A novel approach for distinguishing noise and information in forest <scp>MLS</scp> ‐point clouds

Abstract 3D laser scanning is a cornerstone of modern forest and ecological research, allowing detailed insight into forest structures and dynamics. Although point cloud processing and noise management are crucial steps in the exploitation of LiDAR data, traditional denoising methods are usually based on the assumption of a Gaussian noise distribution, whereas many factors (including structural complexity of forest environments) lead to the opposite By overlooking the complexity of noise distribution within point clouds, these methods often fail to distinguish noise correctly, resulting in a loss of relevant information and introducing potential biases. This is particularly problematic in areas with smaller stems and branches, where their pronounced curvature leads to light diffraction, complicating reliable detection. To address these issues, we propose a new noise compression method designed to better distinguish noise from information‐bearing points within MLS‐point clouds. By self‐optimizing its parameters against ground‐truth data, our method significantly enhances the accuracy and reliability of MLS‐derived metrics while preserving essential structural details. Compared to previous denoising methods, our approach minimizes data loss and ensures a more accurate representation of forest structures across diverse conditions (including small stems and branches), widening the potential contributions of 3D mapping to other ecological spheres.