Semester End Seminar

Modelling and Analyzing the Watershed Dynamics using Cellular Automata (CA) -Markov Model –A Geoinformation Based Approach

SCHOOL OF WATER RESORCES

INDIAN INSTITUTE OF TECHNOLOGY KHARAGPUR

Semester End Seminar

19-11- 2009

Prepared by

SANTOSH BORATE

08WM6002

Under the guidance of

DR. M. D. BEHERA

CONTENTS• Introduction

• Review of Literature

• Aim and Objectives

• Study Area

• Methodology

• Model Description

- Markov Chain Analysis (MCA)

- Cellular Automata(CA)

- CA-Markov model in IDRISI- Andes

• Work Done

• Work to be done

• Conclusion

• Acknowledgement

Introduction

• Watershed, Land Use/ Land CoverDefinition

• Implies the proper use of all land, waterand natural resources of a watershed

Need of Watershed Modelling

• Prerequisite for Land Use Land CoverChange (LULCC) detection

Image classification

• Understand relationships & interactionswith human & natural phenomena tobetter management

Change detection

• Remote sensing & GIS tools providessynoptic coverage & repeatability thus iscost effective

Use of advanced spatial technology

tools

Introduction

Review of Literature

Methodology

Aim and Objectives

Study Area

Model description

Work to be done

Work Done

Acknowledge-ment

Conclusion



IntroductionGautam (2006) done the watershed modelling for Kundapallam watershed usingremote sensing and GIS by considering the main causes like changing of land use fromforest into pasture, agriculture and urban, as a result of population growth and generalscarcity, use of the wood as a source of heat and energy in economically poor area,also general degradation of forests caused by industrial growth, Environmentalpollution, and an increase of consumption.

Alemayehu et al. (2009) assessed the impact of watershed management on land useand land cover dynamics in Eastern Tigray (Ethiopia) and determined the land use andcover dynamics that it has induced.

Daniel G. Brown(2004) Introduced the different type of models for LULCC Modeling inrelation to the purpose of the model, avaibility of data , drivers responsible for LULCC.

Soe W. Myint and Le Wang(2006) This study demonstrates the integration of Markovchain analysis and Cellular Automata (CA) model to predict the Land Use Land CoverChange of Norman in 2000 using multicriteria decision making approach. This studyused the post-classification change detection approach to identify the land use landcover change in Norman, Oklahoma, between September 1979 and July 1989 usingLandsat Multispectral Scanner (MSS) and Thematic Map (TM) images.

Research Papers

Methodology

Aim and Objectives

Study Area

Model description

Work to be done

Work Done

Acknowledge-ment

Conclusion

Review of Literature continue……

Fan et al. (2008) conducted the study of detecting the temporal and spatial change inbetween1998 to 2003 and then predicted land use and land cover in Core corridor ofPearl River Delta (China) by using Markov and Cellular Automata (CA) model.

BOOKS1. Introduction to probability.

- Charles M. Grinstead, J. Laurie Snell2. Probability and statistics for Engineers and Scientists.

- Ronald E. Walpole3. Markov Chains Gibbs Fields, Monte Carlo Simulation and Queues.

- J.E. Marrsden4. Introduction to Geographic Information System(GIS).

-Kang-tsung Chang

Methodology

Aim and Objectives

Study Area

Model description

Work to be done

Work Done


Introduction

Acknowledge-ment

Conclusion

Aim and Objectives

AIMTo Model and Analyze the Watershed Dynamics using Cellular Automata(CA) -Markov Model and predict the change for next 10 years.

