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
We report the world's first all-season training and validation sample sets for global land cover classification with Landsat-8 data.Prior to this,such samples were only available at a single date primarily from th...We report the world's first all-season training and validation sample sets for global land cover classification with Landsat-8 data.Prior to this,such samples were only available at a single date primarily from the growing season.It is unknown how much limitation such a single-date sample has to mapping global land cover in other seasons of the year.To answer this question,we selected available Landsat-8 images from four seasons and collected training and validation samples from them.We compared the performances of training samples in different seasons using Random Forest algorithm.We found that the use of training samples from any individual season would result in the best overall classification accuracy when validated by samples in the same season.The global overall accuracy from combined best seasonal results was 67.2% when classifying the 11 Level-1 classes in the Finer Resolution Observation and Monitoring of Global Land Cover(FROM-GLC) classification system.The use of training samples from all seasons(named all-season training sample set hereafter) produced an overall accuracy of 67.0%.We also tested classification within 10° latitude 60° longitude zones using all-season training subsample within each zone and obtained an overall accuracy of 70.2%.This indicates that properly grouped subsamples in space can help improve classification accuracies.All the results in this study seem to suggest that it is possible to use an all-season training sample set to reach global optimality with universal applicability in classifying images acquired at any time of a year for global land cover mapping.展开更多
Knowledge about climate change impacts on species distribution at national scale is critical to biodi- versity conservation and design of management programs. Although China is a biodiversity hot spot in the world, po...Knowledge about climate change impacts on species distribution at national scale is critical to biodi- versity conservation and design of management programs. Although China is a biodiversity hot spot in the world, potential influence of climate change on Chinese protected birds is rarely studied. Here, we assess the impact of climate change on 108 protected bird species and nature reserves using species distribution modeling at a relatively fine spatial resolution （1 km） for the first time. We found that a large proportion of protected species would have potential suitable habitat shrink and northward range shift by 77-90 km in response to projected future climate change in 2080. Southeastern China would suffer from losing climate suitability, whereas the climate conditions in Qinghai-Tibet Plateau and northeastern China were projected to become suitable for more protected species. On average, each protected area in decline of suitable climate for China would experience a 3-4 species by 2080. Cli- mate change will modify which species each protected area will be suitable for. Our results showed that the risk of extinction for Chinese protected birds would be high, even in the moderate climate change scenario. These findings indicate that the management and design of nature reserves in China must take climate change into consideration.展开更多
Global climate and environmental change studies require detailed land-use and land-cover(LULC)information about the past,present,and future.In this paper,we discuss a methodology for downscaling coarse-resolution(i.e....Global climate and environmental change studies require detailed land-use and land-cover(LULC)information about the past,present,and future.In this paper,we discuss a methodology for downscaling coarse-resolution(i.e.,half-degree)future land use scenarios to finer(i.e.,1 km)resolutions at the global scale using a grid-based spatially explicit cellular automata(CA)model.We account for spatial heterogeneity from topography,climate,soils,and socioeconomic variables.The model uses a global 30 m land cover map(2010)as the base input,a variety of biogeographic and socioeconomic variables,and an empirical analysis to downscale coarse-resolution land use information(specifically urban,crop and pasture).The output of this model offers the most current and finest-scale future LULC dynamics from 2010 to 2100(with four representative concentration pathway(RCP)scenarios--RCP 2.6,RCP 4.5,RCP 6.0,and RCP 8.5)at a 1 km resolution within a globally consistent framework.The data are freely available for download,and will enable researchers to study the impacts of LULC change at the local scale.展开更多
We report on a global cropland extent product at 30-m spatial resolution developed with two 30-m global land cover maps(i.e.FROM-GLC,Finer Resolution Observation and Monitoring,Global Land Cover;FROM-GLC=agg)and a 250...We report on a global cropland extent product at 30-m spatial resolution developed with two 30-m global land cover maps(i.e.FROM-GLC,Finer Resolution Observation and Monitoring,Global Land Cover;FROM-GLC=agg)and a 250-m cropland probability map.A common land cover validation sample database was used to determine optimal thresholds of cropland probability in different parts of the world to generate a cropland/noncropland mask according to the classification accuracies for cropland samples.A decision tree was then applied to combine two 250-m cropland masks:one existing mask from the literature and the other produced in this study,with the 30-m global land cover map FROM-GLC-agg.For the smallest difference with country-level cropland area in Food and Agriculture Organization Corporate Statistical(FAOSTAT)database,a final global cropland extent map was composited from the FROM-GLC,FROM-GLC-agg,and two masked crop=land layers.From this map FROM-GC(Global Cropland),we estimated the global cropland areas to be 1533.83 million hectares(Mha)in 2010,which is 6.95 Mha(0.45%)less than the area reported by the Food and Agriculture Organization(FAO)of the United Nations for the year 2010.A country-by=country comparison between the map and the FAOSTAT data showed a linear relationship(FROM-GC=1.05*FAOSTAT-1.2(Mha)with R^(2)=0.97).Africa,South America,Southeastern Asia,and Oceania are the regions with large discrepancies with the FAO survey.展开更多
As the world strives to reduce the impact of population growth,urbanization,agricultural expansion,and climate change on food security,energy and water shortage,resource over-exploration,biodiversity loss,environmenta...As the world strives to reduce the impact of population growth,urbanization,agricultural expansion,and climate change on food security,energy and water shortage,resource over-exploration,biodiversity loss,environmental pollution,and ultimately human health,timely and higher resolution land cover information is urgently needed to achieve the sustainable development goals of the United Nations.展开更多
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partially supported by the National High Technology Program(2013AA122804)the Special Fund for Meteorology Scientific Research in the Public Welfare(GYHY201506023)of ChinaOpen Fund of State Key Laboratory of Remote Sensing Science(OFSLRSS201514)
文摘We report the world's first all-season training and validation sample sets for global land cover classification with Landsat-8 data.Prior to this,such samples were only available at a single date primarily from the growing season.It is unknown how much limitation such a single-date sample has to mapping global land cover in other seasons of the year.To answer this question,we selected available Landsat-8 images from four seasons and collected training and validation samples from them.We compared the performances of training samples in different seasons using Random Forest algorithm.We found that the use of training samples from any individual season would result in the best overall classification accuracy when validated by samples in the same season.The global overall accuracy from combined best seasonal results was 67.2% when classifying the 11 Level-1 classes in the Finer Resolution Observation and Monitoring of Global Land Cover(FROM-GLC) classification system.The use of training samples from all seasons(named all-season training sample set hereafter) produced an overall accuracy of 67.0%.We also tested classification within 10° latitude 60° longitude zones using all-season training subsample within each zone and obtained an overall accuracy of 70.2%.This indicates that properly grouped subsamples in space can help improve classification accuracies.All the results in this study seem to suggest that it is possible to use an all-season training sample set to reach global optimality with universal applicability in classifying images acquired at any time of a year for global land cover mapping.
