Land cover products of China- A Big Earth Data Platform for Three Poles

China's land cover data set includes 5 products:

1) glc2000_lucc_1km_China.asc, a Chinese subset of global land cover data based on SPOT4 remote sensing data developed by the GLC2000 project. The data name is GLC2000.GLC2000 China's regional land cover data is directly cropped from global cover data. For data description, please refer to http : //www-gvm.jrc.it/glc2000/defaultGLC2000.htm

2) igbp_lucc_1km_China.asc, a Chinese subset of global land cover data based on AVHRR remote sensing data supported by IGBP-DIS, the data name is IGBPDIS; IGBPDIS data was prepared using the USGS method, using April 1992 to March 1992 The AVHRR data developed global land cover data with a resolution of 1km. The classification system adopts a classification system developed by IGBP, which divides the world into 17 categories. Its development is based on continents. Applying AVHRR for 12 months to maximize synthetic NDVI data,

3) modis_lucc_1km_China_2001.asc, a subset of MODIS land cover data products in China, the data name is MODIS; MODIS China's regional land cover data is directly cropped from global cover data, and its data description please refer to http://edcdaac.usgs.gov/ modis / mod12q1v4.asp.

4. umd_lucc_1km_China.asc, a Chinese subset of global land cover data based on AVHRR data produced by the University of Maryland, the data name is UMd; the five bands of UMd based on AVHRR data and NDVI data are recombined to suggest a data matrix, using Methodology carried out global land cover classification. The goal is to create data that is more accurate than past data. The classification system largely adopts the classification scheme of IGBP.

5) westdc_lucc_1km_China.asc, China ’s 2000: 100,000 land cover data organized and implemented by the Chinese Academy of Sciences, combined with Yazashi conversion (the largest area method), and finally obtained a land use data product of 1km across the country, data name WESTDC. WESTDC China's regional land cover data is based on the results of a 1: 100,000 county-level land resource survey conducted by the Chinese Academy of Sciences. The land use data were merged and converted into a vector (the largest area method). The Chinese Academy of Sciences resource and environment classification system is adopted.

2: Data format: ArcView GIS ASCII

3: Mesh parameters:

ncols 4857

nrows 4045

xllcorner -2650000

yllcorner 1876946

cellsize 1000

NODATA_value -9999

4: Projection parameters:

Projection ALBERS

Units METERS

Spheroid Krasovsky

Parameters:

25 00 0.000 / * 1st standard parallel

47 00 0.000 / * 2nd standard parallel

105 00 0.000 / * central meridian

0 0 0.000 / * latitude of projection's origin

0.00000 / * false easting (meters)

0.00000 / * false northing (meters)

File naming and required software

ArcView GIS ASCII format storage

Data Citations Data citation guideline What's data citation?

Cite as:

Ran, Y. (2013). Land cover products of China. A Big Earth Data Platform for Three Poles, DOI: 10.3972/westdc.007.2013.db. CSTR: 18406.11.westdc.007.2013.db. (Download the reference： RIS | Bibtex )

Related Literatures:

1. Youhua Ran, Xin Li & Ling Lu (2010): Evaluation of four remote sensing based land cover products over China, International Journal of Remote Sensing, 31:2, 391-401.( View Details | Download | Bibtex)

Using this data, the data citation is required to be referenced and the related literatures are suggested to be cited.

References literature

1.Liu W, Zhang Q, Liu G. Influences of watershed landscape composition and configuration on lake-water quality in the Yangtze River basin of China[J]. Hydrological Processes, 2012, 26(4): 570–578. doi:10.1002/hyp.8157 (View Details )

2.Li M, Wu Z, Qin L, Meng X. Extracting vegetation phenology metrics in Changbai Mountains using an improved logistic model[J]. Chinese Geographical Science, 2011, 21(3): 304–311. doi:10.1007/s11769-011-0471-3 (View Details )

3.Xu, L., Xu, X., & Meng, X. (2013). Risk assessment of soil erosion in different rainfall scenarios by RUSLE model coupled with Information Diffusion Model: A case study of Bohai Rim, China. CATENA, 100, 74–82. https://doi.org/10.1016/j.catena.2012.08.012. (View Details )

4.Ran, Y.H., Li, X., Lu, L. (2009). China Land Cover Classfication at 1KM Spatial Resolution Based on a Multisource Data Fusion Approach.Advances in earth science.Issue(2), 192-203. (View Details )

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