-B8, 2012 
  
mate Data like 
emperature, 
lumidity etc. 
  
CARTOSAT -1 
en rectified using 
tracted. Different 
r enhancement, 
| on the satellite 
ve been merged 
ility. Land use / 
ing supervised 
ellite data. False 
rous wetlands in 
Vegetation index 
igour of the area 
he vicinity 
  
SS IV covering 
ax & Min) and 
cropping pattern, 
density of vector 
listrict have been 
HD and disease 
and inter-annual 
as been a steady 
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 
increase in the annual number of disease cases. The Annual 
vector density of P.argentipes is reflected in the form of two 
peaks. Disease epidemic starts at summer and peaks in the 
March - April and major peak is seen in August-September 
[1,3]. Dry and humid with higher temperature is favourable 
climatic condition to the vector in the endemic area. Table - 1 
shows population affected and death occurred in twelve blocks 
of Vaishali district which is reported in Public Health Centres 
during the years 2008-2010[9]. The population at risk in Mahua 
block indicates the maximum of 419, 198 and 191 respectively 
during the years 2008-2010. It is also observed that a large 
number of disease incidence is not reported in the PHCs 
regarding disease incidence of the study area. 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
Name of the 2008 2009 2010 
Block Affected Death Affected Death Affected Death 
Hajipur 206 0 129 0 133 0 
Bidupur 126 0 85 1 82 0 
Vaishali 275 0 195 1 185 1 
Goraul 166 1 80 0 95 1 
Mahannar 120 0 98 0 91 1 
Mahua 419 0 198 0 191 1 
Lalganj 241 0 150 0 106 0 
Sadai Buzurg 99 0 75 0 95 0 
Jandaha 204 0 99 0 105 0 
Patepur 198 0 151 0 97 0 
Raghopur 385 1 302 0 368 0 
Sadar Hospital 348 1 321 2 441 3 
Total 2787 3 1883 4 1989 7 
  
  
  
  
  
  
  
  
  
Table 1- Disease incidence in various Blocks / PHC of 
Vaishali District 
Multivariate Analysis: A Systematic study of geo- 
environmental parameters derived from satellite data in 
conjunction with ground intelligence enabled modelling of 
infectious disease and risk villages. High resolution Indian 
satellites data of IRS LISS IV (multi-spectral) and Cartosat-1 
(Pan) have been used for studying environmentally risk 
parameters viz. peri-domestic vegetation, dwelling condition, 
wetland ecosystem, cropping pattern, Normalised Difference 
Vegetation Index (NDVI), detailed land use etc towards risk 
assessment. Univariate analysis of the relationship between 
vector density and various land cover categories and climatic 
variables suggested that all the variables are significantly 
correlated. Using the significantly correlated variables with 
vector density, a seasonal multivariate regression model has 
been carried out incorporating geo-environmental parameters, 
climate variables and seasonal time series disease parameters. 
Linear and non-linear models have been applied for periodicity 
coeff p-value 
correlation 
108(0.063) 
  
Table 2: Statistical analysis of endemic and non-endemic 
area 
and inter-annual temporal scale to predict Man-hour-density 
(MHD). Out-of-fit data have been used for validating the model. 
Multivariate regression analysis has been carried out using geo- 
environmental parameters and climatic variables and derived 
coefficients are shown in equation below. 
  
Multivariate Regression Equation for Vector MHD 
Z = - 42.23 + (Temp. x 0.597) + (Humd. x 0.684) — 
(Dry fallow x 0.170) + (Min. NDVI x 11.44) 
Where, Z is the estimated man-hour-density. 
  
  
  
The statistical analysis of the parameters indicate that average 
maximum temperature, humidity, settlement with vegetation, 
dry fallow, moist fallow, minimum NDVI are significantly 
correlated with MHD (Table-2). 
GIS Model: The patterns in the dynamics of the vector diseases 
are their periodicity and seasonal and inter-annual temporal 
scale forms the basis for the development of Early warning 
system [7]. Satellite data forms the primary input for generation 
of geo-environmental parameters. The following data sets have 
been generated using satellite data processing /digitisation and 
organised in the GIS environment( Table 3). 
  
1| Base layers consisting of road, rails, village 
locations and administrative boundaries like village 
boundary, block / taluk boundary, district boundary 
etc 
Land use / land cover map 
Normalised Difference Vegetation Index 
Soils (type, ph etc) 
Peri-domestic vegetation 
Cropping pattern (crop calendar, crops etc) 
Hydro-meteorological data (rain fall, humidity, 
temperature etc) 
Disease Vector (MHD, incidence etc) 
9| House survey (Nature of the house, domestic 
animals, roofs, type of walls etc.) 
  
  
  
  
  
  
Aal SION 
  
oo 
  
  
  
  
  
Table 3 — List of variables used for the analysis 
To improve the MHD predictive approach, fuzzy model has 
been developed and incorporated in GIS environment 
combining spatial geo-environmental and climatic variables by 
applying a Boolean logic for the members of the membership 
function and the equation is given below. 
The combined Fuzzy membership function is defined as 
u = (Fuzzy algebraic Sum) Y * ( Fuzzy algebraic Product ) FM 
n 
Fuzzy algebraic sum z 1- II. (1- pi) 
i=1 
n 
Fuzzy algebraic product = IT (Hi) 
i= 
where p ; is the fuzzy membership for ji” map and i= 
1,2,3,......n maps are to be combined and where yi combined 
fuzzy membership of Y operation, Y is the parameter chosen in 
the range of (0,1) such that when Y is 1, the output of combined 
fuzzy membership value equals to same as fuzzy algebraic sum 
and when Y is 0, the combination equals to the fuzzy algebraic 
product. Therefore, a judicial choice of Y factor produces 
output values that ensure the compromise between increasive 
tendencies of the fuzzy algebraic sum and the decreasive effect 
of the fuzzy algebraic product. Jeyaram, (2008) has used the 
above techniques successfully in prioritising water conservation