利用点云数据进行单木分割的方法大致可以分为两类：一种是基于归一化点云数

The method of using point cloud data for single-wood segmentation can be roughly divided into two categories: one is segmentation based on normalized point cloud data, called NPC method; the other is rasterized canopy height generated from point cloud data The model, based on this, performs single-wood segmentation. Common methods include watershed segmentation and polynomial fitting segmentation. The NPC method directly sets a reasonable threshold based on the spatial distance between the point clouds for clustering to complete the segmentation, while the method based on the canopy height model uses the height change of the forest canopy surface to detect tree vertices and crown edges. There are many image-based segmentation methods, such as edge detection algorithms, area segmentation, and image threshold segmentation.

The method of single-wood segmentation by using point cloud data can be divided into two categories: one is based on normalized point cloud data, called NPC method, and the other is the grid-based coronary height model generated by point cloud data, on which the single-wood segmentation is carried out, and the commonly used methods are watershed segmentation and polynomial fitted segmentation. The NPC method is to cluster to complete segmentation directly based on the spatial distance between point clouds, while the method based on the canopy height model uses the height variation of the forest canopy surface to detect tree vertices and the edge of the tree crown. There are many methods of image-based segmentation, such as edge detection algorithm, area segmentation, image threshold segmentation, etc.

There are two kinds of single tree segmentation methods based on point cloud data: one is based on normalized point cloud data, which is called NPC method; the other is based on point cloud data to generate grid canopy height model, which is based on single tree segmentation. The common methods are watershed segmentation and polynomial fitting segmentation. NPC method is based on the reasonable threshold set by the space distance between the point clouds to complete the clustering segmentation, while the method based on the canopy height model is based on the height change of the forest canopy surface to detect the tree vertex and crown edge. There are many image-based segmentation methods, such as edge detection algorithm, region segmentation, image threshold segmentation and so on.<br>