## 5 Conclusions  
Existing efforts on large-scale forest mapping generally focus on the use of high temporal and spatial resolution datasets, and our knowledge is still limited in the new frontier of forest shrinking in Northeast China. Based on the object-based decision tree classification approach and all the available HJ-1 images in a single year, this study generated an unprecedented 30-m forest map (including subcategories) in Northeast China in 2010. To our knowledge, this is the first application of HJ-1 imagery on large area (>10,000 km\({}^{2}\)) forest mapping. Its effective implementation in large areas with hundreds of HJ-1 images showcased potentials and prospects of the application of HJ-1 imagery in global and longer temporal scale of land cover and land use changes studies.  
In this study, an object-based decision tree classification approach has been proved to be effective in classifying forest across a large area and was accurate based on validation using ground truth data from field surveys. The results also showed better consistency with the statistical data when compared with the two global land cover products (MCD12Q1 and GlobCover). More importantly, HJ-1 imagery provides intensive observations for the phenology attributes of different forest types. This study demonstrates that HJ-1 imagery can reasonably meet the high temporal observation requirement of the forest subcategories mapping in temperate regions. That suggests HJ-1 imagery is promising in the applications of more detailed land use mapping such as different crop planting types and forest species. Furthermore, comparing the results of the HJ-1 forest map to another global land cover product (MCD12Q1) revealed that the forest type information from the global land cover products should be cautiously used in various ecological models. If MCD12Q1 is used in carbon cycle modeling, large uncertainty can be introduced in the carbon evaluation at regional scales. Considering that HJ-1 data only cover China and some surrounding Asian countries, but not the whole globe, and the sensors only have four wave bands; more sources of remote sensing data should be included, such as SAR (Sentinel-1, PALSAR, ENVISAT-ASAR, etc.), LiDAR, UAV, and high temporal resolution optical remote sensing data (Gaofen series satellite, Landsat 8, etc.) for monitoring forest on a global scale. This approach is expected to gain sufficient robustness and reliability on larger scales of forest remote sensing.  
[PERSON] calculated and analyzed the data in addition to writing the paper. [PERSON] designed the research project and analyzed the data in addition to writing the paper. [PERSON] and [PERSON] gave suggestions for the whole study. [PERSON] contributed to the field survey.  
This study was funded by the National Key Research and Development Program of China (2016 YFC0500301), Field Station Alliance Project of Chinese Academic Sciences (KFJ-SW-YW026) and Strategic Planning Project from Institute of Northeast Geography and Agroecology (IGA), Chinese Academy of Sciences (Y6H2091001).  
Many thanks go to the anonymous reviewers for their valuable comments on the manuscript.  
The authors declare no conflict of interest.