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

Marie-Soleil Fradette,Jean Bégin,Martin Riopel,Antoine Leboeuf

doi:10.3390/rs11070863

Marie-Soleil Fradette, Jean Bégin + Show 2 more

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https://doi.org/10.3390/rs11070863

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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.

Highlights

Airborne LiDAR (Light Detection and Ranging) has been beneficial in the field of forestry for several years because of its ability to produce very accurate information about terrain [1,2]
Among the information obtained from LiDAR data, vegetation height is an important variable for forest management
Treitz et al [23] studied the impact of pulse density (3.2 pulses/m2 decimated to 0.5 pulses/m2) on several forest inventory variables such as tree top height, but concluded that at plot scale, the pulse density had no significant effect on tree top height

Summary

Introduction

Airborne LiDAR (Light Detection and Ranging) has been beneficial in the field of forestry for several years because of its ability to produce very accurate information about terrain [1,2]. Bater et al [24] analyzed, at plot scale, the impact of different pulse densities at the maximum height, and concluded that maximum height was significantly different between pulse densities varying within 2% to 4%. Treitz et al [23] studied the impact of pulse density (3.2 pulses/m2 decimated to 0.5 pulses/m2) on several forest inventory variables such as tree top height, but concluded that at plot scale, the pulse density had no significant effect on tree top height. At a tree scale, Sibona et al [17] concluded that heights obtained by LiDAR were not significantly different to those measured in the field for pulse densities higher than 5 pulses/m2. The authors analyzed the scale effect and concluded that for the same density, the scale significantly impacted the underestimation [10]

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