Finer Resolution Observation and Monitoring of Global Land Cover



News

International Symposium on Land Cover Mapping for the African Continent: http://data.ess.tsinghua.edu.cn/ISLandCoverAfrica.html
(will jump to http://www.cess.tsinghua.edu.cn/publish/essen/7774/2013/20130716143808193384006/20130716143808193384006_.html)



Data Download
(New)
Gong P, Li X C, Zhang W. 40-Year(1978-2017) human settlement changes in China reflected by impervious surfaces from satellite remote sensing. Science Bulletin, 2019, 64,https://doi.org/10.1016/j.scib.2019.04.024 , Data download (Urban only data ),( Urban and Rural data) (Readme).

Gong P., et al., 2019. Stable classification with limited sample: transferring a 30-m resolution sample set collected in 2015 to mapping 10-m resolution global land cover in 2017,Science Bulletin.https://doi.org/10.1016/j.scib.2019.03.002
FROM-GLC10 (2017v0.1.2 with minor improvements, ! An updated version will soon become available!!): fromglc10_2017 data( ! See: Classification System_FROM-GLC10--2017v01 )

FROM-GLC 2017v1 download web URL (longitude, latitude): http://data.ess.tsinghua.edu.cn/fromglc2017v1.html( !!! See: Classification system_2017v1 )
FROM-GLC version2 (2015_v1) download web URL (longitude, latitude): http://data.ess.tsinghua.edu.cn/fromglc2015_v1.html( !!! See: Classification system_2015_v1 )
FROM-GLC 2010 download web URL (WRS-2): http://data.ess.tsinghua.edu.cn/landsat_pathList_fromglc_0_1.html
waterbody-modis-500m-2001-2016: http://data.ess.tsinghua.edu.cn/modis_500_2001_2016_waterbody.html
KML Download - Latest version of FROM-GLC (with preview image and download links): FROM-GLC-Preview.kmz
KML Download - FROM-GLC-seg (with download links): FROM-GLC-seg-DownloadLinks.kmz
KML Download - FROM-GLC-agg (with download links): FROM-GLC-agg-DownloadLinks.kmz
KML Download - FROM-GC (with download links): FROM-GLC-GC-DownloadLinks.kmz
KML Download - FROM-GLC-Hierarchy (with download links): FROM-GLC-Hierarchy-DownloadLinks.kmz
FROM-GLC-seg download web URL (WRS-2): http://data.ess.tsinghua.edu.cn/landsat_pathList_fromglcseg_0_1.html
FROM-GLC-agg download web URL (WRS-2): http://data.ess.tsinghua.edu.cn/landsat_pathList_fromglcagg_0_1.html
FROM-GC download web URL (WRS-2): http://data.ess.tsinghua.edu.cn/data/FROMGLCAggCropBestPick_colormap
FROM-GLC-Hierarchy download web URL (MODIS Tile): http://data.ess.tsinghua.edu.cn/data/FROMGLC_Hierarchy_MODISLIKE_GZ/
2015 US Corn/Soybean Prediction Map(MODIS Tile): http://data.ess.tsinghua.edu.cn/data/temp/2015US_CornSoybean_Map/
2016 US Corn/Soybean Prediction Map(MODIS Tile): http://data.ess.tsinghua.edu.cn/data/temp/2016US_CornSoybean_Map/
Improved Version of FROM-GLC Water Layer: http://data.ess.tsinghua.edu.cn/data/fromglc-water/
Global validation sample set (v1) : http://data.ess.tsinghua.edu.cn/data/temp/GlobalLandCoverValidationSampleSet_v1.xlsx
2010 Cropland map for China: http://data.ess.tsinghua.edu.cn/data/temp/China_LC_update_v5/
* If you do not know the MODIS tile number of your area of interest, please click http://modis-land.gsfc.nasa.gov/MODLAND_grid.html to use their spatial query to find it out.
* If you do not know the Landsat Path and Row number, please click http://landsat.usgs.gov/tools_csf.php to use their spatial query to find it out.


A Circa 2010 Thirty Meter Resolution Forest Map for China (Clas Codes: 211 evergreen broadleaf; 212 deciduous broadleaf; 214 bamboo; 221 evergreen needleleaf; 222 deciduous needleleaf; 230 mixed forest; 255 nonforest. Descriptions see Refrence [9])

An all-season sample database for Africa with two classification schemes (data publishing system is under construction)



About FROM-GLC

Global land cover data are key sources of information for understanding the complex interactions between human activities and global change. FROM-GLC (Finer Resolution Observation and Monitoring of Global Land Cover) is the first 30 m resolution global land cover maps produced using Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) data. Our long-term goal in FROM-GLC is to develop a multiple stage approach to mapping global land cover so that the results can better meet the needs of land process modeling and can be easily cross-walked to existing global land cover classification schemes.



