In a recently published study, National Science Foundation-funded researchers paired satellite imaging data with machine learning techniques to map local tree species and health. The data generated by the project will help inform best practices for managing healthy green spaces as well as trimming programs to avoid power outages following storms. In the forest, a tree that is struck during a storm may just fall over without causing much disruption. When a tree falls in St. Louis, it can cause a power outage. Understanding the species and health condition of trees is important for safety and economic reasons. Millions of dollars are spent on tree trimming each year. The researchers used an airborne LiDAR (Light Detection and Ranging) system, imaging together with satellite imaging techniques to gather data about trees. Then they "trained" a machine-based analytic tool to identify trees based on that data. The researchers' next step will be to look at tree species, tree health data and utility information, overlaying the data and doing hotspot analysis, helping experts create maps of neighborhoods at risk of power outages during storms and helping to restore power quickly.
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