Abstract
Forest plays a significant role in the global carbon budget and ecological processes. The precise mapping of forest cover can help significantly reduce uncertainties in the estimation of terrestrial carbon balance. A reliable and operational method is necessary for a rapid regional forest mapping. In this study, the goal relies on mapping forest and subcategories in Northeast China through the use of high spatio-temporal resolution HJ-1 imagery and time series vegetation indices within the context of an object-based image analysis and decision tree classification. Multi-temporal HJ-1 images obtained in a single year provide an opportunity to acquire phenology information. By analyzing the difference of spectral and phenology information between forest and non-forest, forest subcategories, decision trees using threshold values were finally proposed. The resultant forest map has a high overall accuracy of 0.91 ± 0.01 with a 95% confidence interval, based on the validation using ground truth data from field surveys. The forest map extracted from HJ-1 imagery was compared with two existing global land cover datasets: GlobCover 2009 and MCD12Q1 2009. The HJ-1-based forest area is larger than that of MCD12Q1 and GlobCover and more closely resembles the national statistics data on forest area, which accounts for more than 40% of the total area of the Northeast China. The spatial disagreement primarily occurs in the northern part of the Daxing’an Mountains, Sanjiang Plain and the southwestern part of the Songliao Plain. The compared result also indicated that the forest subcategories information from global land cover products may introduce large uncertainties for ecological modeling and these should be cautiously used in various ecological models. Given the higher spatial and temporal resolution, HJ-1-based forest products could be very useful as input to biogeochemical models (particularly carbon cycle models) that require accurate and updated estimates of forest area and type.
Highlights
Forest covers approximately 31% of the global land surface [1,2] and dramatic changes in forested areas have attracted much attention in the past few decades due to their strong influence on regional climate, water and carbon cycles, as well as biodiversity [3,4]
The training samples and the results with Google Earth images and the field samples in multiple locations, it is suggested that validation samples collected from the Sanjiang Plain were used to verify the forest classification, and forest was distributed in the low hilly terrain within the square region. This indicates that the results proved that a large area of cropland was present (Figure 12), supporting the results in the HJ-1 imagery has an improved capacity to map forest areas, which can be attributed to its higher
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
Summary
Forest covers approximately 31% of the global land surface [1,2] and dramatic changes in forested areas have attracted much attention in the past few decades due to their strong influence on regional climate, water and carbon cycles, as well as biodiversity [3,4]. China [6], which is composed of different types of forest, including evergreen needleleaf forest, deciduous needleleaf forest, deciduous broadleaf forest, and mixed forest [7]. This region has experienced dramatic deforestation due to agricultural activities, urbanization, and water project constructions [8,9]. Northeast China is a pilot region for the Grain for Green Project, the Logging Ban
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