基于树高和树冠因子的立木材积模型研究
Individual Stem Volume Modeling Based on Tree Height and Crown Characteristics
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摘要: [目的]由于激光雷达技术已经能准确测定立木树高及相关树冠因子,应用该技术建立基于树高和树冠因子的立木材积模型,为激光技术在森林蓄积估计中提供技术支撑.[方法]利用云杉、冷杉、栎树、桦树4个树种组的3 010株实测样木数据,分析了立木材积与胸径、树高、树冠因子之间的相关关系;并通过对数回归方法构建了基于树高和树冠因子的立木材积模型,用确定系数R2和平均预估误差MPE等6项指标对模型进行评价.[结果]表明,立木材积与单一因子之间的相关,以胸径最为紧密,其次是树高,再次是冠长和冠幅.基于树高和树冠因子的立木材积模型中,以树高和冠幅作为解释变量的二元模型效果较好,再增加冠长因子的三元模型改进不大.云杉、冷杉、栎树、桦树4个树种组基于树高冠幅的立木材积模型,其R2分别为0.81、0.80、0.76和0.77,MPE分别为4.7%、5.3%、5.4%和5.3%,模型预估精度均能达到95%左右.[结论]本文对材积与林木因子之间相关关系的定量分析,建立了云杉、冷杉、栎树、桦树4个树种的立木材积模型,模型预估精度高.为激光雷达技术定量估测森林参数提供了依据.Abstract: [Objective]Based on the data of tree height and crown characteristics obtained through LiDAR technology, forest volume and biomass can be derived. However, the reliable individual stem volume models based on tree height and crown characteristics are still not available. [Method]Using the mensuration data of 3 010 sample trees of four species groups, i.e., Picea, Abies, Quercus and Betula, the relationships between stem volume and tree size factors such as diameter at breast height (dbh), tree height and crown characteristics were analyzed; and applying logarithmic regression, the individual stem volume models based on tree height and crown characteristics were developed, which were evaluated with six statistics such as coefficient of determination (R2) and mean prediction error (MPE). [Result]The results showed that dbh was the most efficient explanatory variable for volume model, followed by the tree height, and then the crown length and the crown width. The two-variable volume model based on tree height and crown width was highly efficient, and the three-variable model including crown length improved only a little. The R2 values of four two-variable volume models based on tree height and crown width for Picea, Abies, Quercus and Betula were 0.81, 0.80, 0.76 and 0.77, respectively, and MPE's were 4.7%, 5.3%, 5.4% and 5.3%, respectively, indicating that the prediction precisions of the volume models were about 95%. [Conclusion]The quantitative analysis results in this study about relationship between stem volume and tree size factors could provide technical support for applying LiDAR technology to measure forest parameters; and the developed volume models would provide a quantitative basis for estimating individual volume through measurements of tree height and crown characteristics.
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Key words:
- individual volume
- / tree height
- / crown width
- / Picea
- / Abies
- / Quercus
- / Betula
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