International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B8, 2012 
XXII ISPRS Congress, 25 August - 01 September 2012, Melbourne, Australia 
72 
that the preponderance of vector (except winter months) was 2 
to 3 times higher than the critical density in summer and rainy 
months (from March - October) thereby suggesting probability 
of successful transmission subject to presence of host and 
conducive environmental situation.[2, 4, 6, 8, II] 
1.2 Study area and Materials 
In the previous study, endemic area of Vaishali district (5 
villages) and non-endemic area of Lohardaga district (5 
villages) have been taken and studied in detail for 
understanding geo-environmental parameters. Based on the 
encouraging results, the endemic area of Vaishali district 
covering 70.08 sq. km 
comprising 12 blocks of all 
villages have been taken for 
the present investigation (Fig. 
2). These villages mostly have 
mixed dwelling (house with 
cattle shed) with physical 
characteristics of the houses 
consisting of mud walls, mud 
floors, thatched roof etc. 
(Shreekant et al, 2010). 
Indian Remote Sensing 
satellite data of LISS IV (5.8 
m) multispectral and Cartosat 
- 1 panchromatic data (2.5 m) 
acquired during the year 2008 and 2009 over two seasons 
(summer and winter) have been used for the present 
investigation. Village wise Climatic variables viz. temperature 
(min & max) and humidity have also been collected. Field 
investigations have been carried out and information on disease 
incidence, Man-hour-density (MHD) i.c vector density measure, 
peri-domestic vegetation, housing pattern, nature of dwellings, 
and living conditions of the population have been collected. 
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India  
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Jharkhand ^ 
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Bihar 
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Lohardaga 
Vaishali .jMpi 
Fig. 2 Study area 
Fig. 3 Housing pattern near Gurhi village showing 
peri-domestic vegetation and mixed dwellings. 
During the field investigation, it was observed that most of the 
villages are having human dwellings with thatched roofs, mud 
walls with peri-domestic vegetation (Fig. 3). 
1.3 Methodology 
A systematic approach has been designed based on the 
information generated using satellite data, field and collateral 
data and a software package has been developed to estimate 
villages at risk in the endemic area. The overall methodology is 
given in Fig. 4. Land use / Land cover, Normalised Vegetation 
Index, wetlands, rural built up/settlement, peri-domestic 
vegetation etc. have been derived from satellite data using 
digital image processing techniques with reasonable accuracy. 
The understanding of sand fly life cycle became an important 
input for characterising geo-environmental parameters in micro 
scale in and around endemic villages. The overall methodology 
is divided into four different steps namely satellite data 
processing, collateral data analysis, statistical analysis and GIS 
model development for early warning system. 
Fuzzy 
Based Risk 
Otherground 
based 
Dwelling condition, Cattle 
population, Socio Economic 
Conditions 
Satellite 
S,3ËaÊsàm^ 
ik. », 
HP 
*1 
; Xc 
V'c 
v ir. J 
U—s 
-A1 
Visceral form of disease 
development inside the 
gut of Phlebotomous 
Argentipes takes four to 
five days for developing 
motile promastigotes in 
an adult after feeding 
on an infected host. 
Climate Data like 
Temperature, 
Humidity etc. 
Kala-Azar Disease 
Forewarning 
Fig.4 Overall Methodology 
Satellite data processing: IRS LISS IV and CARTOSAT -1 
satellite data pertaining to two seasons have been rectified using 
reference maps and the study area has been extracted. Different 
digital enhancement techniques like linear enhancement, 
histogram equalisation etc. have been applied on the satellite 
data. IRS LISS IV and CARTOSAT - 1 have been merged 
using Brovey transform to increase interpretability. Land use / 
land cover has been generated applying supervised 
classification technique using two seasons satellite data. False 
colour composite of IRS LISS IV shows numerous wetlands in 
the study area (fig.5). Normalised Difference Vegetation index 
has been performed to assess the vegetation vigour of the area 
in two seasons. 
Fig. 5 False color composite of IRS LISS IV covering 
Vaishali distrit 
Collateral data analysis: Temperature (Max & Min) and 
humidity data, Kala a/.ar disease incidence, cropping pattern, 
chemical analysis of soil data and Man-hour-density of vector 
(MHD) from most of the villages of Vaishali district have been 
collected and analysed. The analysis of MHD and disease 
dynamics indicate periodicity at seasonal and inter-annual 
temporal scale. From 2003 to 2007, there has been a steady