Mapping global impervious surface area and green space within urban environments
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摘要
The mapping of impervious surface area(ISA) and urban green space(UGS) is essential for improving the urban environmental quality toward ecological, livable, and sustainable goals. Currently, accurate ISA and UGS products are lacking in urban areas at the global scale. This study established regression models that estimated the fraction of ISA/UGS in global 30 cities for validation using MODIS NDVI and DMSP/OLS nighttime light imageries. A global dataset of ISA and UGS fraction with a spatial resolution of 250 m×250 m was developed using the regression model, with a mean relative error of 0.19 for its ISA. The results showed the global urban area of 76.29×10~4 km~2, which was primarily distributed in central Europe, eastern Asia,and central and eastern North America. The urban land area in North America, Europe, and Asia was 66.3×10~4 km~2, accounting for 86.91% of the world's urban area; the urban land area of the top 50 countries accounted for 59.32% of the total urban land area in the world. The global ISA of 45.26×10~4 km~2 was mainly distributed in central and southern North America, eastern Asia, and Europe, as well as coastal regions around the world. The proportion of ISA situated in built-up areas on the continental scale followed the order of Africa(>70%)>South America>Oceania>Asia(>60%)>North America>Europe(>50%), and these areas were mostly in southeastern North America, southwestern Europe, and eastern and western Asia. North America, Europe, and Asia accounted for 89.44% of the world's total UGS. The cities of developed countries in Europe and North America exposed a dramatic mosaic of ISA and UGS composites in urban construction. Therefore, the proportion of UGS is relatively high in those cities. However, in developing and underdeveloped countries, the proportion of UGS in built-up areas is relatively low, and urban environments need to be improved for livability.
The mapping of impervious surface area(ISA) and urban green space(UGS) is essential for improving the urban environmental quality toward ecological, livable, and sustainable goals. Currently, accurate ISA and UGS products are lacking in urban areas at the global scale. This study established regression models that estimated the fraction of ISA/UGS in global 30 cities for validation using MODIS NDVI and DMSP/OLS nighttime light imageries. A global dataset of ISA and UGS fraction with a spatial resolution of 250 m×250 m was developed using the regression model, with a mean relative error of 0.19 for its ISA. The results showed the global urban area of 76.29×10~4 km~2, which was primarily distributed in central Europe, eastern Asia,and central and eastern North America. The urban land area in North America, Europe, and Asia was 66.3×10~4 km~2, accounting for 86.91% of the world's urban area; the urban land area of the top 50 countries accounted for 59.32% of the total urban land area in the world. The global ISA of 45.26×10~4 km~2 was mainly distributed in central and southern North America, eastern Asia, and Europe, as well as coastal regions around the world. The proportion of ISA situated in built-up areas on the continental scale followed the order of Africa(>70%)>South America>Oceania>Asia(>60%)>North America>Europe(>50%), and these areas were mostly in southeastern North America, southwestern Europe, and eastern and western Asia. North America, Europe, and Asia accounted for 89.44% of the world's total UGS. The cities of developed countries in Europe and North America exposed a dramatic mosaic of ISA and UGS composites in urban construction. Therefore, the proportion of UGS is relatively high in those cities. However, in developing and underdeveloped countries, the proportion of UGS in built-up areas is relatively low, and urban environments need to be improved for livability.
