Title
Estimating Crown Variables of Individual Trees Using Airborne and Terrestrial Laser Scanners
Abstract
In this study, individual tree height (TH), crown base height (CBH), crown area (CA) and crown volume (CV), which were considered as essential parameters for individual stem volume and biomass estimation, were estimated by both an airborne laser scanner (ALS) and a terrestrial laser scanner (TLS). These ALS- and TLS-derived tree parameters were compared because TLS has been introduced as an instrument to measure objects more precisely. ALS-estimated TH was extracted from the highest value within a crown boundary delineated with the crown height model (CHM). The ALS-derived CBH of individual trees was estimated by k-means clustering method using the ALS data within the boundary. The ALS-derived CA was calculated simply with the crown boundary, after which CV was computed automatically using the crown geometric volume (CGV). On the other hand, all TLS-derived parameters were detected manually and precisely except the TLS-derived CGV. As a result, the ALS-extracted TH, CA, and CGV values were underestimated whereas CBH was overestimated when compared with the TLS-derived parameters. The coefficients of determination (R-2) from the regression analysis between the ALS and TLS estimations were approximately 0.94, 0.75, 0.69 and 0.58, and root mean square errors (RMSEs) were approximately 0.0184 m, 0.4929 m, 2.3216 m(2) and 13.2087 m(3) for TH, CBH, CA and CGV, respectively. Thereby, the error rate decreased to 0.0089, 0.0727 and 0.0875 for TH, CA and CGV via the combination of ALS and TLS data.
Year
DOI
Venue
2011
10.3390/rs3112346
REMOTE SENSING
Keywords
Field
DocType
airborne laser scanner,terrestrial laser scanner,digital canopy model,k-means clustering,crown geometric volume
k-means clustering,Laser scanning,Regression analysis,Remote sensing,Laser,Root mean square,Geology,Cluster analysis
Journal
Volume
Issue
ISSN
3
11
2072-4292
Citations 
PageRank 
References 
12
1.73
1
Authors
5
Name
Order
Citations
PageRank
sungeun jung1121.73
Doo-Ahn Kwak2122.07
Taejin Park318624.34
Woo-Kyun Lee4234.08
Seongjin Yoo5212.84