580
LANDSAT) by United States of America in 1972. Since then, remote sensing has
made rapid advances in the last four and a half decades, in terms of the increased
spatial, spectral, radiometric and temporal resolutions. Spatial, spectral and, radiometric resolutions are the key sensor parameters which defines the quality of images
captured by these sensors. These three types of resolutions are responsible for discrimination and identification of different objects on the image. Spatial resolution is
defined by the pixel size and is governed by the instantaneous field of view (IFOV)
of the collecting optics of the sensor system. Basically, it is the capability of the
sensor to discriminate and identify the smallest object on the ground (Joseph 2007).
Spectral resolution is basically the spectral bands in which images are taken by a
particular sensor. Radiometric resolution is the capability of the sensor to discriminate two targets based on their reflectance/emittance difference. It is measured in
terms of the smallest radiance difference, emitted or reflected by two targets that can
be detected. Higher the radiometric resolution, smaller the radiance difference
(reflected/emitted by the two targets), that can be detected. The fourth one, the temporal resolution, is the capability of sensors to view the same ground area at frequent interval of time.
During the last one and a half decades, Earth Observation (EO) satellites have
been providing data with sub meter spatial resolutions in panchromatic (e.g.
IKONOS, QuickBird, Worldview-3, Cartosat-2). Multispectral and stereo data are
available with 2.5–30 m spatial resolutions from remote sensing satellites (e.g.
Resourcesat-2, SPOT-7, LANDSAT-8 Thematic Mapper, ASTER, Cartosat-1). On
the other hand EO satellites also provide medium spatial resolution data at a very
high repeat cycle (MODIS, AWiFS on Resourcesat-2, Oceansat-2). Above mentioned data, available from EO satellites, have been widely used world over, for
inventory, monitoring and management of natural resources, area development
planning, urban and infrastructure development, environment monitoring and
assessment as well as for disaster mitigations.
Remote sensing data collected from space platforms have also been extensively
used in mapping and monitoring of coastal zones, its environment, coastal ecosystems and ecologically sensitive areas (ESA) in addition to assessing the ocean biological resources. Various coastal features, landforms, vital habitats and ecologically
sensitive areas are very well discernible on the images acquired by remote sensing
or Earth Observation (EO) satellites.
As an example, Fig. 19.1 shows IRS LISS-3 FCC (False Colour Composite)
image of the part of gulf of Kachchh, western coast of India. In FCC images, vegetation is seen in red colour, water in blue, barren land in light grey, and sandy area and
salt effected land are seen in white. Tonal variations in red colour (light to dark red)
indicate the increasing vigour or density of the vegetation. Similarly, dark blue tone
represents deep water and light blue represents either the shallow water or turbid
water. Information extraction from these images, through visual interpretation, is
done on the basis of colour, tone, texture, association, shape, size and location of the
features appearing on the image. For example, mangroves can be discriminated
from the other coastal vegetation as well as terrestrial vegetation based on its tone
(red/pale red), association (in warm waters, on mudflat substrate and low energy
Ajai and H.B. Chauhan
LANDSAT) by United States of America in 1972. Since then, remote sensing has
made rapid advances in the last four and a half decades, in terms of the increased
spatial, spectral, radiometric and temporal resolutions. Spatial, spectral and, radiometric resolutions are the key sensor parameters which defines the quality of images
captured by these sensors. These three types of resolutions are responsible for discrimination and identification of different objects on the image. Spatial resolution is
defined by the pixel size and is governed by the instantaneous field of view (IFOV)
of the collecting optics of the sensor system. Basically, it is the capability of the
sensor to discriminate and identify the smallest object on the ground (Joseph 2007).
Spectral resolution is basically the spectral bands in which images are taken by a
particular sensor. Radiometric resolution is the capability of the sensor to discriminate two targets based on their reflectance/emittance difference. It is measured in
terms of the smallest radiance difference, emitted or reflected by two targets that can
be detected. Higher the radiometric resolution, smaller the radiance difference
(reflected/emitted by the two targets), that can be detected. The fourth one, the temporal resolution, is the capability of sensors to view the same ground area at frequent interval of time.
During the last one and a half decades, Earth Observation (EO) satellites have
been providing data with sub meter spatial resolutions in panchromatic (e.g.
IKONOS, QuickBird, Worldview-3, Cartosat-2). Multispectral and stereo data are
available with 2.5–30 m spatial resolutions from remote sensing satellites (e.g.
Resourcesat-2, SPOT-7, LANDSAT-8 Thematic Mapper, ASTER, Cartosat-1). On
the other hand EO satellites also provide medium spatial resolution data at a very
high repeat cycle (MODIS, AWiFS on Resourcesat-2, Oceansat-2). Above mentioned data, available from EO satellites, have been widely used world over, for
inventory, monitoring and management of natural resources, area development
planning, urban and infrastructure development, environment monitoring and
assessment as well as for disaster mitigations.
Remote sensing data collected from space platforms have also been extensively
used in mapping and monitoring of coastal zones, its environment, coastal ecosystems and ecologically sensitive areas (ESA) in addition to assessing the ocean biological resources. Various coastal features, landforms, vital habitats and ecologically
sensitive areas are very well discernible on the images acquired by remote sensing
or Earth Observation (EO) satellites.
As an example, Fig. 19.1 shows IRS LISS-3 FCC (False Colour Composite)
image of the part of gulf of Kachchh, western coast of India. In FCC images, vegetation is seen in red colour, water in blue, barren land in light grey, and sandy area and
salt effected land are seen in white. Tonal variations in red colour (light to dark red)
indicate the increasing vigour or density of the vegetation. Similarly, dark blue tone
represents deep water and light blue represents either the shallow water or turbid
water. Information extraction from these images, through visual interpretation, is
done on the basis of colour, tone, texture, association, shape, size and location of the
features appearing on the image. For example, mangroves can be discriminated
from the other coastal vegetation as well as terrestrial vegetation based on its tone
(red/pale red), association (in warm waters, on mudflat substrate and low energy
Ajai and H.B. Chauhan