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Article

Vegetation Extraction Using Visible-Bands from Openly Licensed Unmanned Aerial Vehicle Imagery

by 1,2
1
Remote Sensing and Geo-Environment Lab, Department of Civil Engineering and Geomatics, Faculty of Engineering and Technology, Cyprus University of Technology, Saripolou 2-8, Limassol 3036, Cyprus
2
Eratosthenes Centre of Excellence, Saripolou 2-8, Limassol 3036, Cyprus
Drones 2020, 4(2), 27; https://doi.org/10.3390/drones4020027
Received: 25 May 2020 / Revised: 25 June 2020 / Accepted: 25 June 2020 / Published: 26 June 2020
(This article belongs to the Collection Feature Papers of Drones)
Red–green–blue (RGB) cameras which are attached in commercial unmanned aerial vehicles (UAVs) can support remote-observation small-scale campaigns, by mapping, within a few centimeter’s accuracy, an area of interest. Vegetated areas need to be identified either for masking purposes (e.g., to exclude vegetated areas for the production of a digital elevation model (DEM) or for monitoring vegetation anomalies, especially for precision agriculture applications. However, while detection of vegetated areas is of great importance for several UAV remote sensing applications, this type of processing can be quite challenging. Usually, healthy vegetation can be extracted at the near-infrared part of the spectrum (approximately between 760–900 nm), which is not captured by the visible (RGB) cameras. In this study, we explore several visible (RGB) vegetation indices in different environments using various UAV sensors and cameras to validate their performance. For this purposes, openly licensed unmanned aerial vehicle (UAV) imagery has been downloaded “as is” and analyzed. The overall results are presented in the study. As it was found, the green leaf index (GLI) was able to provide the optimum results for all case studies. View Full-Text
Keywords: vegetation indices; RGB cameras; unmanned aerial vehicle (UAV); empirical line method; Green leaf index; open aerial map vegetation indices; RGB cameras; unmanned aerial vehicle (UAV); empirical line method; Green leaf index; open aerial map
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MDPI and ACS Style

Agapiou, A. Vegetation Extraction Using Visible-Bands from Openly Licensed Unmanned Aerial Vehicle Imagery. Drones 2020, 4, 27. https://doi.org/10.3390/drones4020027

AMA Style

Agapiou A. Vegetation Extraction Using Visible-Bands from Openly Licensed Unmanned Aerial Vehicle Imagery. Drones. 2020; 4(2):27. https://doi.org/10.3390/drones4020027

Chicago/Turabian Style

Agapiou, Athos. 2020. "Vegetation Extraction Using Visible-Bands from Openly Licensed Unmanned Aerial Vehicle Imagery" Drones 4, no. 2: 27. https://doi.org/10.3390/drones4020027

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