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首页> 外文期刊>ISPRS Journal of Photogrammetry and Remote Sensing >Comparison of airborne laser scanning methods for estimating forest structure indicators based on Lorenz curves
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Comparison of airborne laser scanning methods for estimating forest structure indicators based on Lorenz curves

机译:基于Lorenz曲线的机载激光扫描法估算森林结构指标的比较。

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摘要

The purpose of this study was to compare a number of state-of-the-art methods in airborne laser scanning (ALS) remote sensing with regards to their capacity to describe tree size inequality and other indicators related to forest structure. The indicators chosen were based on the analysis of the Lorenz curve: Gini coefficient (GC), Lorenz asymmetry (LA), the proportions of basal area (BALM) and stem density (NSLM) stocked above the mean quadratic diameter. Each method belonged to one of these estimation strategies: (A) estimating indicators directly; (B) estimating the whole Lorenz curve; or (C) estimating a complete tree list. Across these strategies, the most popular statistical methods for area-based approach (ABA) were used: regression, random forest (RF), and nearest neighbour imputation. The latter included distance metrics based on either RF (NN-RF) or most similar neighbour (MSN). In the case of tree list estimation, methods based on individual tree detection (ITD) and semi-ITD, both combined with MSN imputation, were also studied. The most accurate method was direct estimation by best subset regression, which obtained the lowest cross-validated coefficients of variation of their root mean squared error CV(RMSE) for most indicators: GC (16.80%), LA (8.76%), BALM (8.80%) and NSLM (14.60%). Similar figures [CV(RMSE) 16.09%, 10.49%, 10.93% and 14.07%, respectively] were obtained by MSN imputation of tree lists by ABA, a method that also showed a number of additional advantages, such as better distributing the residual variance along the predictive range. In light of our results, ITD approaches may be clearly inferior to ABA with regards to describing the structural properties related to tree size inequality in forested areas.
机译:这项研究的目的是比较机载激光扫描(ALS)遥感中的多种最新方法,以描述树木大小不平等和与森林结构有关的其他指标的能力。选择的指标是基于洛伦兹曲线的分析得出的:基尼系数(GC),洛伦兹不对称性(LA),在平均二次直径之上存储的基面积比(BALM)和茎密度(NSLM)。每种方法都属于以下估计策略之一:(A)直接估计指标; (B)估计整个洛伦兹曲线;或(C)估算完整的树列表。在这些策略中,使用了最流行的基于区域方法(ABA)的统计方法:回归,随机森林(RF)和最近邻归因法。后者包括基于RF(NN-RF)或最相似邻居(MSN)的距离度量。在树列表估计的情况下,还研究了基于单独树检测(ITD)和半ITD并结合MSN归因的方法。最准确的方法是通过最佳子集回归进行直接估计,对于大多数指标,GC验证均获得了最低的均方根误差CV(RMSE)交叉验证变异系数:GC(16.80%),LA(8.76%),BALM( 8.80%)和NSLM(14.60%)。通过ABA的树列表MSN插补获得了相似的数字[CV(RMSE)16.09%,10.49%,10.93%和14.07%],该方法还显示了许多其他优点,例如更好地分布残差沿着预测范围。根据我们的结果,就描述与森林地区树木大小不平等有关的结构特性而言,ITD方法可能明显不如ABA。

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