Improving the efficiency and accuracy of evaluating aridland riparian habitat restoration using unmanned aerial vehicles
Schlatter, Karen J.
Flessa, Karl W.
AffiliationDepartment of Geosciences, University of Arizona
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CitationGómez‐Sapiens, M., Schlatter, K. J., Meléndez, Á., Hernández‐López, D., Salazar, H., Kendy, E., & Flessa, K. W. (2021). Improving the efficiency and accuracy of evaluating Aridland riparian habitat restoration using unmanned aerial vehicles. Remote Sensing in Ecology and Conservation.
Rights© 2021 The Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License.
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AbstractUnmanned Aerial Vehicles (UAVs) offer new opportunities for accurate, repeatable vegetation assessments, which are needed to adaptively manage restored habitat. We used UAVs, ground surveys, and satellite imagery to evaluate vegetation metrics for three riparian restoration sites along the Colorado River in Mexico and we compared the data accuracy and efficiency (cost and time requirements) between the three methods. We used an off-the-shelf UAV coupled with a multispectral sensor to determine Normalized Difference Vegetation Index (NDVI) and vegetation cover. We were unable to accurately classify vegetation by individual species, but by grouping riparian species of interest (cottonwood-willow, mesquite, shrubs), we achieved high overall model accuracies of 87–96% across sites (Kappa = 0.82–0.95). Producer’s and user’s accuracies were moderate to high for target vegetation classes (69–100%). UAV and ground-survey vegetation percent cover differed due to differences in methodologies (UAVs measure aerial cover; ground surveys measure foliar cover) and sources of error for each method. Correlations between UAV and ground survey vegetation cover were moderate (rs(90) = 0.24–0.58, p < 0.05). UAV NDVI (0.50–0.61) was significantly higher than Landsat NDVI (0.40–0.45) for all sites (p < 0.0001), likely due to presence of shadows with high NDVI values in UAV imagery. UAV NDVI, Landsat NDVI and UAV total vegetation cover were strongly correlated (rs(90) = 0.72–0.85, p < 0.05). UAV surveys were more labor- and cost- intensive than ground surveys in the first year, but were slightly less so in the second year. We conclude that UAVs can provide efficient, accurate assessments of riparian vegetation, which can be used in restoration site management. Due to UAV limitations to assess vegetation in a multi-layered canopy and inability to classify individual riparian species with similar spectral signals, we recommend a combined approach of UAV and ground surveys.
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Except where otherwise noted, this item's license is described as © 2021 The Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License.