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A voxel-based approach for canopy structure characterization using full-waveform airborne laser scanning

机译:使用全波形机载激光扫描的基于体素的冠层结构表征方法

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Forests play a significant role in the global biogeochemical and -physical cycles and particularly the complex three-dimensional forest canopy structure influences the fluxes of energy and matter between the atmosphere and forests. Assessing this structure quantitatively using conventional fieldwork or traditional remote sensing methods is difficult, whereas airborne laser scanning (ALS) systems have proven to be suitable for providing explicit vertical information for large areas. However, most existing ALS based approaches include manual processing steps or need additional data about stand characteristics. To solve these issues, a robust and automatic multi-dimensional clustering method was developed to derive forest canopy structure types (CSTs) based on full-waveform ALS data. The results show that it is possible to develop an automatic, self-sustained and transferable method for: the extraction of CSTs without any previous knowledge about the forest stand; and the extraction of bio-physical parameters based on the resulting CSTs.
机译:森林在全球生物地球化学和物理循环中起着重要作用,尤其是复杂的三维森林冠层结构影响着大气与森林之间的能量和物质通量。使用传统的野外工作或传统的遥感方法对这种结构进行定量评估是困难的,而机载激光扫描(ALS)系统已被证明适用于为大面积区域提供明确的垂直信息。但是,大多数现有的基于ALS的方法都包括手动处理步骤或需要有关林分特性的其他数据。为了解决这些问题,开发了一种鲁棒且自动的多维聚类方法,用于基于全波形ALS数据导出森林冠层结构类型(CST)。结果表明,有可能开发一种自动,自我维持和可转移的方法来:提取CSTs,而无需事先了解林分;并根据生成的CST提取生物物理参数。

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