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Article

Comparison of Vegetation Indices Derived from UAV Data for Differentiation of Tillage Effects in Agriculture

1
Research Institute for Automotive Diagnosis Technology of Multi-scale Organic and Inorganic Structure, Kyungpook National University, 2559 Gyeongsang-daero, Sangju-si, Gyeongsangbuk-do 37224, Korea
2
College of Science & Engineering, Texas A&M University Corpus Christi, 6300 Ocean Drive, Corpus Christi, TX 78412, USA
3
Texas A&M AgriLife Research and Extension Center, 10345 TX-44, Corpus Christi, TX 78406, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(13), 1548; https://doi.org/10.3390/rs11131548
Received: 21 May 2019 / Revised: 24 June 2019 / Accepted: 26 June 2019 / Published: 29 June 2019
Unmanned 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. View Full-Text
Keywords: UAV; vegetation indices comparison; tillage effect; time series analysis UAV; vegetation indices comparison; tillage effect; time series analysis
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MDPI and ACS Style

Yeom, J.; Jung, J.; Chang, A.; Ashapure, A.; Maeda, M.; Maeda, A.; Landivar, J. Comparison of Vegetation Indices Derived from UAV Data for Differentiation of Tillage Effects in Agriculture. Remote Sens. 2019, 11, 1548. https://doi.org/10.3390/rs11131548

AMA Style

Yeom J, Jung J, Chang A, Ashapure A, Maeda M, Maeda A, Landivar J. Comparison of Vegetation Indices Derived from UAV Data for Differentiation of Tillage Effects in Agriculture. Remote Sensing. 2019; 11(13):1548. https://doi.org/10.3390/rs11131548

Chicago/Turabian Style

Yeom, Junho, Jinha Jung, Anjin Chang, Akash Ashapure, Murilo Maeda, Andrea Maeda, and Juan Landivar. 2019. "Comparison of Vegetation Indices Derived from UAV Data for Differentiation of Tillage Effects in Agriculture" Remote Sensing 11, no. 13: 1548. https://doi.org/10.3390/rs11131548

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