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Lidar-based measurement of surface roughness features of single tree crowns

机译：基于激光雷达的单树冠表面粗糙度特征测量

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摘要

In remote sensing data, trees have a low interspecies variability and show a high variability within the tree species. Therefore, specific features that distinguish between unique properties of two tree species are required for a single tree based genera classification. To improve classification results, the suitability of seven surface roughness features, calculated on single tree crown regions, is studied. The algorithms developed to provide roughness parameters can be validated and prototyped in a Virtual Forest testbed. The features are extracted from a normalized digital surface model with a resolution of 0.4m per pixel. Within the test area of 340A;m2 more than 4000 single trees of eleven different species and additionally 200 buildings are available as reference data. Technical standards define several parameters to describe surface properties. These roughness features are evaluated in the context of single tree crowns. All of these features are based on the deviation of the height values of the tree crown to its mean height. As an additional feature the relationship between the crown's surface area and its occupied ground area is used. The evaluation results of these features regarding the discrimination of tree species on different levels - eleven single tree species, seven tree classes, deciduous and coniferous - and also towards discrimination of trees from buildings will be presented.
机译：在遥感数据中，树木的种间变异性较低，并且在树种内显示出较高的变异性。因此，对于基于树的单个属分类，需要区分两种树的独特属性的特定功能。为了提高分类结果，研究了在单个树冠区域上计算的七个表面粗糙度特征的适用性。为提供粗糙度参数而开发的算法可以在Virtual Forest测试平台中进行验证和原型化。这些特征是从归一化的数字表面模型中提取的，每个像素的分辨率为0.4m。在340A; m2的测试区域内，提供11种不同树种的4000多棵单树以及200座建筑物作为参考数据。技术标准定义了几个参数来描述表面特性。这些粗糙度特征是在单树冠的情况下评估的。所有这些特征均基于树冠的高度值与其平均高度的偏差。作为附加功能，使用了表冠的表面积与其占据的地面之间的关系。这些特征的评估结果将涉及不同级别的树种歧视-11种单一树种，7种树种，落叶和针叶树-以及建筑物与树木的区别。

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