Document Type
Technical Report
Publication Date
9-28-2022
Department
College of Forest Resources and Environmental Science
Abstract
Description:
A detailed workflow using Structure from Motion (SfM) techniques for processing high-resolution Unmanned Aerial System (UAS) NIR and RGB imagery in a dense forest environment where obtaining control points is difficult due to limited access and safety issues.
Abstract:
Imagery collected via Unmanned Aerial System (UAS) platforms has become popular in recent years due to improvements in a Digital Single-Lens Reflex (DSLR) camera (centimeter and sub-centimeter), lower operation costs as compared to human piloted aircraft, and the ability to collect data over areas with limited ground access. Many different application (e.g., forestry, agriculture, geology, archaeology) are already using and utilizing the advantages of UAS data. Although, there are numerous UAS image processing workflows, for each application the approach can be different. In this study, we developed a processing workflow of UAS imagery collected in a dense forest (e.g., coniferous/deciduous forest and contiguous wetlands) area allowing users to process large datasets with acceptable mosaicking and georeferencing errors. Imagery was acquired with near-infrared (NIR) and red, green, blue (RGB) cameras with no ground control points. Image quality of two different UAS collection platforms were observed. Agisoft Metashape, a photogrammetric suite, which uses SfM (Structure from Motion) techniques, was used to process the imagery. The results showed that an UAS having a consumer grade Global Navigation Satellite System (GNSS) onboard had better image alignment than an UAS with lower quality GNSS.
Recommended Citation
Bhatt, P.,
Edson, C.,
&
Maclean, A.
(2022).
Image Processing in Dense Forest Areas using Unmanned Aerial System (UAS).
http://doi.org/10.37099/mtu.dc.michigantech-p/16366
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/16366
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Publisher's Statement
This white paper has been published under a CC BY NC ND 4.0 license.