Improving the efficiency and accuracy of evaluating aridland riparian habitat restoration using unmanned aerial vehicles
Abstract Unmanned 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...
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doaj-856438c6f0cd4c0a80dfca6b28cd55552021-09-23T06:41:06ZengWileyRemote Sensing in Ecology and Conservation2056-34852021-09-017348850310.1002/rse2.204Improving the efficiency and accuracy of evaluating aridland riparian habitat restoration using unmanned aerial vehiclesMartha Gómez‐Sapiens0Karen J. Schlatter1Ángela Meléndez2Deus Hernández‐López3Helen Salazar4Eloise Kendy5Karl W. Flessa6Department of Geosciences University of Arizona 1040 E. 4th Street Tucson Arizona 85721 USAUniversity of Florida Water Institute 570 Weil Hall Gainesville Florida 32611 USASonoran Institute, México Calz de las Americas 233, Cuauhtémoc Sur Mexicali 21200 MexicoSonoran Institute, México Calz de las Americas 233, Cuauhtémoc Sur Mexicali 21200 MexicoSonoran Institute, México Calz de las Americas 233, Cuauhtémoc Sur Mexicali 21200 MexicoThe Nature Conservancy, Colorado River Program 2424 Spruce Street Boulder Colorado 80302 USADepartment of Geosciences University of Arizona 1040 E. 4th Street Tucson Arizona 85721 USAAbstract Unmanned 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.https://doi.org/10.1002/rse2.204DronemonitoringrestorationriparianUAVvegetation |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Martha Gómez‐Sapiens Karen J. Schlatter Ángela Meléndez Deus Hernández‐López Helen Salazar Eloise Kendy Karl W. Flessa |
spellingShingle |
Martha Gómez‐Sapiens Karen J. Schlatter Ángela Meléndez Deus Hernández‐López Helen Salazar Eloise Kendy Karl W. Flessa Improving the efficiency and accuracy of evaluating aridland riparian habitat restoration using unmanned aerial vehicles Remote Sensing in Ecology and Conservation Drone monitoring restoration riparian UAV vegetation |
author_facet |
Martha Gómez‐Sapiens Karen J. Schlatter Ángela Meléndez Deus Hernández‐López Helen Salazar Eloise Kendy Karl W. Flessa |
author_sort |
Martha Gómez‐Sapiens |
title |
Improving the efficiency and accuracy of evaluating aridland riparian habitat restoration using unmanned aerial vehicles |
title_short |
Improving the efficiency and accuracy of evaluating aridland riparian habitat restoration using unmanned aerial vehicles |
title_full |
Improving the efficiency and accuracy of evaluating aridland riparian habitat restoration using unmanned aerial vehicles |
title_fullStr |
Improving the efficiency and accuracy of evaluating aridland riparian habitat restoration using unmanned aerial vehicles |
title_full_unstemmed |
Improving the efficiency and accuracy of evaluating aridland riparian habitat restoration using unmanned aerial vehicles |
title_sort |
improving the efficiency and accuracy of evaluating aridland riparian habitat restoration using unmanned aerial vehicles |
publisher |
Wiley |
series |
Remote Sensing in Ecology and Conservation |
issn |
2056-3485 |
publishDate |
2021-09-01 |
description |
Abstract Unmanned 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. |
topic |
Drone monitoring restoration riparian UAV vegetation |
url |
https://doi.org/10.1002/rse2.204 |
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