90202012,40471009 and 30270256)the National Basic Research Program of China(Grant No.2005CB422006)+1 种基金the Knowledge Innovation Project of CAS(Grant No.KZCX3-SW-339)the Core Project of IGSNRR,CAS(Grant No.CXIOG-E01-01).
文摘To link regional land use/cover changes with environmental effects,land cover changes are required to reflect vegetation successions,whereas the land cover classification systems commonly used nowadays cannot serve this purpose.In this paper,a new land cover classification system is established in which land covers are classified by the vegetation succes-sions,taking Zamtang County,Barkam County and Jinchuan County in the upper Dadu River watershed as a study area.Using multi-temporal remote sensing images,the land cover data of 1967,1986 and 2000 are obtained by means of integration of unsupervised classification and visual interpretation methods.The database facilitates the study of land use/cover changes,en-vironmental effects and ecological construction.Land cover changes reflect the main ecological processes in the upper Dadu River watershed.The landscape composed mainly of grasslands,wildwoods and alpine scrubs in 1967 was changed to that of grasslands,secondary forests,al-pine scrubs,fragmentary wildwoods,artificial forests,secondary scrubs in 2000,meanwhile,the landscape got more fragmentized.The total area of the forests decreased by 9.43%.Study results have shown the process of restoration of logged areas in forest centers.From 1967 to 2000,only 6.86 percents of logged areas were converted to shrubs,meadows or crop-lands,and the rest were converted into artificial forests or secondary forests.So the ecological shelter functions will be restored,stage by stage.Firewood collection,charcoal production and overgrazing are the three major triggers for the extensive degradation of alpine oak forests,Sa-bina tibetica forests and meadows.The arid valley grasslands expanded too.The degradation of vegetation in the southern slopes impairs ecological shelter functions and affects livelihood of local residents,so it is essential to find effective measures for ecological restoration and recon-struction.Field investigations have found that the current measures have not concerned with how to keep the livelihood of local farmers and herders.The most important measure for ecological protection and restoration is to help the farmers and herders to raise the living standard,which means that they will never need to rely only on the colonizing of croplands,the logging of forests and the grazing of livestock to make a living.
