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) .