OBJECTIVES To generate land use / land cover database with uniform classification

scheme for 1972, 1990, 1999 and 2004 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

Methodology


Study Area

Model description

Work to be done

Work Done

Aim and Objectives

Introduction

Acknowledge-ment

Conclusion

River basin map of India

STUDY AREA

• Drainage Area = 195 sq.km• latitude- 20 29’33 to 20 40’21 N •Longitude- 85 44’59.33 to 85 54’16.62 E•Growing Industrial Area

Mahanadi River Basin

Methodology


Aim and Objectives

Model description

Work to be done

Work Done

Study Area

Introduction

Acknowledge-ment

Conclusion

Parameters to be considered

A) Biophysical Parameters: B) Socio-economic Parameters

1. Altitude 1. Urban Sprawl2. Slope 2. Population Density3. Soil Type 3. Road Network4. LU/LC classes 4. Socioeconomic Environment

a) Wetlands Policies b) Forest 5. Residential developmentc) Shrubs 6. Industrial Structure d) Agriculture 7. GDPAe) Urban Area 8. Public Sector Policies

5. Extreme Events 9. Literacya) Flood b) Forest Fire

6. Drainage Network 7. Meteorological

a) Rainfall b) Runoff

Methodology


Aim and Objectives

Model description

Work to be done

Work Done

Study Area

Introduction

Acknowledge-ment

Conclusion

Acquired Satellite Data

LandsatMSS

PATH 150

ROW 46

Resolution 79m

LandsatTM

PATH 140

ROW 46

Resolution 30m

Satellite data for time period 1972 – procured from GLCF site



GLCF – Global Land Cover Facility

LandsatETM+

PATH 140

ROW 46

Resolution 30m


LandsatTM

PATH 140

ROW 46

Resolution 30m

Methodology


Aim and Objectives

Model description

Work to be done

Work Done

Study Area

Introduction

Acknowledge-ment

Conclusion

Data Collection

1. Population Density

2. Land Use Land Cover

3. Soil Map

4. Rainfall

5. Road Network

6. Urban Sprawl

7. GDPA

8. Literacy

9. Residential development Methodology


Aim and Objectives

Model description

Work to be done

Work Done

Study Area

Introduction

Acknowledge-ment

Conclusion

METHODOLOGY

Data download and Layer stack

Georeferencing and Reprojection

Area extraction

Multitemporalimage Classification

Preparing Ancillary Data

Statistics

TAM and Suitability Images

Simulation

Analysis

Prediction

Classification of the satellite data

Drainage Network Soil Type Altitude

Population Density

Road network

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

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

Industrial Structure

Urban 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 future watershed dynamics for coming 10 years from the obtained trend

Toposheet 1945 MSS 1972 TM 1990 ETM+ 1999 TM 2004

CA-Markov Model Description

Markov Chain Analysis

Cellular Automata (CA)

CA-Markov Model in IDRISI AndesInput files- 1) Basis land Cover Image ,

2) Transition Area Matrix3) Suitability Image Study Area


Aim and Objectives

Methodology

Work to be done

Work Done

Model description

Introduction

Acknowledge-ment

Conclusion

Work Done


Acquisition, Georeferencing, Reprojection of Remote Sensing Data

Collection of demographic, socioeconomic, Infrastructure parameters data like DEM data, road network, drainage network, LULCC, Population, Rainfall etc.

Generation of spatial layers of demographic, socioeconomic and

Infrastructure parameters

Generation of database of land use land cover in uniform classification scheme

Analysis of Land Use Land Cover Change

Introduction with Geo-informatics software's ERDAS IMAGINE 9.1, ArcGIS 9.1, IDRISI Andes.

Study Area


Aim and Objectives

Methodology

Work to be done

Model description

Work Done

Introduction

Acknowledge-ment

Conclusion

Work to be done

To develop the criteria for model construction

To run CA- Markov model through IDRISI- Andes software

Analysis of drivers responsible for land use land cover change in watershed

To predict the watershed dynamics scenarios for next future 10 years

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

Study Area


Aim and Objectives

Methodology

Work done

Model description

Work to be Done

Introduction

Acknowledge-ment

Conclusion

Conclusion

Watershed modeling 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.

Helps to policymaker and decision maker.

Need of implementation of measure plan

Study Area


Aim and Objectives

Methodology

Work done

Model description

Acknowledge-ment

Conclusion

Introduction

Work to be Done

Acknowledgement

Prof. S.N Panda gave the guidance on Modelling of watershed.

Prof. C Chatterjee guided in selection of watershed

Prof. M.D. Behera guided in developing overall methodology and gave ancillary data.

