Application des techniques de photogrammétrie par drone à la caractérisation des ressources forestières

Abstract : The recent development of operational small unmanned aerial systems (UASs) opens the door for their extensive use in forest mapping, as both the spatial and temporal resolution of UAS imagery better suit local-scale investigation than traditional remote sensing tools.Along with this rising use of drones, dense three-dimensional reconstruction through the combined use of photogrammetry and textit{Structure from Motion} techniques enables now the fine modelization of the canopy surface relief from a set of overlapping images.Forest management is evolving and has to cope with numerous news demands.A sustainable managemnent practice requires beforehand up-to-date and comprehenvise forest inventory.Traditionnal forest ressources inventories are carried out on the field.They are expensive and focus only on an sample of the forest.Information is delivered at the stand level, and specific measurements for individual tree is missing.The use of mapping drones can potentially changes the story by describing forest ecosystems on a tree-level.This thesis aims at investigating the use of unmanned aerial systems for the characterization of temperate forests (in Wallonia, Belgium).Modelization of the vegetation heigth also is investigated by the combinaison of photogrammetric canopy surface measurements with digital terrain elevation acquired by LiDAR.Eventually, the study of a time series of 20 drone fligths through the growing season enables to determine when is the optimal period for automatic classification of deciduous species.Photogrammetric measurements of individual deciduous tree heigth are always less accurate than high density LiDAR measurements (RMSE of 1.04 m versus 0.83 m for the latter).Nevertheless, the versatility of drones is far higher than LiDAR data, with the possibility of flying at the appropriate time and delivering both spectral and 3D information with a very high resolution.Spetral information is relevant among other for tree species identification.The optimal phenology state for the discrimination of deciduous species was demonstrated to be the end of leaf flush.The intra-species phenology is indeed well synchronized during this time windows ranging from late spring to early summer.A global classification error of 16% is reached by using single date UAS imagery, and multitemporal UAS acquisitions still improve the process of species discrimination.Altough precision forestry can largely benefits from UAS technology, legislation constraints limit the operationnal use of drones.Thus, UAS flights are most of the time restricted under a specific altitude and within a certain distance from the remote pilot.These constraints are sub-optimal for the mapping of forest, which requires beyond line of sigth fligth at relatively high altitude.We thus believe that the drone technology will be more developped for scientific investigations at a local scale (dozens or hundreds of hectares) than for forest inventory of large forest estate (thousands of hectares)
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Jonathan Lisein. Application des techniques de photogrammétrie par drone à la caractérisation des ressources forestières. Géographie. Université Paris-Est, 2016. Français. ⟨NNT : 2016PESC1049⟩. ⟨tel-01539627⟩

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