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
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
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DOI：
10.3390/rs6010087
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发表时间：
2013-12
期刊：
影响因子：
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通讯作者：
S. Deng;M. Katoh;Qingwei Guan;Na Yin;Mingyang Li
中科院分区：
文献类型：
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作者：
S. Deng;M. Katoh;Qingwei Guan;Na Yin;Mingyang Li
本研究试图结合GPS、RS和地理信息系统（GIS）技术，使用高分辨率图像测量单木水平的森林资源。这些图像由WorldView-2号卫星获得，全色波段分辨率为0.5米，多光谱波段分辨率为2.0米。90个样地的现场数据被用来验证解释的准确性。分别采用3 × 3像素、5 × 5像素和7 × 7像素的移动窗口滤波器，利用单冠法提取≥10 cm、≥15 cm和≥20 cm胸径3组树木的树冠。在研究区，胸径大于10 cm的树木有1,203,970株，90块样地的平均判读精度为73.68 ± 15.14%。结果表明，≥15 cm和≥20 cm胸径的树木分别为727，887株和548，919株，平均准确率分别为68.74 ± 17.21%和71.92 ± 18.03%。基于像元的分类结果表明，使用8个多光谱波段得到的16类的分类精度高于仅使用4个标准波段得到的分类精度。16个水类的增长率为0.1%~ 17.0%，平均为4.8%。此外，为了克服“混合像素”的问题，基于冠的监督分类，它可以提高分类精度的优势种和较小的类，用于生成树种的专题地图。基于树冠到像素的分类的改进范围从疏林类的-1.6%到水杉类的34.3%，10个类的平均值为20.3%。然后用地图上的物种属性对所有树顶进行注释，对不同物种的树木计数表明，紫金山森林主要以麻栎、枫香和马尾松为主。从这项研究的结果，导致建议使用冠为基础的，而不是基于像素的分类方法在分类混交林。
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 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.