Consumer-grade Drone Utility for Post-fire Vegetation Monitoring at Piney Grove Preserve, Virginia, USA

Keywords: pine savanna, drone, photogrammetry, point cloud
Session Type: Virtual Poster Abstract
Day: Sunday
Session Start / End Time: 2/27/2022 05:20 PM (Eastern Time (US & Canada)) - 2/27/2022 06:40 PM (Eastern Time (US & Canada))
Room: Virtual 32

Abstract

In support of a managed burning program in a pine woodland/savanna preserve, we investigated the utility of small unpiloted aerial systems (sUAS or drones) to assess burn efficacy as viewed through the lens of understory vegetation observations. Fire management objectives at Piney Grove Preserve, owned and managed by The Nature Conservancy, include promoting a low-density woodland/savanna structure, pine sapling regeneration, and reducing certain understory hardwoods. Understory vegetation and regrowth of hardwoods can displace pine species and reduce habitat quality for the federally endangered red-cockaded woodpecker present at the site. We used fine-scale, orthoimagery (<5cm2) and 3-D elevation data produced by drone surveys, along with limited field observations and GPS-resolved ground control points, to reconstruct and classify the understory of a pine savanna ecosystem. Traditionally, such assessments have been accomplished through on-the-ground field analysis, but relatively sparse foliage of the woodland/savanna canopy allowed us to “see” through to the understory. We employed a combination of random-forest classification, point cloud analysis, and both leaf-on and leaf-off imagery in the analysis. Our goal was to establish a protocol for consumer-grade drone-based monitoring of post-fire understory vegetation recovery. Our work shows the potential for bypassing extensive, time-consuming fieldwork and data from more sophisticated multispectral and LiDAR sensors, by using consumer-grade sensors and an efficient and effective workflow that land managers can leverage with limited resources and time to monitor the effects of burning on pine savanna sites.