A collaborative team of researchers led by Ben Weinstein of the University of Florida, Oregon, US, used machine learning to generate highly detailed maps of over 100 million individual trees from 24 sites across the U.S., publishing their findings July 16th in the open-access journal PLOS Biology. These maps provide information about individual tree species and conditions, which can greatly aid conservation efforts and other ecological projects.
Ecologists have long collected data on tree species to better understand a forest's unique ecosystem. Historically, this has been done by surveying small plots of land and extrapolating those findings, though this cannot account for the variability across the whole forest. Other methods can cover broader areas, but often struggle to categorize individual trees.
To generate large and highly detailed forest maps, the researchers trained a type of machine learning algorithm called a deep neural network using images of the tree canopy and other sensor data taken by plane. These training data covered 40,000 individual trees and, like all the data used in this study, were provided by the National Ecological Observatory Network.
The deep neural network was able to classify most common tree species with 75 to 85 percent accuracy. Additionally, the algorithm could also provide other important analyses, such as reporting which trees are alive or dead.
