Title
Estimation of Tree Lists from Airborne Laser Scanning Using Tree Model Clustering and k-MSN Imputation
Abstract
Individual tree crowns may be delineated from airborne laser scanning (ALS) data by segmentation of surface models or by 3D analysis. Segmentation of surface models benefits from using a priori knowledge about the proportions of tree crowns, which has not yet been utilized for 3D analysis to any great extent. In this study, an existing surface segmentation method was used as a basis for a new tree model 3D clustering method applied to ALS returns in 104 circular field plots with 12 m radius in pine-dominated boreal forest (64 degrees 14'N, 19 degrees 50'E). For each cluster below the tallest canopy layer, a parabolic surface was fitted to model a tree crown. The tree model clustering identified more trees than segmentation of the surface model, especially smaller trees below the tallest canopy layer. Stem attributes were estimated with k-Most Similar Neighbours (k-MSN) imputation of the clusters based on field-measured trees. The accuracy at plot level from the k-MSN imputation (stem density root mean square error or RMSE 32.7%; stem volume RMSE 28.3%) was similar to the corresponding results from the surface model (stem density RMSE 33.6%; stem volume RMSE 26.1%) with leave-one-out cross-validation for one field plot at a time. Three-dimensional analysis of ALS data should also be evaluated in multi-layered forests since it identified a larger number of small trees below the tallest canopy layer.
Year
DOI
Venue
2013
10.3390/rs5041932
REMOTE SENSING
Keywords
Field
DocType
LiDAR,ALS,3D analysis,individual trees,stem list
Laser scanning,Segmentation,Remote sensing,Decision tree model,Mean squared error,Lidar,Imputation (statistics),Cluster analysis,Statistics,Geology,Canopy
Journal
Volume
Issue
ISSN
5
4
2072-4292
Citations 
PageRank 
References 
6
0.90
8
Authors
5
Name
Order
Citations
PageRank
eva lindberg1264.41
Johan Holmgren211615.32
Kenneth Olofsson3364.92
Jörgen Wallerman4317.83
Håkan Olsson510215.93