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

TL;DR

This paper introduces a hybrid CNN and active contour method for accurately segmenting dead trees in aerial images, improving ecological monitoring and carbon stock estimation.

Contribution

It combines CNNs with a novel active contour model within an energy minimization framework, achieving higher accuracy than existing methods.

Findings

Superior precision, recall, and IoU in dead tree detection

Enhanced ecological and carbon stock monitoring capabilities

Effective for climate change impact assessment

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 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.