CONFIGURATION AND SPECIFICATIONS OF AN UNMANNED AERIAL VEHICLE FOR PRECISION AGRICULTURE

Abstract

Unmanned Aerial Vehicles (UAVs) with multispectral sensors are increasingly attractive in geosciences for data capture and map updating at high spatial and temporal resolutions. These autonomously-flying systems can be equipped with different sensors, such as a six-band multispectral camera (Tetracam mini-MCA-6), GPS Ublox M8N, and MEMS gyroscopes, and miniaturized sensor systems for navigation, positioning, and mapping purposes. These systems can be used for data collection in precision viticulture. In this study, the efficiency of a light UAV system for data collection, processing, and map updating in small areas is evaluated, generating correlations between classification maps derived from remote sensing and production maps. Based on the comparison of the indices derived from UAVs incorporating infrared sensors with those obtained by satellites (Sentinel 2A and Landsat 8), UAVs show promise for the characterization of vineyard plots with high spatial variability, despite the low vegetative coverage of these crops. Consequently, a procedure for zoning map production based on UAV/UV images could provide important information for farmers.

Full text

CONFIGURATION AND SPECIFICATIONS OF AN UNMANNED AERIAL VEHICLE
FOR PRECISION AGRICULTURE
M. Erena a *, S. Montesinos b, D. Portillo c, J. Alvarez d, C. Marin e, L. Fernandez b, J. M. Henarejos a, L. A. Ruiz f
a GIS and Remote Sensing Group- IMIDA, 30150. La Alberca. Murcia. Spain - manuel.erena@carm.es
b SM Geodim, Torre Albarrana. 50340 Maluenda. Zaragoza. Spain - smontesinos@geodim.es
c Habitat, Avda. Don Juan de Borbón, Nº 98, C.P. 30007, Murcia, Spain – d.portillo@habitatea.es
d Droning, Calle Astronomía 1, Torre 2, 6-11. 41015. Sevilla, Spain - j.alvarez@droning.es
e Bioiberica, Plaça Francesc Macià, 7. 08029. Barcelona, España - cmarin@bioiberica.com
f Geo-Environmental Cartography & Remote Sensing Group, UPV, Calle Vera s/n, 46022. Valencia, Spain - laruiz@cgf.upv.es
Commission I, ICWG I/Vb
KEY WORDS: UAV, calibration, multispectral, multitemporal, viticulture
ABSTRACT:
Unmanned Aerial Vehicles (UAVs) with multispectral sensors are increasingly attractive in geosciences for data capture and map
updating at high spatial and temporal resolutions. These autonomously-flying systems can be equipped with different sensors, such
as a six-band multispectral camera (Tetracam mini-MCA-6), GPS Ublox M8N, and MEMS gyroscopes, and miniaturized sensor
systems for navigation, positioning, and mapping purposes. These systems can be used for data collection in precision viticulture. In
this study, the efficiency of a light UAV system for data collection, processing, and map updating in small areas is evaluated,
generating correlations between classification maps derived from remote sensing and production maps. Based on the comparison of
the indices derived from UAVs incorporating infrared sensors with those obtained by satellites (Sentinel 2A and Landsat 8), UAVs
show promise for the characterization of vineyard plots with high spatial variability, despite the low vegetative coverage of these
crops. Consequently, a procedure for zoning map production based on UAV/UV images could provide important information for
farmers.
1. INTRODUCTION
In the domain of precision farming, the contemporary
generation of aerial images with high spatial resolution is of