Classification system

Level 1 Type Level 1 Coce Level 2 Type Level 1/2 Code Level 2 Type Level 1/2 Code Level 2 Type Level 1/2 Code Level 2 Type Level 1/2 Code Level 2 Type Level 1/2 Code Level 2 Type Level 1/2 Code
Crop 10 Rice 10/11 Greenhouse 10/12 Other 10/13            
Forest 20 Broadleaf 20/21 Needleleaf 20/22 Mixed 20/23 Orchard 20/24        
Grass 30 Managed 30/31 Nature 30/32                
Shrub 40                        
Wetland 50 Grass 30/51 Silt 90/52                
Water 60 Lake 60/61 Pond 60/62 River 60/63 Sea 60/64        
Tundra 70 Shrub 40/71 Grass 30/72                
Impervious 80 High albedo 80/81 Low albedo 80/82                
Bareland 90 Saline-Alkali 90/91 Sand 90/92 Gravel 90/93 Bare-cropland 10/94 Dry river/lake bed 90/95 other 90/96
Snow/Ice 100 Snow 100/101 Ice 100/102                
Cloud 120                        



Classification system (for FROM-GLC-Hierarchy)

Level 1 Type Level 1 Coce Level 2 Type Level 1/2 Code Level 2 Type Level 1/2 Code Level 2 Type Level 1/2 Code Level 2 Type Level 1/2 Code Level 2 Type Level 1/2 Code Level 2 Type Level 1/2 Code Level 2 Type Level 1/2 Code
Crop 10 Rice 10/11 Greenhouse 10/12 Other 10/13 Crop in urban 10/19            
Forest 20 Broadleaf 20/21 Needleleaf 20/22 Mixed 20/23 Orchard 20/24 Forest in urban 20/29        
Grass 30 Managed 30/31 Nature 30/32 Grass in urban 30/39                
Shrub 40 Shrub in urban 40/49                        
Wetland 50 Grass 30/51 Silt 90/52 Wetland in urban 50/59                
Water 60 Lake 60/61 Pond 60/62 River 60/63 Sea 60/64 Water in urban 60/69        
Tundra 70 Shrub 40/71 Grass 30/72                    
Impervious 80 High albedo 80/81 Low albedo 80/82                    
Bareland 90 Saline-Alkali 90/91 Sand 90/92 Gravel 90/93 Bare-cropland 10/94 Dry river/lake bed 90/95 other 90/96 Bareland in urban 90/99
Snow/Ice 100 Snow 100/101 Ice 100/102                    
Cloud 120                            



Legend

Land cover type (Level1) Level 1 Code Level 1 Color R Value G Value B Value
Background   0 0 0
Cropland 10 163 255 115
Forest 20 38 115 0
Grass 30 76 230 0
Shrub 40 112 168 0
Water 60 0 92 255
Impervious 80 197 0 255
Bareland 90 255 170 0
Snow/Ice 100 0 255 197
Cloud 120 255 255 255



