Interpretation of Forest Resources at the Individual Tree Level at Purple Mountain, Nanjing City, China, Using WorldView-2 Imagery by Combining GPS, RS and GIS Technologies

Abstract

This study attempted to measure forest resources at the individual tree level using high-resolution images by combining GPS, RS, and Geographic Information System (GIS) technologies. The images were acquired by the WorldView-2 satellite with a resolution of 0.5 m in the panchromatic band and 2.0 m in the multispectral bands. Field data of 90 plots were used to verify the interpreted accuracy. The tops of trees in three groups, namely ≥10 cm, ≥15 cm, and ≥20 cm DBH (diameter at breast height), were extracted by the individual tree crown (ITC) approach using filters with moving windows of 3 × 3 pixels, 5 × 5 pixels and 7 × 7 pixels, respectively. In the study area, there were 1,203,970 trees of DBH over 10 cm, and the interpreted accuracy was 73.68 ± 15.14% averaged over the 90 plots. The numbers of the trees that were ≥15 cm and ≥20 cm DBH were 727,887 and 548,919, with an average accuracy of 68.74 ± 17.21% and 71.92 ± 18.03%, respectively. The pixel-based classification showed that the classified accuracies of the 16 classes obtained using the eight multispectral bands were higher than those obtained using only the four standard bands. The increments ranged from 0.1% for the water class to 17.0% for Metasequoia glyptostroboides, with an average value of 4.8% for the 16 classes. In addition, to overcome the "mixed pixels" problem, a crown-based supervised classification, which can improve the classified accuracy of both dominant OPEN ACCESSRemote Sens. 2014, 6 88 species and smaller classes, was used for generating a thematic map of tree species. The improvements of the crown-to pixel-based classification ranged from −1.6% for the open forest class to 34.3% for Metasequoia glyptostroboides, with an average value of 20.3% for the 10 classes. All tree tops were then annotated with the species attributes from the map, and a tree count of different species indicated that the forest of Purple Mountain is mainly dominated by Quercus acutissima, Liquidambar formosana and Pinus massoniana. The findings from this study lead to the recommendation of using the crown-based instead of the pixel-based classification approach in classifying mixed forests.

Interpretation of Forest Resources at the Individual Tree Level in Japanese Conifer Plantations Using Airborne LiDAR Data

“…This task has been enabled by the advent of airborne digital sensors and the launch of commercial satellites that can obtain multispectral imageries with a high resolution of 1 m or less in panchromatic mode [1,2,[9][10][11][12][13][14][15]. However, it is difficult to detect the three-dimensional attributes of forests, such as tree height and DBH, using only optical data [16,41]. Accordingly, the LiDAR technology has received increasing attention from researchers in the forest measurement field [17,18,.…”

“…Obtaining spatially detailed forest information at the tree level over large areas is critical for sustainable forest management [4,16]. This task has been enabled by the advent of airborne digital sensors and the launch of commercial satellites that can obtain multispectral imageries with a high resolution of 1 m or less in panchromatic mode [1,2,[9][10][11][12][13][14][15].…”

“…Moreover, several studies suggested that the object-based classification approach is better than the pixel-based approach in classifying mixed forests [16,54,55]. To overcome the "mixed pixels" problem of the pixel-based classification (i.e., some pixels within a tree crown may be classified into two or more different classes), an object-based supervised classifier (called crown-based classification) was designed for tree species classification in the ITC Suite [51] and used to generate thematic maps of the tree species in this study.…”

“…A rule-based system follows the boundary of each segment of crown material to create isolations, which are taken to represent tree crowns, whereas the pixel with the highest gray value at each tree crown is interpreted as the tree top by a local maximum filtering technique [33,51]. To better compare the results interpreted using different data, we only attempted to extract the tops of the canopy trees in the study area using a filter with a moving window of 5ˆ5 pixels, which, in theory, may extract trees with a crown diameter of more than 2.5 m and has been proven effective in previous studies [16].…”

“…The detailed steps of the interpretation are provided in previous studies [1,16,51,53]. First, preprocessing for the tree top interpretation was necessary to normalize the object bands based on their own ranges, which was performed twice to smooth using an averaging filter of 5ˆ5 pixels (2.5ˆ2.5 m) [1,51].…”