The mapping of impervious surface area(ISA) and urban green space(UGS) is essential for improving the urban environmental quality toward ecological, livable, and sustainable goals. Currently, accurate ISA and UGS products are lacking in urban areas at the global scale. This study established regression models that estimated the fraction of ISA/UGS in global 30 cities for validation using MODIS NDVI and DMSP/OLS nighttime light imageries. A global dataset of ISA and UGS fraction with a spatial resolution of 250 m×250 m was developed using the regression model, with a mean relative error of 0.19 for its ISA. The results showed the global urban area of 76.29×10~4 km~2, which was primarily distributed in central Europe, eastern Asia,and central and eastern North America. The urban land area in North America, Europe, and Asia was 66.3×10~4 km~2, accounting for 86.91% of the world's urban area; the urban land area of the top 50 countries accounted for 59.32% of the total urban land area in the world. The global ISA of 45.26×10~4 km~2 was mainly distributed in central and southern North America, eastern Asia, and Europe, as well as coastal regions around the world. The proportion of ISA situated in built-up areas on the continental scale followed the order of Africa(>70%)>South America>Oceania>Asia(>60%)>North America>Europe(>50%), and these areas were mostly in southeastern North America, southwestern Europe, and eastern and western Asia. North America, Europe, and Asia accounted for 89.44% of the world's total UGS. The cities of developed countries in Europe and North America exposed a dramatic mosaic of ISA and UGS composites in urban construction. Therefore, the proportion of UGS is relatively high in those cities. However, in developing and underdeveloped countries, the proportion of UGS in built-up areas is relatively low, and urban environments need to be improved for livability.
引文
Bierwagen B G,Theobald D M,Pyke C R,Choate A,Groth P,Thomas J V,Morefield P.2010.National housing and impervious surface scenarios for integrated climate impact assessments.Proc Natl Acad Sci USA,107:20887-20892
Cao S S,Hu D Y,Zhao W J,Chen S S,Cheng Q W.2017.Spatial structure comparison of urban agglomerations between China and USA in a perspective of impervious surface coverage:A case study of BeijingTianjin-Hebei and Boswash(in Chinese).Acta Geogr Sin,72:1017-1031
Chaudhuri A S,Singh P,Rai S C.2017.Assessment of impervious surface growth in urban environment through remote sensing estimates.Environ Earth Sci,76:541
Chen J,Chen J,Liao A P,Cao X,Chen L J,Chen X H,He C Y,Han G,Peng S,Lu M,Zhang W W,Tong X H,Mills J.2015.Global land cover mapping at 30 m resolution:A POK-based operational approach.ISPRS-J Photogramm Remote Sens,103:7-27
Chen L D,Zhou W Q,Han L J,Sun R H.2016.Developing key technologies for establishing ecological security patterns at the BeijingTianjin-Hebei urban megaregion(in Chinese).Acta Ecol Sin,36:7125-7129
Chi W F,Shi W J,Kuang W H.2015.Spatio-temporal characteristics of intra-urban land cover in the cities of China and USA from 1978 to2010.J Geogr Sci,25:3-18
Creutzig F,Agoston P,Minx J C,Canadell J G,Andrew R M,QuéréC L,Peters G P,Sharifi A,Yamagata Y,Dhakal S.2016.Urban infrastructure choices structure climate solutions.Nat Clim Change,6:1054-1056
Ding Y H.2018.Impact of climate change and urbanization on extreme rainstorm in China’s megacities(in Chinese).China Flood Drought Manage,28:1-2
