Thèse soutenue

Développement du potentiel du Lidar aéroporté pour la gestion durable des forêts : prise en compte et gestion des effets de l’angle de balayage sur les prédictions d’attributs forestiers à l’aide de modèles surfaciques (ABA)

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Auteur / Autrice : Karun Dayal
Direction : Sylvie DurrieuMarc Bouvier
Type : Thèse de doctorat
Discipline(s) : Géomatique
Date : Soutenance le 19/09/2022
Etablissement(s) : Paris, AgroParisTech
Ecole(s) doctorale(s) : Biodiversité, Agriculture, Alimentation, Environnement, Terre, Eau
Partenaire(s) de recherche : Laboratoire : Territoires, Environnement, Télédétection et Information Spatiale (Montpellier)
Jury : Président / Présidente : Pierre Couteron
Examinateurs / Examinatrices : Jocelyn Chanussot, Félix Morsdorf, Richard Fournier
Rapporteurs / Rapporteuses : Jocelyn Chanussot, Félix Morsdorf


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Information measured by lidar depends on the observed vegetation and the acquisition geometry, which is a function of the acquisition parameters and the terrain properties. The thesis aims to understand the relationship between lidar acquisition geometry and forest attribute predictions, focusing on the assessment and management of impacts of lidar scan angle on lidar metrics and ABA models. Four different forest types were studied with three forest types (broadleaf, coniferous and mixed) in mountainous terrain and one forest type (riparian) in relatively flat terrain . The thesis was divided into three parts. The first part assessed the effect of lidar scan angle on lidar metrics commonly used in ABA predictions. It was observed that different lidar metrics behave differently under changing scan angles. Subsequently, the effect of including metrics with different sensitivities to scan angle was investigated in the second part of the study. A model involving a set of predefined metrics with different sensitivities to scan angle was used. Existing lidar datasets were resampled based on the flight lines 1) to simulate lidar acquisitions with different scan geometries, 2) to build models for a set of scan patterns and 3) to further compare the quality of estimations resulting from each scan pattern. These comparisons highlighted that introducing metrics sensitive to scan angle led to a decrease in model robustness. Also, the variation in the accuracy of ABA models was found to be higher for datasets consisting of point clouds scanned from only one flight line as opposed to those consisting of point clouds scanned from multiple flight lines. The normalisation of lidar metrics sensitive to scan angle was also attempted using voxelisation. Voxel-based metrics contributed by increasing either the precision or the accuracy, or both. In the last part of the study, the terrain properties and acquisition parameters were considered explicitly. As the interaction between lidar acquisition parameters, terrain, and vegetation properties can be complex, neural networks were used to model the relationships between various lidar metrics and the acquisition geometry, resulting in significantly better ABA predictions.