Dense Canopy Height Model from a low-cost photogrammetric platform and LiDAR data

Low-cost methodology to obtain CHMs integrating terrain data from LiDAR into photogrammetric point clouds with greater spatial, radiometric and temporal resolution due to a correction model. This study focuses on developing a methodology to generate a Dense Canopy Height Model based on the registration of point clouds from LiDAR open data and the photogrammetric output from a low-cost flight. To minimise georeferencing errors from dataset registration, terrain data from LiDAR were refined to be included in the photogrammetric point cloud through a correction model supported by a statistical analysis of heights. As a result, a fusion point cloud was obtained, which applies LiDAR to characterize the terrain in areas with high vegetation and utilizes the greater spatial, radiometric and temporal resolution of photogrammetry. The obtained results have been successfully validated: the accuracy of the fusion cloud is statistically consistent with the accuracies of both clouds based on the principles of classical photogrammetry and LiDAR processing. The resulting point cloud, through a radiometric and geometric segmentation process, allows a Dense Canopy Height Model to be obtained.