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Article

Optimal Timing Assessment for Crop Separation Using Multispectral Unmanned Aerial Vehicle (UAV) Data and Textural Features

Department of Geography, Remote Sensing Laboratories (RSL), University of Zürich, Winterthurerstrasse 190, 8057 Zürich, Switzerland
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Remote Sens. 2019, 11(15), 1780; https://doi.org/10.3390/rs11151780
Received: 17 May 2019 / Revised: 25 July 2019 / Accepted: 27 July 2019 / Published: 30 July 2019
(This article belongs to the Special Issue Remote Sensing for Crop Mapping)
The separation of crop types is essential for many agricultural applications, particularly when within-season information is required. Generally, remote sensing may provide timely information with varying accuracy over the growing season, but in small structured agricultural areas, a very high spatial resolution may be needed that exceeds current satellite capabilities. This paper presents an experiment using spectral and textural features of NIR-red-green-blue (NIR-RGB) bands data sets acquired with an unmanned aerial vehicle (UAV). The study area is located in the Swiss Plateau, which has highly fragmented and small structured agricultural fields. The observations took place between May 5 and September 29, 2015 over 11 days. The analyses are based on a random forest (RF) approach, predicting crop separation metrics of all analyzed crops. Three temporal windows of observations based on accumulated growing degree days (AGDD) were identified: an early temporal window (515–1232 AGDD, 5 May–17 June 2015) with an average accuracy (AA) of 70–75%; a mid-season window (1362–2016 AGDD, 25 June–22 July 2015) with an AA of around 80%; and a late window (2626–3238 AGDD, 21 August–29 September 2015) with an AA of <65%. Therefore, crop separation is most promising in the mid-season window, and an additional NIR band increases the accuracy significantly. However, discrimination of winter crops is most effective in the early window, adding further observational requirements to the first window. View Full-Text
Keywords: crop type separation; temporal window; small structured agricultural area; uncalibrated consumer-grade camera; unmanned aerial vehicle (UAV); very high resolution (VHR); random forest (RF) classifier; spectral and textural features crop type separation; temporal window; small structured agricultural area; uncalibrated consumer-grade camera; unmanned aerial vehicle (UAV); very high resolution (VHR); random forest (RF) classifier; spectral and textural features
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MDPI and ACS Style

Böhler, J.E.; Schaepman, M.E.; Kneubühler, M. Optimal Timing Assessment for Crop Separation Using Multispectral Unmanned Aerial Vehicle (UAV) Data and Textural Features. Remote Sens. 2019, 11, 1780. https://doi.org/10.3390/rs11151780

AMA Style

Böhler JE, Schaepman ME, Kneubühler M. Optimal Timing Assessment for Crop Separation Using Multispectral Unmanned Aerial Vehicle (UAV) Data and Textural Features. Remote Sensing. 2019; 11(15):1780. https://doi.org/10.3390/rs11151780

Chicago/Turabian Style

Böhler, Jonas E., Michael E. Schaepman, and Mathias Kneubühler. 2019. "Optimal Timing Assessment for Crop Separation Using Multispectral Unmanned Aerial Vehicle (UAV) Data and Textural Features" Remote Sensing 11, no. 15: 1780. https://doi.org/10.3390/rs11151780

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