## 4 Discussion
### Uncertainties of Forest Classification in Northeast China  
Similarly to other forest classifications, error and uncertainty cannot be avoided in the HJ-1-based forest map produced in this study. One source of uncertainty is how to define individual forest types and describe their characteristics with remote sensing images. Concerning the forest cover in Northeast China, this study sought to determine which forest type is dominant, mixed or deciduous broadleaf forest. This study used the scheme of forest types modified from the IPCC. Deciduous broadleaf forest refers to natural or semi-natural vegetation with a tree canopy cover of more than 15% and tree height between 3 and 30 m. Mixed forest refers to natural or semi-natural vegetation with a tree canopy cover of more than 15% and tree height between 3 and 30 m, in addition to a ratio of needleleaf forest and broadleaf forest that is between 25% and 75%. Notably, MCD12Q1 and GlobCover use different forest definitions [68; 69]. MCD12Q1 uses 60% as the low limit of tree canopy cover, whereas GlobCover uses 15%; and the tree height threshold used in MCD12Q1 was 2 m, whereas that in GlobCover was 5 m. For forest cover mapping at large areas, these two factors (tree canopy cover and tree height) are difficult to accurately measure with moderate resolution optical remote sensing data [16]. The important difference between mixed forest and deciduous broadleaf forest is the ratio of needleleaf forest and broadleaf forest. However, the extraction of forest cover from remote sensing is based on objects or pixels, and it is difficult to identify the area ratio of needleleaf to broadleaf forest within a homogeneous area. Therefore, the discrimination of mixed forest and deciduous broadleaf forest primarily depends on the knowledge of experts and the small spectral difference between these forest types in remote sensing images. This naturally leads to another source of uncertainty, i.e., the assessment of accuracy. A large number of field survey samples were applied to perform the accuracy assessment of the HJ-1-based forest map in this study. However, determining the ratio of needleleaf to broadleaf forest largely relies on the subjective judgments of the field survey participants. This validation dataset collection was performed simultaneously by different field survey groups, implying the same forest type may have been identified as a different forest type, especially, deciduous broadleaf forest and mixed forest. Deciduous broadleaf forest, which is dominated by deciduous broadleaf species, also includes needleleaf species, but the quantity of needleleaf forest species is less than that for mixed forest. When only a small portion of forest is captured and viewed, the forest type can be misclassified. To avoid this problem, it is better to have the same survey group to classify and verify the same region. Furthermore, the training data and validation data were both collected in 2011, while the images used in this study were acquired in 2010. The inconsistency of the data acquisition time may lead to minor uncertainties of estimation. Although in one year the forest land may not have great area changes, the field survey samples used for training and validation should be selected from large, homogeneous forest patches combined with remote sensing images. In addition, the validation data were provided by individual provincial environmental protection departments of Northeast China. The spatial distribution of these field survey samples is even more than the training data which were collected by our research teams. Theoretically, the classification algorithm was influenced by the richness, spatial distribution, and spatial representativeness of the training samples. Because of limits on time, human-resources, and financial support, the training data was only obtained from the positions with convenient transportation. The precision of the estimates in no training data region may be lower, especially relevant to forest types.