  Developing a robust algorithm for automatic individual tree crown (ITC)
detection from laser scanning datasets is important for tracking the responses
of trees to anthropogenic change. Such approaches allow the size, growth and
mortality of individual trees to be measured, enabling forest carbon stocks and
dynamics to be tracked and understood. Many algorithms exist for structurally
simple forests including coniferous forests and plantations. Finding a robust
solution for structurally complex, species-rich tropical forests remains a
challenge; existing segmentation algorithms often perform less well than simple
area-based approaches when estimating plot-level biomass. Here we describe a
Multi-Class Graph Cut (MCGC) approach to tree crown delineation. This uses
local three-dimensional geometry and density information, alongside knowledge
of crown allometries, to segment individual tree crowns from LiDAR point
clouds. Our approach robustly identifies trees in the top and intermediate
layers of the canopy, but cannot recognise small trees. From these
three-dimensional crowns, we are able to measure individual tree biomass.
Comparing these estimates to those from permanent inventory plots, our
algorithm is able to produce robust estimates of hectare-scale carbon density,
demonstrating the power of ITC approaches in monitoring forests. The
flexibility of our method to add additional dimensions of information, such as
spectral reflectance, make this approach an obvious avenue for future
development and extension to other sources of three-dimensional data, such as
structure from motion datasets.

Computer Vision and Pattern Recognition;Machine Learning