=Paper=
{{Paper
|id=Vol-1498/HAICTA_2015_paper42
|storemode=property
|title=Management Zones Delineation in Olive Grove Using an Unmanned Aerial Vehicle (UAV)
|pdfUrl=https://ceur-ws.org/Vol-1498/HAICTA_2015_paper42.pdf
|volume=Vol-1498
|dblpUrl=https://dblp.org/rec/conf/haicta/GertsisVZ15
}}
==Management Zones Delineation in Olive Grove Using an Unmanned Aerial Vehicle (UAV)==
https://ceur-ws.org/Vol-1498/HAICTA_2015_paper42.pdf
Management Zones Delineation in Olive Grove Using an
Unmanned Aerial Vehicle (UAV)
Athanasios Gertsis1, Christos Vasilikiotis2 and Konstantinos Zoukidis2
1
Department of Environmental Systems Management, Precision Agriculture Laboratory,
Perrotis College, American Farm School, Thessaloniki, Greece, e-mail: agerts@afs.edu.gr
2
Department of Environmental Systems Management, Precision Agriculture Laboratory,
Perrotis College, American Farm School, Thessaloniki, Greece
Abstract. The use of aerial photos taken in a low altitude with UAVs
(Unmmaned Aerial Vehicles) is recently becoming a common practice in many
areas. The use for agricultural related applications is applied in this study,
using a common vegetation index, namely NDVI, to identify areas of large
differences in crops grown in order to delineate Management Zones to be
eventually used for Precision Agriculture applications. A UAV equipped with
an infrared camera was used to develop maps of NDVI in an olive grove along
with ground measurements of NDVI, to provide ground truthing information.
The results were used to identify areas of small or large differences and to
establish Management Zones (MZs) for further evaluation and application of
precision agriculture inputs.
Keywords: UAV (Unmanned Aerial Vehicle), NDVI (Normalized Difference
Vegetation Index, Management Zones (MZs), remote sensing, ground truthing,
olive grove, vineyard, Precision Agriculture
1 Introduction
The first step in application of Precision Agriculture’s (PA) methods is to evaluate if
any significant “spatial” and “temporal” variability exists in the farmer’s field. This
results to establishment of digital maps and delineation of Management Zones (MZs)
for important soil and crop properties affecting growth and productivity. The
technologies developed and become commercially available in the in the last ca.20
years, provided tools to achieve this evaluation. In addition and in the recent years, a
new tool became available, the use of unmanned aerial vehicles (UAVs) which has
extended to many applications such as agricultural management, civilian
applications, homeland security, forest fire monitoring, quick response surveillance
for emergency disasters et al. This preliminary study attempts to combine “aerial
sensors” to facilitate delineation of MZ in a recently established olive grove.
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2 Materials & Methods
The study area is located at the premises of the American Farm School,
Thessaloniki Greece and includes a recently established olive grove, where high and
super-high density systems adapted for mechanical harvesting are evaluated in a
long-term assessment. A UAV (Figure 1) equipped with a camera with R-G-B-NIR
filters and a GPS for georeferencing the pictures, was used to obtain the NDVI
(Normalized Difference Vegetation Index) data. In addition a hand help NDVI sensor
(Trimble® GreenSeeker) was used to measure NDVI at ground level, to provide
similar “ground” data for correlation with the “aerial” values.
Fig 1. The UAV used for the study to obtain “aerial” NDVI data.
Fig 2. The hand held sensor use to obtain NDVI data at “ground” level.
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Fig. 3. The NDVI range of the entire olive grove- data by UAV’s camera
Fig. 4. A selected portion of NVDI from olive trees – data by UAV’s camera
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Fig. 5. The NDVI of the selected area shown in Fig.4 in the olive grove, measured using the
ground NDVI sensor.
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3 Results & Discussion
The aerial data are shown for the entire olive grove in Fig. 3 and for a small
selected section, magnified, in Fig. 4. The ground measured NDVI of the selected
area is shown in Fig.5, where two distinct areas are visualized. These two areas
appear to be similar with the more detailed NDVI values obtained from the UAV’s
camera. Further analysis of data will provide more close evaluation of the two MZ’s
established. The results from a general approach used – to facilitate extrapolation for
farmer’s use- to delineate MZ indicated that a combination of aerial and ground data
can be used to provide a “manageable” size MZ. It is important to “think at the
farmer’s level” when designing MZ, especially considering the small size farm area
at which most farmers exercise cultivations and other management practices. This
study presents preliminary data, to evaluate “spatial variability”. More data are in
progress to further validate the “temporal variability” needed, in order to conclude
the delineation of MZs on a time basis. The correlation was good for the olive grove
section- partial data section used only in this report-, while there is no consistent
correlation in the vineyard, due to inadequate database. Note: Data analysis of new
datasets for both crop species, is under evaluation but not presented in this report,
due to time constraints.
The ground data provided also in previous year measurement from the olive
grove, indicated two very distinct areas of NDVI (Gertsis et al. 2013). Candiago et al.
(2015) demonstrated he great potential in terms of speed, cost and reliability, of high
resolution UAV data, combined with additional photogrammetric methods. Bendig et
al. (2014) used UAV for estimating crop biomass, another area of important
application. However, none of these studies had the aerial NDVI to be correlated
with ground NDVI data, to provide a means of comparison. In general, there is a gap
in validation studies for remote and ground data; therefore, more work is required on
this subject, to provide simple and reliable means of delineating MZs at farmer’s
field level with either aerial or ground sensors.
4 Conclusions
The use of UAV’s is currently applied in many areas of interest with a exponential
increase of uses. Particularly the applications in agricultural production and
environmental studies indicates a high prospect of significant contribution; however,
the use of data provided by UAV’s should be coupled and validated in most cases, by
related data taken at ground level (soil-crop) in order to expand and verify the
“extrapolation” to potential beneficial uses. This study is an example of such a
“relational” verification. The study is in progress and more data are collected for
further and more accurate validation data sets.
Acknowledgments. The authors express their sincere gratitude to the Sky Squirrel
Technologies Inc., (www.skysquirell.ca) for their help with the UAV and NDVI data
processing.
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