great interest. Useful in particular are aerial images in the
thermal (TIRS) infrared (Suarez et al., 2010) and near-infrared
(NIR) spectrum (Nebiker et al., 2008). Unmanned Aerial
Vehicles (UAVs) with multispectral sensors are becoming
increasingly attractive in viticulture for data capture and map
updating at high spatial and temporal resolutions. These
systems can be used for data collection in precision viticulture
(Arno, 2008), (Bellvert, 2014) and (Montesinos, 2015).
Airborne thermal and multispectral images have also been
applied successfully to the detection of water stress at larger
scales. Normalized canopy temperature, chlorophyll content,
and photochemical reflectance indices were demonstrated to be
the best indicators of early and advanced water stress.
Canopy temperature: Remote sensing of crop temperature
was proposed (Tanner, 1963) through the use of thermal
infrared sensors. The temperature was a valuable index for the
determination of water regimes as well for estimating crop
production. Subsequently, the temperature was used as an index
for crop water status, relating it to the productivity and water
requirements of the plants (Jackson, Reginato, & Idso, 1977).
The "temperature" parameter provided by UAV/aircraft flight
allows detection of crop areas with higher temperatures, relating
this to their water status. Validation in crops with different
water supplies has demonstrated the relationship between
temperature and water potential and stomatal conductance, thus
showing the feasibility of the detection of areas with water
stress by UAV images taken with thermal cameras (Suarez et
al., 2010) and (Bellvert, 2014).
Chlorophyll content: Estimation by remote sensing of the
chlorophyll content in vegetation has proven to be of great
interest because it is an indicator of stress directly related to
photosynthetic processes as well as an indicator of nutritional
deficiencies. Spectroscopic methods at the leaf and canopy
levels allow estimation of the chlorophyll content based on the
reflected radiation in the spectral regions of green (550 nm) and
red edge (690-750 nm) (Suarez et al., 2010), therefore allowing
the detection of chlorosis in vegetation. The chlorophyll
content, in addition to other biochemical constituents such as
water content or dry matter, can be estimated using remote
sensors installed on the UAV.
TCARI index: The TCARI index (Berni et al., 2009) is one of
the products generated after an airborne flight over the
vineyard: the spatial variability of the nutritional status
indicator is obtained by means of the demonstrated relationship
between the chlorophyll content and leaf nitrogen.
PRI: The Photochemical Reflectance Index (Gamon et al.,
1992) is an indicator of stress related to fruit quality, due to its
relationship with plant photosynthesis. The PRI has been used
as a pre-visual indicator of water stress at the leaf scale (Thenot
et al., 2010) and canopy scale (Dobrowski et al., 2005). The
PRI is calculated to obtain the average per plant of that index.
Its association with photosynthesis provides good relationships
between the index and fruit quality parameters such as the
sugar/acid ratio in the grapes of the vineyard (Suarez et al.,
2008)
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLI-B1, 2016
XXIII ISPRS Congress, 12–19 July 2016, Prague, Czech Republic
This contribution has been peer-reviewed.
doi:10.5194/isprsarchives-XLI-B1-809-2016
809

