Qin Y., Ferraz A., Mallet C., Iovan Corina. (2015). Individual tree segmentation over large areas using airborne LiDAR point cloud and very high resolution optical imagery. In :
Multitemp 2015. IEEE, p. 800-803. International Workshop on the Analysis of Multitemporal Remote Sensing Images, 8., Annecy (FRA), 2015/07/22-24. ISBN 978-1-4673-7119-3.
Titre du document
Individual tree segmentation over large areas using airborne LiDAR point cloud and very high resolution optical imagery
Année de publication
2015
Type de document
Partie d'ouvrage
Auteurs
Qin Y., Ferraz A., Mallet C., Iovan Corina
In
Multitemp 2015
Source
IEEE, 2015,
p. 800-803 ISBN 978-1-4673-7119-3
Colloque
International Workshop on the Analysis of Multitemporal Remote Sensing Images, 8., Annecy (FRA), 2015/07/22-24
Timely and accurate measuremen ts of forest parameters are critical for ecosystem studies, sustainable forest resources management, monitoring and planning. This paper presents a processing chain for individual tree segmentation over large areas with airborne LiDAR 3D point cloud and very high resolution (VHR) optical imagery. The proposed processing chain consists of fo rest stand level delineation with optical imagery, individual tree segmentation with Canopy Height Model (CHM) derived from LiDAR point cloud, rough characterization of trees at forest stand level, and point clustering of individual tree with an Adaptive Mean Shift 3D (AMS3D) algorithm. The processing chain is developed with the expectation of supporting operational forest inventory at individual tree level. Experiment is conducted using LiDAR data acquired in Ventoux region, France. Results suggest that the proposed processing chain can be successfully adopted for individual tree characterization over large areas with different forest stands.
Plan de classement
Etudes, transformation, conservation du milieu naturel [082]
;
Télédétection [126]
