ASSESSING RED PINE SEEDLINGS USING UAV POINT CLOUDS AND FIELD-VERIFIED DATA

Abstract

Accurate, reliable, and cost-efficient approaches to forest monitoring are critical for sustainable forest management. The use of digital photogrammetry for tree height estimation is well-known among forest managers and remote sensing researchers. Satellite remote sensing has not been very successful in providing detailed and reliable estimates of tree height. Unmanned Aerial Vehicles (UAVs) are one of the latest remote sensing platforms to get forest attributes information at very high temporal and spatial resolution. This study assessed the potential of using digital aerial photogrammetry point clouds and UAV acquired high-resolution imagery to estimate red pine seedlings' height in Adirondacks, New York. Seedling's location, height, crown width, and diameter were measured from 16 fixed area sample plots, and multispectral imagery was acquired with DJI Matrice 100- UAV fitted with Micasense RedEdge-M camera. UAV was flown under clear sky conditions at 93-meter height in a single grid pattern with 80% front and side overlap. PIX4D software was used to process UAV multispectral imagery and generate Digital Surface Model (DSM) and Orthomosiac at 6.08 cm/pixel resolution along with 3D Digital Terrain Model (DTM). 3D densified point cloud layers of regeneration canopy were generated at an average density of 1.54 per m3. Seedlings were differentiated from bare ground cover through supervised image classification methods. Preliminary results of this study highlight that multispectral imagery acquired from UAVs has the potential to characterize and provide detailed structural information to estimate red pine seedlings' height.