for various natural resources applications in India. The following are theme
specific potentials of IRS-1A data (DOS, 1988, 1988A).
2.1 Agriculture
Crop acreage and production using IRS LISS-I data for paddy and wheat has been
estimated covering several districts in India for the States of Punjab, Haryana (for
wheat), Orissa and Tamil Nadu (for paddy). For example, the remote sensing based
wheat acreage estimation for Punjab for Kharif crop of 1989 gave an estimate of
3.128 + 0.172 M.ha compared well with Bureau of Economic and Statistics (BES)
estimate of 3.126 M.Ha. The crop acreage estimates using IRS data have provided
better results in comparison to Landsat MSS data. All the estimates have satisifed
the 90/90 accuracy criterion.
In case of acreage estimation for dryland crops like Sorghum, the results have shown
that IRS LISS-I shows improved accuracy over Landsat MSS data due to higher
radiometric sensitivity and detectivity determined by their S/N ratios. Digital
analysis of IRS-1A data has been found to provide highly accurate estimates of
acreage under mulberry and cotton crops. These two plant species provide either
directly or indirectly the raw material for natural fibre. The Ministry of
Textiles, Govt, of India is making use of the IRS-1A data, on an experimental basis,
to forecast the production of silk and cotton so as to determine the exportable
surplus. The IRS-1A data has also been tried for locating the illegal cultivation
of narcotic plants and in the inventory of commercial agricultural plantations like
tea and coffee in the hill areas of southern and north-eastern regions of India.
The IRS data is also being applied for crop yield estimation. The yield models are
being developed and the operational packages for mono cropped areas of Punjab and
Haryana have been transferred for routine operational annual crop acreage
estimations. Efforts related to multi-crop estimation using IRS data are presently
on.
2.2 Forestry
Several case studies have been carried out in the field of forestry using IRS-1A
data both by visual and digital interpretation techniques to study forest type
descrimiantion and forest cover mapping. The studies indicate that details on
forest types on LISS-I data are comparable with that on Landsat MSS data.
Interclass boundary discrimination is found to be better on LISS-II data as compared
with Landsat Thematic Mapper imagery. Very good discrmination of forest cover
species such as Deodar, Pines (old), Pines (young), Oak and Shorea forests have been
noted in Himalayan areas specially around Shimla using supervised classification of
LISS-II data. The results are also corroborated from other studies (Roy, et.al,
1988) .
Feasibility of the use of magnified LISS-II imagery in preparation and updating of
forest stock maps within 90% accuracy has also been demonstrated through a case
study in Jatga range. Case study carried out in Haliyal and Belgaum forest
divisions of Karnataka indicated that differentiation between evergreen, moist
deciduous and dry deciduous forest types is better observed on LISS-I data than the
Landsat MSS data. (Jadhav, et.al, 1988). The Biennial monitoring for forest cover
over India being carried out by Forest Survey of India is planned to be conducted
using IRS-1A data.
2.3 Landuse/Land Cover Mapping
A large number of Landuse/Land Cover projects using IRS data have been carried out
in India and many are in progress. The results of landuse/land cover and urban
landuse case studies carried out using IRS LISS-II data for different
towns/districts using visual and digital interpretation techniques indicate that
Landuse details upto Level-2 are possible using LISS-II data. LISS-I data is useful
for Level-I mapping and is comparable in information to Landsat MSS data. Image
processing techniques such as stratification approach used for landuse
classification, HSI transformation and colour composites, edge enhancement,
supervised classification, contract stretching, filtering, principal Component
analysis and colour compositing and FCC (Band 2,3,4 and Band 1,3,4) were found very
effective in discrimination/classification of landuse categories. Landuse features
such as Irrigated and Non-irrigated lands, open and closed forests, open scrub,
wastelands, waterbodies, orchards, plantations, vacant lands, grasslands, dense and
sparsely built up areas, rail-road network, recreational areas, parks, airports,
golf course, industrial areas, city growth directions, settlements, cropland etc.
are easily differentiated on LISS-II data. The hard copy of Gaussian stretched IRS
LISS-II data offers good discrimination between different landuse features (DOS,
1989) .