Elvidge C D,Keith D M,Tuttle B T,Baugh K E.2010.Spectral identification of lighting type and character.Sensors,10:3961-3988
Elvidge C D,Tuttle B T,Sutton P C,Baugh K E,Howard A T,Milesi C,Bhaduri B,Nemani R.2007.Global distribution and density of constructed impervious surfaces.Sensors,7:1962-1979
Fang C L,Yang J Y,Kuang W H.2017.Basic schemes and suggestions of multi-planning integration in progress of Xiongan New Area planning(in Chinese).Bull Chin Acad Sci,32:1192-1198
Georgescu M,Morefield P E,Bierwagen B G,Weaver C P.2014.Urban adaptation can roll back warming of emerging megapolitan regions.Proc Natl Acad Sci USA,111:2909-2914
Georgescu M,Moustaoui M,Mahalov A,Dudhia J.2013.Summer-time climate impacts of projected megapolitan expansion in Arizona.Nat Clim Change,3:37-41
Grimm N B,Faeth S H,Golubiewski N E,Redman C L,Wu J,Bai X,Briggs J M.2008.Global change and the ecology of cities.Science,319:756-760
Homer C H,Fry J A,Barnes C A.2012.The national land cover database.USGS Fact Sheet,3020:1-4
Jones B,O’Neill B C,Mc Daniel L,Mc Ginnis S,Mearns L O,Tebaldi C.2015.Future population exposure to US heat extremes.Nat Clim Change,5:652-655
Kuang W H,Chen L J,Liu J Y,Xiang W N,Chi W F,Lu D S,Yang T R,Pan T,Liu A L.2016a.Remote sensing-based artificial surface cover classification in Asia and spatial pattern analysis.Sci China Earth Sci,59:1720-1737
Kuang W H,Chi W F,Lu D S,Dou Y Y.2014.A comparative analysis of megacity expansions in China and the U.S.:Patterns,rates and driving forces.Landscape Urban Plan,132:121-135
Kuang W H,Liu J Y,Dong J W,Chi W F,Zhang C.2016b.The rapid and massive urban and industrial land expansions in China between 1990and 2010:A CLUD-based analysis of their trajectories,patterns,and drivers.Landscape Urban Plan,145:21-33
Kuang W H,Liu J Y,Zhang Z X,Lu D S,Xiang B.2013.Spatiotemporal dynamics of impervious surface areas across China during the early 21st century.Chin Sci Bull,58:1691-1701
Lee C,Kim K,Lee H.2018.GIS based optimal impervious surface map generation using various spatial data for urban nonpoint source management.J Environ Manage,206:587-601
Li L W,Lu D S,Kuang W H.2016.Examining urban impervious surface distribution and its dynamic change in Hangzhou metropolis.Remote Sens,8:265
Liu X P,Hu G H,Chen Y M,Li X,Xu X C,Li S Y,Pei F S,Wang S J.2018.High-resolution multi-temporal mapping of global urban land using Landsat images based on the Google Earth Engine Platform.Remote Sens Environ,209:227-239
Liu Z F,He C Y,Zhou Y Y,Wu J G.2014.How much of the world’s land has been urbanized,really?A hierarchical framework for avoiding confusion.Landscape Ecol,29:763-771
Lu D S,Hetrick S,Moran E.2011.Impervious surface mapping with Quickbird imagery.Int J Remote Sens,32:2519-2533
Lu D S,Tian H Q,Zhou G M.2008.Regional mapping of human settlements in southeastern China with multisensor remotely sensed data.Remote Sens Environ,112:3668-3679
Lu D S,Weng Q H.2006.Use of impervious surface in urban land-use classification.Remote Sens Environ,102:146-160
Pan J H,Li X X,Liu C Y.2009.Urban impervious surface abundance estimation in Lanzhou City based on remote sensing(in Chinese).JNorthwest Nor Univ-Nat Sci,45:95-100
Ridd M K.1995.Exploring a V-I-S(vegetation-impervious surface-soil)model for urban ecosystem analysis through remote sensing:Comparative anatomy for cities.Int J Remote Sens,16:2165-2185
