Additionally, many global land-cover products are available at different spatial resolutions, ranging from 300 m to 1 km, including UMD 1992/1993 (1-km resolution), GLC2000 (1-km resolution), GlobCover 2004/2009 (300-m resolution), and MODIS Land Cover Type (500 m resolution) [20]. The derivation of forest areal statistics with these global products for Northeast China requires a number of GIS operations such as re-projection, mosaicking, sub-setting, and recoding. Given that the overall classification accuracies of global products range between 65% and 75% [21], coarse resolution datasets fail to provide reliable area estimates of forest extent and change [22]. Landsat TM/ETM+ imagery has been widely used to map forest areas in Northeast China [23; 24; 25; 26]. Because of frequent cloud cover and the long re-visit time (16 days) of Landsat, it is difficult to obtain cloud-free images over the entirety of Northeast China during a certain period. Furthermore, successive acquisition of the Landsat data in a single year is very difficult and then temporal or phenological information cannot be incorporated into the procedure for forest mapping, although phenological information is very important for improving vegetation cover classification [5; 27]. The Chinese satellite constellation HJ-1 was launched in 2008. Due to the fine spatial resolution (30 m), large swath (700 km), and short revisit cycle (2 days), it has been used to map saltmarsh and species [28], rice growth parameters [29], and land cover [30], and it is expected to be an important data source for rapid, large area land cover information extraction. In the most recent decades, there are various remote sensing-based methodologies that have been widely used to obtain the extent and subcategories of forest, including conventional supervised and unsupervised classification [31; 32], the phenology-based method [19], and object-based classification [33]. Using these methods, classification accuracies of 70-90% for forest cover or types were reported. The object-based classification is especially becoming more popular compared with traditional pixel-based image analysis [34], because it allows for segmentation, attribution, classification, and establishment of relationships among defined objects that are not possible in pixel-based analysis [35]. The advantages of object-based classification also include overcoming salt-and-pepper effects [36] and providing geo-information that can be immediately stored into Geographical Information System (GIS) databases [37]. Furthermore, many studies revealed that the object-based classification provided higher accuracy than the pixel-based classification approaches [38; 39; 40]. Some research that incorporated other sources of remotely sensed data (suchas LiDAR and Hyperspectral data) into the object-based forest classification process has been reported [41; 42; 43]. However, little research has explored the combination of phenology analysis and the object-based approach, which may increase the accuracy of classification and contribute towards operational large-scale forest mapping. Based on the necessity of forest information in Northeast China and the existing challenges in large-scale forest mapping in high resolution, the innovative goal tackled in this paper relies on the use of multi-temporal HJ-1 CCD imagery, within the context of an object-based classification approach, to map the extent and subcategories of forest through a decision tree classifier. By taking the spatio-temporal advantages of HJ-1 CCD data, a hierarchical classification approach coupled with time series Vegetation Indices and DEM information was developed to map forest in Northeast China. The other specific objective of this study is to compare the HJ-1-based forest map with two global land cover datasets (GlobCover 2009 and MODIS MCD12Q1) produced for similar years, which will help explain the error and uncertainty of forest monitoring at large spatial scales from these three datasets.