基金supported by the National High Technology Research and Development Program of China(‘‘863’’Program)(2009AA12200101)the National Natural Science Foundation of China(41471347)
文摘Knowledge about climate change impacts on species distribution at national scale is critical to biodi- versity conservation and design of management programs. Although China is a biodiversity hot spot in the world, potential influence of climate change on Chinese protected birds is rarely studied. Here, we assess the impact of climate change on 108 protected bird species and nature reserves using species distribution modeling at a relatively fine spatial resolution （1 km） for the first time. We found that a large proportion of protected species would have potential suitable habitat shrink and northward range shift by 77-90 km in response to projected future climate change in 2080. Southeastern China would suffer from losing climate suitability, whereas the climate conditions in Qinghai-Tibet Plateau and northeastern China were projected to become suitable for more protected species. On average, each protected area in decline of suitable climate for China would experience a 3-4 species by 2080. Cli- mate change will modify which species each protected area will be suitable for. Our results showed that the risk of extinction for Chinese protected birds would be high, even in the moderate climate change scenario. These findings indicate that the management and design of nature reserves in China must take climate change into consideration.
基金partially supported by the National Natural Science Foundation of China(41301445)Research Grant from Tsinghua University(20151080351)a Meteorological Public Benefit project of China(GYHY201506010)
文摘Global climate and environmental change studies require detailed land-use and land-cover(LULC)information about the past,present,and future.In this paper,we discuss a methodology for downscaling coarse-resolution(i.e.,half-degree)future land use scenarios to finer(i.e.,1 km)resolutions at the global scale using a grid-based spatially explicit cellular automata(CA)model.We account for spatial heterogeneity from topography,climate,soils,and socioeconomic variables.The model uses a global 30 m land cover map(2010)as the base input,a variety of biogeographic and socioeconomic variables,and an empirical analysis to downscale coarse-resolution land use information(specifically urban,crop and pasture).The output of this model offers the most current and finest-scale future LULC dynamics from 2010 to 2100(with four representative concentration pathway(RCP)scenarios--RCP 2.6,RCP 4.5,RCP 6.0,and RCP 8.5)at a 1 km resolution within a globally consistent framework.The data are freely available for download,and will enable researchers to study the impacts of LULC change at the local scale.
基金This research was partially supported by an Open Fund of State Key Laboratory of Remote Sensing Science(OFSLRSS201202)a National High Technology Grant from China(2009AA12200101).
文摘We report on a global cropland extent product at 30-m spatial resolution developed with two 30-m global land cover maps(i.e.FROM-GLC,Finer Resolution Observation and Monitoring,Global Land Cover;FROM-GLC=agg)and a 250-m cropland probability map.A common land cover validation sample database was used to determine optimal thresholds of cropland probability in different parts of the world to generate a cropland/noncropland mask according to the classification accuracies for cropland samples.A decision tree was then applied to combine two 250-m cropland masks:one existing mask from the literature and the other produced in this study,with the 30-m global land cover map FROM-GLC-agg.For the smallest difference with country-level cropland area in Food and Agriculture Organization Corporate Statistical(FAOSTAT)database,a final global cropland extent map was composited from the FROM-GLC,FROM-GLC-agg,and two masked crop=land layers.From this map FROM-GC(Global Cropland),we estimated the global cropland areas to be 1533.83 million hectares(Mha)in 2010,which is 6.95 Mha(0.45%)less than the area reported by the Food and Agriculture Organization(FAO)of the United Nations for the year 2010.A country-by=country comparison between the map and the FAOSTAT data showed a linear relationship(FROM-GC=1.05*FAOSTAT-1.2(Mha)with R^(2)=0.97).Africa,South America,Southeastern Asia,and Oceania are the regions with large discrepancies with the FAO survey.
基金partially supported by the National Key Research and Development Program of China(2016YFA0600103)Delos Living LLCthe Cyrus Tang Foundation
文摘As the world strives to reduce the impact of population growth,urbanization,agricultural expansion,and climate change on food security,energy and water shortage,resource over-exploration,biodiversity loss,environmental pollution,and ultimately human health,timely and higher resolution land cover information is urgently needed to achieve the sustainable development goals of the United Nations.
