The science and management of terrestrial ecosystems require accurate,high-resolution mapping of surface water.We produced a global,30-m-resolution inland surface water dataset with an automated algorithm using Landsa...The science and management of terrestrial ecosystems require accurate,high-resolution mapping of surface water.We produced a global,30-m-resolution inland surface water dataset with an automated algorithm using Landsat-based surface reflectance estimates,multispectral water and vegetation indices,terrain metrics,and prior coarse-resolution water masks.The dataset identified 3,650,723 km2 of inland water globally–nearly three quarters of which was located in North America(40.65%)and Asia(32.77%),followed by Europe(9.64%),Africa(8.47%),South America(6.91%),and Oceania(1.57%).Boreal forests contained the largest portion of terrestrial surface water(25.03%of the global total),followed by the nominal‘inland water’biome(16.36%),tundra(15.67%),and temperate broadleaf and mixed forests(13.91%).Agreement with respect to the Moderate-resolution Imaging Spectroradiometer water mask and Landsat-based national land-cover datasets was very high,with commission errors<4%and omission errors<14%relative to each.Most of these were accounted for in the seasonality of water cover,snow and ice,and clouds–effects which were compounded by differences in image acquisition date relative to reference datasets.The Global Land Cover Facility(GLCF)inland surface water dataset is available for open access at the GLCF website(http://www.landcover.org).展开更多
The compilation of global Landsat data-sets and the ever-lowering costs of computing now make it feasible to monitor the Earth’s land cover at Landsat resolutions of 30 m.In this article,we describe the methods to cr...The compilation of global Landsat data-sets and the ever-lowering costs of computing now make it feasible to monitor the Earth’s land cover at Landsat resolutions of 30 m.In this article,we describe the methods to create global products of forest cover and cover change at Landsat resolutions.Nevertheless,there are many challenges in ensuring the creation of high-quality products.And we propose various ways in which the challenges can be overcome.Among the challenges are the need for atmospheric correction,incorrect calibration coefficients in some of the data-sets,the different phenologies between compila-tions,the need for terrain correction,the lack of consistent reference data for training and accuracy assessment,and the need for highly automated character-ization and change detection.We propose and evaluate the creation and use of surface reflectance products,improved selection of scenes to reduce phenological differences,terrain illumination correction,automated training selection,and the use of information extraction procedures robust to errors in training data along with several other issues.At several stages we use Moderate Resolution Spectro-radiometer data and products to assist our analysis.A global working prototype product of forest cover and forest cover change is included.展开更多
We developed a global,30-m resolution dataset of percent tree cover by rescaling the 250-m MOderate-resolution Imaging Spectroradiometer(MODIS)Vegetation Continuous Fields(VCF)Tree Cover layer using circa-2000 and 200...We developed a global,30-m resolution dataset of percent tree cover by rescaling the 250-m MOderate-resolution Imaging Spectroradiometer(MODIS)Vegetation Continuous Fields(VCF)Tree Cover layer using circa-2000 and 2005 Landsat images,incorporating the MODIS Cropland Layer to improve accuracy in agricultural areas.Resulting Landsat-based estimates maintained consistency with the MODIS VCF in both epochs(RMSE=8.6%in 2000 and 11.9%in 2005),but showed improved accuracy in agricultural areas and increased discrimination of small forest patches.Against lidar measurements,the Landsat-based estimates exhibited accuracy slightly less than that of the MODIS VCF(RMSE=16.8%for MODIS-based vs.17.4%for Landsat-based estimates),but RMSE of Landsat estimates was 3.3 percentage points lower than that of the MODIS data in an agricultural region.The Landsat data retained the saturation artifact of the MODIS VCF at greater than or equal to 80%tree cover but showed greater potential for removal of errors through calibration to lidar,with post-calibration RMSE of 9.4%compared to 13.5%in MODIS estimates.Provided for free download at the Global Land Cover Facility(GLCF)website(www.landcover.org),the 30-m resolution GLCF tree cover dataset is the highest-resolution multi=temporal depiction of Earth’s tree cover available to the Earth science community.展开更多
Long-term observation of the earth is essential for studying the factors affecting global environmental changes.Digital earth technology can facilitate the monitoring of global environmental change with its ability to...Long-term observation of the earth is essential for studying the factors affecting global environmental changes.Digital earth technology can facilitate the monitoring of global environmental change with its ability to process vast amounts of information.In this study,we map the forest cover change of Myanmar from 2000 to 2005 using a training data automation procedure and support vector machines algorithm.Our results show that Myanmar’s forests have declined 0.68%annually over this six-year period.We validated our derived change results and found the overall accuracy to be greater than 88%.We also assessed forest loss from protected areas,areas close to roads,and areas subject to fire,which were most likely to lose forested area.The results revealed the main reasons for forest losses in some hotspots to be increased agricultural conversion,fire,and the construction of highways.This information is useful for identifying the driving forces behind forest changes and to support environmental policy development in Myanmar.展开更多
