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Mapping and Characterizing Selected Canopy Tree Species at the Angkor World Heritage Site in Cambodia Using Aerial Data

机译:使用航空数据在柬埔寨吴哥世界遗产地上绘制和表征选定的树冠树种

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摘要

At present, there is very limited information on the ecology, distribution, and structure of Cambodia’s tree species to warrant suitable conservation measures. The aim of this study was to assess various methods of analysis of aerial imagery for characterization of the forest mensuration variables (i.e., tree height and crown width) of selected tree species found in the forested region around the temples of Angkor Thom, Cambodia. Object-based image analysis (OBIA) was used (using multiresolution segmentation) to delineate individual tree crowns from very-high-resolution (VHR) aerial imagery and light detection and ranging (LiDAR) data. Crown width and tree height values that were extracted using multiresolution segmentation showed a high level of congruence with field-measured values of the trees (Spearman’s rho 0.782 and 0.589, respectively). Individual tree crowns that were delineated from aerial imagery using multiresolution segmentation had a high level of segmentation accuracy (69.22%), whereas tree crowns delineated using watershed segmentation underestimated the field-measured tree crown widths. Both spectral angle mapper (SAM) and maximum likelihood (ML) classifications were applied to the aerial imagery for mapping of selected tree species. The latter was found to be more suitable for tree species classification. Individual tree species were identified with high accuracy. Inclusion of textural information further improved species identification, albeit marginally. Our findings suggest that VHR aerial imagery, in conjunction with OBIA-based segmentation methods (such as multiresolution segmentation) and supervised classification techniques are useful for tree species mapping and for studies of the forest mensuration variables.
机译:目前,关于柬埔寨树种的生态,分布和结构的信息非常有限,需要采取适当的保护措施。这项研究的目的是评估各种航空影像分析方法,以表征在柬埔寨吴哥城庙宇周围森林区域发现的选定树种的森林测定变量(即树高和树冠宽度)。使用基于对象的图像分析(OBIA)(使用多分辨率分割)从超高分辨率(VHR)航空影像和光检测与测距(LiDAR)数据中描绘出单个树冠。使用多分辨率分割提取的树冠宽度和树高值与树木的实测值显示出很高的一致性(分别为Spearman的rho 0.782和0.589)。使用多分辨率分割从航空影像中描绘出的单个树冠具有很高的分割精度(69.22%),而使用分水岭分割来描绘的树冠则低估了实地测量的树冠宽度。光谱角度映射器(SAM)和最大似然(ML)分类都应用于航空影像,用于映射选定的树种。发现后者更适合于树种分类。可以准确地识别单个树种。包括纹理信息可进一步改善物种识别,尽管程度不高。我们的研究结果表明,VHR航空影像与基于OBIA的分割方法(例如多分辨率分割)和监督分类技术相结合,可用于树种标测和森林测定变量的研究。

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