56 
Coal fire problem: The near surface upper seams, 
consisting of good quality coking coal, have been 
extensively mined in the past before nationalisation 
(1971-1973) by the erstwhile private mine operators. 
The upper seams, consisting of good quality coking 
coals are more prone to self combustion when 
exposed to atmosphere due to mining. As a result, 
continued heating over a prolonged period gave rise 
to mine fires. 
In order to provide a solution to the coal fire 
problem, it is necessary to know the exact location 
and extent of fire affected areas. Remote sensing in 
thermal region is an appropriate cost and time- 
effective technique to locate fire areas by virtue of 
their surface thermal anomalies. Remote sensing 
techniques also play a significant role in monitoring 
fire areas and fire fighting operations. For mine fire 
mapping and classification, both TM and airborne 
TIR scanner data were used. When time series data 
was analysed for year-wise fire area statistics, it was 
estimated that in 1985, the total fire area was 5.57 sq. 
km which increased to 15.92 in 1991, further 
decreased to 8.48 in 1994 and again increased to 8.92 
in 1995 (Bhattacharya et al, 1995a; Bhattacharya 
and Reddy, 1995b). Fire source depth modelling was 
also attempted following downward continuation and 
heat flow methods (Bhattacharya and Reddy, 1992). 
G1S based analysis: The year-wise mine fire maps 
obtained from remotely sensed data processing are 
subjected to GIS based analysis with a view to assess 
the temporal behavior of mine fire areas. For this 
purpose, the multilayer modelling module available 
in EASI/PACE software and also the 1DRISI GIS 
packages have been used. Initially, year-wise, 
geological formation-wise and colliery-wise fire area 
statistics have been generated by using appropriate 
GIS coverages. The mine fire maps corresponding to 
years 1985, 1987, 1989, 1991 and 1993 have been 
combined based on a logical overlay model approach 
and a final composite fire dynamics map of Jharia 
coalfield has been derived. 
Land subsidence: Due to acute problem of 
underground coal mine fire, many areas in Jharia 
coalfield are facing land subsidence. This problem is 
comparatively less in Raniganj coal field. All the 
land subsidence areas and the different 
infrastructures endangered from the fire point of 
view are mapped. 
Lead-Zinc Underground Mining 
The study area (Rajpura-Dariba, Rajasthan) is 
bounded by N latitude 24°50’ to 25°05’ and E longitude 
74°05’ to 74° 15’, falling in SOI toposheet nos. 45K/4 
and L/l. 
Due to underground mining for metallic minerals, 
impact felt is much less as compared to bauxite and coal 
mining districts, except some loss of agricultural land 
and soil loss due to overburden dump. Air pollution is 
minimal. Lowering of ground water table has occurred 
in limited areal extent near the mining activity. 
CONCEPTUAL MODEL FOR 
LAND DEGRADATION 
It is obvious from the above case studies that 
remote sensing can provide enough information on 
impact of mining over land degradation. Considering the 
various factors related to type of land loss due to mining, 
a conceptual model can be developed. While mining 
operation is already on without a prior environmental 
plan, it is a hard task at that time to take up measures for 
land conservation and rehabilitation. Hence, it is wise to 
prepare such a model and plan before the operation starts 
so that the mining activity can be taken up smoothly. It 
is statistically observed that in any mining operation, 
whether opencast or underground, metallic or non- 
metallic, the different types of lands affected or 
environmental hazards created are: agricultural land, 
forest cover, soil loss, infrastructural facilities, land 
subsidence, ground water table lowering and pollution, 
air pollution and so on. For coal mining, apart from this, 
another very special situation may crop up, i.e. coal 
mine fire. Before mining operation to be taken up in any 
particular area, data base on such themes/factors as 
mentioned can be created in GIS environment. Each 
factor contributes to the overall ‘Land Degradation’ 
(LD) with varying amount of intensity. Thematic maps 
of all the factors can then be integrated using GIS 
approach. In GIS, data from various sources are 
generated, stored and analysed at a particular geographic 
location. The data is then superimposed using a common 
reference geographic grid. Depending on the areal extent 
of mining influence, the grid size can be selected and the 
entire area can be gridded. ‘LD’ weightages 10-1 
(maximum - minimum) can be assigned to the different 
factors prioritising the contribution to land degradation, 
e.g. in a particular grid of a particular area, if forest 
cover loss contributes maximum to land degradation, its 
weightage is assigned as 10 whereas if infrastructure 
facility has no effect, the weightage is given as I. 
Accordingly, weightages for other themes/factors are 
set. Thus, e.g. considering, five thematic qualitatively 
indexed maps, five quantified maps in raster (grid) 
format can be generated and superimposed over each 
other. The number of maps can go even to ‘n’ depending 
on the number of factors/themes responsible for land 
degradation. For each grid centre, there are five values 
from five maps. All these values are multiplied and log 
of that value is put on the composite map. The