Abstract. LiDAR remote sensing data combined with machine learning (ML) techniques have presented great potential for large-scale modeling of tropical forest attributes. However, the large amount of information that can be derived from an aerial LiDAR survey, summed with the intrinsic heterogeneity of tropical environments (e.g., the Amazon), makes it a challenge to accurately estimate forest biophysical variables. The aim of our work is to investigate the potential and accuracy of different ML techniques and a generalized linear model (GLM) to learn the relationships between LiDAR-derived metrics and forest inventory data for aboveground biomass (AGB) prediction in Amazon forest sites under selective logging regimes. The predictive performance of three ML techniques, namely random forest (RF), support vector machine (SVM), and artificial neural network (ANN), was compared against result from the GLM technique, across 85 sample plots. Interestingly, the GLM retrieved the most accurate estimations of forest AGB (rho Spearman’s coefficient = 0.87), compared with the ML techniques (RF = 0.77, SVM = 0.67, and ANN = 0.50). A number of possible factors affecting such results are listed and discussed in the text, including sample size and number of predictor variables. Continued research is necessary to improve the confidence of AGB estimation, especially over complex forest structures.

中文翻译：

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使用激光雷达数据预测亚马逊雨林地上生物量的机器学习和广义线性模型技术

摘要。激光雷达遥感数据与机器学习（ML）技术相结合，为热带森林属性的大规模建模提供了巨大的潜力。然而，可以从航空 LiDAR 调查中获得的大量信息加上热带环境（例如亚马逊）的内在异质性，使得准确估计森林生物物理变量成为一项挑战。我们工作的目的是研究不同 ML 技术和广义线性模型 (GLM) 的潜力和准确性，以了解 LiDAR 衍生指标与亚马逊森林地点选择性下地上生物量 (AGB) 预测的森林库存数据之间的关系。伐木制度。三种 ML 技术的预测性能，即随机森林 (RF)、支持向量机 (SVM)、和人工神经网络 (ANN) 与 GLM 技术的结果进行比较，跨越 85 个样本地块。有趣的是，与 ML 技术（RF = 0.77、SVM = 0.67 和 ANN = 0.50）相比，GLM 检索了森林 AGB 的最准确估计（rho Spearman 系数 = 0.87）。文中列出并讨论了许多影响此类结果的可能因素，包括样本大小和预测变量的数量。需要继续研究以提高 AGB 估计的置信度，尤其是在复杂的森林结构上。文中列出并讨论了许多影响此类结果的可能因素，包括样本大小和预测变量的数量。需要继续研究以提高 AGB 估计的置信度，尤其是在复杂的森林结构上。文中列出并讨论了许多影响此类结果的可能因素，包括样本大小和预测变量的数量。需要继续研究以提高 AGB 估计的置信度，尤其是在复杂的森林结构上。