...
首页> 外文期刊>Remote Sensing of Environment: An Interdisciplinary Journal >ARBOR: A new framework for assessing the accuracy of individual tree crown delineation from remotely-sensed data
【24h】

ARBOR: A new framework for assessing the accuracy of individual tree crown delineation from remotely-sensed data

机译:乔木:一种评估从远程感测数据的单个树冠描绘的准确性的新框架

获取原文
获取原文并翻译 | 示例
           

摘要

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 delineati
机译:为了评估各个树冠(ITC)的准确性(ITC)描绘技术,需要在两个不同的数据集中识别相同的树,例如,从LIDAR描绘的地面参考(GR)数据和冠状物。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木,而不会定量协议级别。例如,通常声称成功的匹配配对,其中表示不同数据集中的同一树的两个数据点位于彼此的5米范围内。这种简单的措施不足以代表ITC划分的多变化性质,并产生划分精度的误导性测量。在这项研究中,我们开发了一个新的框架,用于客观地量化GR和远程感测的树数据集之间的协议:远程感测的生物物理观察和检索(arbor)框架的准确性。使用ITC划定的树木(位置,高度和冠区域)的常见生物物理特性,在不同数据集中表示的树木被建模为重叠的高斯曲线,以促进对协议水平的更全面的评估。广泛的测试量化了一些常用的匹配配对方法的局限,特别是Hausdorff距离算法。我们证明,在arbor框架内,匈牙利组合优化算法改善了数据集之间的匹配,而Jaccard相似度系数对于测量匹配数据群体之间的对应关系是有效的。从林地研究现场应用了arbor框架,从林地研究现场应用了Arbor如何识别最佳ITC描绘技术,其中基于两种统计准确度的两种不同方法测试。使用arbor将进一步依赖任意阈值,因为它提供了量化ITC Delineati的开发和应用的准确性的客观方法

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号