Sanchez Rodriguez R,ürge-Vorsatz D,Barau A S.2018.Sustainable development goals and climate change adaptation in cities.Nat Clim Change,8:181-183
Schneider A,Friedl M A,Potere D.2009.A new map of global urban extent from MODIS satellite data.Environ Res Lett,4:044003-44011
Schneider A,Friedl M A,Potere D.2010.Mapping global urban areas using MODIS 500-m data:New methods and datasets based on‘urban ecoregions’.Remote Sens Environ,114:1733-1746
Tigges J,Lakes T,Hostert P.2013.Urban vegetation classification:Benefits of multitemporal Rapid Eye satellite data.Remote Sens Environ,136:66-75
ürge-Vorsatz D,Rosenzweig C,Dawson R J,Rodriguez R S,Bai X M,Barau A S,Seto K C,Dhakal S.2018.Locking in positive climate responses in cities.Nat Clim Change,8:174-177
Wang L,Li C C,Ying Q,Cheng X,Wang X Y,Li X Y,Hu Y Y,Liang L,Yu L,Huang H B,Gong P.2012.China’s urban expansion from 1990 to2010 determined with satellite remote sensing(in Chinese).Chin Sci Bull,57:1388-1399
Wickham J D,Stehman S V,Gass L,Dewitz J,Fry J A,Wade T G.2013.Accuracy assessment of NLCD 2006 land cover and impervious surface.Remote Sens Environ,130:294-304
William S,Cynthia R,Shobhakar D,Debra R,Aliyu S B,Seth S,ürgeVorsatz D.2018.City transformations in a 1.5°C warmer world.Nat Clim Change,8:177-181
Wu C S,Murray A T.2003.Estimating impervious surface distribution by spectral mixture analysis.Remote Sens Environ,84:493-505
Wu L Y.2018.Planning and constructing healthy cities is the key to improve the livability of cities(in Chinese).Chin Sci Bull,63:985-985
Yang L M,Huang C G,Homer C G,Wylie B K,Coan M J.2003.An approach for mapping large-area impervious surfaces:Synergistic use of Landsat-7 ETM+and high spatial resolution imagery.Canadian JRemote Sens,29:230-240
Yu S S,Sun Z X,Guo H D,Zhao X W,Sun L,Wu M F.2017.Monitoring and analyzing the spatial dynamics and patterns of megacities along the Maritime Silk Road(in Chinese).J Remote Sens,21:169-181
Yue W Z,Xu J H,Xu L H.2006.An analysis on eco-environmental effect of urban land use based on remote sensing images:A case study of urban thermal environment and NDVI(in Chinese).Acta Ecol Sin,26:1450-1460
Zhang J,Zhou Y K,Li R Q,Zhou Z J,Zhang L Q,Shi Q D,Pan X L.2010.Accuracy assessments and uncertainty analysis of spatially explicit modeling for land use/cover change and urbanization:A case in Beijing metropolitan area.Sci China Earth Sci,53:173-180
Zhang L,Weng Q H.2016.Annual dynamics of impervious surface in the Pearl River Delta,China,from 1988 to 2013,using time series Landsat imagery.ISPRS-J Photogramm Remote Sens,113:86-96
Zhuo L,Shi Q L,Tao H Y,Zheng J,Li Q P.2018.An improved temporal mixture analysis unmixing method for estimating impervious surface area based on MODIS and DMSP-OLS data.ISPRS-J Photogramm Remote Sens,142:64-77
Cao S S,Hu D Y,Zhao W J,Chen S S,Cheng Q W.2017.Spatial structure comparison of urban agglomerations between China and USA in a perspective of impervious surface coverage:A case study of BeijingTianjin-Hebei and Boswash(in Chinese).Acta Geogr Sin,72:1017-1031
Chaudhuri A S,Singh P,Rai S C.2017.Assessment of impervious surface growth in urban environment through remote sensing estimates.Environ Earth Sci,76:541
Chen J,Chen J,Liao A P,Cao X,Chen L J,Chen X H,He C Y,Han G,Peng S,Lu M,Zhang W W,Tong X H,Mills J.2015.Global land cover mapping at 30 m resolution:A POK-based operational approach.ISPRS-J Photogramm Remote Sens,103:7-27
