AI and deep learning will transform forest inventory tasks to be more accurate, scalable, and accessible, supporting sustainable resource management.
Forests play a critical role in climate change adaptation due to their ability to trap and store carbon. To determine a forest’s capacity for carbon sequestration, it is important to inventory and monitor forested areas regularly. Tree species classification is a vital component of forest management and can assist with calculating carbon sequestration potential.
In-person monitoring of forests can be difficult, especially in remote locations or large areas. Remote sensing techniques have been proven effective at assisting with forest management, notably LiDAR (Light Detection and Ranging). LiDAR uses a pulsed laser to measure ranges, which results in a three-dimensional point cloud that can be used for data analysis. When LiDAR data is collected over a large area with an aircraft operating at a high elevation, the density of the point cloud can be sparse. These datasets can be difficult to conduct accurate individual tree-level species classification.
Lanying Wang, a PhD candidate in the Department of Geography and Environmental Management, is combining remotely sensed data and deep learning (DL) models to improve data accuracy and applicability.
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