A hybrid convolutional neural network/active contour approach to segmenting dead trees in aerial imagery

Jacquelyn A Shelton, Przemyslaw Polewski, Wei Yao

Abstract:

The stability and ability of an ecosystem to withstand climate change is directly
linked to its biodiversity. Dead trees are a key indicator of overall forest health,
housing one-third of forest ecosystem biodiversity, and constitute 8% of the global
carbon stocks. They are decomposed by several natural factors, e.g. climate, insects
and fungi. Accurate detection and modeling of dead wood mass is paramount to
understanding forest ecology, the carbon cycle and decomposers. We present
a novel method to construct precise shape contours of dead trees from aerial
photographs by combining established convolutional neural networks with a novel
active contour model in an energy minimization framework. Our approach yields
superior performance accuracy over state-of-the-art in terms of precision, recall,
and intersection over union of detected dead trees. This improved performance is
essential to meet emerging challenges caused by climate change (and other manmade perturbations to the systems), particularly to monitor and estimate carbon
stock decay rates, monitor forest health and biodiversity, and the overall effects of
dead wood on and from climate change