Centre de documentation
scientifique

Individual tree identification using a new cluster-based approach with discrete-return airborne LiDAR data / Haijian Liu in Remote sensing of environment, vol 258 (June 2021)  

Public [article]  inRemote sensing of environment > vol 258 (June 2021) . - n° 112382 Titre : Individual tree identification using a new cluster-based approach with discrete-return airborne LiDAR data Type de document : Article/Communication Auteurs : Haijian Liu, Auteur ; Pinliang Dong, Auteur ; Changshan Wu, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 112382 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] analyse de groupement [Termes IGN] arbre (flore) [Termes IGN] détection d'arbres [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] houppier [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] modèle numérique de surface de la canopée [Termes IGN] peuplement mélangé [Termes IGN] semis de points [Termes IGN] surveillance forestière [Termes IGN] Wisconsin (Etats-Unis) Résumé : (auteur) Individual tree identification is a key step for forest surveying and monitoring. To identify individual trees with airborne LiDAR data, a local maximum (LM) filter technique is typically performed. With LM, the highest point in a filtering window is generally considered to represent the tree position. This assumption, however, has great limitations, especially for mixed forests. To address this problem, we developed a new approach, the cluster center of higher points (CCHP), for tree position detection with LiDAR data. CCHP assumes that a tree position is located at the clustering center of higher points within a spatial neighborhood, and the center can be detected by a location-based recursive algorithm. The developed CCHP method was applied to a simulated forest and then verified in two real urban forests. In comparison with the variable window-sized LM filter method and layer stacking method, CCHP successfully identified 97% of trees in the simulated forest, while only 78% and 81% of the trees were recognized by LM and layer stacking methods respectively. The average absolute and relative offsets of CCHP are 0.33 m and 6.59%, respectively, and over 80% of the detected trees have an offset of less than 10% of the tree crown radius. CCHP also correctly detected 93.80% and 88.74% of individual trees in the first and second real forests, respectively, but the detection rates from the variable window-sized LM approach and layer stacking were less than 80%. In addition, the tree positions located by CCHP are considerably more accurate than the other two methods. Therefore, CCHP is proven to be promising for detecting individual tree positions from airborne LiDAR data for both simulated and real forests. Numéro de notice : A2021-443 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.rse.2021.112382 Date de publication en ligne : 06/03/2021 En ligne : https://doi.org/10.1016/j.rse.2021.112382 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97850 [article]

Incorporating crown shape information for identifying ash tree species / Haijian Liu in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 8 (août 2018)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 84 n° 8 (août 2018) . - pp 495 - 503 Titre : Incorporating crown shape information for identifying ash tree species Type de document : Article/Communication Auteurs : Haijian Liu, Auteur ; Changshan Wu, Auteur Année de publication : 2018 Article en page(s) : pp 495 - 503 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] Fraxinus (genre) [Termes IGN] fusion de données [Termes IGN] hauteur des arbres [Termes IGN] houppier [Termes IGN] image aérienne [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] Milwaukee [Termes IGN] photo-interprétation assistée par ordinateur [Termes IGN] zone urbaine Résumé : (Auteur) Identifying ash trees from other common deciduous trees is challenging due to subtle spectral differences of foliage among species. Although many researchers have integrated lidar-derived tree height and crown size metrics to improve tree species classification accuracy, these simple biophysical attributes provide inadequate explanatory power in distinguishing ash trees (Fraxinus, spp.) in urban ecosystems. To address this issue, shape-related features, including crown shape index (SI) and coefficient of variation (CV) of crown height, were extracted from lidar data, and fused with treetopbased spectra for ash tree species identification in Milwaukee City, Wisconsin, United States. Analysis results indicate shape features including SI and CV play a big role in improving the accuracy for ash tree identification. Specifically, Fusion of CV and treetop-based spectra improved the overall accuracy from 81.9 percent to 89 percent, and McNemar tests indicated the differences in accuracy between CV fusion and tree height fusion was statistically significant (p = 0.016). Numéro de notice : A2018-360 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.84.8.495 Date de publication en ligne : 01/08/2018 En ligne : https://doi.org/10.14358/PERS.84.8.495 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90600 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
105-2018081	RAB	Revue	Centre de documentation	En réserve L003	Disponible