Synthetic Forest Stands and Point Clouds for Model Selection and Feature Space Comparison
Synthetic Forest Stands and Point Clouds for Model Selection and Feature Space Comparison
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DOI：
10.3390/rs15184407
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发表时间：
2023-09
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通讯作者：
Michelle S. Bester;Aaron E. Maxwell;Isaac Nealey;Michael R. Gallagher;N. Skowronski;Brenden E. McNeil
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作者：
Michelle S. Bester;Aaron E. Maxwell;Isaac Nealey;Michael R. Gallagher;N. Skowronski;Brenden E. McNeil
现场验证数据以及现实世界的光探测和测距（激光雷达）收集所固有的挑战使得评估使用激光雷达表征林分体积的最佳算法变得困难。在这里，我们演示了合成林分和模拟地面激光扫描（TLS）的使用，目的是评估哪些机器学习算法、扫描配置和特征空间可以最好地表征林分体积。无论输入特征空间或扫描次数如何，随机森林 (RF) 和支持向量机 (SVM) 算法在估计地块级植被体积方面通常优于 k 最近邻 (kNN)。此外，设计用于使用球形体素表征遮挡的测量通常比使用高度箱的汇总统计来表征返回的垂直分布的测量提供更高的预测性能。鉴于收集大量扫描来训练模型以及收集准确且一致的现场验证数据的困难，我们认为合成数据提供了参数化模型和确定适当的采样策略的重要手段。
The challenges inherent in field validation data, and real-world light detection and ranging (lidar) collections make it difficult to assess the best algorithms for using lidar to characterize forest stand volume. Here, we demonstrate the use of synthetic forest stands and simulated terrestrial laser scanning (TLS) for the purpose of evaluating which machine learning algorithms, scanning configurations, and feature spaces can best characterize forest stand volume. The random forest (RF) and support vector machine (SVM) algorithms generally outperformed k-nearest neighbor (kNN) for estimating plot-level vegetation volume regardless of the input feature space or number of scans. Also, the measures designed to characterize occlusion using spherical voxels generally provided higher predictive performance than measures that characterized the vertical distribution of returns using summary statistics by height bins. Given the difficulty of collecting a large number of scans to train models, and of collecting accurate and consistent field validation data, we argue that synthetic data offer an important means to parameterize models and determine appropriate sampling strategies.