This article presents a multiscale approach to obtain different
vegetative parameters. Characterization of the spatial variability
in water status across vineyards is a prerequisite for precision
irrigation. In this paper we used simultaneous UAV and aircraft
surveys and Landsat 8 and sentinel II satellite images, acquired
over vineyards in southeast of Spain, to assess the capability of
each system to represent the intra-vineyard vegetation patterns,
to evaluate the similarities of images taken at different spatial
resolutions, and to perform an evaluation that combines
operational and economic factors. The final outcome of this
assessment is the development of a logical framework with the
aim of providing guidelines for the choice of the appropriate
detection platform as a function of the scale of analysis in
precision viticulture. The efficiency of a UAV system for data
collection, processing, and map updating in small areas is
evaluated, using different indices like enhanced normalized
difference vegetation ENDVI (Matsushita et al., 2007).
The investigation was based on a set of aerial images recorded
during the flights performed with a UAV system over a
commercial vineyard plot in Jumilla and an experimental
vineyard in Bullas (Murcia, Spain). Additionally, biophysical
indices obtained with the UAV were compared with those
obtained by processing Landsat 8 and Sentinel 2A images and
using multispectral and thermal cameras on-board an airplane.
These tests show the preliminary results for the configuration of
a UAV-based system to be used for practical applications in
precision agriculture (Pierce, 1999), (Reuter and Kersebaum,
2009), (Matese, 2013), (Bellvert et al., 2014) and (Matese & Di
Gennaro, 2015).
2. MATERIALS
2.1 Study Site
Two vineyards, hereafter referred to as V1 (38°34′05′′N,
1°21′14′′E) and V2 (38°06′39′′N, 1°40′59′′E), were chosen as
test sites in the Murcia Region (Spain). The vineyards have
similar agronomic characteristics. A Monastrel (Vitis vinifera
L.) vine is trained to a free cordon with a single horizontal wire
1.5 m high. Vines spacing is 2.5 × 1.3 m between rows and
plants, respectively, while the row orientation is Northeast (V1)
or Northwest (V2) with flat topography. The climatic
characterization used the data collected by the
agrometeorological station JU12 (38°02′38′′N, 1°58′46′′E). The
study was performed in summer 2015, one of the warmest of
the period 2000-2015, with annual mean temperatures 17.08 °C,
a cumulative rainfall of 349 mm, and an annual
evapotranspiration (ETo) of 1377 mm (Figures 1 and 2).
Figure 1 Situation maps of vineyard plots.
The commercial vineyard plot is divided into four zones,
according to their lithology and the age of the plantation on the
plot. It has a clay soil with low organic matter content (about
1.5 %), an active silt content of 20 %, and a pH of 7.6.
Figure 2 Lithology from the Geological Map of Spain-IGME.
The V. vinifera varieties studied were Monastrell, Merlot, and
Syrah. The hours and days of the UAV flight and the
acquisition of AV, L8, and Sentinel II images are shown in
Figure 3.
Figure 3. Average hourly temperature at the JU12 station and
acquisition dates of the images.
2.2. UAVs system description
Two different models of UAV were used: in the commercial
vineyard (V1) the Droning D650 UAV (Figure 3) was used and
in the experimental vineyard (V2) the Droning D820 UAV
(Figure 7) was used.
Figure 4. Droning D650 UAV-Canon IXUS 125 HS.
The Droning D650 UAV can carry any sensor weighing less
than 0.5 kg, although the maximum recommended payload is
0.30 kg. Two sensors with different spectral and spatial
resolutions were mounted on the UAV to be tested separately in
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLI-B1, 2016
XXIII ISPRS Congress, 12–19 July 2016, Prague, Czech Republic
This contribution has been peer-reviewed.
doi:10.5194/isprsarchives-XLI-B1-809-2016
810