SAL (Spatial Analytical Lab) of CORAL Department and JRF and SRF in Lab.

GLCF (Global Land Cover Facility) – RS data download.

SRTM (Shuttle Radar Topography Mission )- DEM data download.

NRSC (National Remote Sensing Centre)- LULC data

Study Area


Aim and Objectives

Methodology

Work done

Model description

Work to be done

Acknowledge-ment

Introduction

Conlclusion

18


Subdivide area into a number of cells

On the basis of observed data between time periods, MCA computes the probability that a cell will change from one land 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.

Let set of states, S = { S1,S2, ……., Sn}.

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

Study Area


Aim and Objectives

Methodology

Work to be done

Work Done

Model description

Introduction

Acknowledge-ment

Conclusion


Example: Wetland class in 2000 changes into two major classes in2004, agriculture class and settlement; 33 % of wetland is changing toagriculture, while 20 % changing to settlement.

Wetland

Settlement

Agriculture

2000 2004

W A S

W .47 .33 .20

P= A PRF PRR PRP

S PPF PPR PPP

transition probability matrix

Study Area


Aim and Objectives

Methodology

Work to be done

Work Done

Model description

Introduction

Acknowledge-ment

Conclusion


Transition Area Matrix: is produced by multiplication of each column inTransition Probability Matrix (P) by no. of pixels of corresponding class inlater image

Disadvantages:

Markov analysis does not account the causes of land use change.

An even more serious problem of Markov analysis is that it is insensitiveto space: it provides no sense of geography.

W A S

W 94 66 40

A= A ARF ARR ARP

S APF APR APP

Study Area


Aim and Objectives

Methodology

Work to be done

Work Done

Model description

Introduction

Acknowledge-ment

Conclusion

Cellular Automata (CA) Model

Spatial component is incorporated

Powerful tool for Dynamic modelling

St+1 = f (St,N,T)

where St+1 = State at time t+1

St = State at time t

N = Neighbourhood

T = Transition Rule

Study Area


Aim and Objectives

Methodology

Work to be done

Work Done

Model description

Introduction

Transition Rules Heart of Cellular Automata Each cell’s evolution is affected by its own state and the state of its

immediate neighbours to the left and right.

Fig. Von Neumann’s Neighbor and Moore’s Neighbor Acknowledge-

ment

Conclusion

Cellular Automata(CA) –MCA in IDRISI -Andes

• Combines cellular automata and the Markov change landcover prediction.

• Adds knowledge of the likely spatial distribution oftransitions to Markov change analysis.

• The CA process creates a suitability map for each classbased on the factors (Biophysical and Proximate) andensuring that land use change occurs in proximity toexisting like land use classes, and not in a wholly randommanner.

Study Area


Aim and Objectives

Methodology

Work to be done

Work Done

Model description

Introduction

Acknowledge-ment

Conclusion

Fig. (a)Spatial layer of slope b) Slope aspect

Fig. Spatial layer of Road network, Drainage Network

Fig. Spatial layer of Land Use Land Cover of the watershed

Fig. Spatial layer of Soil classes in watershed

Dense forest

Open forest

Agriculture

Water Body

Wetland

Settlement

Marshy land

Fallow land

1972 1990 1999 2004

Fig. Unsupervised classification of Land use land cover

WaterWetland

Marshy land

Dense forest

Openforest settlement agriculture

Fallow land

1972 493.5231 898.9983 1426.311 8597.823 5276.701 584.82 2266.1775 833.6934

1990 507.0877 959.9171 1156.969 7398.054 4156.04 780.7347 3633.84405 661.1715

1999 585.6323 823.784 680.5031 8383.313 3478.379 793.1621 4936.611825 311.01053

2004 471.87 687.51 340.74 6539.49 2959.74 1110.33 7338.42 554.4

Fig. Land Use Land Cover Trend

Agriculture

Settlement

Forest

Wetland

Marshy land

Fallow and Barren Land

Water Body

Legend

Water Body

wetland

Marshyland

Forest

Settlement

Agriculture

Fallow and Barren Land

road rail network

Technology

Semester End Seminar