M.Tech Thesis Presentation

Presented By

Mr. SANTOSH NAVNATH BORATE

08WM6002

Modelling and Analyzing the Watershed Dynamics using Cellular Automata (CA) -Markov Model –A Geo-information

Based Approach

SCHOOL OF WATER RESOURCES

INDIAN INSTITUTE OF TECHNOLOGY KHARAGPUR

Date: 04-05-2010

Supervisor

DR. M. D. BEHERA

• Introduction

• Aim and Objectives

• Study Area

• Methodology

• Model Description

• Results and Discussions

• Watershed Management Plan

• Conclusions

Outline of Presentation

Introduction

Watershed Dynamics

Watershed Resources

Land Uses

Agricultural

Settlement

Industrial Development

Artificial Structures

Land Covers

Wetlands

Forests

Bare soils

Natural streams, Lakes

Drivers affecting LULC

A) Biophysical Drivers B) Socio-economic Drivers

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. Public Sector Policies e) Urban Area 8. Literacy

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

6. Drainage Network 7. Meteorological

a) Rainfall b) Runoff

Impact of change in watershed Dynamics

Changes in land use and land cover- feedback system

Patchiness in forest- due to agriculture

Deterioration of water quality- water usage

Shortage of water resources- spatial patterns of LU

Biodiversity loss- due to loss in forest, wetland etc.

Need of Watershed Modelling Improper LU practices Drivers complex interaction

Geo-information based ApproachRemote Sensing- gives spatial and temporal dataGIS- integrate spatial and non spatial data

Aim and Objectives

Aim : To 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,

etc parameters Analysis of socioeconomic and biophysical drivers impact on

watershed dynamics To derive the Transition Area matrix and suitability images based on

classification To generate scenarios for projecting future watershed dynamics

scenarios using CA- Markov Model To prepare Management Plan to minimize change in watershed

dynamics

River basin map of India

• 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

Study Area- Choudwar Watershed

Problems of Choudwar Watershed

Transformation

-wetland is transferring in to Agriculture

-Unavailability of water

Land Use Land Cover (LULC) Dynamics

1972 1990 1999 2004

Land use and

Land Cover

Categories

Area

(ha)

Area

(%)

Area

(ha)

Area

(%)

Area

(ha)

Area

(%)

Area

(ha)

Area

(%)

Agriculture 3055 15.35 4500.0 22.82 8194 41.57 8878 44.93

Settlement 422 2.12 549.73 2.79 575.9 2.92 738.6 3.74

Forest 11608 58.35 108182 54.86 8624 43.76 8098 40.98

Wetland 1043 5.24 693.17 3.52 430 2.18 160.9 0.81

Marshy Land 1578 7.93 1427.2 7.24 331.3 1.68 313.3 1.59

Fallow and

Barren Land 1749 8.79 1354.5 6.87 1124 5.70 1119 5.66

Water 442 2.22 377.29 1.91 430.9 2.19 451 2.28

LULC Distribution for year 1972, 1990, 1999 and 2004

Methodology

Data download and Layer stack

Geo-referencing and Reprojection

Area extraction

Multi-temporal image

Classification

Preparing Ancillary Data

Statistics

TAM and Suitability Images

Simulation

Analysis

Prediction

Management Plan

Classification of the satellite data

Drainage Network Road and Rail Network

Distance from Road and Rail Network

Population

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

Settlement Distance

Residential Development

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 2014 from the obtained trend

