Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Author ORCID Identifier

  1. https://orcid.org/0000-0003-1974-7529
  2. https://orcid.org/0000-0002-4606-5630
  3. https://orcid.org/0000-0003-3891-2163

Abstract

The pairing of Unpiloted Aerial Systems (UAS) and Structure from Motion (SfM) has provided new capabilities for modeling freshwater environments. Applications of UAS-SfM range from water quality monitoring to the mapping of aquatic vegetation. The models produced provide users with the ability to analyze features at ultra-high-resolutions and across scales not easily achieved through in situ sampling. Despite the demonstrated benefits of UAS-SfM in freshwater and other natural resource disciplines, there remain fundamental technical challenges in the modeling of environments with homogenous surfaces (e.g., water). In this research, the effectiveness of several image collection and processing approaches for the modelling of three freshwater lakes using UAS-SfM were tested to evaluate their ability to generate models capable of mapping cyanobacteria concentrations. These approaches demonstrate quantitative and qualitative impacts resulting from the selection of the photogrammetric software, the flying height, and the geometry-based image processing techniques. The results quantified differences in model completeness and the presence of image artifacts dependent on flight planning and software processing choices (12.3% and 5.2% respectively). Additionally, regardless of modelling technique, the orthoimagery was often subject to portions of incompleteness or image artifacts. The lack of a clear and reliable workflow for generating UAS-SfM of freshwater environments stands as a major barrier to many stakeholders. The lessons learned regarding the image collection and processing approaches tested here are highlighted to facilitate a discussion for meeting these challenges and formalizing accepted practices.

Date Created

October 23rd 2024

Department

Department of Natural Resources and the Environment

Publication Date

Fall 2024

Subject

Remote Sensing

Language

English

Document Type

Article

Rights

None.

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