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Application of remote sensing and GIS for sustainable development

integrated assessment of natural resources. Integrated
assessment can be defined as an interdisciplinary process
of combining, interpreting and communicating
knowledge from diverse scientific disciplines. The aim
is to describe the entire cause-effect chain of a problem
so that it can be evaluated from a synoptic perspective.
Integrated assessment has two characteristics: (i) it
should provide added value compared to single
disciplinary assessment; and (ii) it should offer decision
makers useful information (Rotmans and Dowlatabadi
(1996). Integrated assessment is an iterative, continuing
process, whereby on the one hand comprehensive
insights from the scientific community are communi
cated to the decision making community, and on the
other, lessons learned by decision-makers contribute to
the input for scientific investigations.
The information on nature, extent, spatial
distribution, and potential and limitations of natural
resources is a pre-requisite for planning the strategy for
sustainable development. In addition, socio-economic
and meteorological, and other related ancillary
information is also required while recommending locale-
specific prescriptions for taking up curative or
preventive measures. By virtue of synoptic view of a
fairly large area at regular interval, spaceborne
multispectral data have been used at operational level for
generating base line information on mineral resources,
soils, ground water and surface water, land use/iand
cover, forests, etc. at scales ranging from regional to
micro level i.e. 1:250,000 to 1:12,500 scale and
monitoring the changes, if any, over a period of time.
Beginning with the Landsat-MSS data with a 60x80 m
spatial resolution and four spectral bands spanning from
green to near infrared in early seventies, the natural
resources scientists had access to Landsat-TM data with
a 30m spatial resolution and seven spectral bands spread
over between blue and thermal infrared region of the
electromagnetic spectrum in early eighties which helped
further refinement and generation of thematic
information at further larger scale. Further, high spatial
resolution HRV-MLA and PLA data with 20m and 10m
spatial resolution, respectively from SPOT series of
satellite in later half of eighties have supplemented the
effort of generating information on natural resources.
The indigenous effort on design and development
of satellites and sensors led initially to the launch of
Indian Remote Sensing Satellite (1RS-1A and B),
carrying Linear Imaging Self-scanning Sensors (LISS-I
and II) with the spatial resolution comparable with those
of Landsat MSS and TM, respectively in late eighties
and early nineties. Further development in the sensor
technology had resulted in the launch of the state-of-the-
art satellite (IRS-IC) in December, 1995 with the
following three unique sensors:
(i) Wide Field, sensor (WiFS) with 188 m spatial
resolution, two spectral bands - red and near
infrared, 810 km swath and a repetivity of 5 days.
(ii) Linear Imaging Self-scanning Sensor (LISS-III)
with 23.5 m spatial resolution in the green, red and
near infrared region, and 70.5 m in the middle
infrared region, and 140 km swath.
(iii) Panchromatic (PAN) camera with 5.8 m spatial
resolution, 70 km swath and stereo capability.
While WiFS with 5-day repetivity and large swath
to provide regional level monitoring of crop condition
assessment, LISS-I 11 multispectral sensor with 140 km.
swath provides detailed level crop acreage estimation
and crop condition assessment. PAN data with 5.8m
spatial resolution and stereo capability enables
appreciation of terrain’s relief. Merging LISS-III data
with PAN offers additional advantage of exploiting both
spectral information from LISS-III and high spatial
resolution from PAN for such applications as
geomorphological mapping, soil resources mapping and
terrain analyses. The uniqueness of these sensors lies in
the fact that all the sensors with regional and local level
coverage are mounted on the same platform and collect
data under similar illumination conditions, hence
avoiding the need for radiometric normalization.
Further, the development of launch vehicles
especially Polar Satellite Launch Vehicle (PSLV) has
enabled India, launching three experimental satellites,
namely IRS-IE in September, 1993, IRS-P2 in October
1994 and IRS-P3 in March, 1996. The IRS-P3 has two
payloads namely Wide Field Sensors (WiFS) same as
the one aboard IRS-1C/1D, and Modular Electro-optical
Scanner (MOS) with 13 channels spanning from blue to
middle infrared region of the electromagnetic spectrum.
For visual interpretation, the standard false colour
composite (FCC) prints generated from green, red and
near infra-red bands have been used. Flowever, special
products with varying combination of spectral bands
have also been tried out for certain specific applications.
For instance, red, near infrared and short wave infrared
combination has been found to help improved
delineation of lithological boundaries - an important
element in soil resources mapping.
Apart from supervised classification of digital
multispectral data, new classification algorithms like
fuzzy logic, artificial neural, network, etc have been
developed which help refining the information generated
on natural resources using Gaussian maximum
likelihood per-pixel classifier. Further, using advanced
image fusion techniques like Intensity, Hue and