Lidar point cloud filtering is the process of separating ground points from non-ground points and is a particularly important part of point cloud data processing. Forest filtering has always been a difficult topic in point cloud filtering research. Given that vegetation cannot be completely summarized according to the structure of ground objects, and given the diversity and complexity of the terrain in woodland areas, filtering in the forest area is a particularly difficult task. However, only few studies have tested the application of the point cloud filtering method for forest areas, the parameter setting of filtering methods is highly complex, and their terrain adaptability is weak. This paper proposes a new filtering method for forest areas that effectively combines iterative minima with machine learning, thereby greatly reducing the degree of manual participation. Through filtering tests on three types of woodlands, the filtering results were evaluated based on the filtering error definition proposed by ISPRS and were compared with the filtering results of other classical methods. Experimental results highlight the advantages of the proposed method, including its high accuracy, strong terrain universality, and limited number of parameters.

With the rapid development of LiDAR technology in recent years, high-resolution LiDAR data possess a great capability to describe fine surface morphology in detail; thus, differencing multi-temporal datasets becomes a powerful tool to explain the surface deformation process. Compared with other differencing methods, ICP algorithms can directly estimate 3D displacements and rotations; thus, surface deformation parameters can be obtained by aligning window point clouds. However, the traditional ICP algorithm usually requires a good initial pose of the point cloud and relies on calculating the spatial distance to match the corresponding points, which can easily lead the algorithm to the local optimum. To address the above problems, we introduced the color information of the point cloud and proposed an improved ICP method that fuses RGB (RGB-ICP) to reduce the probability of matching errors by filtering color-associated point pairs, thus improving the alignment accuracy. Through simulated experiments, the ability of the two algorithms to estimate 3D deformation was compared, and the RGB-ICP algorithm could significantly reduce the deformation deviation (30%–95%) in the three-dimensional direction. In addition, the RGB-ICP algorithm was applicable to different terrain structures, especially for smooth terrain, where the improvement was the most effective in the horizontal direction. Finally, it is worth believing that the RGB-ICP algorithm can play a unique role in surface change detection and provide a reliable basis for explaining the surface motion process.