# Land Cover Mapping in Southwestern China Using the HC-MMK Approach
## 3 Methodology
### Remote Sensing Data Collection and Mapping Units  
A total of 170 Landsat TM scenes and 202 HJ-CCD scenes were collected respectively from the USGS EarthExplorer and the China Center for Resources Satellite Data and Application. These images were acquired in 2009, 2010 and 2011 (Figure 3). For each mapping unit, at least two free-cloud satellite images (cloud coverage below 10%), one acquired in leaf-on conditions (May to October) and the other acquired in leaf-off conditions (November to following April), were selected to form the multi-temporal satellite images. The acquired Landsat TM images have been calibrated radiometrically and geometrically. The atmospheric correction for the Landsat images were processed by the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS, [36]). Although systematic geometric correction has been conducted, there are still some residual geometric errors existing in the acquired HJ-CCD images. The precise geometric correction for these HJ-CCD images was conducted by Auto-Registration and Orthorectification Algorithm [23], which was specially designed for satellite images over mountainous areas. After processing, the geometric errors between HJ-CCD images and Landsat TM images were less than one pixel [37]. The topographical data were incorporated in the classification, including elevation, aspect and slope. Despite some geo-location errors (approximately 1-3 pixels) existing in the 30-m ASTER GDEM in this region [38], it was still used in this study having less missing data, higher spatial resolution and better topographic representation than other elevation data (Such as SRTM) [39]. Using the ASTER GDEM data, the slope and aspect were calculated by the ERDAS software. The high spatial resolution Google Earth images and existing thematic maps, including the Vegetation Map of the People's Republic of China (1:1,000,000) and the Chinese Land Use Database at 1:100,000 scale, were also used for generating the CLC-SW2010 product. Although these thematic maps and high spatial resolution images were not directly used in the automatically classification due to the scale and quality, they could provide lots of useful reference information for sample selection and classification error identification. To conveniently and efficiently organize a large number of satellite images and relevant auxiliary data, the study area needs to be partitioned into a series of mapping units according to the image scenes. Because distinct differences have existed between the HJ image scenes and the Landsat image \begin{table}
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**Code I** & **Category I** & **Code II** & **Category II** & **Code I** & **Category I** & **Code II** & **Category II** \\ \hline \multirow{8}{*}{1} & & 101 & Evergreen Broadleaf Forests & \multirow{8}{*}{3} & 34 & Lakes & \multirow{8}{*}{35} \\  & & 102 & Dediduous Headleaf Forests & & & 35 & Reservoirs \\  & & 103 & Evergreen Needleaf Forests & & & 36 & Werers \\  & & 104 & Dediduous Needleaf Forests & & & 37 & Cands \\ \cline{1-1} \cline{3-6}  & Woodlands & 105 & Mixed Forests & & & 41 & Paddy Fields \\  & & 106 & Evergreen Broadleaf Shubs & & & 42 & Dry Lands \\  & & 107 & Dediduous Broadleaf Shubs & & & 51 & Residential Lands \\  & & 108 & Evergreen Needleaf Shubs & & & 52 & Industrial Lands \\  & & 109 & Woody Plantations & & & 53 & Transportation Lands \\  & & 110 & Strub Plantations & & & 54 & Mineral Land \\  & & 111 & Woody Greenland & & & 61 & Sparse Forests \\  & & 112 & Shrub Greenland & & & 62 & Sparse Forests \\ \hline \multirow{2}{*}{2} & & 21 & Medadows & & & 63 & Sparse grasslands \\  & & 22 & Strippers & & 64 & Lichens/Mosses \\  & & 23 & Herbous Greenland & & & 65 & Bare Rocks \\  & & 24 & Herbaceous Greenland & & & 66 & Bare Soils \\ \hline \multirow{2}{*}{3} & & 31 & Woody Wetlands & & & 67 & Deserts \\  & & 32 & Shrub Wetlands & & & 68 & Saling Lands \\  & & 33 & Herbaceous Wetlands & & & 69 & Permanent Snow and \\ \hline \hline \end{tabular}
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