ABSTRACT

This study evaluated the suitability of different airborne laser scanning (ALS) datasets for the prediction of forest canopy fuel parameters in managed boreal forests in Finland. The ALS data alternatives were leaf-off and leaf-on unispectral and leaf-on multispectral data, alone and combined with aerial images. Canopy fuel weight, canopy base height, biomass of living and dead trees, and height and biomass of the understory tree layer were predicted using regression analysis. The considered categorical forest parameters were dominant tree species, site fertility and vertical forest structure layers. The canopy fuel weight was modeled based on crown biomass with an RMSE% value of 20–30%. The canopy base heights were predicted separately for pine and spruce stands with satisfactory results the RMSE% values being 9–10% and 15–17%, respectively. Following the initial classification of the existence of an understory layer (with kappa-values of 0.47–0.53), the prediction of understory height performed well (RMSE% 20–25%) but the understory biomass was predicted with larger RMSE% values (about 60–70%). Site fertility was classified with kappa-values of 0.5–0.6. The most accurate results were obtained using multispectral ALS data, although the differences between the datasets were minor.

摘要

这项研究评估了不同的机载激光扫描（ALS）数据集对于预测芬兰受管理的北方森林中森林冠层燃料参数的适用性。ALS数据替代方案是单独使用叶图和叶图单光谱以及叶图多光谱数据，并与航拍图像组合。使用回归分析预测冠层燃料重量，冠层基部高度，活树和枯树的生物量以及林下树木层的高度和生物量。认为的分类森林参数是优势树种，立地肥力和垂直森林结构层。冠层燃料重量基于树冠生物量建模，RMSE％值为20–30％。分别对松树和云杉林的树冠基部高度进行了预测，结果令人满意，RMSE％值分别为9-10％和15-17％。在对地下层的存在进行初步分类（kappa值为0.47–0.53）之后，对地下高度的预测效果很好（RMSE％20–25％），但对地下生物量的预测则具有较大的RMSE％值（大约60–70％）。场地肥力的kappa值为0.5-0.6。使用多光谱ALS数据可获得最准确的结果，尽管数据集之间的差异很小。