Comparing RGB - based vegetation indices from UAV imageries to estimate hops canopy area
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Kuupäev
2020
Kättesaadav alates
Ajakirja pealkiri
Ajakirja ISSN
Köite pealkiri
Kirjastaja
Abstrakt
Remote estimation of hops plants in hop gardens is imperative in field of precision
agriculture, because of precise imaging of hop garden structure. Monitoring of hop plant volume
and area can help to predict the condition and yield of hops. In this study, two unmanned aerial
vehicles (UAV) – eBee X senseFly UAV equipped with Red Green Blue (RGB) S.O.D.A. camera
and Vertical Take-Off Landing (VTOL) UAV FireFly6 Pro by BirdsEyeView Aerobotics
equipped with MicaSense RedEdge MX camera were used to acquire images of hop garden at
phenology stage maturity of cones (24 th July) before harvest. Seven commonly used RGB
vegetation indices (VI) were derived from these RGB and multispectral (MS) images after
photogrammetric pre-processing and orthophoto mosaic extraction using Pix4Dmapper software.
Vegetation Indices as the Green Percentage Index (G%), Excess of Green Index (ExGreen),
Green Leaf Index (GLI), Visible Atmospherically Resistant Index (VARI), Red Green Blue
Vegetation Index (RGBVI), Normalised Green Red Difference Index (NGRDI) and Triangular
Greenness Index (TGI) were derived from both data sets. Binary model from each of VI was
derived and threshold value for green vegetation was set. The results showed significant
differences in hop plant area based on the specifications of cameras, especially wavelengths
centres, and design and flight parameters of both UAV types. The comparison of various indices
showed, that ExG and TGI indices has the highest congruity of estimated vegetation indices in
hop garden canopy area for both used cameras. Further processing by Fuzzy Overlay tool proved
high accuracy in green canopy area estimation for ExG and TGI vegetation indices.
Kirjeldus
Märksõnad
unmanned aerial vehicles, hop garden, vegetation indices, canopy area, articles