  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 man-made 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.
