*2.6. High-Density Laser Scanning Features*

We applied the binning interpolation method based on the maximum cell-assignment method to generate a digital surface model (DSM) from the high-density LiDAR points. The linear interpolation method was used to fill void areas in the DSM. The digital terrain model (DTM) was created based on the interpolation from ground points as well [47]. The features representing the surface topography and canopy conditions, such as slope gradient, aspect, topography/canopy position, plan curvature, profile curvature, and mean curvature [48,49], were derived from the DTM and DSM (Figure 3).

We subtracted the DTM from the DSM to reach the canopy height model (CHM) [50]. The canopy density was generated based on the ratio of the number of nonground points to the total number of points in an object [51]. The tree canopy was delineated from the CHM and used for measuring the canopy cover within an object [52]. The intensity image was created from the range of pulse-intensity values of the laser points [53]. It was applied in combination with orthophotos to determine species types within the objects.

**Figure 3.** Object features in an example area of the study area. (**a**) 3D perspectives of high-density Li-DAR point clouds, its metric derivatives including (**b**) the digital terrain model (DTM) and (**c**) digital surface model (DSM). (**d**) The ground-surface features were extracted from the DTM and (**e**) the canopy characteristics and (**f**) the canopy-surface features were extracted from the LiDAR points and the DSM, respectively.

#### *2.7. Object Features*

A buffer of 10 m was delineated around the logging trails, and then the area was segmented into the homogenous units (objects) based on the similarity in the spectral properties of the adjacent cells in the high-resolution orthophotos. The average size of the objects was obtained around 18 sq.m. The values of the derived features from the LiDAR data (Table 1) and the accuracy of positioning by u-blox ZED-F9P were summarized within the objects. This single database was used for analyzing the relationship between the target and the features using TreeNet.
