Développement et utilisation des outils d'analyse LiDAR pour la cartographie des gisements forestiers et l'évaluation des volumes sur pied en zone de montagne

[Departement_IRSTEA]Territoires [TR1_IRSTEA]SEDYVINCe projet 2 (convention cadre ONF-Cemagref, télédétection, sous-action 1) constitue le volet appliqué du travail de recherche effectué par Jean-Matthieu Monnet dans le cadre d'une thèse subventionnée par la région Rhône-Alpes. Un certain nombre d'outils ont été développés et testés sur des surfaces réduites. Cette action conjointe Cemagref-ONF est motivée par l'application de ces outils à une plus grande variété de peuplements forestiers d'intérêt et par leur utilisation sur des données à grande échelle, ce qui pose plusieurs questions théoriques et pratiques : - le choix de la méthode de paramétrage des algorithmes permettant d'obtenir une performance convenable sans passer par une longue et coûteuse phase de calibration ; - la fiabilité des résultats obtenus à grande échelle, qui passe par une complémentarité à définir entre les méthodes de relevés terrain et le traitement de données LiDAR; - la capacité des algorithmes (et logiciels) à traiter un volume de données considérable (environ 90 millions de points pour la vallée de Chamonix). La question du niveau d'analyse et de rendu de l'information permettant une lecture optimale pour l'utilisateur final qu'est le gestionnaire forestier doit également rester un objectif primordial. Ce rapport s'organise en quatre parties : - rappel du contexte et des enjeux liés au LiDAR pour les forêts de montagne (chapitre 1) ; - présentation de la méthodologie et des résultats obtenus par une approche arbre (chapitre 2) ; - présentation de la méthodologie et des résultats obtenus par une approche surfacique sur des taillis de montagne (chapitre 3) ; - rapide bilan et quelques perspectives concernant le travail effectué (chapitre 4)

Lidar aéroporté et modélisation spatiale pour la prévention du risque de chutes de bloc

[Departement_IRSTEA]Territoires [TR1_IRSTEA]SEDYVINNational audienceLes données laser aéroporté à haute résolution permettent désormais de modéliser finement la propagation des blocs rocheux en intégrant les effets de la topographie et de la végétation

Evaluation of a semi-automated approach for the co-registration of forest inventory plots and airborne laser scanning data

[Departement_IRSTEA]Territoires [TR1_IRSTEA]SEDYVINInternational audienceContinuous maps of forest parameters can be derived from Airborne Laser Scanning (ALS) data with the so-called area-based method. A prediction model is calibrated between local ALS statistics and forest parameters measured on field sampling plots. Unfortunately, inaccurate GPS positioning often leads to a bad matching of forest measures with ALS data, which results in poor calibration datasets. This article has two main objectives: first to present and test a novel semi-automated co-registration approach, and second to evaluate how the number of positioned trees per plot affects the efficiency of co-registration. The co-registration is based on the computation of correlation coefficients between the ALS-derived canopy height model and the rasterized forest plot map for different offsets applied to the GPS position. The plot map raster is computed by affecting to each pixel the height or diameter of the tree located in this pixel. Others pixels are set to zero. The final offset to be applied to plot coordinates is the one which results in the highest correlation coefficient. The validation dataset is composed of 139 17m-radius plots located in a 1000-ha forest of the Jura mountain (France). The algorithm was used to search the best offset within a 40m square centered on the GPS position for each plot. The resulting co-registration was evaluated visually. 127 plots (91.4%) were successfully co-registered by the algorithm. Six (resp. two) additional plots were correctly co-registered when the searched window was increased to 80 (resp. 200) meters. Three of the four remaining plots could be manually co-registered. For two of them the algorithm also found the correct position when trees felled between the inventory and the ALS flight were removed from the inventory. For the 138 identified plots, mean distance between the GPS and the validated position was 9.0 ± 8.7 m. Bootstrap cross-validations showed that the area-based prediction model for basal area calibrated with the validated positions was significantly better than when calibrated with the GPS positions. Root mean square errors were respectively 5.84 ± 0.04 and 6.94 ± 0.06 m²/ha. The influence of the number of georeferenced trees on the efficiency of automatic co-registration was assessed by including in the field plot inventory only the N largest or nearest trees, with N varying from 3 to 15. The search window was set to 40m around the validated position and a plot was considered successfully matched when the distance between the new and the validated position was less than two meters. When the three largest trees were used, 89% of the 138 plots were correctly co-registered. This proportion raised to 100% when six trees were used, but decreased to 99% when more than 14 trees were included. Using the N nearest trees yielded a lower proportion of correct co-registration with only 86% for 15 trees. These results show that the proposed approach is efficient and robust, as a high proportion of plots are properly co-registered with only diameter information of a small number of big trees. This semi-automatic method could thus contribute to better calibrated ALS prediction models while saving inventory time on the field. Future developments include the automatic assessment of co-registration liability, based on the distribution of correlation coefficients within the search windows, and the evaluation of the effect of plot radius on the accuracy of co-registration

