Remote Sensing (Jan 2021)

Assessing Geomorphic Change in Restored Coastal Dune Ecosystems Using a Multi-Platform Aerial Approach

  • Zach Hilgendorf,
  • M. Colin Marvin,
  • Craig M. Turner,
  • Ian J. Walker

DOI
https://doi.org/10.3390/rs13030354
Journal volume & issue
Vol. 13, no. 3
p. 354

Abstract

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Uncrewed aerial systems (UAS) provide an effective method to examine geomorphic and vegetation change in restored coastal dune ecosystems. Coupling structure-from-motion (SfM) photogrammetry with RGB orthomosaic imagery allows researchers to characterize spatial-temporal geomorphic responses associated with differences in vegetation cover. Such approaches provide quantitative data on landscape morphodynamics and sediment erosion and deposition responses that allow scientists and land managers to assess the efficacy of dynamic restoration efforts and, in turn, make informed decisions for future restoration projects. Two different restored coastal foredune sites in Humboldt County, California were monitored between 2016–20 with UAS (quadcopter and fixed-wing), kite aerial photogrammetry (KAP), and terrestrial laser scanning (TLS) platforms. We compared our KAP- and UAS-SfM elevation models to concurrently collected TLS bare earth models for five of our fifteen collections. The goal of this study was to inform on the potential of a multi-platform aerial approach for calculating geomorphic differences (i.e., topographic differencing), in order to quantify sediment erosion and deposition, and vegetation change over a coastal dune ecosystem. While UAS-SfM datasets were relatively well fit to their TLS counterparts (2.1–12.2% area of difference), the KAP-SfM surfaces exhibited higher deviations (23.6–27.6%) and suffered from systematic collection inconsistencies related to methods and susceptibility to external factors (e.g., the influence of wind speed and direction on variable altitude, image overlap, and coverage extent). Finally, we provide commentary on the logistical considerations regarding KAP and UAS data collection and the construction of uncertainty budgets for geomorphic change detection (GCD), while providing suggestions for standardizing methods for uncertainty budgeting. While we propose an approach that incorporates multiple levels of collection- and processing-based uncertainty, we also recognize that uncertainty is often project-specific and outline the development of potential standards for incorporating uncertainty budgets in SfM projects.

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