Sensitivity of voxel-based estimations of leaf area density with terrestrial LiDAR to vegetation structure and sampling limitations: A simulation experiment - INRAE - Institut national de recherche pour l’agriculture, l’alimentation et l’environnement Access content directly
Journal Articles Remote Sensing of Environment Year : 2021

Sensitivity of voxel-based estimations of leaf area density with terrestrial LiDAR to vegetation structure and sampling limitations: A simulation experiment

Abstract

Highlights:• Stand-scale estimates of leaf density from voxelized LiDAR data can be biased.• We identified a negative bias caused by the oversampling of light voxels.• Its magnitude depends on location, vegetation structure, scan design and voxel size• Voxel-scale measurement accuracy is too low in small voxels.• 0.5 m voxel size is the best option because corrections are more straightforward.Abstract:The need for fine scale description of vegetation structure is increasing as Leaf Area Density (LAD, m(2)/m(3)) becomes a critical parameter to understand ecosystem functioning and energy and mass fluxes in heterogeneous ecosystems. Terrestrial Laser Scanning (TLS) has shown great potential for retrieving the foliage area at stand, plant or voxel scales. Several sources of measurement errors have been identified and corrected over the past years. However, measurements remain sensitive to several factors, including, 1) voxel size and vegetation structure within voxels, 2) heterogeneity in sampling from TLS instrument (occlusion and shooting pattern), the consequences of which have been seldom analyzed at the scale of forest plots. In the present paper, we aimed at disentangling biases and errors in plot-scale measurements of LAD with TLS in a simulated vegetation scene. Two negative biases were formerly attributed to (i) the unsampled voxels and to (ii) the subgrid vegetation heterogeneity (i.e. clumping effect), and then quantified, thanks to a the simulation experiment providing known LAD references at voxel scale, vegetation manipulations and unbiased point estimators. We used confidence intervals to evaluate voxel-scale measurement accuracy. We found that the unsampled voxel effect (i) led to underestimations with the "mean layer" method -commonly used to fill unsampled voxels- for small voxels (0.1-0.2 m) and/or low number of scans (<4). It was explained by the spatial correlations in vegetation, which induced that dense voxels were more often occluded by dense neighbors than light voxels. The distribution of the bias was heterogeneous in canopy, the bias being stronger at mid canopy where occlusion started, but smaller in highly-occluded upper layers. This somehow counterintuitive result was explained by a more random sampling of upper layers, but could highly depend on vegetation structure. The subgrid vegetation heterogeneity effect (ii) was confirmed to increase with voxel size, yet, the magnitude of this bias -quantified with vegetation manipulation- was found to be more homogeneously-distributed than the unsampled voxel effect. Overall, we found that no scenario was unbiased. However, an intermediate voxel size (0.5 m) was the best option, because the relatively homogeneous subgrid effect could be handled with a single correction factor and voxel-scale measurements errors were reasonable. On the contrary, smaller voxels led to poor voxel-scale measurements and variable biases in magnitude and spatial distribution with sampling design. However, more similar research in other context is required to adapt these conclusions to other forest plots.
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Dates and versions

hal-03353461 , version 1 (09-03-2023)

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Attribution - NonCommercial

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Maxime Soma, François Pimont, Jean-Luc Dupuy. Sensitivity of voxel-based estimations of leaf area density with terrestrial LiDAR to vegetation structure and sampling limitations: A simulation experiment. Remote Sensing of Environment, 2021, 257, pp.112354. ⟨10.1016/j.rse.2021.112354⟩. ⟨hal-03353461⟩
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