Applications forestières du scanner laser aéroporté : état de l'art

[Departement_IRSTEA]Territoires [TR1_IRSTEA]SEDYVINCe rapport scientifique dresse un état des lieux des connaissances sur l'utilisation du scanner laser aéroporté pour les applications forestières

Cartographie de l'Erable américain dans les ripisylves insulaires de Loire

National audienceThis presentation presents the results of a study to automatically detect American Maple from Lidar and multispectral data. The model selects the standard deviation of the heights and the vegetation index Red / (Green + Blue). Acer negundo is detected at over 90%.Cette présentation retrace les résultats d'une étude visant à détecter automatiquement l'Erable américain à partir de données Lidar et multispectrales. Le modèle sélectionne l'écart-type des hauteurs et de l'indice Rouge/(Vert+Bleu). Acer negundo est détecté à plus de 90 %

Corrélation de mesures de diamètre pour le géoréférencement de placettes d'inventaire forestier avec des données de scanner laser aéroporté

[Departement_IRSTEA]Territoires [TR1_IRSTEA]SEDYVINInternational audienceContinuous 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

Utilisation de métriques LIDAR orienté-objet pour caractériser les habitats d'oiseaux dans une perspective de gestion.

International audienceAdvances in remote sensing technologies are today making it essential for ecological modeling improvement. One of the most promising technique is Light Detection and Ranging (LiDAR) which provides accurate and highly precise data of the three dimensional structure of the environment. First, LiDAR was mostly used to better characterize forest stand (estimation of stem density, basal area) and was mainly used by forestry industries. However, nowadays LiDAR is increasingly used to improve habitat modeling for a wide variety of species including birds (Kathleen M. Bergen, Gilboy, and Brown 2007; K. M. Bergen et al. 2009). Especially it could be used to improve Species Distribution Models (SDMs) accuracy (He et al. 2015; Tattoni, Rizzolli, and Pedrini 2012). To obtain a good predicting model, it is acknowledged that we should use metrics that are meaningful from the species point of view and will accordingly explain best their distribution within the landscape (Johnson and Gillingham 2005). However, is obtaining a model that predicts well the habitat suitability of a species enough to impact deeply local conservation actions? In order to take appropriate and more efficient management decisions, we believe that the metrics explaining the species distribution must also be meaningful by for managers. Indeed, if some LiDAR metrics such as canopy cover (Graf et al. 2007), can be well understood , most of LiDAR extracted metrics used so far in SDMs such as the standard-deviation of penetration ratio between 0.5-10m (Bae et al. 2014) or the proportion of echo above five meters (Melin et al. 2016) are not easy to interpret once on the field. Therefore, the aim of this study is to improve forest management actions planning by using appropriate LiDAR predictors for both the species and managers. We are here focusing on the case of an avian species of conservation concern occurring in the French Jura Mountains: the Capercaillie (Tetrao urogallus). Capercaillies favor old mixed forest constituted of a mosaic of structurally different habitat (gap openings, moderate canopy cover area, isolated resting trees, presence of shelters) and is threatened mostly by habitat degradation and loss. Habitat restoration planning is then a fundamental point of the species conservation actions. To achieve our objectives we extracted numerous oriented-object metrics from LiDAR datasets, defined with the help of expert and forest managers. Habitat suitability models using maximum entropy methods (Phillips, Anderson, and Schapire 2006) will be compared using both commonly used “points clouds LiDAR metrics” and new “object-oriented LiDAR metrics”. Preliminary results show that both categories of metrics give quietly accurate predicting models of Capercaillie habitat suitability. Thus, we hope that the use of object-oriented variable over a large area will allow a wide diffusion of the different results and help future forest management planning in favor of Capercaillie conservation

Cartographie forestière avec des données nationales de lidar aéroporté et d'inventaire forestier

International audienceAirborne laser scanning (ALS) remote sensing data are now available for entire countries such as Switzerland. Methods for the estimation of forest parameters from ALS have been intensively investigated in the past years. However, the implementation of a forest mapping workflow based on available data at a regional level still remains challenging. A case study was implemented in the Canton of Valais (Switzerland). The national ALS dataset and field data of the Swiss National Forest Inventory were used to calibrate estimation models for mean and maximum height, basal area, stem density, mean diameter and stem volume. When stratification was performed based on ALS acquisition settings and geographical criteria, satisfactory prediction models were obtained for volume (R2 = 0.61 with a root mean square error of 47 %) and basal area (respectively 0.51 and 45 %) while height variables had an error lower than 19%. This case study shows that the use of nationwide ALS and field datasets for forest resources mapping is cost efficient, but additional investigations are required to handle the limitations of the input data and optimize the accuracy