Land-cover mapping is one of the foundations of Earth science.As a result of the combined efforts of many scientists,numerous global land-cover(GLC)products with a resolution of 30 m have so far been generated.However...Land-cover mapping is one of the foundations of Earth science.As a result of the combined efforts of many scientists,numerous global land-cover(GLC)products with a resolution of 30 m have so far been generated.However,the increasing number of fineresolution GLC datasets is imposing additional workloads as it is necessary to confirm the quality of these datasets and check their suitability for user applications.To provide guidelines for users,in this study,the recent developments in currently available 30 m GLC products(including three GLC products and thematic products for four different land-cover types,i.e.,impervious surface,forest,cropland,and inland water)were first reviewed.Despite the great efforts toward improving mapping accuracy that there have been in recent decades,the current 30 m GLC products still suffer from having relatively low accuracies of between 46.0%and 88.9%for GlobeLand30-2010,57.71%and 80.36%for FROM_GLC-2015,and 65.59%and 84.33%for GLC_FCS30-2015.The reported accuracies for the global 30 m thematic maps vary from 67.86%to 95.1%for the eight impervious surface products that were reviewed,56.72%to 97.36%for the seven forest products,32.73%to 98.3%for the six cropland products,and 15.67%to 99.7%for the six inland water products.The consistency between the current GLC products was then examined.The GLC maps showed a good overall agreement in terms of spatial patterns but a limited agreement for some vegetation classes(such as shrub,tree,and grassland)in specific areas such as transition zones.Finally,the prospects for fine-resolution GLC mapping were also considered.With the rapid development of cloud computing platforms and big data,the Google Earth Engine(GEE)greatly facilitates the production of global fine-resolution land-cover maps by integrating multisource remote sensing datasets with advanced image processing and classification algorithms and powerful computing capability.The synergy between the spectral,spatial,and temporal features derived from multisource satellite datasets and s展开更多
30-m Global Land Cover(GLC)data products permit the detection of land cover changes at the scale of most human land activities,and are therefore used as fundamental information for sustainable development,environmenta...30-m Global Land Cover(GLC)data products permit the detection of land cover changes at the scale of most human land activities,and are therefore used as fundamental information for sustainable development,environmental change studies,and many other societal benefit areas.In the past few years,increasing efforts have been devoted to the accuracy assessment of GlobeLand30 and other finer-resolution GLC data products.However,most of them were conducted either within a limited percentage of map sheets selected from a global scale or in some individual countries(areas),and there are still many areas where the uncertainty of 30-m resolution GLC data products remains to be validated and documented.In order to promote a comprehensive and collaborative validation of 30-m GLC data products,the GEO Global Land Cover Community Activity had organized a project from 2015 to 2017,to examine and explore its major problems,including the lack of international agreed validation guidelines and on-line tools for facilitating collaborative validation activities.With the joint effort of experts and users from 30 GEO member countries or participating organizations,a technical specification for 30-m GLC validation was developed based on the findings and experiences.An on-line validation tool,GLCVal,was developed by integrating land cover validation procedures with the service computing technologies.About 20 countries(regions)have completed the accuracy assessment of GlobeLand30 for their territories with the guidance of the technical specification and the support of GLCVal.展开更多
Accurate and reliable cropland surface information is of vital importance for agricultural planning and food security monitoring. As several global land cover datasets have been independently released, an inter-compar...Accurate and reliable cropland surface information is of vital importance for agricultural planning and food security monitoring. As several global land cover datasets have been independently released, an inter-comparison of these data products on the classification of cropland is highly needed. This paper presents an assessment of cropland classifications in four global land cover datasets, i.e., moderate resolution imaging spectrometer （MODIS） land cover product, global land cover map of 2009 （GlobCover2009）, finer resolution observation and monitoring of global cropland （FROM-GC） and 30-m global land cover dataset （GlobeLand30）. The temporal coverage of these four datasets are circa 2010. One of the typical agricultur- al regions of China, Shaanxi Province, was selected as the study area. The assessment proceeded from three aspects： accuracy, spatial agreement and absolute area. In accuracy assessment, 506 validation samples, which consist of 168 cropland samples and 338 non-cropland ones, were automatically and systematically selected, and manually interpreted by referencing high-resolution images dated from 2009 to 2011 on Google Earth. The results show that the overall accuracy （OA） of four datasets ranges from 61.26 to 80.63%. GlobeLand30 dataset, with the highest accuracy, is the most accurate dataset for cropland classification. The cropland spatial agreement （mainly located in the plain ecotope of Shaanxi） and the non-cropland spatial agreement （sparsely distributed in the south and middle of Shaanxi） of the four datasets only makes up 33.96% of the whole province. FIROM-GC and GlobeLand30, obtaining the highest spatial agreement index of 62.40%, have the highest degree of spatial consistency. In terms of the absolute area, MODIS underestimates the cropland area, while GlobCover2009 significantly overestimates it. These findings are of value in revealing to which extent and on which aspect that these global land cover datasets may agree with each other at small scale on eac展开更多