Chen L D,Zhou W Q,Han L J,Sun R H.2016.Developing key technologies for establishing ecological security patterns at the BeijingTianjin-Hebei urban megaregion(in Chinese).Acta Ecol Sin,36:7125-7129
Chi W F,Shi W J,Kuang W H.2015.Spatio-temporal characteristics of intra-urban land cover in the cities of China and USA from 1978 to2010.J Geogr Sci,25:3-18
Creutzig F,Agoston P,Minx J C,Canadell J G,Andrew R M,QuéréC L,Peters G P,Sharifi A,Yamagata Y,Dhakal S.2016.Urban infrastructure choices structure climate solutions.Nat Clim Change,6:1054-1056
Ding Y H.2018.Impact of climate change and urbanization on extreme rainstorm in China’s megacities(in Chinese).China Flood Drought Manage,28:1-2
Elvidge C D,Keith D M,Tuttle B T,Baugh K E.2010.Spectral identification of lighting type and character.Sensors,10:3961-3988
Elvidge C D,Tuttle B T,Sutton P C,Baugh K E,Howard A T,Milesi C,Bhaduri B,Nemani R.2007.Global distribution and density of constructed impervious surfaces.Sensors,7:1962-1979
Fang C L,Yang J Y,Kuang W H.2017.Basic schemes and suggestions of multi-planning integration in progress of Xiongan New Area planning(in Chinese).Bull Chin Acad Sci,32:1192-1198
Georgescu M,Morefield P E,Bierwagen B G,Weaver C P.2014.Urban adaptation can roll back warming of emerging megapolitan regions.Proc Natl Acad Sci USA,111:2909-2914
Georgescu M,Moustaoui M,Mahalov A,Dudhia J.2013.Summer-time climate impacts of projected megapolitan expansion in Arizona.Nat Clim Change,3:37-41
Grimm N B,Faeth S H,Golubiewski N E,Redman C L,Wu J,Bai X,Briggs J M.2008.Global change and the ecology of cities.Science,319:756-760
Homer C H,Fry J A,Barnes C A.2012.The national land cover database.USGS Fact Sheet,3020:1-4
Jones B,O’Neill B C,Mc Daniel L,Mc Ginnis S,Mearns L O,Tebaldi C.2015.Future population exposure to US heat extremes.Nat Clim Change,5:652-655
Kuang W H,Chen L J,Liu J Y,Xiang W N,Chi W F,Lu D S,Yang T R,Pan T,Liu A L.2016a.Remote sensing-based artificial surface cover classification in Asia and spatial pattern analysis.Sci China Earth Sci,59:1720-1737
Kuang W H,Chi W F,Lu D S,Dou Y Y.2014.A comparative analysis of megacity expansions in China and the U.S.:Patterns,rates and driving forces.Landscape Urban Plan,132:121-135
Kuang W H,Liu J Y,Dong J W,Chi W F,Zhang C.2016b.The rapid and massive urban and industrial land expansions in China between 1990and 2010:A CLUD-based analysis of their trajectories,patterns,and drivers.Landscape Urban Plan,145:21-33
Kuang W H,Liu J Y,Zhang Z X,Lu D S,Xiang B.2013.Spatiotemporal dynamics of impervious surface areas across China during the early 21st century.Chin Sci Bull,58:1691-1701
Lee C,Kim K,Lee H.2018.GIS based optimal impervious surface map generation using various spatial data for urban nonpoint source management.J Environ Manage,206:587-601
Li L W,Lu D S,Kuang W H.2016.Examining urban impervious surface distribution and its dynamic change in Hangzhou metropolis.Remote Sens,8:265
Liu X P,Hu G H,Chen Y M,Li X,Xu X C,Li S Y,Pei F S,Wang S J.2018.High-resolution multi-temporal mapping of global urban land using Landsat images based on the Google Earth Engine Platform.Remote Sens Environ,209:227-239
Liu Z F,He C Y,Zhou Y Y,Wu J G.2014.How much of the world’s land has been urbanized,really?A hierarchical framework for avoiding confusion.Landscape Ecol,29:763-771
Lu D S,Hetrick S,Moran E.2011.Impervious surface mapping with Quickbird imagery.Int J Remote Sens,32:2519-2533
