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4.2.6 CONCLUSIONS 
Temporal satellite imagery of IRS-C/ID PAN, LISS-III, WIFS 
and Radarsat had been analyzed in order to find out the 
probable causes related to the Sutlej River flash floods of 
Himachal Pradesh during August 1, 2000. It was observed 
during the study that more than one factor, namely 
impoundment and subsequent breaching of water bodies, 
cloudburst/snowmelt, landslides and earthquakes together 
responsible for that flash flood. Further, it is important to note 
that many lakes in the Tibet region are formed by blockade of 
river valleys by large alluvial deposits in the form of landslides/ 
mudflows, which in turn were activated by earthquakes/tectonic 
movements. Hence, there is a need to monitor the water bodies 
(lakes), snow cover/ glacier movements, earthquakes and 
associated affects using temporal satellite imagery at regular 
intervals. 
4.3 Landslide Hazard Zonation mapping- A case study of 
Pithoragarh area, Uttaranchal State 
4.3.1 Introduction: 
The case study covering a part of Pithoragarh district, 
Uttaranchal state lying between 29?30' &  29940'North 
Latitudes and 80°15°& 80°25’ East Longitudes. The area is 
falling under the tectonically active Himalayan belt. Tectonic 
activity together high rainfall has cumulative effect on the 
landslide process. The various thematic data layers (12 Nos.) 
were integrated using the Decision Space software in order to 
derive the output of Landslide Hazard Zonation (LHZ) Map 
(table —2). 
SL.NO INPUT DATA DATA SOURCE 
01 Rock Weathering Satellite data / Field data 
02 Land cover Satellite data 
03 Slope Toposheet / Aerial Photo 
04 Soil Texture Satellite Data / Field data 
05 Drainage density based on length = Toposheet /Satellite data 
06 Drainage density based on number Toposheet /Satellite data 
Lineament Density based on 
07 Satellite data 
Length 
08 Lineament Density based on Satellite data 
Number 
09 Lineament Density based on Satellite data 
Intersections 
10 Proxity to fault Satellite data 
11 Lithology Satellite data /Field data 
12 Road Network Toposheet /satellite data 
Remotely sensed data (IRS-1C/ID PAN and SPOT PLA) is the 
main source for the preparation of various thematic maps. Apart 
from SOI toposheets, geological map and collateral data 
(literature) also used for deriving thematic information. 
Landslide scar map interpreted visually based on the total 
variations, vertical disturbances, and accumulation of loose 
sediments in the toe of hill slopes and valley sides. Removal of 
loose material over debris slopes, arcuate scars on the steep 
slopes and partial blockade of roads, are some of the evidences 
associated with landslides. 
4.3.2 Methodology: 
The Decision Space was developed for handling complex 
spatial modeling problems like landslide hazard zonation. It 
provides tools for the decision-maker to define his spatial 
modeling problem under consideration, consolidate the list of 
parameters/ categories influencing the problem, and define the 
interdependency amongst the parameters. All the terrain 
related parameters like slope, landuse, geology, etc., are 
interdependent. One cannot model the problem close to reality 
if their interdependencies are not considered. Parameters are 
637 
spatial entities that influence the modeling problem and 
categories are subclasses within the parameters. For example, 
slope, land use, landforms, geology are some of the parameters 
that influence the landslide hazard zonation problem. While 
built-up land, Agricultural land, Forest etc., are the categories 
within the parameters of land use. Once, the model definition is 
complete, Decision Space poses a series of simple. queries 
regarding the relative importance of parameters/ categories 
towards the objective (landslide hazard zonation in the present 
case). The expert quantifies his/her opinion on a standard nine 
or three point scale. Once all comparisons are made, Decision 
Space will processes the data, synthesizes expert's judgment 
and calculates the priorities for parameters and categories. To 
help the expert refine his/her judgment, Decision Space 
performs a consistency check to identify any inconsistencies in 
the judgments. The expert can reconsider his/her judgments, 
revise them and see how it affects the decision. Each expert 
gives his/her own judgments and makes a distinct contribution. 
The expert can quantify his/her opinion on a standard nine- 
point (AHP method) or three-point (FOI method) scale. 
Different weights assigned by various experts can be integrated 
and ultimately a single set of final weights can be arrived at. In 
the present study, consensus of opinion was given to get single 
set of weights. The model thus created can be applied on 
different study areas to generate composite spatial output grid. 
The Decision Space also provides resource checking which 
enables users to know the parameter/ category names and the 
suitability rank at any point on the output grid. The problem 
was divided into a 5 level hierarchy and they are: 
Level 1 Objective 
Level 2 Experts 
Level 3 Parameters 
Level | 4 Interdependency among parameters 
Level 5 Classes within parameters 
Weightages were obtained using Factor of Importance method 
for the classes within parameters. Interdependency among the 
parameters was also considered and multiplied with the 
parameter weightage (in consideration) that has been obtained 
using AHP method. Following are the weightages obtained 
using Decision Space for the parameters and classes within 
each parameter (table-2). The weightages thus obtained were 
applied on spatial data and integrated with ideal point analysis, 
which resulted in a composite grid. This composite grid was 
reclassified into 6 hazard classes. Sensitivity analysis was 
performed by integrating the data layers with independent 
weightages for the parameters. Output obtained by Decision 
Space considering the interdependency among the parameters. 
The output map of the Decision Space was found perfectly 
matched with field conditions. Remedial measures were 
suggested for each hazard class by looking at the terrain 
conditions. VALIDATE, a module in Decision Space helped us 
to look back at the resource setting at each grid cell. 
4.3.4 Conclusions: 
The analytical methods like Analytical Hierarchy Process 
(AHP) and Compromise Programming method used in 
Decision Space to model the landslide problem in a realistic 
way. Adoption of AHP method has helped us to solve multi- 
criteria, multi-person problem efficiently. Adoption of 
compromise programming method gave non-compensatory 
solution where an increase in one unit in one criterion will not 
be compensated by a decrease by equal amount in another 
criteria. Since Decision Space is a general-purpose modeling 
framework any other natural hazards other than landslides can 
also be modeled. 
4.4 Decision Space software to locate favourable water 
harvesting sites: A case study of Alwar district, Rajasthan 
4.4.1 Introduction: 
In the past, dug wells had been used for drinking & agricultural 
requirements in the rural areas. With ever-increasing population 
pressure for natural resources, especially on agricultural and 
drinking water. Industrial needs also increased the demand for