Abstract
Continuous maps of forest parameters can be derived from airborne laser scanning (ALS) remote sensing data. A prediction model is calibrated between local point cloud statistics and forest parameters measured on field plots. Unfortunately, inaccurate positioning of field measures lead to a bad matching of forest measures with remote sensing data. The potential of using tree diameter and position measures in cross-correlation with ALS data to improve co-registration is evaluated. The influence of the correction on ALS models is assessed by comparing the accuracy of basal area prediction models calibrated or validated with or without the corrected positions. In a coniferous, uneven-aged forest with high density ALS data and low positioning precision, the algorithm co-registers 91% of plots within two meters from the operator location when at least the five largest trees are used in the analysis. The new coordinates slightly improve the prediction models and allow a better estimation of their accuracy. In a forest with various stand structures and species, lower ALS density and differential Global Navigation Satellite System measurements, position correction turns out to have only a limited impact on prediction models.
Highlights
Traditional forest inventory practices mainly rely on statistical descriptions obtained from field sample plots [1]
The area-based approach aims at combining the high resolution of airborne laser scanning (ALS) data with the sample field plots in order to provide statistically calibrated, continuous maps of forest parameters [2,3]
Two-tailed Wilcoxon rank-sum tests show that the proportion of coniferous trees weighted by basal area (p = 0.012), the altitude (p = 0.003) and, to a lesser extent, the amount of pit-filling of the canopy height model (CHM) Qf (p = 0.078) have significantly different distributions for the “doubtful” and “non-doubtful” plots in the Vosges area
Summary
Traditional forest inventory practices mainly rely on statistical descriptions obtained from field sample plots [1]. This method provides reliable estimates at the forest level, while limiting fieldwork cost. The area-based approach aims at combining the high resolution of ALS data with the sample field plots in order to provide statistically calibrated, continuous maps of forest parameters [2,3]. Relationships between local descriptors of the ALS point cloud and forest parameters, such as basal area, stem volume or dominant height, are investigated based on the available field data. Once a prediction model is validated, it is applied to the whole ALS dataset in order to produce the map of the desired forest parameters
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