Summary
Published in Remote Sensing, vol 11(7), p. 863.
Underestimation of LiDAR heights, which is linked to the pulse’s probability of reaching the top of vegetation and the ground, is widely known but has never been evaluated for different sensors and for diverse types of ecological conditions. The main causes for this underestimation are: pulse density, scan pattern (sensors), scan angles, and specific contract parameters (flight altitude, pulse repetition frequency), together with territory characteristics (slope, stand density, species composition). This study, which was conducted at a 1 × 1 m resolution, calculated the bias of both the digital terrain model (DTM) and canopy height model (CHM) by subtracting two LiDAR datasets: high-density pixels with 21 pulses/m² (first return) and more (DTM or CHM reference value pixels); and low-density pixels (DTM or CHM value to correct). After preliminary analyses, we concluded that the DTM did not need any specific adjustment; only CHM needed it. Among the variables studied, three were selected for the final CHM adjustment model and an empirical equation using a non-linear mixed model was developed. CHM underestimation correction could be applied before using the CHM for volume calculations, in forest growth models or for multi-temporal analysis.
File
Sector(s):
Forests
Categorie(s):
Scientific Article
Theme(s):
Cartography and Data, Imaging and LiDAR
Departmental author(s):
Author(s)
FRADETTE, Marie-Soleil, Antoine LEBOEUF, Martin RIOPEL and Jean BÉGIN
Year of publication :
2019
Keywords :
LiDAR, canopy height model, digital terrain model, pulse density, LiDAR metrics, stand structure
