Abstract
More than 50% of the national lands in Japan have been surveyed by airborne laser scanning (ALS) data with different point densities; and developing an effective approach to take full advantage of these ALS data for forest management has thus become an urgent topic of study. This study attempted to assess the utility of ALS data for individual tree detection and species classification in a mixed forest with a high canopy density. For comparison, two types of tree tops and tree crowns in the study area were delineated by the individual tree crown (ITC) approach using the green band of the orthophoto imagery and the digital canopy height model (DCHM) derived from the ALS data, respectively. Then, the two types of tree crowns were classified into four classes—Pinus densiflora (Pd), Chamaecyparis obtusa (Co), Larix kaempferi (Lk), and broadleaved trees (Bl)—by a crown-based classification approach using different combinations of the three orthophoto bands with intensity and slope maps as follows: RGB (red, green and blue); RGB and intensity (RGBI); RGB and slope (RGBS); and RGB, intensity and slope (RGBIS). Finally, the tree tops were annotated with species attributes from the two best-classified tree crown maps, and the number of different tree species in each compartment was counted for comparison with the field data. The results of our study suggest that the combination of RGBIS yielded greater classification accuracy than the other combinations. In the tree crown classifications delineated by the green band and DCHM data, the improvements in the overall accuracy compared to the RGB ranged from 5.7% for the RGBS to 9.0% for the RGBIS and from 8.3% for the RGBS to 11.8% for the RGBIS. The laser intensity and slope derived from the ALS data may be valuable sources of information for tree species classification, and in terms of distinguishing species for the detection of individual trees, the findings of this study demonstrate the advantages of using DCHM instead of optical data to delineate tree crowns. In conclusion, the synthesis of individual tree delineation using DCHM data and species classification using the RGBIS combination is recommended for interpreting forest resources in the study area. However, the usefulness of this approach must be verified in future studies through its application to other forests.
Highlights
The forest land in Japan has an area of approximately 25.1 million ha and accounts for approximately 66% of the country’s area [1]
The results suggest that the reflectance of the trees in the laser scanning may be a valuable source of information for the tree species classification of Pinus densiflora, Chamaecyparis obtusa and Larix kaempferi, which are the main tree species in Japan
We assessed the utility of airborne laser scanning (ALS) data for individual tree detection and species classification in a mixed forest with a high canopy density in
Summary
The forest land in Japan has an area of approximately 25.1 million ha and accounts for approximately 66% of the country’s area [1]. These main plantations with 35- to 55-year-old trees are managed by thinning or selection cutting. Forest resource information, such as species composition, stem density and volume, is the basis of sustainable forest management. One plot is typically established in each subcompartment (the minimum unit of forest management in Japan). This method is too costly and time consuming and less accurate for large forests in which stand conditions, species and stem densities vary [2,3]. More accurate information on the condition of forest resources is required for forestry officers and landowners [1]
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