Predicting Tree Attributes and Quality Characteristics of Scots Pine Using Airborne Laser Scanning Data
Predicting Tree Attributes and Quality Characteristics of Scots Pine Using Airborne Laser Scanning Data
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DOI：
10.14214/sf.203
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发表时间：
2009-01-01
期刊：
影响因子：
1.8
通讯作者：
Tokola, Timo
中科院分区：
文献类型：
--
作者：
Maltamo, Matti;Peuhkurinen, Jussi;Tokola, Timo
近十年来，机载激光扫描技术的发展，为精确描述现存树木提供了新的可能性。ALS数据的森林资源清查应用包括两个树种面积为基础的地块水平的方法。这种应用的主要目标通常是估计关于木材数量的准确资料。木材质量的预测没有得到同样的重视。因此，在这项研究中，我们认为在这里预测的基本树木属性（树径，高度和体积）和特性描述树质量更密切（冠高，高度的最低死分支和锯木比例的树体积）的高分辨率ALS数据。考虑的树种是苏格兰松（樟子松），和现场数据来自14个位于北卡累利阿，芬兰东部的科利国家公园的样地。该材料包括133棵树，这些树的大小和质量变量进行了建模，使用大量的潜在的独立变量从ALS数据计算。这些变量包括个体树识别和基于区域的特征。然后使用非参数k-MSN方法或同时通过看似不相关回归（SUR）方法构建的参数集模型构建要考虑的依赖树特征的模型。结果表明，k-MSN方法可以提供更准确的树级估计比SUR模型。k-MSN估计实际上是高度准确的一般，RMSE小于10%，除了在树的体积和高度的最低死亡分支的情况下。
The development of airborne laser scanning (ALS) during last ten years has provided new possibilities for accurate description of the living tree stock. The forest inventory applications of ALS data include both tree kind area-based plot level approaches. The main goal of such applications has usually been to estimate accurate information on timber quantities. Prediction of timber quality has not been focused to the same extent. Thus, in this study we consider here the prediction of both basic tree attributes (tree diameter, height and volume) and characteristics describing tree quality more closely (crown height, height of the lowest dead branch and sawlog proportion of tree volume) by means of high resolution ALS data. The tree species considered is Scots pine (Pinus sylvestris), and the field data originate from 14 sample plots located in the Koli National Park in North Karelia, eastern Finland. The material comprises 133 trees, and size and quality variables of these trees were modeled using a large number of potential independent variables calculated from the ALS data. These variables included both individual tree recognition and area-based characteristics. Models for the dependent tree characteristics to be considered were then constructed using either the non-parametric k-MSN method or a parametric set of models constructed simultaneously by the Seemingly Unrelated Regression (SUR) approach. The results indicate that the k-MSN method can provide more accurate tree-level estimates than SUR models. The k-MSN estimates were in fact highly accurate in general, the RMSE being less than 10% except in the case of tree volume and height of the lowest dead branch.