this experiment: a stationary point-and-shoot camera, model
Canon IXUS 125 HS. The Canon camera acquires 16.1
megapixel images (Infrared, IR; Green, G; and Blue, B, bands)
with 8-bit radiometric resolution and is equipped with a 4.3-
21.5 mm zoom lens.
2.3 Aircraft description
The aerial images were collected on June 26th 2015, T13:30Z to
T13:55Z, using a six-band multispectral camera (model
Tetracam mini-MCA-6) and FLIR thermal cameras on-board an
airplane. The calculated indices are: canopy temperature,
NDVI, TCARI, and PRI (Suarez et al., 2008).
Figure 5. Commercial vineyard plot 2015/06/26T1300Z
2.4 Satellite description
Two types of satellite image have been used: Sentinel 2A is a
European wide-swath, high-resolution, multi-spectral imaging
mission. The full mission specification of the twin satellites,
flying in the same orbit but phased at 180°, is designed to give a
high revisit frequency of 5 days at the Equator (ESA, 2015).
Sentinel 2A has an optical instrument payload that samples 13
spectral bands: four bands at 10 m, six bands at 20 m, and three
bands at 60 m spatial resolution. The orbital swath width is 290
km.
Spectral Band; Wavelength (µm); Band width (µm); Resolution(m)
B1 0.443 0.020 60
B2 Blue 0.490 0.065 10
B3 Green 0.560 0.035 10
B4 Red 0.665 0.030 10
B5 0.705 0.015 20
B6 0.740 0.015 20
B7 0.783 0.020 20
B8 NIR 0.842 0.115 10
B8a 0.865 0.020 20
B9 0.945 0.020 60
B10 1.380 0.030 60
B11 1.610 0.090 20
B12 2.190 0.180 20
Table 1: Spectral bands of SENTINEL-2 MSI
The Level-1C product provides orthorectified Top-Of-
Atmosphere (TOA) reflectance values, with sub-pixel
multispectral registration.
The aerial images were collected on July 6th 2015, at T11:03Z,
using the spectral band numbers 2, 3, 4, and 8 (10 m) of the
MSI sensor.
Landsat 8: Landsat 8 products provided by the USGS EROS
Center consist of quantized and calibrated, scaled Digital
Numbers (DN) representing multispectral image data acquired
by both the Operational Land Imager (OLI) and Thermal
Infrared Sensor (TIRS). The products are delivered in 16-bit
unsigned integer format and can be rescaled to the Top Of
Atmosphere (TOA) reflectance and/or radiance using
radiometric rescaling coefficients provided in the product
metadata file. The MTL file contains the thermal constants
needed to convert TIRS data to the at-satellite brightness
temperature. Landsat 8 satellite images cover Earth every 16
days. Landsat 8 data products are consistent with the all
standard Level-1 specifications: OLI multispectral bands 1-7, 9:
30-meters; OLI panchromatic band 8: 15-meters; TIRS bands
10-11: 100 meters; Orthorectified; 16-bit pixel value. (USGS,
2015).
Spectral B Wavelength (µm) Resolution (m)
B1 –Aero. 0.433 - 0.453 30
B2 - Blue 0.450 - 0.515 30
B3 - Green 0.525 - 0.600 30
B4 - Red 0.630 - 0.680 30
B5 - NIR 0.845 - 0.885 30
B6 - IR 1.560 - 1.660 30
B7 - IR 2.100 - 2.300 30
B 8 - Pan 0.500 - 0.680 15
B 9 - Cirrus 1.360 - 1.390 30
B10 – LI 10.30 - 11.30 100
B11 – LI 11.50 - 12.50 100
Table 2. Spectral bands of Landsat 8 (OLI/TIRS)
3. METHODS
3.1 UAV image acquisition
The set of aerial images was collected on June 26th 2015,
T12:00Z - T13:30Z (Figure 6), with a stationary point-and-
shoot camera (Canon, model IXUS 125HS). The index obtained
was ENDVI (Figure 9).
Figure 6. Flight plan of the vineyard plot 2015/06/26T1300Z (0.1m)
A sequence of images was collected in each flight mission to
cover the whole commercial crop field.
An important task prior to image analysis was the combination
of all these individual and overlapped images by applying two
consecutive processes of orthorectification and mosaicking.
Agisoft Photoscan Professional Edition (Agisoft, 2016)
software was employed in this task, together with the
information related to the roll, pitch, and yaw of the vehicle in
each acquired image. Then, the orthorectification and
mosaicking of the imagery set into a single image of the whole
experimental field was performed.
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLI-B1, 2016
XXIII ISPRS Congress, 12–19 July 2016, Prague, Czech Republic
This contribution has been peer-reviewed.
doi:10.5194/isprsarchives-XLI-B1-809-2016
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The Droning D820 UAV can carry any sensor weighing less
than 1.5 kg, although the maximum recommended payload is
0.80 kg. Two sensors with different spectral and spatial
resolutions were mounted on the UAV to be tested separately in
this experiment: a stationary point-and-shoot mirrorless camera,
