Please use this identifier to cite or link to this item: https://ktisis.cut.ac.cy/handle/10488/18644
Title: Vegetation Extraction Using Visible-Bands from Openly Licensed Unmanned Aerial Vehicle Imagery
Authors: Agapiou, Athos 
Major Field of Science: Engineering and Technology
Field Category: Electrical Engineering - Electronic Engineering - Information Engineering
Keywords: Vegetation indices;RGB cameras;Unmanned aerial vehicle (UAV);Empirical line method;Green leaf index;Open aerial map
Issue Date: 26-Jun-2020
Source: Drones, 2020, vol. 4, no. 2, pp. 27
Volume: 4
Issue: 2
Start page: 27
Project: NAVIGATOR: Copernicus Earth Observation Big Data for Cultural Heritage 
Journal: Drones 
Abstract: 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.
URI: https://ktisis.cut.ac.cy/handle/10488/18644
ISSN: 2504-446X
DOI: 10.3390/drones4020027
Rights: Attribution-NonCommercial-ShareAlike 4.0 International
Type: Article
Affiliation : Cyprus University of Technology 
Remote Sensing and Geo-Environment Lab 
ERATOSTHENES Centre of Excellence 
Appears in Collections:Άρθρα/Articles

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