Six widely used coarse-resolution global land cover data-sets–Global Land Cover Characterization(GLCC),Global Land Cover 2000(GLC2000),GlobCover land cover product(GlobCover),MODIS land cover product(MODIS LC),the Un...Six widely used coarse-resolution global land cover data-sets–Global Land Cover Characterization(GLCC),Global Land Cover 2000(GLC2000),GlobCover land cover product(GlobCover),MODIS land cover product(MODIS LC),the University of Maryland land cover product(UMD LC),and the MODIS Vegetation Continuous Fields tree cover layer(MODIS VCF)disagree substantially in their estimates of forest cover.Employing a regression tree model trained on higher-resolution,Landsat-based data,these multisource multiresolution maps were integrated for an improved characterization of forest cover over North America.Evaluated using a withheld test sample,the integrated percent forest cover(IPFC)data-set has a root mean square error of 11.75%–substantially better than the 17.37% of GLCC,17.61% of GLC2000,17.96% of GlobCover,15.23% of MODIS LC,19.25%of MODIS VCF,and 15.15%of UMD LC,respectively.Although demonstrated for forest,this approach based on integration of multiple products has potential for improved characterization of other land cover types as well.展开更多
基金supported by the following NASA programs:Making Earth System Data Records for Use in Research Environments(MEaSUREs)[NNX08AP33A]Land-Cover and Land-Use Change(LCLUC)[NNX08AN72G]Advancing Collaborative Connections for Earth System Science(ACCESS)[NNX12AH18A].
文摘The science and management of terrestrial ecosystems require accurate,high-resolution mapping of surface water.We produced a global,30-m-resolution inland surface water dataset with an automated algorithm using Landsat-based surface reflectance estimates,multispectral water and vegetation indices,terrain metrics,and prior coarse-resolution water masks.The dataset identified 3,650,723 km2 of inland water globally–nearly three quarters of which was located in North America(40.65%)and Asia(32.77%),followed by Europe(9.64%),Africa(8.47%),South America(6.91%),and Oceania(1.57%).Boreal forests contained the largest portion of terrestrial surface water(25.03%of the global total),followed by the nominal‘inland water’biome(16.36%),tundra(15.67%),and temperate broadleaf and mixed forests(13.91%).Agreement with respect to the Moderate-resolution Imaging Spectroradiometer water mask and Landsat-based national land-cover datasets was very high,with commission errors<4%and omission errors<14%relative to each.Most of these were accounted for in the seasonality of water cover,snow and ice,and clouds–effects which were compounded by differences in image acquisition date relative to reference datasets.The Global Land Cover Facility(GLCF)inland surface water dataset is available for open access at the GLCF website(http://www.landcover.org).
基金support from the NASATerrestrial Ecology Program which led to the creation of LEDAPS on which much of this work is based.We acknowledge the help of two people in particular from USGS EROS:Gyanesh Chander helped to identify the GLS 1990 images that have most recent USGS calibration coefficients(?50%of the GLS 1990 data-set).Rachel Headley helped us obtain the GLS data-sets.She also helped significantly with our reordering of the GLS 1990 images that had good calibration coefficients。
文摘The compilation of global Landsat data-sets and the ever-lowering costs of computing now make it feasible to monitor the Earth’s land cover at Landsat resolutions of 30 m.In this article,we describe the methods to create global products of forest cover and cover change at Landsat resolutions.Nevertheless,there are many challenges in ensuring the creation of high-quality products.And we propose various ways in which the challenges can be overcome.Among the challenges are the need for atmospheric correction,incorrect calibration coefficients in some of the data-sets,the different phenologies between compila-tions,the need for terrain correction,the lack of consistent reference data for training and accuracy assessment,and the need for highly automated character-ization and change detection.We propose and evaluate the creation and use of surface reflectance products,improved selection of scenes to reduce phenological differences,terrain illumination correction,automated training selection,and the use of information extraction procedures robust to errors in training data along with several other issues.At several stages we use Moderate Resolution Spectro-radiometer data and products to assist our analysis.A global working prototype product of forest cover and forest cover change is included.
