论文标题
高光谱和激光雷达数据，用于通过树种，体积和生物量的机器学习预测：更新森林管理计划的可能贡献
Hyperspectral and LiDAR data for the prediction via machine learning of tree species, volume and biomass: a possible contribution for updating forest management plans
论文作者
论文摘要
这项工作旨在使用当前可用的遥感解决方案来确定自治省特伦托（Trento）私人森林库存中的森林单位的基础。特别是，获得和处理了PAT提供的2014年LIDAR和2014年高光谱调查的数据。在森林管理方案的背景下，此类研究非常重要。该方法包括通过用多边形概述单个树冠来定义树种地面真实并标记它们。使用了两个有监督的机器学习分类器，K-Nearest社区和支持向量机（SVM）。结果表明，通过设置特定的超参数，SVM方法在树种分类方面给出了最好的结果。使用冠层参数和高层生物质（AGB）和Scrinzi的jucker方程来估计生物质。将预测的值与11个固定半径的11个田间图进行了比较，其中在2017年估计了体积和生物量。结果显示出显着的相关系数：茎体积为0.94，总地上树生物量为0.90。
This work intends to lay the foundations for identifying the prevailing forest types and the delineation of forest units within private forest inventories in the Autonomous Province of Trento (PAT), using currently available remote sensing solutions. In particular, data from LiDAR and hyperspectral surveys of 2014 made available by PAT were acquired and processed. Such studies are very important in the context of forest management scenarios. The method includes defining tree species ground-truth by outlining single tree crowns with polygons and labeling them. Successively two supervised machine learning classifiers, K-Nearest Neighborhood and Support Vector Machine (SVM) were used. The results show that, by setting specific hyperparameters, the SVM methodology gave the best results in classification of tree species. Biomass was estimated using canopy parameters and the Jucker equation for the above ground biomass (AGB) and that of Scrinzi for the tariff volume. Predicted values were compared with 11 field plots of fixed radius where volume and biomass were field-estimated in 2017. Results show significant coefficients of correlation: 0.94 for stem volume and 0.90 for total aboveground tree biomass.
