sUAS for 3D vegetation mapping: lessons learned and best practices captured

Keywords: sUAS, personal remote sensing, sUAS point cloud, orthoimage, vegetation health
Session Type: Virtual Paper Abstract
Day: Saturday
Session Start / End Time: 2/26/2022 02:00 PM (Eastern Time (US & Canada)) - 2/26/2022 03:20 PM (Eastern Time (US & Canada))
Room: Virtual 5

Abstract

Rapid advancement of sUAS technology enables its quantitative application in public and private sectors. Defined as “personal remote sensing”, sUAS is superior to regular aerial/satellite remote sensing not only in its flexibility of data collection and the captured spatial details, but the capability of deriving both 3D information of the surveyed landscape. This study tests the feasibility of sUAS on extracting 3D vegetation structures and healthiness for management practices. A comprehensive experiment utilizing different drones, sensors and flight parameters is conducted on different sites (forest, grass, marsh). Three DJI drones: Mavic Pro, Phantom 4 Pro, and M100/RedEdge-M, were used to collect imagery in multiple missions in 2019-2020. Beyond the RGB observations from Phantom 4 Pro and Mavic Pro, the 5-band RedEdge camera can be radiometrically calibrated to extract surface reflectance. The comparative analysis reveals the RedEdge-M has higher spectral sensitivity and achieves better performance. Although at lower accuracy, the Phantom 4 Pro is recommended as an optimal drone for operational vegetation surveying due to its low cost and easy deployment. Results also show that the Normalized Difference Vegetation Index (NDVI) from the RedEdge orthoimage is prone to overestimation and saturation in high-biomass fields. Taking advantage of the camera’s red edge band centered at 717nm, the red edge NDVI (ReNDVI) can be better applied for healthiness assessment. This study reveals that sUAS has good potential of operationally deployment for best management solutions. With 3D imaging, sUAS remote sensing can be counted as a reliable, consumer-oriented tool of vegetation mapping.