Silva Carlos Alberto, Klauberg Carine, Hudak Andrew T, Vierling Lee A, Liesenberg Veraldo, Bernett Luiz G, Scheraiber Clewerson F, Schoeninger Emerson R
USDA Forest Service, Rocky Mountain Research Station/RMRS, 1221, South Main Street, 83843 Moscow, Idaho, USA.
Department of Natural Resources and Society, College of Natural Resources, University of Idaho/UI, 875 Perimeter Drive, 83843 Moscow, Idaho, USA.
An Acad Bras Cienc. 2018 Jan-Mar;90(1):295-309. doi: 10.1590/0001-3765201820160071.
Accurate forest inventory is of great economic importance to optimize the entire supply chain management in pulp and paper companies. The aim of this study was to estimate stand dominate and mean heights (HD and HM) and tree density (TD) of Pinus taeda plantations located in South Brazil using in-situ measurements, airborne Light Detection and Ranging (LiDAR) data and the non- k-nearest neighbor (k-NN) imputation. Forest inventory attributes and LiDAR derived metrics were calculated at 53 regular sample plots and we used imputation models to retrieve the forest attributes at plot and landscape-levels. The best LiDAR-derived metrics to predict HD, HM and TD were H99TH, HSD, SKE and HMIN. The Imputation model using the selected metrics was more effective for retrieving height than tree density. The model coefficients of determination (adj.R2) and a root mean squared difference (RMSD) for HD, HM and TD were 0.90, 0.94, 0.38m and 6.99, 5.70, 12.92%, respectively. Our results show that LiDAR and k-NN imputation can be used to predict stand heights with high accuracy in Pinus taeda. However, furthers studies need to be realized to improve the accuracy prediction of TD and to evaluate and compare the cost of acquisition and processing of LiDAR data against the conventional inventory procedures.
准确的森林资源清查对于优化制浆造纸公司的整个供应链管理具有重要的经济意义。本研究的目的是利用实地测量、机载激光雷达（LiDAR）数据和非k近邻（k-NN）插补法，估算巴西南部火炬松人工林的林分优势高和平均高（HD和HM）以及树木密度（TD）。在53个常规样地计算了森林资源清查属性和LiDAR衍生指标，并使用插补模型在样地和景观层面获取森林属性。预测HD、HM和TD的最佳LiDAR衍生指标是H99TH、HSD、SKE和HMIN。使用选定指标的插补模型在检索高度方面比树木密度更有效。HD、HM和TD的模型决定系数（adj.R2）和均方根差（RMSD）分别为0.90、0.94、0.38m和6.99、5.70、12.92%。我们的结果表明，LiDAR和k-NN插补法可用于高精度预测火炬松的林分高度。然而，需要进一步开展研究，以提高TD的预测精度，并评估和比较LiDAR数据采集和处理成本与传统清查程序的成本。
