Drone-Based LiDAR for High-Resolution Forest Modeling
Fisheries and Aquatic Sciences Tate Bruckhart
Light Detection and Ranging (LiDAR) is an evolving technology that allows expedient construction of accurate 3D models up to the landscape level. This technology has previously been applied to forestry research to produce metrics such as canopy cover, complexity metrics, and canopy height maps by utilizing data available from state and federal agencies such as USGS. In this preliminary study we aim to prove the feasibility of using drone (UAS) based LiDAR sensors to provide useful and cost-effective high-resolution models of forestry units. Traditional LiDAR data is usually collected by a fixed wing aircraft, and research with this data is often done using cells as large as 30m x 30m due to the coarse nature of the data, making it difficult to accurately assess finer aspects of the forest. The UAV-sensed LiDAR data produced for this project allows us to work on a sub-meter scale. The extremely high resolution of our data allows us to more accurately assess forest structure, canopy cover, and identify individual trees. We provide an overview of the workflow for collecting and processing LiDAR data using UAS, including ArcGIS, R, and proprietary industry software. We aim to demonstrate a sample of the deliverables we have produced thus far, and explore potential future uses in forest management and research, from stand thinning to fire ecology to wildlife habitat identification and restoration.
Advisors: Michael Tiller Ph.D.
Subject Area: Forestry
Type: Poster
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Jim and Katie Krause CNR Student Research Symposium
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