Lu D S,Tian H Q,Zhou G M.2008.Regional mapping of human settlements in southeastern China with multisensor remotely sensed data.Remote Sens Environ,112:3668-3679
Lu D S,Weng Q H.2006.Use of impervious surface in urban land-use classification.Remote Sens Environ,102:146-160
Pan J H,Li X X,Liu C Y.2009.Urban impervious surface abundance estimation in Lanzhou City based on remote sensing(in Chinese).JNorthwest Nor Univ-Nat Sci,45:95-100
Ridd M K.1995.Exploring a V-I-S(vegetation-impervious surface-soil)model for urban ecosystem analysis through remote sensing:Comparative anatomy for cities.Int J Remote Sens,16:2165-2185
Sanchez Rodriguez R,ürge-Vorsatz D,Barau A S.2018.Sustainable development goals and climate change adaptation in cities.Nat Clim Change,8:181-183
Schneider A,Friedl M A,Potere D.2009.A new map of global urban extent from MODIS satellite data.Environ Res Lett,4:044003-44011
Schneider A,Friedl M A,Potere D.2010.Mapping global urban areas using MODIS 500-m data:New methods and datasets based on‘urban ecoregions’.Remote Sens Environ,114:1733-1746
Tigges J,Lakes T,Hostert P.2013.Urban vegetation classification:Benefits of multitemporal Rapid Eye satellite data.Remote Sens Environ,136:66-75
ürge-Vorsatz D,Rosenzweig C,Dawson R J,Rodriguez R S,Bai X M,Barau A S,Seto K C,Dhakal S.2018.Locking in positive climate responses in cities.Nat Clim Change,8:174-177
Wang L,Li C C,Ying Q,Cheng X,Wang X Y,Li X Y,Hu Y Y,Liang L,Yu L,Huang H B,Gong P.2012.China’s urban expansion from 1990 to2010 determined with satellite remote sensing(in Chinese).Chin Sci Bull,57:1388-1399
Wickham J D,Stehman S V,Gass L,Dewitz J,Fry J A,Wade T G.2013.Accuracy assessment of NLCD 2006 land cover and impervious surface.Remote Sens Environ,130:294-304
William S,Cynthia R,Shobhakar D,Debra R,Aliyu S B,Seth S,ürgeVorsatz D.2018.City transformations in a 1.5°C warmer world.Nat Clim Change,8:177-181
Wu C S,Murray A T.2003.Estimating impervious surface distribution by spectral mixture analysis.Remote Sens Environ,84:493-505
Wu L Y.2018.Planning and constructing healthy cities is the key to improve the livability of cities(in Chinese).Chin Sci Bull,63:985-985
Yang L M,Huang C G,Homer C G,Wylie B K,Coan M J.2003.An approach for mapping large-area impervious surfaces:Synergistic use of Landsat-7 ETM+and high spatial resolution imagery.Canadian JRemote Sens,29:230-240
Yu S S,Sun Z X,Guo H D,Zhao X W,Sun L,Wu M F.2017.Monitoring and analyzing the spatial dynamics and patterns of megacities along the Maritime Silk Road(in Chinese).J Remote Sens,21:169-181
Yue W Z,Xu J H,Xu L H.2006.An analysis on eco-environmental effect of urban land use based on remote sensing images:A case study of urban thermal environment and NDVI(in Chinese).Acta Ecol Sin,26:1450-1460
Zhang J,Zhou Y K,Li R Q,Zhou Z J,Zhang L Q,Shi Q D,Pan X L.2010.Accuracy assessments and uncertainty analysis of spatially explicit modeling for land use/cover change and urbanization:A case in Beijing metropolitan area.Sci China Earth Sci,53:173-180
Zhang L,Weng Q H.2016.Annual dynamics of impervious surface in the Pearl River Delta,China,from 1988 to 2013,using time series Landsat imagery.ISPRS-J Photogramm Remote Sens,113:86-96
Zhuo L,Shi Q L,Tao H Y,Zheng J,Li Q P.2018.An improved temporal mixture analysis unmixing method for estimating impervious surface area based on MODIS and DMSP-OLS data.ISPRS-J Photogramm Remote Sens,142:64-77
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