ABSTRACT

With cut-to-length harvesters, tree stems are measured and cut into different timber assortments at the time of felling. These measurement data collected from harvested trees can be used for decision-support at different levels of the forest industry chain and also for forest planning when combined with remote sensing data. The aim of this study was to examine the operational application for predicting merchantable stem volume, basal area, basal area-weighted mean tree height, basal area-weighted mean stem diameter and diameter distribution at stand level with airborne laser scanning data and harvester data from final felling operations. The area-based approach using k-MSN estimation was evaluated for six different variants of spatial partitioning. The results were stand level predictions with relative root mean square errors of 11–14%, 10–15%, 3–4% and 6–7% for merchantable stem volume, basal area, basal area-weighted mean tree height and basal area-weighted mean stem diameter, respectively. Predictions of stem diameter distributions resulted in error indices of 0.13–0.14. The results demonstrate that harvester data from cut forests may serve as ground truth to airborne laser scanning data and provide accurate forest estimates at stand level. The predicted diameter distributions could be useful for improving yield estimates and bucking simulations.

摘要

使用定长切割机，在砍伐时测量树干并切割成不同的木材分类。从采伐的树木中收集的这些测量数据可用于森林产业链不同层次的决策支持，也可与遥感数据结合用于森林规划。本研究的目的是检查利用机载激光扫描数据和采伐机数据在林分水平预测适销性茎体积、基面积、基面积加权平均树高、基面积加权平均茎直径和直径分布的操作应用。最后的砍伐作业。使用k的基于区域的方法-MSN 估计评估了空间分区的六种不同变体。结果是林分水平预测，可销售的茎体积、基面积、基面积加权平均树高和基面积的相对均方根误差为 11-14%、10-15%、3-4% 和 6-7% - 加权平均茎直径，分别。茎直径分布的预测导致误差指数为 0.13-0.14。结果表明，来自砍伐森林的收割机数据可以作为机载激光扫描数据的地面实况，并在林分级别提供准确的森林估计。预测的直径分布可用于改进产量估计和屈曲模拟。