recommended, to maximize the utility of this method. Future
efforts will focus on identifying tiny twigs and branches by
testing various circle-fitting procedures (Simonse
et al
. 2003;
Mass
et al
., 2008). We believe that the intensity approach has
great potential for wood-leaf separation if multi-band lidar is
available (Kaasalainen
et al.
, 2007; Chen
et al
., 2010).
Conclusions
The objective of the present research was to introduce a geo-
metric method for wood-leaf separation, using only the
x-
,
y-
,
and
z-
coordinates of each point. In the proposed method, the
tree is sliced horizontally, and then geometric primitives, i.e.,
circles, circle-like shapes such as arcs and incomplete circles,
and line segments, are detected from each sliced bin. We
found that thresholding the sizes of these geometric primi-
tives enabled wood points to be extracted from the raw point
cloud. Our method performed well for broad-leaved trees of
different sizes and heights. First-, second-, and third-order
branches were extracted from the raw point cloud with high
accuracy, but tiny twigs shaded by leaf clusters were misclas-
sified as leaves. A comparison of the results obtained using
our method and those obtained using the intensity approach
suggested that our method is superior; it produced a Cohen’s
kappa coefficient ranging from 0.80 to 0.90. Future efforts will
be made to improve the detection rate for tiny twigs, and to
test the robustness of the method using more trees.
Acknowledgments
We are grateful for the constructive comments from the
anonymous reviewers of an earlier version of the manuscript.
This study is partially supported by NSF (DBI 1356077) and
the National Science Foundation of China (No. 31270563 and
41471363).
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