Title of the paper [14pt Times New Roman, bold

SilviLaser 2013, October 9-11, 2013 –Beijing, China
Ensemble classification of tree genera using airborne LiDAR point clouds
Connie Ko1, Gunho Sohn2, Tarmo K. Remmel3 & John R. Miller4
Earth and Space Science and Engineering, York University, 4700 Keele Street, Toronto,
Ontario, M3J 1P3, Canada, Tel: 416-736-2100 x20290, Fax: 416-736-5988;
GeoICT Lab; Earth and Space Science and Engineering Department, York University,
Toronto, Canada, gsohn@yorku.ca
Department of Geography, York University, 4700 Keele Street, Toronto, Ontario, M3J
1P3, Canada, Tel: 416-736-2100 x22496, Fax: 416-736-5988; remmelt@yorku.ca
Ensemble classification is a classification strategy for combining two or more base classifiers in
one of two architectures: 1) sequential ensemble classification, or 2) parallel ensemble
classification. A sequential ensemble classifier makes the final decision about the assigned class
label by combining a sequence of base classifiers where information from one classifier will be
able to only transmit to the successive one. Conversely, a parallel ensemble classifier makes the
final decision by considering results from the base classifiers collaboratively. The objective of
this paper is to classify tree genera into 1) pine, 2) poplar, 3) maple and 4) other (which contains
birch, spruce, oak and larch) from LiDAR point clouds. We built three binary base classifiers
from 30% of the data (for training purpose) and leave 70% of the data for validation purpose.
This simple scheme of breaking down a classification problem into k smaller problem is known
as a one-versus-all (OVA) approach where each classifier will distinguish a single class from the
other instances. We then combine the three classifiers in parallel and sequentially; all
arrangements of the base classifier sequence are considered. The base classifiers used in this
paper are based on the Random Forests algorithm, working with six features derived from
LiDAR data related to the geometry of the tree. Random Forests itself is an ensemble classifier
combining multiple classification trees for categorical classification. In this paper we argue this
approach is better than a single classifier approach because each classifier will only need to
focus on one class (simple); also, another class label can easily be added by adding another base
classifier rather than rebuilding the entire classification model. Also, this method can
accommodate the “other” class label that is often problematic. We apply and discuss both
ensemble architectures and compare them with a regular Random Forests classification where
the “other” class cannot be predicted since it does not exist in the training data. Our study site is
located north of Thessalon, Ontario (east of Sault Ste. Marie), Canada, and spans a major utility
corridor right of way and seven surrounding woodlots. LiDAR data was collected by a Riegl
LMS-Q560 scanner with original point density of 40 pulses per m2, however we iteratively
reduce the point density to 20, 10, and 5 pulses per m2 to assess the sensitivity of LiDAR point
density on classification accuracy.