Abstract This study investigated the influence of sampling design parameters on biomass prediction accuracy obtained from airborne lidar data. A one-factor-at-a-time and a global sensitivity analyses were applied to identify the parameters most impacting model accuracy. We focused on several lidar and field survey parameters that can be easily controlled by users. In this pine plantations study site, a decrease in pulse density (4 to 0.5 pulse/m2) led to a small decrease in prediction accuracy (−3%). However, variability in the number of field plots, positioning accuracy, and plot size, significantly impacted model performance. To obtain a robust model, a minimum of 40 field plots, along with field plot position accuracy of 5 m or lower, and field plot radius exceeding 13 m are recommended. The minimum diameter at breast height (DBH) threshold and the choice of the allometric biomass equation were found to have lesser impacts on model accuracy. In addition, accuracies of DBH and tree height measurements were respectively shown to have a minor and negligible contribution to the prediction error. Significant field measurement costs will still be needed to ensure good-quality models for biomass mapping. However, by reducing pulse density, cost savings can be made on lidar acquisition.

中文翻译：

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采样设计参数对基于机载 LiDAR 数据的生物量预测的影响

摘要 本研究调查了采样设计参数对从机载激光雷达数据获得的生物量预测精度的影响。应用一次一个因素和全局敏感性分析来确定对模型准确性影响最大的参数。我们专注于用户可以轻松控制的几个激光雷达和现场调查参数。在这个松树种植园研究地点，脉冲密度的降低（4 到 0.5 脉冲/平方米）导致预测精度的小幅下降 (-3%)。然而，现场图的数量、定位精度和图大小的可变性显着影响了模型性能。为了获得可靠的模型，建议至少 40 个现场图，以及 5 m 或更低的现场图位置精度，以及超过 13 m 的现场图半径。发现胸高最小直径 (DBH) 阈值和异速生长生物量方程的选择对模型精度的影响较小。此外，DBH 和树高测量的准确性分别显示出对预测误差的影响很小且可以忽略不计。仍然需要大量的现场测量成本来确保生物量绘图的高质量模型。但是，通过降低脉冲密度，可以节省激光雷达采集的成本。