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Tis the season to be… Sneezing: 4 ways winter air quality…New Web Hosting Pioneer Emerges, Offering Affordable Hosting Solutions Coupled with…Scientists have used high-resolution satellite images to create a map of global canopy heights, and to also develop an AI model that can predict canopy heights.
Canopy height is an important indicator of forest biomass and aboveground carbon stock, and is used to measure the progress of forest restoration efforts. “We wanted to see how spatially granular we can get for canopy height data,” John Brandt, data science associate at WRI, told Mongabay in a video interview. “Our hope is to help in the monitoring of systems that aren’t well-served by forest structure data that currently exists in the Earth observation community.”
“If you look at traditional forest carbon data, it makes sense for some kinds of forests,” Brandt said. “If you are looking at an agroforestry system or a dryland forest or a really heterogeneous forest, it won’t tell you much about carbon storage because, well, it doesn’t tell you the canopy height distribution within a coarse pixel.”
The process of training the model consisted of two steps: first, a self-supervised learning stage, where the model trained itself using the input data; followed by a fine-tuning stage where the team fed the model publicly available lidar data on canopy heights in the United States. In the second stage, “the model is essentially shown an RGB image of a region, and the corresponding lidar height map, and it learns the relationship between the two,” Tolan said.
Brandt said both the model and the global map the team produced can be used in different applications. “You can use the map where the existing data is of good quality,” he said. “Where it’s not, you can use the model. You can take field data and, with 10 or 15 field data points, train the canopy height model to get really good predictions.”
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