Comparison of vegetation indices derived from UAV data for differentiation of tillage effects in agriculture

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dc.contributor.authorYeom, Junho
dc.contributor.authorJung, Jinha
dc.contributor.authorChang, Anjin
dc.contributor.authorAshapure, Akash
dc.contributor.authorMaeda, Murilo
dc.contributor.authorMaeda, Andrea
dc.contributor.authorLandivar, Juan
dc.creator.orcidhttps://orcid.org/0000-0001-7571-1155en_US
dc.creator.orcidhttps://orcid.org/0000-0001-6870-3771en_US
dc.creator.orcidhttps://orcid.org/0000-0001-7571-1155
dc.creator.orcidhttps://orcid.org/0000-0001-6870-3771
dc.creator.orcidhttps://orcid.org/0000-0001-7571-1155
dc.creator.orcidhttps://orcid.org/0000-0001-6870-3771https://orcid.org/0000-0001-7571-1155
dc.creator.orcidhttps://orcid.org/0000-0001-6870-3771
dc.date.accessioned2021-10-27T21:41:34Z
dc.date.available2021-10-27T21:41:34Z
dc.date.issued2019-06-29
dc.description.abstractUnmanned aerial vehicle (UAV) platforms with sensors covering the red-edge and near-infrared (NIR) bands to measure vegetation indices (VIs) have been recently introduced in agriculture research. Consequently, VIs originally developed for traditional airborne and spaceborne sensors have become applicable to UAV systems. In this study, we investigated the difference in tillage treatments for cotton and sorghum using various RGB and NIR VIs. Minimized tillage has been known to increase farm sustainability and potentially optimize productivity over time; however, repeated tillage is the most commonly-adopted management practice in agriculture. To this day, quantitative comparisons of plant growth patterns between conventional tillage (CT) and no tillage (NT) fields are often inconsistent. In this study, high-resolution and multi-temporal UAV data were used for the analysis of tillage effects on plant health and the performance of various vegetation indices investigated. Time series data over ten dates were acquired on a weekly basis by RGB and multispectral (MS) UAV platforms: a DJI Phantom 4 Pro and a DJI Matrice 100 with the SlantRange 3p sensor. Ground reflectance panels and an ambient illumination sensor were used for the radiometric calibration of RGB and MS orthomosaic images, respectively. Various RGB and NIR-based vegetation indices were then calculated for the comparison between CT and NT treatments. In addition, a one-tailed Z-test was conducted to check the significance of VIs’ difference between CT and NT treatments. The results showed distinct differences in VIs between tillage treatments during the whole growing season. NIR-based VIs showed better discrimination performance than RGB-based VIs. Out of 13 VIs, the modified soil adjusted vegetation index (MSAVI) and optimized soil adjusted vegetation index (OSAVI) showed better performance in terms of quantitative difference measurements and the Z-test between tillage treatments. The modified green red vegetation index (MGRVI) and excess green (ExG) showed reliable separability and can be an alternative for economic RGB UAV application.en_US
dc.description.abstractUnmanned aerial vehicle (UAV) platforms with sensors covering the red-edge and near-infrared (NIR) bands to measure vegetation indices (VIs) have been recently introduced in agriculture research. Consequently, VIs originally developed for traditional airborne and spaceborne sensors have become applicable to UAV systems. In this study, we investigated the difference in tillage treatments for cotton and sorghum using various RGB and NIR VIs. Minimized tillage has been known to increase farm sustainability and potentially optimize productivity over time; however, repeated tillage is the most commonly-adopted management practice in agriculture. To this day, quantitative comparisons of plant growth patterns between conventional tillage (CT) and no tillage (NT) fields are often inconsistent. In this study, high-resolution and multi-temporal UAV data were used for the analysis of tillage effects on plant health and the performance of various vegetation indices investigated. Time series data over ten dates were acquired on a weekly basis by RGB and multispectral (MS) UAV platforms: a DJI Phantom 4 Pro and a DJI Matrice 100 with the SlantRange 3p sensor. Ground reflectance panels and an ambient illumination sensor were used for the radiometric calibration of RGB and MS orthomosaic images, respectively. Various RGB and NIR-based vegetation indices were then calculated for the comparison between CT and NT treatments. In addition, a one-tailed Z-test was conducted to check the significance of VIs’ difference between CT and NT treatments. The results showed distinct differences in VIs between tillage treatments during the whole growing season. NIR-based VIs showed better discrimination performance than RGB-based VIs. Out of 13 VIs, the modified soil adjusted vegetation index (MSAVI) and optimized soil adjusted vegetation index (OSAVI) showed better performance in terms of quantitative difference measurements and the Z-test between tillage treatments. The modified green red vegetation index (MGRVI) and excess green (ExG) showed reliable separability and can be an alternative for economic RGB UAV application.
dc.identifier.citationYeom, J., Jung, J., Chang, A., Ashapure, A., Maeda, M., Maeda, A. and Landivar, J., 2019. Comparison of vegetation indices derived from UAV data for differentiation of tillage effects in agriculture. Remote Sensing, 11(13), p.1548.en_US
dc.identifier.citationYeom, J., Jung, J., Chang, A., Ashapure, A., Maeda, M., Maeda, A. and Landivar, J., 2019. Comparison of vegetation indices derived from UAV data for differentiation of tillage effects in agriculture. Remote Sensing, 11(13), p.1548.
dc.identifier.doihttps://doi.org/10.3390/rs11131548
dc.identifier.urihttps://hdl.handle.net/1969.6/89876
dc.language.isoen_USen_US
dc.language.isoen_US
dc.publisherMDPIen_US
dc.publisherMDPI
dc.rightsAttribution 4.0 International*
dc.rightsAttribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectuaven_US
dc.subjectvegetation indices comparisonen_US
dc.subjecttillage effecten_US
dc.subjecttime series analysisen_US
dc.subjectuav
dc.subjectvegetation indices comparison
dc.subjecttillage effect
dc.subjecttime series analysis
dc.titleComparison of vegetation indices derived from UAV data for differentiation of tillage effects in agricultureen_US
dc.titleComparison of vegetation indices derived from UAV data for differentiation of tillage effects in agriculture
dc.typeArticleen_US
dc.typeArticle

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