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
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/temp/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.


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                        



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



Methods

[1] FROM-GLC
FROM-GLC (Gong et al., 2013) was produced using 91433 training samples and 38664 test samples collected via human interpretation of TM/ETM+ images. The interpretation was carried out based primarily on a color composite of TM images of Bands 4, 3, and 2 displayed with the red, green and blue color guns respectively. In addition, the spectral curve based on the 6 optical bands of TM/ETM+, the MODIS time series during the whole year of 2010, and high resolution images and field photos found in Google Earth were used as reference.
Four sets of global land cover maps were produced based respectively on four types of supervised classifiers including the conventional maximum likelihood classifier (MLC), the J4.8 decision tree classifier, the random forests ensemble classifier (RF) and the support vector machine (SVM). The SVM produced the highest overall classification accuracy of approximately 64.9% that was assessed with a set of test samples independently collected. The random forests produced the second highest classification accuracy of 59.8%, with J4.8 and the MLC ranked the third to the fourth.

[2] FROM-GLC-seg
FROM-GLC-seg (Yu et al., 2013) is an improved version of FROM-GLC. A segmentation approach was used in FROM-GLC-seg to integrate multi-resolution datasets, including Landsat TM/ETM+ (30 meter), MODIS EVI time series (250 meter), Bioclimatic variables (1km) (Hijmans et al., 2005), global DEM (1km) (Hijmans et al., 2005), Soil-water variables (1km) (Zomer et al., 2007; 2008; Trabucco & Zomer, 2010). FROM-GLC-seg used the same training/test samples as FROM-GLC, and followed the same classification system with slight modification (The impervious land cover type was not mapped, due to severe spectral mixing effects and its small coverage. In addition, the clouds, which temporally exist on Landsat TM/ETM+, were removed as well). The Random Forest (RF) classifier was used and achieved better overall accuracy. Accuracies for vegetation land cover types (i.e. cropland, forest) and bareland were improved. However, mapping accuracies for water bodies, snow/ice land cover types are slightly lower because coarser resolution MODIS (250 meter) and Bioclimatic, DEM, Soil-Water variables (1km) are not ideal for recognizing small scale objects.

[3] FROM-GLC-agg
FROM-GLC-agg (Yu et al., 2014) is a further improvement by aggregating FROM-GLC and FROM-GLC-seg, together with two coarse resolution impervious maps, i.e. Nighttime Light Impervious Surface Area (Elvidge et al., 2007) and MODIS urban extent (Schneider et al., 2009; 2010). FROM-GLC-agg has an overall accuracy of 65.51%, which is significantly better than FROM-GLC (63.69%) and FROM-GLC-seg (64.42%). Accuracies for individual land cover types in FROM-GLC-agg have been increased or better balanced compared to FROM-GLC and FROM-GLC-seg.

[4] FROM-GC
FROM-GC (Yu et al., 2013) is a 30-m spatial resolution global cropland extent (with other land cover types) product 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 (Pittman et al., 2010). A common land cover validation sample database (Zhao et al., 2014) 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 cropland 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 R2=?0.97). Africa, South America, Southeastern Asia, and Oceania are the regions with large discrepancies with the FAO survey.

[5] FROM-GLC-Hierarchy
FROM-GLC-Hierarchy (Yu et al., 2014) is land cover dataset with multi-resolution (i.e. 30 m, 250 m, 500 m, 1 km, 5 km, 10 km, 25 km, 50 km, 100 km) to meet requirements for different resolutions from different applications. The 30 m base map was improved from FROM-GLC-agg with additional coarse resolution datasets (i.e., MCD12Q1 (Friedl et al., 2010), GlobCover2009 (Bontemps et al., 2010) etc.) to reduce land cover type confusion. Around 1.1% pixels were replaced by coarse resolution products. Validation based assessments indicate the accuracy for land cover maps at 30 m, 250 m, 500 m, 1 km resolutions are 69.50%, 76.65%, 74.65%, and 73.47%, respectively. Further analysis of area-estimation biases for different land cover types at different resolutions suggests that maps at coarser than 5 km resolution contain at least 5% area estimation error for most land cover types. Proportion layers, which contain precise information on land cover percentage, are suggested for use when coarser resolution land cover data are required.



References

[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: 23 June 2016