Comparing the Performance of Ground Filtering Algorithms for Terrain Modeling in a Forest Environment Using Airborne LiDAR Data

ABSTRACT The aim of this study was to evaluate the performance of four ground filtering algorithms to generate digital terrain models (DTMs) from airborne light detection and ranging (LiDAR) data. The study area is a forest environment located in Washington state, USA with distinct classes of land use and land cover (e.g., shrubland, grassland, bare soil, and three forest types according to tree density and silvicultural interventions: closed-canopy forest, intermediate-canopy forest, and open-canopy forest). The following four ground filtering algorithms were assessed: Weighted Linear Least Squares (WLS), Multi-scale Curvature Classification (MCC), Progressive Morphological Filter (PMF), and Progressive Triangulated Irregular Network (PTIN). The four algorithms performed well across the land cover, with the PMF yielding the least number of points classified as ground. Statistical differences between the pairs of DTMs were small, except for the PMF due to the highest errors. Because the forestry sector requires constant updating of topographical maps, open-source ground filtering algorithms, such as WLS and MCC, performed very well on planted forest environments. However, the performance of such filters should also be evaluated over complex native forest environments.摘要 本研究旨在评估四种地面滤波算法从机载激光雷达（LiDAR）数据生成数字地形模型（DTMs）的性能。研究区域为美国华盛顿州的一处森林环境，拥有分类明确的土地利用与土地覆被类型，涵盖灌丛、草地、裸地，以及依据树木密度与营林措施划分的三种森林类型：密冠森林、中冠森林与疏冠森林。本研究评估的四种地面滤波算法分别为：加权线性最小二乘（WLS）、多尺度曲率分类（MCC）、渐进形态学滤波（PMF）以及渐进不规则三角网（PTIN）。四种算法在各类土地覆被条件下均表现良好，其中渐进形态学滤波（PMF）分类为地面点的数量最少。除渐进形态学滤波（PMF）因误差最高外，各数字地形模型（DTM）对之间的统计差异均较小。鉴于林业领域需持续更新地形地图，诸如加权线性最小二乘（WLS）与多尺度曲率分类（MCC）这类开源地面滤波算法在人工造林环境中表现优异。然而，此类滤波算法的性能仍需在复杂的原生森林环境中开展进一步评估。