Predicting the Elevation of Canopy Occluded Ground Points in Dense Forest Regions

Forest regions are still considered as complex environments for measuring terrain information by aerial surveys. Tree canopies occluded most of the ground surface and limit sensors’ capabilities for capturing ground data. For instance, vision systems (e.g., cameras) cannot record any information about the ground below the canopy. This lack of knowledge might decrease the accuracy of aerial surveys’ products such as digital terrain models (DTMs). Therefore, to outperform the ground surface knowledge, we proposed a method for computing the elevation of occluded ground points using canopy data and estimated tree height. To this aim, individual tree crowns are identified from a 3-D point cloud, retrieved by discrete light detection and ranging (LiDAR) sensors (i.e., Riegl LMSQ560, ALTM 3100, and Riegl LMSQ5600). Then, ground elevation is predicted by taking advantage of the logarithmic relationship between crown diameter (CD) and tree height. Results have shown that the mean, minimum, and maximum ground elevation errors are about 3.01, 1.28, and 6.38, respectively. Conversely, if one attempts to determine a ground surface without the proposed method, the mean elevation difference is about 11.68.