## 2 Materials and Methods
### Forest Classification Method
#### 2.5.3 Object-Based Decision Tree Algorithm  
The overall idea of this approach is to develop a simple decision tree algorithm that uses threshold values from variables, which include the HJ-1 image bands 1-4, NDVI, NDWI, RVI, EVI, DEM and LBV transformation images (Figure 4). Thresholds in the decision tree rules were determined according to a statistical analysis of training areas created from the ground truth data. At first, water areas were distinguished using the Normalized Difference Water Index (NDWI) derived from an intermediate season. Non-water areas were then further classified into vegetation and non-vegetation classes using the Normalized Difference Vegetation Index (NDVI) derived from plant-growing seasons. The non-vegetation class included settlements and other land, distinguished using nearest neighbor (NN) classifiers. The bulk vegetation class was then re-classified into forest and non-forest classes (including cropland, grassland and wetland). The LBV transformation was proposed by [PERSON] (2016). Among the three transformation images, L image reflects the general radiance level, B image represents the visible-infrared radiation balance, and V image refers to the band radiance variation vector (direction and speed). The L, B, and V images were generated, and it was found that forest tended to have a relatively lower V component than other vegetation. Therefore, the V component, and NDVI were combined to separate the forest from non-forest classes. Forested areas were further classified into various forest types under the segmentation level 2 using multi-temporal HJ-1 composite images, vegetation index images, and DEM. The mean value of EVI and NDVI for different forest types monthly was calculated within the context of objects. By analyzing the multi-temporal EVI and NDVI of various forests, NDVI conclusively has advantages for separating evergreen and deciduous forest in early spring and winter. EVI is more sensitive to deciduous forest and mixed forest than NDVI in the summer. In addition, the distribution of needleleaf and broadleaf mixed forest has elevation limits in mountain regions, i.e., lower than 1000 m in the Changbai Mountains, lower than 900 m in the Zhangguangcai Mountains, and lower than 700 m in the Xiaoxing'an Mountains ([PERSON] et al., 2017). The EVI, NDVI, DEM, and expert knowledge were applied to classify deciduous broadleaf forest, deciduous needle-leaf forest, and mixed forest. Shrubland always mixes with deciduous broadleaf forest or mixed forest. Based on the relevant literature and vegetation maps of China, the total shrubland area in Northeast China is relatively smaller than other forest types. According to the training samples for shrubland, the ratio of band 3 and band 1, the RVI, and DEM were used to extract the shrubland group from deciduous broadleaf forest or mixed forest.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Weight**} & \multicolumn{2}{c}{**Levels**} \\ \cline{2-3}  & **Level 1** & **Level 2** \\ \hline Scale & 30/900 & 10/300 \\ (Units: pixels/meters) & & \\ Color & 0.7 & 0.9 \\ Shape & 0.3 & 0.1 \\ Smooth & 0.4 & 0.4 \\ Compact & 0.6 & 0.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Multi-resolution segmentation parameters used in this study.