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

Mid-Semester Seminar

15-10- 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

- Cellular Automata(CA)

- Cellular automata-MCA in IDRISI- Andes

• Work Done

• Work to be done

• Conclusion

Introduction

• Watershed, Land Use/ Land CoverDefinition

• In order to maintain equilibriumbetween surrounding environment andclimate

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

Review of Literature

Review of Literature

IntroductionAnuj Kumar Singh (2003) conducted study of LULCC with Cellular Automata(CA)which has advantage, that it incorporate the spatial component. Suggest thatCellular Automata(CA) Model is highly depend on Spatial variables taken in toconsideration . More variables can increase the accuracy of the model.

Daniel G. Brown(2004) Introduced the different type of models for LULCCModeling in relation to the purpose of the model, avaibility of data , driversresponsible for LULCC.

Antonius B. Wijanarto(2006) Described Markov Change Detection is oneapplication of change detection that can be used to predict future changes basedon the rates of past change. The method is based on probability that a given pieceof land will change from one mutually exclusive state to another. Theseprobabilities are generated from past changes and then applied to predict futurechange.

Thomas HOUET, Laurence HUBERT-MOY(2006) Cellular automata (CA), thatprovide a powerful tool for the dynamic modeling of land use changes, is acommon method to take spatial interactions into account. They have beenimplemented in land use models that are able to simulate multiple land usetypes.

Research Papers

Methodology

Aim and Objectives

Study Area

Model description

Work to be done

Work Done

Acknowledge-ment

Review of Literature continue……

Soe W. Myint and Le Wang(2006) This study demonstrates the integration ofMarkov chain analysis and Cellular Automata (CA) model to predict the Land UseLand Cover Change of Norman in 2000 using multicriteria decision makingapproach. This study used the post-classification change detection approach toidentify the land use land cover change in Norman, Oklahoma, betweenSeptember 1979 and July 1989 using Landsat Multispectral Scanner (MSS) andThematic Map (TM) images.

Huiping Liu (2008) Research shows that Land use/land cover change detectionusing multi-temporal images by means of remote sensing and ration research ofmodel of urban expansion by GIS are good means of research of urban expansion.

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

Acknowledge-ment

Review of Literature

Introduction

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, Infrastructureparameters

To derive the Transition Area matrix and suitability images based onclassification

Analysis of indicators and drivers and their impact on watersheddynamics

Projecting future watershed dynamics scenarios using CA-Markov Model

Methodology

Review of Literature

Study Area

Model description

Work to be done

Work Done

Acknowledge-ment

Aim and Objectives

Introduction

River basin map of India

STUDY AREA

• 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

Methodology

Review of Literature

Aim and Objectives

Model description

Work to be done

Work Done

Acknowledge-ment

Study Area

Introduction

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

Review of Literature

Aim and Objectives

Model description

Work to be done

Work Done

Acknowledge-ment

Study Area

Introduction

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

Satellite data for time period 1990 – procured from GLCF site

Satellite data for time period 1999 – procured from GLCF site

GLCF – Global Land Cover Facility

LandsatETM+

PATH 140

ROW 46

Resolution 30m

Satellite data for time period 2004 – procured from GLCF site

LandsatTM

PATH 140

ROW 46

Resolution 30m

Methodology

Review of Literature

Aim and Objectives

Model description

Work to be done

Work Done

Acknowledge-ment

Study Area

Introduction

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

Review of Literature

Aim and Objectives

Model description

Work to be done

Work Done

Acknowledge-ment

Study Area

Introduction

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

METHODOLOGY

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

IDRISI Software

Markov Chain Analysis

Cellular Automata (CA)

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

2) Transition Area Matrix3) Suitability Image from MCE

Study Area

Review of Literature

Aim and Objectives

Methodology

Work to be done

Work Done

Acknowledge-ment

Model description

Introduction

Work Done

Review of Literature

a) Research papers

b) Books

Formulation of Methodology

Analysis of parameters which to be consider

Acquisition, Georeferencing, Reprojection of Remote Sensing Data

Collection of data like DEM data, road network, drainage network, LULCC, Population, Rainfall etc.

Extraction of Study Area.

Unsupervised Classification of reprojected images

Introduction with Geoinfomatics software's ERDAS IMAGINE 9.1,

ArcGIS 9.1 , IDRISI Andes.

Study Area

Review of Literature

Aim and Objectives

Methodology

Work to be done

Model description

Acknowledge-ment

Work Done

Introduction

Work to be done

Prepare the spatial layers of socio-economic parameters considered.