model Sony QX1 with a Rosco #2007 VS Blue filter, and a six-
band multispectral camera, model Tetracam mini-MCA-6.
The Sony camera acquires 20.1-megapixel images (Infrared, IR;
Green, G; and Blue, B, bands) with 8-bit radiometric resolution
and is equipped with a 16–50 mm zoom lens. The Rosco #2007
filter allows much blue light (0.4-0.5 µm) and much near
infrared light (> 0.7 µm) to pass, but very little red light (0.6-
0,7 µm) (Public Lab, 2016)
The Tetracam mini-MCA-6 (Tetracam, 2016) is a lightweight
(700 g) multispectral sensor composed of six individual digital
channels arranged in a 2×3 array. The images can be acquired
with 10-bit radiometric resolution. The camera has user
configurable band pass filters of 10-nm full-width at half-
maximum and center wavelengths at B (0.49 µm), G (0.55 µm),
R (0.68 µm), R edge (0.72 µm), and near-infrared (NIR, 0.80
µm NIR, 0.90 µm). These bandwidth filters were selected
across the visible and NIR regions, considering well-known
biophysical indices developed for vegetation monitoring. Image
triggering is activated by the UAV according to the
programmed flight route. At the moment of each shoot, the on-
board computer system records a timestamp, the GPS location,
the flight altitude, and the vehicle principal axes. The flight
time was restricted to approx. 30-45 min in our conditions of
use (altitude 120 m, payload 700 g, battery capacity 22000
mAh) (Figure 7).
Figure 7. Droning D820 UAV with Tetracam mini-MCA-6
The Tetracam PixelWrench 2 (PW2) software (Tetracam,
2016), supplied with the multispectral camera, was used to
perform the alignment process. The PW2 software provides a
band-to-band registration file that contains information about
the translation, rotation, and scaling between the master and
slave channels.
Another interesting sensor to increase the functionality of the
UAV is the FLIR Tau® 2 thermal imaging camera, which
includes radiometry, increased sensitivity (<30mK), and
640/60Hz frame rates (Flir, 2016).
3.2 Aircraft image acquisition
The flight with the plane was performed with sensors, a 6-band
multispectral imager (Tetracam, model mini-MCA), and a
thermal camera (FLIR model TAU) mounted on a Cessna 150
aerobat aircraft, flying at 200 m above ground level for a
resolution of 50 cm. Date: 2015/06/26T1330Z.
All images were then processed with QGIS software (Quantum
GIS Development Team (2.8.6)). Quantum GIS Geographic
Information System. Open Source Geospatial Foundation
Project. http://qgis.osgeo.org/es/site/.
3.3 Satellite image processing
The automatic IMIDA processing system generates agro-
meteorological products from Landsat 8. These images were
downloaded by http and stored in a directory; the products are
automatically detected and are inserted in the products archive.
The sensor network registers the air temperature observations
each hour; these data are collected and stored by the automatic
system of Fig. 8.
Figure 8. An automatic image processing system
Each Landsat 8 image received is processed to generate the
following products covering Murcia: Radiance, Reflectance,
NDVI, Land Surface Temperature –LST (B10 and B11), Soil-
adjusted vegetation index-SAVI, Leaf Area Index-LAI, and Air
Temperature –AT.
The system makes use of OGC standard web services: the SOS
(Sensor Observation Service) is used to store and make
available the in-situ data observations. It is responsible for
collecting the observations from the sensor network. It is
foreseen that this implementation will be based on SOS 52º
North (Erena et al., 2015).
Further details can be found in the LDCM Cal/Val algorithm
description document and the Landsat 8 Science Users’
Handbook available from the Landsat website (USGS, 2015),
(Chander et al., 2009), and (Ariza, 2013).
The description of the processing system and products is
available on the website http://idearm.imida.es/. (Erena, M., &
García, S., 2014)
Three indices were used in this study:
The Normalized Difference Vegetation Index (NDVI),
proposed by (Rouse, 1972), is one of the most widely used
vegetation indices. It is based on the distinctive radiometric
behavior of vegetation throughout certain spectral windows.
Healthy vegetation shows a characteristic spectral signature
with a clear contrast between the visible bands, especially the
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLI-B1, 2016
XXIII ISPRS Congress, 12–19 July 2016, Prague, Czech Republic
This contribution has been peer-reviewed.
doi:10.5194/isprsarchives-XLI-B1-809-2016
812

