Une gestion raisonnée et multifonctionnelle des forêts n'est possible qu'avec une description à jour de l'état de la ressource naturelle.Les inventaires forestiers traditionnels, réalisés sur le terrain, sont couteux et ne couvrent qu'un échantillonnage de la surface boisée.L'essor des drones civils pour la cartographie a initié une révolution dans le domaine de la télédétection environnementale.La polyvalence et la diversité des systèmes drones sont une aubaine pour la foresterie de précision.Ceux-ci sont utilisé pour la réalisation de cartographie très fine des habitats naturels avec une résolution temporelle et spatiale sans précédent.Nous explorons les possibilités d’utilisation de mini-drones pour la caractérisation quantitative et qualitative de la ressource forestière.Nous nous intéressons en particulier à l’estimation de la hauteur des arbres et à la caractérisation de la composition spécifique au sein de peuplements forestiers.La hauteur de la canopée est une variable dendrométrique de première importance : elle est un bon indicateur du stade de développement des peuplements et intervient notamment dans les estimations de biomasse ou de niveau de productivité.La composition spécifique est une information essentielle en regard des principales fonctions que remplit la forêt (conservation, production, récréation, etc).Nous avons comparé l'estimation de la hauteur des peuplements à partir de mesures LiDAR et celles obtenues par photogrammétrie.Bien que permettant une mesure de hauteur individuelle avec une incertitude de l'ordre de 1.04 m (RMSE) en feuillus, la photogrammétrie par drone sur des zones forestières est systématiquement moins précise que les mesures par LiDAR (RMSE de 0.83 m).Néanmoins, la grande flexibilité que confère les petits drones permet d'acquérir, au moment propice du stade de végétation, et l'information de relief de la canopée, et l'information spectrale.La période de fin de feuillaison, au début du mois de juin, s'est avéré le moment le plus propice à une discrimination automatique de cinq groupes d'essences feuillues (le chênes pédonculé, les bouleaux, l'érable sycomore, le frêne commun et les peupliers).Une erreur globale de classification des houppiers de 16% est obtenue avec des acquisitions monotemporelles, alors que l'utilisation d'imageries acquises à différentes dates permet encore d'améliorer cette classification.Les contraintes de la législation régissant l'utilisation des aéronefs sans pilote à bord restreignent le champs d'action des drones civils.Ainsi, les opérations avec un drone sont limitées sous un seuil d'altitude et à une distance maximale du télépilote, ce qui ne permet pas une utilisation optimale de cette technologie pour la couverture de grands domaines forestiers (plusieurs milliers d'hectares).C'est pourquoi nous pensons que les drones resterons un outils d'analyse de petites surfaces (dizaines voire centaines d'hectares), plus utiles à des fins de recherches scientifiques qu'à une utilisation en gestion forestière / 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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京都大学 / 新制・課程博士 / 博士(農学) / 甲第23942号 / 農博第2491号 / 新制||農||1090(附属図書館) / 学位論文||R4||N5377(農学部図書室) / 京都大学大学院農学研究科森林科学専攻 / (主査)教授 神﨑 護, 教授 北島 薫, 教授 市岡 孝朗 / 学位規則第4条第1項該当 / Doctor of Agricultural Science / Kyoto University / DGAM

Forest species determination from satellite data Abstract Examining the species composition of forests from satellite imagery is constantly evolving. The new ways of exploring forests from the satellites make it easier for foresters to maintain a more accurate and up-to-date overview of the state of forests. In this work, the research was made on the forests in the cadastral territories of Osvětimany and Buchlovice in the Chřiby Mountains in the Czech Republic. In this work, data from the Landsat-8 satellite from three seasons and the Maximum Likelihood Classification method were used. The reference maps were used as reference data. The method of work consists in the fact that 6 frames were classified with the help of training sets using Maximum Likehood Classification. Subsequently, the pixels which were at least 4 times out of 6 ranked in the same class after the classification were selected. Based on these pixels, artificial training sets were calculated for each of the 6 frames, and they were used for another classification with the expectation of better results. The accuracy of the individual classification frames was verified by an error matrix on the crop maps. Keywords: remote sensing, forest canopy, forest tree types, forestry map

Forest species determination from satellite data Abstract This thesis examines the species composition of forests from satellite images using the pixel classification. The research was done on 24 forest locations in The Ustecký Region, The Karlovarský Region, The Plzeňský Region and The Central Bohemian Region in the Czech Republic. In this thesis, data from the Landsat-8 and Sentinel-2 satellites from summer season and the Random Forest Classifier method were used. The layer of species composition of forests from map portal LhpoMap was used as reference data. The method of work consisted of a broad literature search to select the most favourable classifier and to choose the most advantageous input parameter values to achieve the highest overall accuracy of the classification. The practical part was focused on creating a software classification process. The accuracy of the individual image values was verified using matrix errors. Based on the literature search, the Random Forest classifier was used to classify the images. Parameter values were used for the Gini criterion, 500 decision trees, and the other parameters were left with default values. The entire classification process was performed in ArcMap and ArcGIS Pro software using Python programming language with the help of the sklearn.ensemble module...

The Cerulean Warbler is a small migratory that is currently considered a species of special concern. Cerulean Warbler response to two types of silvicultural treatments in southern Indiana was assessed by comparing relative abundance and territory data collected during the two years prior to tree harvest to data collected one year immediately following the harvest. ArcGIS was then used to identify spatial attributes of territories that appeared to be important to the species. Lastly, a method of sampling canopy openings by using a Global Positioning System (GPS) unit was introduced. The results suggest that Cerulean Warblers were not negatively affected by the harvest and that moderate prescribed silviculture treatments that create small canopy openings can benefit the species. / Immediate Cerulean warbler response to silviculture in southern Indiana -- Effects of silviculture on spatial characteristics of Cerulean warbler territories -- A method of sampling canopy openings associated with Cerulean warbler territories. / Department of Biology

Thesis (Ph. D.)--Oregon State University, 2007. / Printout. Includes bibliographical references (leaves 204-236). Also available on the World Wide Web.

Thesis (M.S.)--Baylor University, 2009. / Includes bibliographical references (p. 53-55).

This thesis describes a series of 1:75 scale wind tunnel experiments investigating the wind flow over, and through, three different forest models and the resultant wind loading on individual model trees. The experiments were designed to lead to a quantitative assessment of the wind stability of the particular forest arrangements and also to permit a study of the coherent gust structures in the flow. Forest canopy flow is dominated by a plane mixing layer flow regime with a shear layer close to the canopy top. It has been confirmed that data can be correlated usefully in terms of a shear length, Ls, related to the form of this shear layer. Frequency analysis has confirmed that the flow structures have the same frequency as the swaying of the tallest trees in each forest. A mechanism is proposed whereby upstream turbulence induces swaying of trees at and near the upwind edge region of the forest, which in turn perturbs the air in the unstable shear layer. This leads to a roll-up of the shear layer and the creation of coherent flow structures. Conditional sampling of the gust structures, using wavelet analysis, has also supported the theory of a plane mixing layer type flow. An eddy-pair structure was revealed, the arrangement of which accounts for the intermittent strong downward sweeps of air into the canopy that have been reported by many observers. The large downward sweep of air was also shown to be responsible for the highest bending moments experienced by individual trees. Assessments of the different forest formations showed that in a forest consisting of a 50/50 mix of 200 mm and 100 mm model trees, gusts did not penetrate the lower forest. This arrangement should improve the protection of younger trees and may be worth investigating in field trials.