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Remote Sens. 2019, 11(7), 863; https://doi.org/10.3390/rs11070863

Method to Reduce the Bias on Digital Terrain Model and Canopy Height Model from LiDAR Data

1
Department of Wood and Forest Sciences, Université Laval, Quebec City, QC G1V 0A6, Canada
2
Direction des Inventaires Forestiers, Ministère des Forêts, de la Faune et des Parcs du Québec, Quebec City, QC G1H 6R1, Canada
*
Author to whom correspondence should be addressed.
Received: 5 March 2019 / Revised: 3 April 2019 / Accepted: 7 April 2019 / Published: 10 April 2019
(This article belongs to the Special Issue 3D Point Clouds in Forests)
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Abstract

Underestimation of LiDAR heights is widely known but has never been evaluated for several sensors and for diverse types of ecological conditions. This underestimation is mainly linked to the probability of the pulse to reach the ground and the top of vegetation. Main causes of this underestimation are pulse density, pattern of scan (sensors), scan angles, specific contract parameters (flying altitude, pulse repetition frequency) and characteristics of the territory (slopes, stand density and species composition). This study, carried out at a resolution of 1 × 1 m, first assessed the possibility of making an adjustment model to correct the bias of the digital terrain model (DTM), and then proposed a global adjustment model to correct the bias on the canopy height model (CHM). For this study, the bias of both DTM and CHM were calculated 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, it was concluded that the DTM did not need specific adjustment. In contrast, the CHM needed adjustments. Among the variables studied, three were selected for the final CHM adjustment model: the maximum height of the pixel (H2Corr); the density of first returns by m2 (D_first); and the standard deviation of nine maximum heights of the neighborhood cells (H_STD9). The modeling occurred in three steps. The first two steps enabled the determination of significant variables and the shape of the equation to be defined (linear mixed model and non-linear model). The third step made it possible to propose an empirical equation using a non-linear mixed model that can be applied to a 1 × 1 m CHM. The CHM underestimation correction could be used for a preliminary step to several uses of the CHM such as volume calculation, forest growth models or multi-temporal analysis. View Full-Text
Keywords: LiDAR; canopy height model; digital terrain model; pulse density; LiDAR metrics; stand structure LiDAR; canopy height model; digital terrain model; pulse density; LiDAR metrics; stand structure
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Fradette, M.-S.; Leboeuf, A.; Riopel, M.; Bégin, J. Method to Reduce the Bias on Digital Terrain Model and Canopy Height Model from LiDAR Data. Remote Sens. 2019, 11, 863.

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