Using Vegetation Indices and a UAV Imaging Platform to Quantify the Density of Vegetation Ground Cover in Olive Groves (Olea Europaea L.) in Southern Spain

In olive groves, vegetation ground cover (VGC) plays an important ecological role. The EU Common Agricultural Policy, through cross-compliance, acknowledges the importance of this factor, but, to determine the real impact of VGC, it must first be quantified. Accordingly, in the present study, eleven...

Full description

Saved in:
Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2019-11, Vol.11 (21), p.2564
Main Authors: Lima-Cueto, Francisco J., Blanco-Sepúlveda, Rafael, Gómez-Moreno, María L., Galacho-Jiménez, Federico B.
Format: Article
Language:English
Subjects:
Agricultural policy
agro-environmental measures
Altitude
Common Agricultural Policy
Density
Farms
Fieldwork
Ground cover
Mosaics
Mountains
multispectral
olive groves
Physiology
Remote sensing
Sampling
southern spain
Spatial discrimination
Spatial resolution
Spectral bands
Trees
uav
Unmanned aerial vehicles
Vegetation
vegetation ground cover
vegetation indices
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In olive groves, vegetation ground cover (VGC) plays an important ecological role. The EU Common Agricultural Policy, through cross-compliance, acknowledges the importance of this factor, but, to determine the real impact of VGC, it must first be quantified. Accordingly, in the present study, eleven vegetation indices (VIs) were applied to quantify the density of VGC in olive groves (Olea europaea L.), according to high spatial resolution (10–12 cm) multispectral images obtained by an unmanned aerial vehicle (UAV). The fieldwork was conducted in early spring, in a Mediterranean mountain olive grove in southern Spain presenting various VGC densities. A five-step method was applied: (1) generate image mosaics using UAV technology; (2) apply the VIs; (3) quantify VGC density by means of sampling plots (ground-truth); (4) calculate the mean reflectance of the spectral bands and of the VIs in each sampling plot; and (5) quantify VGC density according to the VIs. The most sensitive index was IRVI, which accounted for 82% (p < 0.001) of the variability of VGC density. The capability of the VIs to differentiate VGC densities increased in line with the cover interval range. RVI most accurately distinguished VGC densities > 80% in a cover interval range of 10% (p < 0.001), while IRVI was most accurate for VGC densities < 30% in a cover interval range of 15% (p < 0.01). IRVI, NRVI, NDVI, GNDVI and SAVI differentiated the complete series of VGC densities when the cover interval range was 30% (p < 0.001 and p < 0.05).
ISSN:2072-4292
2072-4292
DOI:10.3390/rs11212564