Estimating Forest Ground Vegetation Cover From Nadir Photographs Using Deep Convolutional Neural Networks (Papers Track)
Pranoy Panda (Indian Institute of Technology, Hyderabad); Martin Barczyk (University of Alberta); Jen Beverly (University of Alberta)
Forest fires, such as those on the US west coast in September 2020, are an important factor in climate change. Wildfire modeling and mitigation require mapping vegetation ground cover over large plots of land. The current forestry practice is to send out human ground crews to collect photos of the forest floor at precisely determined locations, then manually calculate the percent cover of ground fuel types. In this work, we propose automating this process using a supervised learning-based deep convolutional neural network to perform image segmentation. Experimental results on a real dataset show this approach delivers very promising performance.