ARBOR: A new framework for assessing the accuracy of individual tree crown delineation from remotely-sensed data
ARBOR: A new framework for assessing the accuracy of individual tree crown delineation from remotely-sensed data
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DOI：
10.1016/j.rse.2019.111256
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发表时间：
2019-09
影响因子：
13.5
通讯作者：
Jon Murray;D. Gullick;G. A. Blackburn;J. D. Whyatt;Christopher Edwards
中科院分区：
文献类型：
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作者：
Jon Murray;D. Gullick;G. A. Blackburn;J. D. Whyatt;Christopher Edwards
为了评估单个树冠（ITC）圈定技术的准确性，需要在两个不同的数据集中识别同一棵树，例如，地面参考（GR）数据和激光雷达圈定的树冠。许多研究使用任意度量或简单的线性距离阈值来匹配不同数据集中的树，而没有量化一致性的水平。例如，当两个数据点（代表不同数据集中的同一棵树）彼此位于5 m以内时，通常声称匹配配对成功。这种简单的措施不足以代表ITC划定的多变量性质，并产生误导性的划定准确性措施。在本研究中，我们建立了一个新的框架来客观量化GR与遥感树木数据集之间的一致性：遥感生物物理观测与检索（ARBOR）的准确性框架。利用ITC绘制的树木的共同生物物理特性（位置、高度和树冠面积），将不同数据集中表示的树木建模为重叠的高斯曲线，以便更全面地评估一致性水平。大量的测试量化了一些常用匹配配对方法的局限性，特别是Hausdorff距离算法。在ARBOR框架下，匈牙利组合优化算法改善了数据集之间的匹配，而Jaccard相似系数可以有效地衡量匹配数据群体之间的对应关系。我们将ARBOR框架应用于一个林地研究地点的GR和遥感树木数据，以证明基于两种统计精度测量，在四种不同的测试方法中，ARBOR如何识别出最佳的ITC描绘技术。使用ARBOR将限制对任意阈值的进一步依赖，因为它为ITC描述算法的开发和应用提供了一种量化准确性的客观方法。
To assess the accuracy of individual tree crown (ITC) delineation techniques the same tree needs to be identified in two different datasets, for example, ground reference (GR) data and crowns delineated from LiDAR. Many studies use arbitrary metrics or simple linear-distance thresholds to match trees in different datasets without quantifying the level of agreement. For example, successful match-pairing is often claimed where two data points, representing the same tree in different datasets, are located within 5 m of one another. Such simple measures are inadequate for representing the multi-variate nature of ITC delineations and generate misleading measures of delineation accuracy. In this study, we develop a new framework for objectively quantifying the agreement between GR and remotely-sensed tree datasets: the Accuracy of Remotely-sensed Biophysical Observation and Retrieval (ARBOR) framework. Using common biophysical properties of ITC delineated trees (location, height and crown area), trees represented in different data sets were modelled as overlapping Gaussian curves to facilitate a more comprehensive assessment of the level of agreement. Extensive testing quantified the limitations of some frequently used match-pairing methods, in particular, the Hausdorff distance algorithm. We demonstrate that within the ARBOR framework, the Hungarian combinatorial optimisation algorithm improves the match between datasets, while the Jaccard similarity coefficient is effective for measuring the correspondence between the matched data populations. The ARBOR framework was applied to GR and remotely-sensed tree data from a woodland study site to demonstrate how ARBOR can identify the optimum ITC delineation technique, out of four different methods tested, based on two measures of statistical accuracy. Using ARBOR will limit further reliance on arbitrary thresholds as it provides an objective approach for quantifying accuracy in the development and application of ITC delineation algorithms.