Annex 5.2 Development of regional database
CONTENTS
Executive summary
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Introduction
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Image crop classification
3
Vegetation Indices
4
Results of satellite images
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Constraints and assumptions
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Executive summary
The deliverable of Action 5 reports on development of regional database based on satellite data. Two
WV-2 satellite images (26/03/2010 and 22/07/2010) have been analysed in order to ensure that crop
and soil information is readily available to support management practice planning and development.
Analysis of the satellite imagery also provides the extent of the watershed area covered with cotton
leading to the development of a GIS database that processes soil type characteristics, water
consumption, agri-chemical inputs and attributes of the field sites to be integrated at the end of the
project.
Introduction
The second satellite image WV-2 (22/7/2010, after planting) was used to identify and calculate the
area covered by cotton cultivation in the watershed, as well as the mapping of vegetation indices
NDVI and NIR/RED. This task was performed by unsupervised and supervised crop classification. The
Normalized Difference Vegetation Index (NDVI) and NIR/RED index was computed through
GEOMATICA software.
Image crop classification
An unsupervised classification organizes image information into discrete classes of spectrally similar
pixel values. Unsupervised classification of satellite image WV2 performed to GEOMATICA software.
The unsupervised classification map is presented below.
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In supervised classification, one must rely on his own pattern recognition skills and knowledge of the
data in determining the statistical criteria (signatures) for data classification. To select reliable training
sites, one should have some information, either spatial or spectral, about the pixels that he wants to
classify.
The location of a specific characteristic, such as a land cover type, may be known through reports on
ground truth. Ground truthing refers to the acquisition of knowledge about the study area from fieldwork analysis, aerial photography, or personal experience. Ground truth data is considered to be the
most accurate (true) data available about the area you want to study and should be collected at the
same time as the remotely-sensed data.
The available sampling ground truth measurements for supervised classification have been limited
and there is a need to increase them in order to achieve fully reliable classification, which is expected
to be conducted in collaboration with NAGREF in the immediate future.
Vegetation Indices
The development of vegetation indices from satellite images have facilitated the process of
differentiating and mapping vegetation by providing valuable information about structure and
composition. In mediterranean green areas, the Normalized Difference vegetation Index (NDVI) from
very high spatial resolution satellite data such as IKONOS and Quickbird has demonstrated to be an
indicator of overall green biomass, canopy closure, tree density, and tree species diversity, and thus, it
seems a promising tool for studying the vegetation of selected agricutural fields of the study area.
NDVI capitalizes on the strong energy absorbed by the chlorophyll in the red portion of the
electromagnetic spectrum (RED), and on the energy scattered by the internal structure of leaves in
the near-infrared (NIR), and uses this contrast as an estimate of vegetation greenness, by the formula:
NDVI=(NIR–RED)/(NIR+RED)
The result is an image with a continuum of pixel values that range from –1 to 1. In general term,
negative values correspond to non-vegetated surfaces, while positive correspond to vegetated ones,
although the lower NDVI values for vegetation usually start in 0.2-0.3. In areas where vegetation
canopies do not achieve complete coverage, the NDVI is susceptible to the spectral influence of the
soil, giving the possibility of uncertainties in interpretation. The NIR/RED index had different behavior
from NDVI. NDVI and NIR/RED indices are presented below.
The NDVI map of WV-2 is classified in four indicative classes where the first class (dark green)
represents water and bare soil and the other three represent vegetation of increasing density. Since
cotton ad the end of July is usually at the highest vegetation cover this means that the red class
represents cotton crop. Similarly the yellow class could also partly represent cotton crop as well as
other crops.
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This product as is expressed in the above mentioned description and constrains and assumptions is
based on crop (cotton) classification and digital soil maps to be provided by NAGREF. Since there is a
need to increase the ground truth measurements in order to achieve fully reliable cotton classification
and the digital soil maps constitute a very large database and due to the great significance of this
product it was decided to arrange a working session (probably a few days) between UTH and NAGREF
staff at the installation of NAGREF in order to resolve this matter.
Results of satellite images
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Maps of NDVI of satellite image for Elefterio, Gyrtoni, Gyrtoni (new), Gentiki and Omorfohori (new)
fields.
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Maps of Nir/Red of satellite image for Elefterio, Gyrtoni, Gyrtoni (new), Gentiki and Omorfohori (new)
fields.
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Constraints and assumptions
Time constraints for the availability of high quality satellite imagery for before planting and at the
stage of crop development, and for access to available digital soil data. The classification of the
management zones using the satellite imagery depend on the variability of SOM and other soil
characteristics in the plot areas, as possible homogeneity in soil conditions make the management
zone delineation difficult to be achieved. Thus, time is needed in order to assess the performance of
different methodologies for management zone delineation using satellite images, and also for the
choice of the satellite image product most suitable for the action’s scope. This action also depends
greatly on the availability of ground measurements and information at the plots from previous
actions.
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