red band (0.630 - 0.680 µm) and near-infrared (0.845 - 0.885
µm). This marked difference between the absorption spectra in
the visible and near-infrared (NIR) of healthy vegetation allows
it to be distinguished from vegetation suffering some kind of
stress (water stress, for example, caused by drought), in which
there is less reflectance in the NIR and greater absorption in the
visible. The NDVI is calculated using the expression proposed
by (Rouse, 1972):
redNIR
redNIR
NDVI





 (1)
The Soil-Adjusted Vegetation Index - SAVI (Huete, 1988)
is calculated by the expression:
L)(1 *
)( 


LREDNIR
REDNIR
SAVI (2)
Where NIR is the reflectance value of the near infrared band,
RED is the reflectance of the red band, and L is the soil
brightness correction factor. The value of L varies according to
the amount or cover of green vegetation: in regions with a very
dense vegetation cover, L=0; and in areas with no green
vegetation, L=1.
ENDVI is calculated using the expression:


bluegreenNIR
bluegreenNIR
ENDVI






2
2 (3)
Where ρ = reflectance in the corresponding band.
4. RESULTS
4.1. UAV
This paper first presents a camera system designed for
georeferenced NIR orthophoto generation, which was reliably
used on a UAV (Figure 9). Although the orthophotos seemed to
be self-consistent, we carried out a ground control measurement
with GNSS to examine the relative and absolute accuracy.
Figure 9. ENDVI 2015/06/26T1300Z (0.1m)
4.2. Aircraft
The results obtained with the thermal camera highlight the great
differences in water stress among the different plots of the farm,
which condition the production - in terms of quantity and
quality - of the vineyard (Figures 10, 11, and 12).
Figure 10. Canopy temperature (ºC) 2015/06/26T1330Z (0.5 m)
Figure 11. TCARI index 2015/06/26T1330Z (0.5m)
Figure 12. PRI index 2015/06/26T1330Z (0.5 m)
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLI-B1, 2016
XXIII ISPRS Congress, 12–19 July 2016, Prague, Czech Republic
This contribution has been peer-reviewed.
doi:10.5194/isprsarchives-XLI-B1-809-2016
813

4.3. Satellites
4.3.1 Sentinel 2A-MSI: 2015/07/6: T1103Z (10m), one image
without clouds is available. In the preliminary results with
bands 2, 3, 4, and 8 of the MSI sensor, four distinct areas
attending to the geological origin of the plot can be
distinguished in Figure 13.
Figure 13. SII-MSI B8 2015/07/6:T1103Z (10 m).
4.3.2. Landsat 8-OLI Products: The NDVI was calculated for
all available dates and plot levels, for this initial study -
06.05.2015 to 11.09.2015 - and nine images without clouds are
available.
Using the NDVI/SAVI OLI based on the sensor, good
characterization of the plot is also achieved and the lower
spatial resolution is compensated by the higher frequency of the
index (Figures 14 and 15).
Conversion of the digital levels of radiance, brightness, and
temperature was performed using the methodology proposed by
(Chander et al., 2009). Using TIRS band 10, the four parcel,
differentiated by the geological origin of the plot, can be
distinguished Figure 16).
Figure 14. NDVI L8-OLI 2015/07/09:T1725Z (30 m).
Figure 15. SAVI L8-OLI 2015/06/23:T1725Z (30 m).
Figure 16. LSTB10 (ºC) L8-OLI 2015/07/09:T1725Z (30 m)
4.4. Recommendations for UAV design
The wide range of affordable UAV hardware and software now
available provides the opportunity to acquire high resolution
aerial photographic and video imagery of vineyards. The results
demonstrate that even simple approaches can lead to the
detection of slightly different canopy management methods
which have an impact on yield and quality. Thus, the use of
UAV-based imagery for small and fragmented vineyards has
potential for vineyard managers, especially in wine regions of
difficult access (Primicerio et al., 2013) and (Norzahari et al.,
2011).
This UAV platform is proposed as a tool that can meet the
needs of precision viticulture in terms of remote sensing, being
distinguished by the low cost, timeliness, and flexibility of the
measurements, customization of the equipment, full automation
of the flight plan, and high precision quality of the data
acquired. The UAV system developed by DRONING is a low-
cost solution that is open source and fully customizable.
The tests performed show that the UAV platform may provide a
tool that can be implemented at the farm level, even for small
businesses (Montesinos, 2015).
For future research, the development of advanced UAV systems
with thermal and spectral cameras (Flir-Tau 2/Tetracam mini-
MCA) would allow water stress to be monitored plant by plant.
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLI-B1, 2016
XXIII ISPRS Congress, 12–19 July 2016, Prague, Czech Republic
This contribution has been peer-reviewed.
doi:10.5194/isprsarchives-XLI-B1-809-2016
814