The Belt and Road Initiative （BRI）-a development strategy proposed by China - provides unprecedented opportunities for multi-dimensional communication and cooperation across Asia, Africa and Europe. In this study, w...The Belt and Road Initiative （BRI）-a development strategy proposed by China - provides unprecedented opportunities for multi-dimensional communication and cooperation across Asia, Africa and Europe. In this study, we analyse the spatio-temporal changes in cul- tivated land in the BRI countries （64 in total） to better understand the land use status of China along with its periphery for targeting specific collaboration. We apply FAd statistics and GlobeLand30 （the world＇s finest land cover data at a 30-m resolution）, and develop three indicator groups （namely quantity, conversion, and utilization degree） for the analysis. The results show that cultivated land area in the BRI region increased 3.73x10^4 km2 between 2000 and 2010. The increased cultivated land was mainly found in Central and Eastern Europe and Southeast Asia, while the decreased cultivated land was mostly concentrated in China. Russia ranks first with an increase of 1.59x10^4 km2 cultivated land area, followed by Hungary （0.66x10^4 km2） and India （0.57x10^4 km2）. China decreased 1.95x10^4 km2 cultivated land area, followed by Bangladesh （-0.22x10^4 km2） and Thailand （-0.22x10^4 km2）. Cultivated land was mainly transferred to/from forest, grassland, artificial surfaces and bare land, and transfer types in different regions have different characteristics： while large amount of culti- vated land in China was converted to artificial surfaces, considerable forest was converted to cultivated land in Southeast Asia. The increase of multi-cropping index dominated the region except the Central and Eastern Europe, while the increase of fragmentation index was prevailing in the region except for a few South Asian countries. Our results indicate that the negative consequence of cultivated land loss in China might be underestimated by the domestic-focused studies, as none of its close neighbours experienced such obvious cultivated land losses. Nevertheless, the increased cultivated land area in Southeast Asia and the extensive c展开更多
基金funded by the National Natural Science Foundation of China(41825002)the Strategic Priority Research Program of the Chinese Academy of Sciences(XDA19090200)the Key Research Program of the Chi-nese Academy of Sciences(grant number ZDRW-ZS-2019-1).
文摘Land-cover mapping is one of the foundations of Earth science.As a result of the combined efforts of many scientists,numerous global land-cover(GLC)products with a resolution of 30 m have so far been generated.However,the increasing number of fineresolution GLC datasets is imposing additional workloads as it is necessary to confirm the quality of these datasets and check their suitability for user applications.To provide guidelines for users,in this study,the recent developments in currently available 30 m GLC products(including three GLC products and thematic products for four different land-cover types,i.e.,impervious surface,forest,cropland,and inland water)were first reviewed.Despite the great efforts toward improving mapping accuracy that there have been in recent decades,the current 30 m GLC products still suffer from having relatively low accuracies of between 46.0%and 88.9%for GlobeLand30-2010,57.71%and 80.36%for FROM_GLC-2015,and 65.59%and 84.33%for GLC_FCS30-2015.The reported accuracies for the global 30 m thematic maps vary from 67.86%to 95.1%for the eight impervious surface products that were reviewed,56.72%to 97.36%for the seven forest products,32.73%to 98.3%for the six cropland products,and 15.67%to 99.7%for the six inland water products.The consistency between the current GLC products was then examined.The GLC maps showed a good overall agreement in terms of spatial patterns but a limited agreement for some vegetation classes(such as shrub,tree,and grassland)in specific areas such as transition zones.Finally,the prospects for fine-resolution GLC mapping were also considered.With the rapid development of cloud computing platforms and big data,the Google Earth Engine(GEE)greatly facilitates the production of global fine-resolution land-cover maps by integrating multisource remote sensing datasets with advanced image processing and classification algorithms and powerful computing capability.The synergy between the spectral,spatial,and temporal features derived from multisource satellite datasets and s
基金This work is funded by the National Natural Science Foundation of China[Grant Nos.41930650,41631178]the Program of International S&T Cooperation,the Ministry of Science and Technology of China[Grant No.2015DFA11360]。
文摘30-m Global Land Cover(GLC)data products permit the detection of land cover changes at the scale of most human land activities,and are therefore used as fundamental information for sustainable development,environmental change studies,and many other societal benefit areas.In the past few years,increasing efforts have been devoted to the accuracy assessment of GlobeLand30 and other finer-resolution GLC data products.However,most of them were conducted either within a limited percentage of map sheets selected from a global scale or in some individual countries(areas),and there are still many areas where the uncertainty of 30-m resolution GLC data products remains to be validated and documented.In order to promote a comprehensive and collaborative validation of 30-m GLC data products,the GEO Global Land Cover Community Activity had organized a project from 2015 to 2017,to examine and explore its major problems,including the lack of international agreed validation guidelines and on-line tools for facilitating collaborative validation activities.With the joint effort of experts and users from 30 GEO member countries or participating organizations,a technical specification for 30-m GLC validation was developed based on the findings and experiences.An on-line validation tool,GLCVal,was developed by integrating land cover validation procedures with the service computing technologies.About 20 countries(regions)have completed the accuracy assessment of GlobeLand30 for their territories with the guidance of the technical specification and the support of GLCVal.
