Classifying ecosystems with metaproperties from terrestrial laser scanner data.
Classifying ecosystems with metaproperties from terrestrial laser scanner data.
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DOI：
10.1111/2041-210x.12854
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发表时间：
2018-03
影响因子：
6.6
通讯作者：
Schaaf C
中科院分区：
文献类型：
--
作者：
Paynter I;Genest D;Saenz E;Peri F;Boucher P;Li Z;Strahler A;Schaaf C
在本研究中，我们介绍了地面激光扫描仪（TLS）数据的元属性分析，并通过几个生态分类问题展示了其应用。元属性分析考虑从典型当代仪器的单次扫描中存在的数十万到数百万个激光雷达脉冲得出的脉冲水平和空间指标。在如此大的聚合中，激光雷达数据群体的属性反映了生态系统潜在生态条件的属性。在本研究中，我们提供元属性分类模型，以采用 TLS 元属性分析来解决生态学中的分类问题。我们将其应用到一项概念验证研究中，该研究对来自房间和森林的 88 个扫描进行了 100% 的准确率分类，作为模板。然后，我们认真应用元属性分类模型，以 97.09% 的准确率 (N = 224) 区分温带和热带森林的扫描，并对内陆和沿海热带雨林的扫描进行分类，准确率 84.07% (N = 270)。结果表明，元属性分析具有识别微妙而重要的生态系统条件（包括疾病和人为干扰）的潜力。元属性分析是生态学中 TLS 当代对象重建应用的增强，并且可以表征区域异质性。
In this study, we introduce metaproperty analysis of terrestrial laser scanner (TLS) data, and demonstrate its application through several ecological classification problems. Metaproperty analysis considers pulse level and spatial metrics derived from the hundreds of thousands to millions of lidar pulses present in a single scan from a typical contemporary instrument. In such large aggregations, properties of the populations of lidar data reflect attributes of the underlying ecological conditions of the ecosystems. In this study, we provide the Metaproperty Classification Model to employ TLS metaproperty analysis for classification problems in ecology. We applied this to a proof‐of‐concept study, which classified 88 scans from rooms and forests with 100% accuracy, to serve as a template. We then applied the Metaproperty Classification Model in earnest, to separate scans from temperate and tropical forests with 97.09% accuracy (N = 224), and to classify scans from inland and coastal tropical rainforests with 84.07% accuracy (N = 270). The results demonstrate the potential for metaproperty analysis to identify subtle and important ecosystem conditions, including diseases and anthropogenic disturbances. Metaproperty analysis serves as an augmentation to contemporary object reconstruction applications of TLS in ecology, and can characterize regional heterogeneity.