基于树高和树冠因子的立木材积与地上生物量相容模型研究

【目的】无人机机载激光雷达能够准确地测定单木、林分乃至大尺度森林结构参数(树高和树冠因子)。为应用无人机激光雷达技术准确估测森林蓄积量、生物量和碳储量提供计量依据和技术支撑。【方法】以150株实测马尾松生物量样本数据为研究对象,采用非线性回归估计方法和度量误差联立方程组方法,分析立木材积和地上生物量与树高、树冠因子的相关性,并在此基础上研究建立基于树高和树冠因子的立木材积与地上生物量相容模型。【结果】单株材积和地上生物量与树高因子的相关性最为紧密,其次才是树冠因子;基于树高和冠幅因子的二元材积和地上生物量模型预估精度较高,达到92%以上,再考虑冠长因子的三元模型预估精度改进不大;基于树高和冠幅因子的二元立木材积与地上生物量相容模型估计效果更好,相对于一元相容模型系统而言,二元相容模型拟合效果有较大幅度提高,预估精度达到92%以上。【结论】采用度量误差联立方程组方法可以有效解决基于树高和树冠因子的立木材积与地上生物量相容问题,并且预估精度达到92%以上,所建二元立木材积与地上生物量相容模型可为应用激光雷达技术反演森林蓄积量和生物量提供计量依据。

Compatibility model of stand volume and above-ground biomass based on tree height and crown characteristics

【Objective】Unmanned aerial vehicle LiDAR can accurately measure forest structure parameters such as tree height and crown characteristics for individual tree,stand and even large-scale forest.It provides measurement basis and technical support for the application of UAV LiDAR technology to accurately estimate forest storage,biomass and carbon storage.【Method】150 measured biomass data of Pinus massoniana were taken as the research object,the correlation between stand volume and aboveground biomass with tree height and crown factors was analyzed and the compatibility model between stand volume and above-ground biomass based on tree height and crown characteristics was established by using the methods of non-linear regression estimation and measurement error simultaneous equations.【Result】The correlation between individual volume and above-ground biomass and tree height factor was the closest;the prediction accuracy of two-variable volume and above-ground biomass model based on tree height and crown width factor was higher than 92%,and the effect of three-variable model considering crown length factor was not improved;the compatibility model of two-variable stand volume and above-ground biomass based on tree height and crown width factor was improved slightly.The model has better estimation effect and higher prediction accuracy,reaching more than 92%.【Conclusion】The method of simultaneous equations of measurement errors can effectively solve the problem of compatibility between stand volume and above-ground biomass,The prediction accuracy of model systems are over 92%.These models can provide quantitative basis for using LiDAR technology to estimate forest stock and biomass.