On the basis of observed data between time periods, MCA computes the probability that a cell will change from oneland use type (state) to another within a specified period of time.

The probability of moving from one state to another state is called a transition probability. From which exact areaexpected to be change is calculated.

Modelling and Analyzing the Watershed Dynamics using Cellular Automata (CA) -Markov Model –A Geoinformation Based ApproachSANTOSH .N. BORATEUnder the Guidance of

Prof. M D BEHERASCHOOL OF WATER RESOURCES

IIT KHARAGPUR, KHARAGPUR-721 302

INTRODUCTION

Watersheds are very crucial as they provide water that meets the different water demand ranging from

drinking, irrigation, industry, power generation etc. The effects of change in watershed dynamics are leading to a series of

environmental problems, such as deterioration of water quality, biodiversity loss, extinction of aquatic species, alternation of

river flows, shortage of water resources, and so on.

For sustaining development of watershed there is need of Watershed Modeling which implies the proper use of all land, water

and natural resources of a watershed for optimum production with minimum hazard to eco-system and natural resources.

Modelling of watershed dynamics helps to policymaker and decision maker in making the policies and taking the decisions for

optimum utilization and sustainable development and management of resources in watershed respectively.

A Remote sensing technique and GIS tool is used taking and process the images of watershed of different time periods. CA-

Markov model approach is used that integrates the Markov and CA models with the use of a multicriteria decision-making

technique is used in predicting the future watershed resources information.

OBJECTIVES

The objective of the study to model and analyze the watershed dynamics change using Cellular Automata (CA) -Markov

Model and predict scenarios for next 10 years . The specific objectives are:

• To generate land use / land cover database with uniform classification scheme using satellite data

• To create database on demographic, socioeconomic, Infrastructure parameters

• Analysis of indicators and drivers and their impact on watershed dynamics

• To derive the Transition Area matrix and suitability images based on classification

• To project future watershed dynamics scenarios using CA-Markov Model

• To give the plan of measures for minimize the future watershed dynamics change

STUDY AREA

River basin map of India

• Drainage Area = 195 sq.km• latitude- 20 29 33.39 to 20 40 21.09 N•Longitude- 85 44 59.33 to 85 54 16.62 E•Growing Industrial Area

Mahanadi River Basin

DATA AND METHODOLOGY

Classification of the satellite data

Drainage Network Soil Type Altitude

Population Density

Road network

Calculation of LU/LC area statistics (for different periods)

Obtain Transition Area Matrix (TAM) by Markov Chain Analysis and Suitability Images by MCE

Industrial StructureUrban Sprawl Slope

Run CA- Markov model in IDRISI- Andes by giving -1) Basis land Cover Image, 2) TAM and 3) Suitability Image as inputs

Analysis of drivers responsible for watershed change

Predict Watershed Dynamics for future 10-Years from the obtained trend

Toposheet 1945 MSS 1972 TM 1990 ETM+ 1999 TM 2004

CA-Markov model operation

CA-Markov model is used for predicting the land cover changes. This is a holistic approach that integrates the Markov and

CA models in which Markov Model gives the Transition Area Matrix (Area expected to change) while Cellular Automata

gives the Suitability maps for each class. These both the outputs are used as inputs in CA-Markov Model

Markov Chain Analysis (MCA)

Table 1: Shows the year wise area of different classes in watershed

1972 1990 1999 2004

Fig1. Unsupervised Classification for different time periods

Year

Area (ha)

Water Wet land Marshy land Dense forest Open forest Settle-ment Agricul-ture

1972 493.52 1013.03 2922.47 7329.09 5405.68 432.766 1926.33

1990 507.08 959.023 1574.05 6353.09 5725.06 653.049 3963.78

1999 790.88 550.46 1120.17 5709.63 5186.21 1160.62 5403.49

2004 472.68 281.88 818.91 5296.32 4763.25 1382.85 6532.56

a) Spatial layer of Soil classes in watershed

Spatial Layer Generation of Socioeconomic, Infrastructure parameters

b) Spatial layer of Land Use Land Cover of the watershed

c) Spatial layer of Road network, Drainage

Network

d) Spatial layer of slope

0

2000

4000

6000

8000

1960 1980 2000 2020

Marshy Land

Dense Forest

Open Forest

Wetland0

1000200030004000500060007000

1960 1980 2000 2020

Settlement

Agriculture

0

200

400

600

800

1000

1200

1970 1980 1990 2000 2010

Water

Fallow Land

Decreasing Trend of LULCIncreasing Trend of LULC

Land Use/Land Cover Change trends:

Area (ha)

Year

Area (ha)

Year

Area (ha)

Year

CONCLUSION

ACKNOWLEDGEMENTS

From the table and graphs it is observed that Dense forest, open forest, wet lands, Marshy Land are drastically are

decreasing and transformed in to other classes.

While the Settlement and Agriculture classes are drastically increasing which obtains the area from above four

classes which are decreasing in area.

A combined use of RS/GIS technology, therefore, can be invaluable to address a wide variety of resource

management problems including land use and landscape changes in watershed

I express my sincere gratitude to Prof. M D Behera for his proper and timely guidance through out the period of work.

I am thankful to Prof. S N Panda and JRF and SRF in SAL (Spatial Analytical Lab) of CORAL Department

for their help and support.

Data download and Layer stack

Georeferencing and Reprojection

Area extraction

Multitemporalimage

Classification

Preparing Ancillary Data

Statistics

TAM and Suitability Images

Simulation

Analysis

Prediction

Cellular Automata (CA)

Spatial component is incorporated

Powerful tool for Dynamic modelling

Each row represents a single time step of the automaton’s evolution.

St+1 = f (St,N,T) where St+1 = State at time t+1

St = State at time t

N = Neighbourhood

T = Transition Rule

where P = Markov transition probability matrix

P i, j = the land type of the first and second time

period

Pij = the probability from land type i to land type j