References

Gong P., Liu H., Zhang M., Li C., Wang J., Huang H., Clinton N., ..., Song L. (2019) Stable classification with limited sample: transferring a 30-m resolution sample set collected in 2015 to mapping 10-m resolution global land cover in 2017,Science Bulletin,2019, https://doi.org/10.1016/j.scib.2019.03.002.
[Technical Report (2017.12)]Automated Global Land Cover Mapping -- FROM-GLC Version 2: the production of the 30 m circa 2015 global land cover map
[1] Gong, P., Wang, J., Yu, L., Zhao, Y.C., Zhao, Y.Y., Liang, L., Niu, Z.G., Huang, X.M., Fu, H.H., Liu, S., Li, C.C., Li, X.Y., Fu, W., Liu, C.X., Xu, Y., Wang, X.Y., Cheng, Q., Hu, L.Y., Yao, W.B., Zhang, H., Zhu, P., Zhao, Z.Y., Zhang, H.Y., Zheng, Y.M., Ji, L.Y., Zhang, Y.W., Chen, H., Yan, A., Guo, J.H., Yu, L., Wang, L., Liu, X.J., Shi, T.T., Zhu, M.H., Chen, Y.L., Yang, G.W., Tang, P., Xu, B., Ciri, C., Clinton, N., Zhu, Z.L., Chen, J., Chen, J. 2013. Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data, International Journal of Remote Sensing. vol.34, n.7, pp.2607-2654.
[2] Yu, L., Wang, J., Gong, P. 2013. Improving 30 meter global land cover map FROM-GLC with time series MODIS and auxiliary datasets: a segmentation based approach, International Journal of Remote Sensing. vol.34,n.16, pp.5851-5867.
[3] Chen, Y., Gong, P. 2013. Clustering based on eigenspace transformation - CBEST for efficient classification, ISPRS Journal of Photogrammetry and Remote Sensing. vol.83, pp.64-80. (Software-CBEST(32bit, 64bit), This software requires matlab compiler runtime (MCR) for R2013a to be installed prior to running. MCR can be downloaded at http://www.mathworks.com/products/compiler/mcr/. Choose R2013a either 32 bit or 64 bit in according to your operating system (Windows only)).
[4] Yu, L., Wang, J., Clinton, N., Xin, Q.C., Zhong, L.H., Chen, Y.L., Gong, P. 2013. FROM-GC: 30 m global cropland extent derived through multisource data integration, International Journal of Digital Earth,6(6):521-533.
[5] Yang, J., Gong, P., Fu, R., Zhang, M.H., Chen, J.M., Liang, S.L., Xu, B., Shi, J.C., Dickinson, R. 2013. The role of satellite remote sensing in climate change studies, Nature Climate Change, 3, 875-883.
[6] Sun, F., Zhao, Y.Y., Gong, P., Ma, R., Dai, Y. 2014. Monitoring dynamic changes of global land cover types: fluctuations of major lakes in China every 8 days during 2000-2010, Chinese Science Bulletin, 59(189), 171-189.
[7] Li, C.C,, Wang, J., Wang, L., Hu, L.Y., Gong, P. 2014. Comparison of Classification Algorithms and Training Sample Sizes in Urban Land Classification with Landsat Thematic Mapper Imagery, Remote Sensing, 6(2), 964-983.
[8] Wang, J., Zhao, Y.Y., Li, C.C., Yu, L., Liu, D.S., Gong, P. 2014. Mapping global land cover in 2001 and 2010 with spatial-temporal consistency at 250 m resolution. ISPRS Journal of Photogrammetry and Remote Sensing.
[9] Li, C.C., Wang, J., Hu, L.Y., Yu, L., Clinton, N., Huang, H.B., Yang, J., Gong, P. 2014. A circa 2010 thirty meter resolution forest map for China, Remote Sensing, 6(6), 5325-5343.
[10] Yu, L., Liang, L., Wang, J., Zhao, Y.Y., Cheng, Q., Hu, L.Y., Liu, S., Yu, L., Wang, X.Y., Zhu, P., Li, X.Y., Xu, Y., Li, C.C., Fu, W., Li, X.C., Li, W.Y., Liu, C.X., Cong, N., Zhang, H., Sun, F.D., Bi, X.F., Xin, Q.C., Li, D.D., Yan, D.H., Zhu, Z.L., Goodchild, M.F., Gong, P. 2014. Meta-discoveries from a synthesis of satellite-based land cover mapping research. International Journal of Remote Sensing, 35(13): 4573-4588
[11] Zhao, Y.Y., Gong, P., Yu, L., Hu, L., Li, X.Y., Li, C.C., Zhang, H.Y., Zheng, Y.M., Wang, J., Zhao, Y.C., Cheng, Q., Liu, C.X., Liu, S., Wang, X.Y. 2014. Towards a common validation sample set for global land-cover mapping. International Journal of Remote Sensing, 35(13): 4795-4814
[12] Yu, L., Wang, J., Li, X.C., Li, C.C., Gong, P. 2014. A multi-resolution global land cover dataset through multisource data aggregation. Science China Earth Sciences, 57(10):2317-2329.
[13] Zhu, P, Gong, P. 2014. Suitability mapping of global wetland areas and validation with remotely sensed data. Science China Earth Sciences, 57(10):2283-2292.
[14] Hu, L.Y., Chen, Y.L., Xu, Y., Zhao, Y.Y., Yu, L., Wang, J., Gong, P. 2014. 30 meter land cover mapping of China with an efficient clustering algorithm CBEST. Science China Earth Sciences,57(10):2293-2304.



Acknowledgment

This research was partially supported by a National High Technology Grant from China (2009AA12200101) and a National Key Basic Research Program of China (2010CB530300).

Last updated: 2019-05-01