Centre de documentation
scientifique

Estimating forest biomass using small footprint LiDAR data: An individual tree-based approach that incorporates training data / Z.J. Bortolot in ISPRS Journal of photogrammetry and remote sensing, vol 59 n° 6 (November 2005)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 59 n° 6 (November 2005) . - pp 342 - 360 Titre : Estimating forest biomass using small footprint LiDAR data: An individual tree-based approach that incorporates training data Type de document : Article/Communication Auteurs : Z.J. Bortolot, Auteur ; R. Wynne, Auteur Année de publication : 2005 Article en page(s) : pp 342 - 360 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] couvert forestier [Termes IGN] données de terrain [Termes IGN] données lidar [Termes IGN] forêt tempérée [Termes IGN] houppier [Termes IGN] lasergrammétrie [Termes IGN] masse végétale [Termes IGN] optimisation (mathématiques) [Termes IGN] Pinus taeda [Termes IGN] régression [Termes IGN] sylviculture [Termes IGN] Virginie (Etats-Unis) Résumé : (Auteur) A new individual tree-based algorithm for determining forest biomass using small footprint LiDAR data was developed and tested. This algorithm combines computer vision and optimization techniques to become the first training data-based algorithm specifically designed for processing forest LiDAR data. The computer vision portion of the algorithm uses generic properties of trees in small footprint LiDAR canopy height models (CHMs) to locate trees and find their crown boundaries and heights. The ways in which these generic properties are used for a specific scene and image type is dependent on 11 parameters, nine of which are set using training data and the Nelder-Mead simplex optimization procedure. Training data consist of small sections of the LiDAR data and corresponding ground data. After training, the biomass present in areas without ground measurements is determined by developing a regression equation between properties derived from the LiDAR data of the training stands and biomass, and then applying the equation to the new areas. A first test of this technique was performed using 25 plots (radius = 15 m) in a loblolly pine plantation in central Virginia, USA (37.42N, 78.68W) that was not intensively managed, together with corresponding data from a LiDAR canopy height model (resolution = 0.5 m). Results show correlations (r) between actual and predicted aboveground biomass ranging between 0.59 and 0.82, and RMSEs between 13.6 and 140.4 t/ha depending on the selection of training and testing plots, and the minimum diameter at breast height (7 or 10 cm) of trees included in the biomass estimate. Correlations between LiDAR-derived plot density estimates were low (0.22 Numéro de notice : A2005-490 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2005.07.001 En ligne : https://doi.org/10.1016/j.isprsjprs.2005.07.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27626 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-05041	SL	Revue	Centre de documentation	Revues en salle	Disponible

Seeing the trees in the forest: Using Lidar and multispectral data fusion with local filtering and variable window size for estimating tree height / S.C. Pospecu in Photogrammetric Engineering & Remote Sensing, PERS, vol 70 n° 5 (May 2004)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 70 n° 5 (May 2004) . - pp 589 - 604 Titre : Seeing the trees in the forest: Using Lidar and multispectral data fusion with local filtering and variable window size for estimating tree height Type de document : Article/Communication Auteurs : S.C. Pospecu, Auteur ; R. Wynne, Auteur Année de publication : 2004 Article en page(s) : pp 589 - 604 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] couvert forestier [Termes IGN] données lidar [Termes IGN] estimation statistique [Termes IGN] feuillu [Termes IGN] fusion d'images [Termes IGN] hauteur des arbres [Termes IGN] identification automatique [Termes IGN] image multibande [Termes IGN] modèle de régression [Termes IGN] Pinus (genre) Résumé : (Auteur) The main study objective was to develop robust processing and analysis techniques to facilitate the use of small-footprint lidar data for estimating plot-level tree height by measuring individual trees identifiable on the three-dimensional lidar surface. Lidar processing techniques included data fusion with multispectral optical data and local filtering with both square and circular windows of variable size. The lidar system used for this study produced an average footprint of 0.65 m and an average distance between laser shots of 0.7 m. The lidar data set was acquired over deciduous and coniferous stands with settings typical of the southeastern United States. The lidar-derived tree measurements were used with regression models and cross-validation to estimate tree height on 0.017-ha plots. For the pine plots, lidar measurements explained 97 percent of the variance associated with the mean height of dominant trees. For deciduous plots, regression models explained 79 percent of the mean height variance for dominant trees. Filtering for local maximum with circular windows gave better fitting models for pines, while for deciduous trees, filtering with square windows provided a slightly better model fit. Using lidar and optical data fusion to differentiate between forest types provided better results for estimating average plot height for pines. Estimating tree height for deciduous plots gave superior results without calibrating the search window size based on forest type. Numéro de notice : A2004-181 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.14358/PERS.70.5.589 En ligne : https://doi.org/10.14358/PERS.70.5.589 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26708 [article]