Comparison of Field and Virtual Vegetation Surveys Conducted Using Uncrewed Aircraft System (UAS) Imagery at Two Coastal Marsh Restoration Projects

Abstract

Traditional field vegetation plot surveys are critical for monitoring ecosystem restoration performance and include visual observations to quantitatively measure plants (e.g., species composition and abundance). However, surveys can be costly, time-consuming, and only provide data at discrete locations, leaving potential data gaps across a site. Uncrewed aircraft system (UAS) technology can help fill data gaps between high-to-moderate spatial resolution (e.g., 1–30 m) satellite imagery, manned airborne data, and traditional field surveys, yet it has not been thoroughly evaluated in a virtual capacity as an alternative to traditional field vegetation plot surveys. This study assessed the utility of UAS red-green-blue (RGB) and low-altitude imagery for virtually surveying vegetation plots in a web application and compared to traditional field surveys at two coastal marsh restoration sites in southeast Louisiana, USA. Separate expert botanists independently observed vegetation plots in the field vs. using UAS imagery in a web application to identify growth form, species, and coverages. Taxa richness and assemblages were compared between field and virtual vegetation plot survey results using taxa resolution (growth-form and species-level) and data collection type (RGB imagery, Anafi [low-altitude] imagery, or field data) to assess accuracy. Virtual survey results obtained using Anafi low-altitude imagery compared better to field data than those from RGB imagery, but they were dependent on growth-form or species-level resolution. There were no significant differences in taxa richness between all survey types for a growth-form level analysis. However, there were significant differences between each survey type for species-level identification. The number of species identified increased by approximately two-fold going from RGB to Anafi low-altitude imagery and another two-fold from Anafi low-altitude imagery to field data. Vegetation community assemblages were distinct between the two marsh sites, and similarity percentages were higher between Anafi low-altitude imagery and field data compared to RGB imagery. Graminoid identification mismatches explained a high amount of variance between virtual and field similarity percentages due to the challenge of discriminating between them in a virtual setting. The higher level of detail in Anafi low-altitude imagery proved advantageous for properly identifying lower abundance species. These identifications included important taxa, such as invasive species, that were overlooked when using RGB imagery. This study demonstrates the potential utility of high-resolution UAS imagery for increasing marsh vegetation monitoring efficiencies to improve ecosystem management actions and outcomes. Restoration practitioners can use these results to better understand the level of accuracy for identifying vegetation growth form, species, and coverages from UAS imagery compared to field data to effectively monitor restored marsh ecosystems.