4.5. UAV flight specifications
Regarding the existing studies on the application of UAVs to
plant monitoring, the UAV flight must be configured to achieve
an optimal spatial resolution between 20 and 50 cm, adequate
for precision viticulture (Primicerio et al., 2013).
The UAV must be able to carry the payload of the camera and
to cover the area of interest in a short time (about 30-45 min), to
avoid changes in atmospheric and illumination conditions
(Torres-Sánchez et al., 2013).
4.6. Camera specifications
To calculate the indicators required for successful detection of
water stress with this state of the art technology, the UAV
should carry a sensor with thermal bands. Some of the
indicators, like NDVI, could be calculated when using just a
multispectral camera with appropriate bands. To calculate the
normalized canopy temperature, a thermal camera is required.
5. CONCLUSIONS
The understanding of the intra-vineyard variability is a
keystone to implement effective precision agriculture practices,
especially in Mediterranean environments where the land-use
patterns are highly fragmented and vineyards present high
heterogeneity because of variability in the soil, morphology,
and microclimate. Our study, based on the comparison of
different remote sensing platforms, shows that different
resolutions provide similar results in the case of vineyards
characterized by pronounced vegetation gradients and large
vegetation clusters. On the contrary, in vineyards characterized
by low vegetation gradients and high vegetation patchiness,
low-resolution images fail to represent intra-vineyard variability
and its patterns. Furthermore, considering the peculiarity of the
crop structure of vineyards, our work points out the
impossibility of distinguishing the canopy and inter-rows in the
case of low-resolution images. The cost analysis shows that,
beyond technical aspects, an economic break-even between
UAV and the other platforms exists between 1 and 30 ha of area
coverage, and that aircraft remote sensing remains competitive
with satellites above this threshold.
Using the periodic satellite images of Landsat 8 and Sentinel II
and the higher-resolution images obtained with UAVs, the
following can be achieved in precision viticulture:
- Identification and calculation of the spatial variability in crop
plots.
- Subdivision of the plots into homogeneous units for
agricultural management.
- Implementation of criteria for selective harvesting, according
to the quality parameters of the grapes in each homogeneous
management area.
- Establishment of sampling points in the field, taking into
account the variability of the crop to increase the
representativeness of the sample.
ACKNOWLEDGEMENTS
This work was done with the financial support of the research
project FEDER 14-20-15 (Design and implementation of spatial
data infrastructure on agriculture and water in the Murcia
Region-IDEaRM), 80% co-funded by the European Regional
Development Fund (ERDF). The authors are grateful to
Bodegas Juan Gil & Gil Family Estates for hosting the
experimentation.
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XXIII ISPRS Congress, 12–19 July 2016, Prague, Czech Republic
This contribution has been peer-reviewed.
doi:10.5194/isprsarchives-XLI-B1-809-2016
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