Airborne Laser Scanning (ALS) results in point-wise measurements of canopy height, which can further be used for Individual Tree Detection (ITD). However, ITD cannot find all trees because small trees can hide below larger tree crowns. Here we discuss methods where the plot totals and means of tree-level characteristics are estimated in such context. The starting point is a previously presented Horvitz–Thompson-like (HT-like) estimator, where the detectability is based on the larger tree crowns and a tuning parameter $\alpha$ that models the detection condition. We propose a new method which is based on modeling the spatial pattern of hidden tree locations using a sequential spatial point process model, with a tuning parameter $\theta$. We also explore whether the variability of the tuning parameters $\alpha$ and $\theta$ can be predicted using ALS features to improve the predictions. The accuracy of stand density, dominant height and mean height is used as comparison criteria in a cross-validation procedure. The HT-like estimator with empirically estimated tuning parameter $\alpha$ performed the best. The overall performance of the new method was comparable. The new method was computationally less demanding, which makes it attractive for practical use.

机载激光扫描 (ALS) 可对树冠高度进行逐点测量，可进一步用于单棵树检测 (ITD)。但是，ITD 无法找到所有树木，因为小树可以隐藏在较大的树冠之下。在这里，我们讨论在这种情况下估计图总数和树级特征均值的方法。起点是之前提出的 Horvitz-Thompson-like (HT-like) 估计器，其中可检测性基于较大的树冠和调整参数$\alpha$模拟检测条件。我们提出了一种新方法，该方法基于使用顺序空间点过程模型对隐藏树位置的空间模式进行建模，并带有调整参数$\theta$. 我们还探讨了调整参数的可变性是否$\alpha$和$\theta$可以使用 ALS 特征来预测以改进预测。林分密度、优势高度和平均高度的准确性用作交叉验证程序中的比较标准。具有经验估计调整参数的类 HT 估计器$\alpha$表现最好。新方法的整体性能相当。新方法对计算的要求较低，这使其在实际应用中具有吸引力。