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

Run CA- Markov model in IDRISI- Andes

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

Predict the watershed dynamics for next future 10 years

Study Area

Review of Literature

Aim and Objectives

Methodology

Work done

Model description

Acknowledge-ment

Work to be Done

Introduction

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.

Christina Connolly who gave the trial version of IDRISI Software from Clark Lab.

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

Review of Literature

Aim and Objectives

Methodology

Work done

Model description

Work to be done

Acknowledge-ment

Introduction

17

Markov Chain Analysis

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, ……., Sr }.

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

Review of Literature

Aim and Objectives

Methodology

Work to be done

Work Done

Acknowledge-ment

Model description

Introduction

Markov Chain Analysis

Example: Forest in 2000 is change into two major classes in 2001,paddy field and residential; 33 % of forest is changing to residential,while 20 % changing to paddy field.

Forest

Residential

Paddy

2000 2001

F R P

F .47 .33 .20

P= R PRF PRR PRP

P PPF PPR PPP

transition probability matrix

Study Area

Review of Literature

Aim and Objectives

Methodology

Work to be done

Work Done

Acknowledge-ment

Model description

Introduction

Markov Chain Analysis

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.

- Although the transition probabilities may be accurate for a particular class as a whole, there is no spatial element to the modeling process.

- Using cellular automata adds a spatial dimension to the model.

F R P

F 94 66 40

A= R ARF ARR ARP

P APF APR APP

Study Area

Review of Literature

Aim and Objectives

Methodology

Work to be done

Work Done

Acknowledge-ment

Model description

Introduction

Cellular Automata (CA) Model

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

Study Area

Review of Literature

Aim and Objectives

Methodology

Work to be done

Work Done

Acknowledge-ment

Model description

Introduction

Cellular Automata (CA) Model

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

Suitability Maps: Ex- To check the suitability of pixel for Settlement or Agriculture It depends on various Factors : biophysical and Proximity Factor like

altitude, rainfall, distance from road etcSc = Su + N……………………(1)

Su = (∑Wi * fi) ……………………………………(2)∑ Wi

Study Area

Review of Literature

Aim and Objectives

Methodology

Work to be done

Work Done

Acknowledge-ment

Model description

Introduction

Cellular Automata (CA) Model

ClassesBiophysicalFactors

Settlement(Weights)

Agriculture(Weights)

Rainfall 4 8

Slope 8 2

Altitude 5 1

ClassesProximateFactors

Settlement(Weights)

Agriculture(Weights)

Distance From Road 10 6

Distance From City 5 7

Distance From Industry 3 3

Table.1. Allotment of Weights for Settlement and agricultural class

If SSet ≥ SAg ………then state = Settlement

If SSet ≤ SAg ………then state = Agriculture

Study Area

Review of Literature

Aim and Objectives

Methodology

Work to be done

Work Done

Acknowledge-ment

Model description

Introduction

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 landuse change occurs in proximity to existinglike landuse classes, and not in a wholly random manner.

• In each iteration of the simulation each class will normallygain land from one or more of the other classes or it maylose some to one or more of the other classes.

Study Area

Review of Literature

Aim and Objectives

Methodology

Work to be done

Work Done

Acknowledge-ment

Model description

Introduction

Conclusion

Morkov Model does not incorporate the spatial component inmodelling Land Use and Land Cover prediction Integration of Markovchain analysis and Cellular Automata (CA) model adds knowledge ofthe likely spatial distribution of transitions to Markov change analysis.

Integration of Markov chain analysis and Cellular Automata (CA)model to predict the Land Use Land Cover Change is reasonablyaccurate , since it produces overall accuracy above the 85% whencomparing predicted map to the original satellite image

Study Area

Review of Literature

Aim and Objectives

Methodology

Work to be done

Work Done

Acknowledge-ment

Model description

Introduction

1. First (earlier) land cover image2. Second (Last) land cover image3. Prefix for output Conditional Probability Image4. No. of time period between first and last land cover image5. No. of time period to project forward from second image

Markov Spacelessness

Study Area

Review of Literature

Aim and Objectives

Methodology

Work to be done

Work Done

Acknowledge-ment

Model description

Introduction

1. Basic Land Cover image2. Markov Area Transition File3. Transition Suitability Image Collection4. Out Put Land Cover Projection5. No. of Cellular Automata iterations

1972 1990 1999 2004

Forest

Agriculture

Settlement

Wetland

Water Body