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

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 locati...

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Main Authors: Aaron N. Schad, Molly K. Reif, Joseph H. Harwood, Christopher L. Macon, Lynde L. Dodd, Katie L. Vasquez, Kevin D. Philley, Glenn E. Dobson, Katie M. Steinmetz
Format: Article
Language:English
Published: MDPI AG 2025-01-01
Series:Remote Sensing
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Online Access:https://www.mdpi.com/2072-4292/17/2/223
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author Aaron N. Schad
Molly K. Reif
Joseph H. Harwood
Christopher L. Macon
Lynde L. Dodd
Katie L. Vasquez
Kevin D. Philley
Glenn E. Dobson
Katie M. Steinmetz
author_facet Aaron N. Schad
Molly K. Reif
Joseph H. Harwood
Christopher L. Macon
Lynde L. Dodd
Katie L. Vasquez
Kevin D. Philley
Glenn E. Dobson
Katie M. Steinmetz
author_sort Aaron N. Schad
collection DOAJ
description 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.
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spelling doaj-art-343e157fdcdc4809afff06c929750d392025-01-24T13:47:46ZengMDPI AGRemote Sensing2072-42922025-01-0117222310.3390/rs17020223Comparison of Field and Virtual Vegetation Surveys Conducted Using Uncrewed Aircraft System (UAS) Imagery at Two Coastal Marsh Restoration ProjectsAaron N. Schad0Molly K. Reif1Joseph H. Harwood2Christopher L. Macon3Lynde L. Dodd4Katie L. Vasquez5Kevin D. Philley6Glenn E. Dobson7Katie M. Steinmetz8Environmental Laboratory, US Army Engineer Research and Development Center, Vicksburg, MS 39180, USAJoint Airborne Lidar Bathymetry Technical Center of Expertise, Environmental Laboratory, US Army Engineer Research and Development Center, Kiln, MS 39556, USAJoint Airborne Lidar Bathymetry Technical Center of Expertise, Mobile District, US Army Corps of Engineers, Kiln, MS 39556, USAJoint Airborne Lidar Bathymetry Technical Center of Expertise, Mobile District, US Army Corps of Engineers, Kiln, MS 39556, USAEnvironmental Laboratory, US Army Engineer Research and Development Center, Vicksburg, MS 39180, USAEnvironmental Laboratory, US Army Engineer Research and Development Center, Vicksburg, MS 39180, USAEnvironmental Laboratory, US Army Engineer Research and Development Center, Vicksburg, MS 39180, USANew Orleans District, US Army Corps of Engineers, New Orleans, LA 70118, USASt. Louis District (Formally), US Army Corps of Engineers, St. Louis, MO 63118, USATraditional 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.https://www.mdpi.com/2072-4292/17/2/223uncrewed aircraft systemsremote sensingcoastal vegetationmarsh restorationmarsh vegetation monitoring
spellingShingle Aaron N. Schad
Molly K. Reif
Joseph H. Harwood
Christopher L. Macon
Lynde L. Dodd
Katie L. Vasquez
Kevin D. Philley
Glenn E. Dobson
Katie M. Steinmetz
Comparison of Field and Virtual Vegetation Surveys Conducted Using Uncrewed Aircraft System (UAS) Imagery at Two Coastal Marsh Restoration Projects
Remote Sensing
uncrewed aircraft systems
remote sensing
coastal vegetation
marsh restoration
marsh vegetation monitoring
title Comparison of Field and Virtual Vegetation Surveys Conducted Using Uncrewed Aircraft System (UAS) Imagery at Two Coastal Marsh Restoration Projects
title_full Comparison of Field and Virtual Vegetation Surveys Conducted Using Uncrewed Aircraft System (UAS) Imagery at Two Coastal Marsh Restoration Projects
title_fullStr Comparison of Field and Virtual Vegetation Surveys Conducted Using Uncrewed Aircraft System (UAS) Imagery at Two Coastal Marsh Restoration Projects
title_full_unstemmed Comparison of Field and Virtual Vegetation Surveys Conducted Using Uncrewed Aircraft System (UAS) Imagery at Two Coastal Marsh Restoration Projects
title_short Comparison of Field and Virtual Vegetation Surveys Conducted Using Uncrewed Aircraft System (UAS) Imagery at Two Coastal Marsh Restoration Projects
title_sort comparison of field and virtual vegetation surveys conducted using uncrewed aircraft system uas imagery at two coastal marsh restoration projects
topic uncrewed aircraft systems
remote sensing
coastal vegetation
marsh restoration
marsh vegetation monitoring
url https://www.mdpi.com/2072-4292/17/2/223
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