基金Work was performed in service of the Global Forest Cover Change Project(www.forestcover.org),a partnership of the University of Maryland Global Land Cover Facility(www.landcover.org)NASA Goddard Space Flight Center,with funding from the NASA MEaSUREs program.Work was performed at the Global Land Cover Facility+5 种基金The authors would like to thank especially the providers of datasets used in this project:Global Land Survey data were provided by Rachel Headley at the USGS Eros Data CenterMODIS Collection-5 VCF data were provided by Charlene DiMiceli in the University of Maryland Department of Geographical SciencesLidar data were provided by OpenTopography(www.opentopography.org)by Bruce Cook at the NASA Goddard Space Flight Center and Paul Bolstad in the University of Minnesota Department of Forest Resouces with funding from NASA and the University of Minnesota Initiative for Renewable Energy and the Environment(IREE)by James Kellner at the University of Maryland,and by R.Dubayah at the University of Maryland from work funded by the NASA Terrestrial Ecology Program.Lidar processing was done using the US Forest Service FUSION software,Version 3.21.Statistical analyses were conducted in R(R Core Team 2012)using the‘raster’package(Hijmans and Etten 2012)Jeff Masek and Eric Vermote at the NASA Goddard Space Flight Center and Matthew Hansen and Amanda Whitehurst and Hao Tang at the University of Maryland Department of Geographical Sciences provided feedback and insights on algorithms and datasets.T.B.Murphy provided assistance with algorithm development.
文摘We developed a global,30-m resolution dataset of percent tree cover by rescaling the 250-m MOderate-resolution Imaging Spectroradiometer(MODIS)Vegetation Continuous Fields(VCF)Tree Cover layer using circa-2000 and 2005 Landsat images,incorporating the MODIS Cropland Layer to improve accuracy in agricultural areas.Resulting Landsat-based estimates maintained consistency with the MODIS VCF in both epochs(RMSE=8.6%in 2000 and 11.9%in 2005),but showed improved accuracy in agricultural areas and increased discrimination of small forest patches.Against lidar measurements,the Landsat-based estimates exhibited accuracy slightly less than that of the MODIS VCF(RMSE=16.8%for MODIS-based vs.17.4%for Landsat-based estimates),but RMSE of Landsat estimates was 3.3 percentage points lower than that of the MODIS data in an agricultural region.The Landsat data retained the saturation artifact of the MODIS VCF at greater than or equal to 80%tree cover but showed greater potential for removal of errors through calibration to lidar,with post-calibration RMSE of 9.4%compared to 13.5%in MODIS estimates.Provided for free download at the Global Land Cover Facility(GLCF)website(www.landcover.org),the 30-m resolution GLCF tree cover dataset is the highest-resolution multi=temporal depiction of Earth’s tree cover available to the Earth science community.
文摘Long-term observation of the earth is essential for studying the factors affecting global environmental changes.Digital earth technology can facilitate the monitoring of global environmental change with its ability to process vast amounts of information.In this study,we map the forest cover change of Myanmar from 2000 to 2005 using a training data automation procedure and support vector machines algorithm.Our results show that Myanmar’s forests have declined 0.68%annually over this six-year period.We validated our derived change results and found the overall accuracy to be greater than 88%.We also assessed forest loss from protected areas,areas close to roads,and areas subject to fire,which were most likely to lose forested area.The results revealed the main reasons for forest losses in some hotspots to be increased agricultural conversion,fire,and the construction of highways.This information is useful for identifying the driving forces behind forest changes and to support environmental policy development in Myanmar.
基金This study is a contribution to the Global Forest Cover Change project funded by NASA’s Making Earth System Data Records for Use in Research Environments(MEaSUREs)Program[NNX08AP33A]Additional support is provided by the NASA Earth and Space Science Fellowship(NESSF)Program[NNX12AN92H]+2 种基金the Land-Cover/Land-Use Change Program[NNH07ZDA001N]the Earth System Science from EOS Program[NNH06ZDA001N]the MODIS Science Team.
文摘Six widely used coarse-resolution global land cover data-sets–Global Land Cover Characterization(GLCC),Global Land Cover 2000(GLC2000),GlobCover land cover product(GlobCover),MODIS land cover product(MODIS LC),the University of Maryland land cover product(UMD LC),and the MODIS Vegetation Continuous Fields tree cover layer(MODIS VCF)disagree substantially in their estimates of forest cover.Employing a regression tree model trained on higher-resolution,Landsat-based data,these multisource multiresolution maps were integrated for an improved characterization of forest cover over North America.Evaluated using a withheld test sample,the integrated percent forest cover(IPFC)data-set has a root mean square error of 11.75%–substantially better than the 17.37% of GLCC,17.61% of GLC2000,17.96% of GlobCover,15.23% of MODIS LC,19.25%of MODIS VCF,and 15.15%of UMD LC,respectively.Although demonstrated for forest,this approach based on integration of multiple products has potential for improved characterization of other land cover types as well.