基金supported by the National High-Tech R&D Program of China (2012AA12A408)the Independent Scientific Research of Tsinghua University,China (20131089277,553302001)
文摘Accurate and reliable cropland surface information is of vital importance for agricultural planning and food security monitoring. As several global land cover datasets have been independently released, an inter-comparison of these data products on the classification of cropland is highly needed. This paper presents an assessment of cropland classifications in four global land cover datasets, i.e., moderate resolution imaging spectrometer （MODIS） land cover product, global land cover map of 2009 （GlobCover2009）, finer resolution observation and monitoring of global cropland （FROM-GC） and 30-m global land cover dataset （GlobeLand30）. The temporal coverage of these four datasets are circa 2010. One of the typical agricultur- al regions of China, Shaanxi Province, was selected as the study area. The assessment proceeded from three aspects： accuracy, spatial agreement and absolute area. In accuracy assessment, 506 validation samples, which consist of 168 cropland samples and 338 non-cropland ones, were automatically and systematically selected, and manually interpreted by referencing high-resolution images dated from 2009 to 2011 on Google Earth. The results show that the overall accuracy （OA） of four datasets ranges from 61.26 to 80.63%. GlobeLand30 dataset, with the highest accuracy, is the most accurate dataset for cropland classification. The cropland spatial agreement （mainly located in the plain ecotope of Shaanxi） and the non-cropland spatial agreement （sparsely distributed in the south and middle of Shaanxi） of the four datasets only makes up 33.96% of the whole province. FIROM-GC and GlobeLand30, obtaining the highest spatial agreement index of 62.40%, have the highest degree of spatial consistency. In terms of the absolute area, MODIS underestimates the cropland area, while GlobCover2009 significantly overestimates it. These findings are of value in revealing to which extent and on which aspect that these global land cover datasets may agree with each other at small scale on eac
基金National Natural Science Foundation of China,No.41501111Fundamental Research Funds for Central Non-profit Scientific Institution,No.IARRP-2017-27,No.IARRP-2017-65
文摘The Belt and Road Initiative （BRI）-a development strategy proposed by China - provides unprecedented opportunities for multi-dimensional communication and cooperation across Asia, Africa and Europe. In this study, we analyse the spatio-temporal changes in cul- tivated land in the BRI countries （64 in total） to better understand the land use status of China along with its periphery for targeting specific collaboration. We apply FAd statistics and GlobeLand30 （the world＇s finest land cover data at a 30-m resolution）, and develop three indicator groups （namely quantity, conversion, and utilization degree） for the analysis. The results show that cultivated land area in the BRI region increased 3.73x10^4 km2 between 2000 and 2010. The increased cultivated land was mainly found in Central and Eastern Europe and Southeast Asia, while the decreased cultivated land was mostly concentrated in China. Russia ranks first with an increase of 1.59x10^4 km2 cultivated land area, followed by Hungary （0.66x10^4 km2） and India （0.57x10^4 km2）. China decreased 1.95x10^4 km2 cultivated land area, followed by Bangladesh （-0.22x10^4 km2） and Thailand （-0.22x10^4 km2）. Cultivated land was mainly transferred to/from forest, grassland, artificial surfaces and bare land, and transfer types in different regions have different characteristics： while large amount of culti- vated land in China was converted to artificial surfaces, considerable forest was converted to cultivated land in Southeast Asia. The increase of multi-cropping index dominated the region except the Central and Eastern Europe, while the increase of fragmentation index was prevailing in the region except for a few South Asian countries. Our results indicate that the negative consequence of cultivated land loss in China might be underestimated by the domestic-focused studies, as none of its close neighbours experienced such obvious cultivated land losses. Nevertheless, the increased cultivated land area in Southeast Asia and the extensive c
