Detecting Dead Standing Eucalypt Trees from Voxelised Full-Waveform Lidar Using Multi-Scale 3D-Windows for Tackling Height and Size Variations
Journal
Forests
Date Issued
February 1, 2020
DOI
10.3390/f11020161
Abstract
In southern Australia, many native mammals and birds rely on hollows for sheltering,
while hollows are more likely to exist on dead trees. Therefore, detection of dead trees could be
useful in managing biodiversity. Detecting dead standing (snags) versus dead fallen trees (Coarse
Woody Debris—CWD) is a very different task from a classification perspective. This study focuses
on improving detection of dead standing eucalypt trees from full-waveform LiDAR. Eucalypt trees
have irregular shapes making delineation of them challenging. Additionally, since the study area is a
native forest, trees significantly vary in terms of height, density and size. Therefore, we need methods
that will be resistant to those challenges. Previous study showed that detection of dead standing
trees without tree delineation is possible. This was achieved by using single size 3D-windows to
extract structural features from voxelised full-waveform LiDAR and characterise dead (positive
samples) and live (negative samples) trees for training a classifier. This paper adds on by proposing
the usage of multi-scale 3D-windows for tackling height and size variations of trees. Both the
single 3D-windows approach and the new multi-scale 3D-windows approach were implemented
for comparison purposes. The accuracy of the results was calculated using the precision and recall
parameters and it was proven that the multi-scale 3D-windows approach performs better than the
single size 3D-windows approach. This open ups possibilities for applying the proposed approach
on other native forest related applications.
while hollows are more likely to exist on dead trees. Therefore, detection of dead trees could be
useful in managing biodiversity. Detecting dead standing (snags) versus dead fallen trees (Coarse
Woody Debris—CWD) is a very different task from a classification perspective. This study focuses
on improving detection of dead standing eucalypt trees from full-waveform LiDAR. Eucalypt trees
have irregular shapes making delineation of them challenging. Additionally, since the study area is a
native forest, trees significantly vary in terms of height, density and size. Therefore, we need methods
that will be resistant to those challenges. Previous study showed that detection of dead standing
trees without tree delineation is possible. This was achieved by using single size 3D-windows to
extract structural features from voxelised full-waveform LiDAR and characterise dead (positive
samples) and live (negative samples) trees for training a classifier. This paper adds on by proposing
the usage of multi-scale 3D-windows for tackling height and size variations of trees. Both the
single 3D-windows approach and the new multi-scale 3D-windows approach were implemented
for comparison purposes. The accuracy of the results was calculated using the precision and recall
parameters and it was proven that the multi-scale 3D-windows approach performs better than the
single size 3D-windows approach. This open ups possibilities for applying the proposed approach
on other native forest related applications.
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