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Mapping the height and spatial cover of features beneath the forest canopy at small-scales using airborne scanning discrete return Lidar / Matthew Sumnall in ISPRS Journal of photogrammetry and remote sensing, vol 133 (November 2017)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 133 (November 2017) . - pp 186 - 200 Titre : Mapping the height and spatial cover of features beneath the forest canopy at small-scales using airborne scanning discrete return Lidar Type de document : Article/Communication Auteurs : Matthew Sumnall, Auteur ; Thomas R. Fox, Auteur ; Randolph H. Wynne, Auteur ; Valerie A. Thomas, Auteur Année de publication : 2017 Article en page(s) : pp 186 - 200 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] couvert forestier [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] estimation statistique [Termes IGN] Etats-Unis [Termes IGN] hauteur des arbres [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] lidar à retour d'onde complète [Termes IGN] Pinophyta [Termes IGN] Pinus taeda [Termes IGN] sous-bois Résumé : (Auteur) The objective of the current study was to develop methods for estimating the height and horizontal coverage of the forest understorey using airborne Lidar data in three managed pine plantation forest typical of the south eastern USA. The current project demonstrates a two-step approach applied automatically across a given study site extent. The first operation divided the study site extent into a regularly spaced grid (25 × 25 m) and identified the potential height range of the main Loblolly pine canopy layer for each grid-cell through aggregating Lidar return height measurements into a ‘stack’ of vertical height bins describing the frequency of returns by height. Once height bins were created, the resulting vertical distributions were smoothed with a regression curve line function and the main canopy vertical layer was identified through the detection of local maxima and minima. The second operation sub-divided the 25 × 25 m grid-cell into 1 × 1 m horizontal grid, for which height-bin stacks were created for each cell. Vertical features below the main canopy were then identified at this scale in the same manner as in the previous step, and classified as understorey features if they were lower in height than the 25 × 25 m estimate of the main canopy layer. The heights of the tallest understorey and sub-canopy layers were kept, and used to produce a rasterized map of the understorey layer height at the 1 × 1 m scale. Lidar derived estimates of the 25 × 25 m lowest vertical extent of the coniferous canopy correlated highly with field data (R2 0.87; RMSE 2.1 m). Estimates of understorey horizontal cover ranged from R2 0.80 to 0.90 (RMSE 6.6–11.7%), and maximum understorey layer height ranged from R2 0.69 to 0.80 (RMSE 1.6–3.4 m) for the three study sites. The automated method deployed within the current study proved sufficient in determining the presence and absence of vegetation and artificial structures within the understorey portion of the current forest context, in addition to height and horizontal cover to a reasonable accuracy. Issues were encountered within older stands (e.g. more than 30 years old) where understorey deciduous vegetation layers intersected with the coniferous canopy layer, resulting in an underestimation of sub-dominant heights. Numéro de notice : A2017-726 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.10.002 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.10.002 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88411 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2017111	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2017112	DEP-EAF	Revue	Nancy	Dépôt en unité	Exclu du prêt
081-2017113	DEP-EXM	Revue	Saint-Mandé	Dépôt en unité	Exclu du prêt

Approximating prediction uncertainty for random forest regression models / John W. Coulston in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 3 (March 2016)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 82 n° 3 (March 2016) . - pp 189 - 197 Titre : Approximating prediction uncertainty for random forest regression models Type de document : Article/Communication Auteurs : John W. Coulston, Auteur ; Christine E. Blinn, Auteur ; Valerie A. Thomas, Auteur ; Randolph H. Wynne, Auteur Année de publication : 2016 Article en page(s) : pp 189 - 197 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] incertitude des données [Termes IGN] méthode de Monte-Carlo [Termes IGN] modèle de régression [Termes IGN] prédiction [Termes IGN] variable Résumé : (auteur) Machine learning approaches such as random forest have increased for the spatial modeling and mapping of continuous variables. Random forest is a non-parametric ensemble approach, and unlike traditional regression approaches there is no direct quantification of prediction error. Understanding prediction uncertainty is important when using model-based continuous maps as inputs to other modeling applications such as fire modeling. Here we use a Monte Carlo approach to quantify prediction uncertainty for random forest regression models. We test the approach by simulating maps of dependent and independent variables with known characteristics and comparing actual errors with prediction errors. Our approach produced conservative prediction intervals across most of the range of predicted values. However, because the Monte Carlo approach was data driven, prediction intervals were either too wide or too narrow in sparse parts of the prediction distribution. Overall, our approach provides reasonable estimates of prediction uncertainty for random forest regression models. Numéro de notice : A2016-176 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.82.3.189 En ligne : https://doi.org/10.14358/PERS.82.3.189 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80506 [article]