## 2 Materials and Methods
### HJ-1 Satellite Images and Preprocessing  
The satellite constellation HJ-1 was designed for environmental and disaster monitoring and forecasting, and includes three satellites: HJ-1A, HJ-1B, and HJ-1C. HJ-1A and HJ-1B carry two identical CCD sensors onboard, each with 30-m spatial resolution [45]. The HJ-1 CCDs have three visible bands (430-520 nm, 520-600 nm, and 630-690 nm) and one near-infrared band (760-900 nm), which are equivalent to Landsat TM bands 1, 2, 3, and 4, respectively. The two satellite CCD sensors acquire images with a swath of approximately 700 km and have a revisiting time of 48 h. Therefore, the HJ-1 CCDs can provide images that encompass all of China's territory every two or three days. The HJ-1 CCD data have the potential to be an important data source for forest mapping, as they are freely available to the public at the official websites of the Ministry of Environmental Protection's Satellite Environment Center (www.secmep.cn) or China's Center for Resources Satellite Data and Application ([[http://www.cresda.com](http://www.cresda.com)]([http://www.cresda.com](http://www.cresda.com))).  
A total of 96 scenes of HJ-1 CCD images was acquired to cover the entirety of Northeast China for the period from late April to November of 2010. At least three scenes from spring to fall were required to cover the same forest region, thus ensuring the detection of seasonal changes of different forest types, less cloud cover, and an absence of significant aerosols. Data preprocessing, including geometric correction, atmospheric correction, and radiometric calibration, was performed to create a high-quality image dataset. Although HJ-1 CCD data have a spatial reference, the spatial error is approximately 200-300 m larger than that for geo-referenced Landsat TM imagery. Therefore, the Landsat TM L1T products were used as the base images downloaded from the USGS website ([[http://glovis.usgs.gov/](http://glovis.usgs.gov/)]([http://glovis.usgs.gov/](http://glovis.usgs.gov/))). All HJ-1 CCD images were automatically geometrically corrected according to the image-to-image registration method integrated into the Auto Sync module of the ERDAS software version 9.2 and the RMS errors of the control points were limited to within two pixels. In the areas with rough terrain, ASTER GDEM (30-m resolution) obtained from the USGS website ([[http://gdex.cr.usgs.gov/gdex/](http://gdex.cr.usgs.gov/gdex/)]([http://gdex.cr.usgs.gov/gdex/](http://gdex.cr.usgs.gov/gdex/))) was used for image ortho-rectification. All corrected images were projected onto an Albers projection with a WGS-84 ellipsoid. The FLAASH module in the ENVI software version 5.3 [46] was applied to alleviate the atmospheric effects on the HJ-1 CCD images. The atmospheric water vapor contents were estimated with one of the MODIS/Terra products (MOD05) [47]. The other parameters were constructed based on the acquisition time and location for the imagery and other ancillary data. The DN values of the HJ-1 CCD images were transferred into the top of atmosphere (TOA) radiance with given spectral response functions under the platform of IDL in the ENVI software.  
Time series of vegetation indices can capture the indices variation in a specific period, and have proven valuable for forest cover classification [48]. In this study, the NDVI, NDWI, RVI and EVI were calculated for each of the HJ-1 composite images and were used in the forest classification. These indices are defined by the following equations:  
\[\mathrm{NDVI_{HJ}}=(\mathrm{B4-B3})/(\mathrm{B4+B3}) \tag{1}\]  
\[\mathrm{NDWI_{HJ}}=(\mathrm{B2-B4})/(\mathrm{B2+B4}) \tag{2}\]  
\[\mathrm{RVI_{HJ}}=\mathrm{B4}/\mathrm{B3} \tag{3}\]  
\[\mathrm{EVI_{HJ}}=2.5\times(\mathrm{B4-B3})/(\mathrm{B4+6\times B3-7.5\times B1 +1}) \tag{4}\]  
where B1, B2, B3 and B4 represent band 1, band 2, band 3, and band 4 of the HJ-1 CCD images, respectively. These VI images and HJ-1 CCD composite images for the same region at different seasons were input to the eCognition Developer 8.64 [49] for land cover classification.