Toposheet 1945 MSS 1972 TM 1990 ETM+ 1999 TM 2004

Land Use Land Cover

Preparation of management plan to minimize change in watershed dynamics

Data Required

Period Satellite and data type Resolution (m) Path Row

1972 Landsat, MSS 79 150 46

1990 Landsat, TM 30 140 46

1999 Landsat, ETM+ 30 140 46

2004 Landsat, TM 30 140 46

Acquired Satellite Data

Sl. Data Type Date of Production Source

1 Population 1971, 1981, 1991, 2002Census of India

Bhubaneswar

2 Residential Development 1971, 1981, 1991, 2002Statistical Handbook

data

3 Industrial development 1991, 2001, 2004, 2007Statistical Handbook

data

4 Road Network 2001 NRIS

5 Railway Network 2001 NRIS

6Total Area under Winter

Crops1991, 2001, 2004

Statistical Handbook

data

Sl. Data Type Date of Production Source

1 Drainage Network 2001 NRIS

2 Slope 2001 NRIS

Socioeconomic data

Biophysical Parameters

Legend

Water Body

wetland

Marshyland

Forest

Settlement

Agriculture

Fallow and Barren Land

road rail network

1972 1990

1990

2004Land use Land Cover Classification

Accuracy Assessment

Class

Name 1972 1990 1999 2004

Producers

Accuracy

Users

Accuracy

Producers

Accuracy

Users

Accuracy

Producers

Accuracy

Users

Accuracy

Producers

Accuracy

Users

Accuracy

Water Body100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

Wetland 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

Marshy

land 100.0 75.0 100.0 75.0 100.0 100.0 100.0 100.0

Forest 96.4 93.1 89.7 96.3 87.5 91.3 91.7 91.7

Settlement 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

Agriculture80.0 100.0 90.9 90.9 94.7 85.7 95.7 95.7

Fallow and

Barren land75.0 75.0 100.0 75.0 50.0 100.0 75.0 75.0

1972 1990 1999 2004

Overall Classification Accuracy (%) 92 92 90 92.31

Overall Kappa Statistics 0.8725 0.8723 0.8377 0.8931

Accuracy Assessment of classified LULC of years 1972, 1990, 1999 and 2004.

Overall Classification Accuracy and Overall Kappa Statistics

Trends

Population trend line from 1972 to 2004

Area under winter crops trend line from 1972 to 2004

Correlation between different factors

Population Settlement Agriculture

No of

House

hold

Total Area

under Winter

Crops

Number of

Industries and

Mining’s

Forest

Population1 0.89 0.91 - - - -0.99

Settlement 0.89 1 0.89 0.94

Agriculture 0.91 0.87 1 - 0.95 0.97 -

No of House

hold

- 0.94 - 1

Total Area

under winter

crops

- - 0.95 - 1 - -

Number of

Industries

and Mining’s

- -0.97

- - 1 -

Forest - - -0.99 - - - 1

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 atransition probability.

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

Transition Probability

Matrix

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

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

Markov Chain Analysis (MCA)

Transition Area Matrix of for prediction of LULC in year 2004 .

Agriculture Settlement Forest Wetland

Marshy

land

Fallow and

Barren Land

Water

Body

Agriculture 0.7765 0.0328 0.0781 0.0066 0.0344 0.0715 0

Settlement 0.3302 0.5473 0.0631 0.0035 0.0142 0.0417 0

Forest 0.223 0.016 0.7199 0.0027 0.0079 0.0305 0

Wetland 0.4068 0 0.0095 0.5483 0.0144 0 0.021

Marshy land 0.6715 0.0158 0.1074 0.0227 0.1718 0.0015 0.0093

Fallow and

Barren Land 0.2049 0.0341 0.1998 0.0026 0.001 0.4945 0.0632

Water Body 0.0234 0.0005 0 0.0285 0.0072 0.1979 0.7425

Agriculture Settlement Forest Wetland

Marshy

land

Fallow and

Barren Land

Water

Body

Agriculture 67984 2875 6842 581 3010 6264 0

Settlement 2092 3466 399 22 90 264 0

Forest 21976 1576 70953 269 781 3005 100

Wetland 1930 0 45 2602 68 0 34

Marshy land 2450 58 392 83 627 5 779

Fallow and

Barren Land 2523 419 2460 32 12 6090 3527

Water Body 111 2 0 135 34 940 3527

Transition Probability Matrix of for prediction of LULC in year 2004

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 tN = Neighbourhood

T = Transition Rule

• 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

Cellular Automata(CA) –MCA in IDRISI -Andes

• Combines cellular automata and the Markov change land coverprediction.

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

Input files required- 1) Basis land Cover Image , 2) Transition Area Matrix3) Suitability Images

Transition Suitability Maps

Drivers Considered

Biophysical drivers

Slope

Drainage Network

Vegetative Cover

Socio-economic

Factors

Population Growth

Residential Development

Agricultural Expansion

Proximate Factors

Distances to road and rail network

Distances to town

Constraints

River Course

Existing Settlement

Road and rail network

Transition suitability implies the suitability of a cell for a particular land cover.

Factors

Slope Population

Road Rail Network Distance

Settlement Distance

Weights Applied for Drivers by AHP

Land use and land

cover classes Factors

Relative

Weight Constraints

Agriculture

Population 0.1837 River Course

Residential

Development 0.206 Settlement

settlement

Distance 0.5668

Road and rail

network

slope 0.0435

Settlement

Population 0.1617 River Course

Residential

Development 0.1703 Settlement

Road rail network

distance 0.0908

Road and rail

network

Slope 0.057

Settlement

Distance 0.5202

Forest

Population 0.1188 River Course

Residential

Development 0.1188 Settlement

Road rail network

distance 0.0678

Road and rail

network

Slope 0.3897 Agriculture

Settlement

Distance 0.3049

Land use and

land cover

classes Factors

Relative

Weight Constraints

Wetland

Population 0.1031 River Course

Residential

Development 0.1078 Settlement

Slope 0.7891

Road and rail

network

Marshy Land

Population 0.0744 River Course

Drainage

distance 0.6042 Settlement

Slope 0.2007

Road and rail

network

Road rail

network distance 0.1207

Fallow and

barren land

Population 0.2202 River Course

Residential

Development 0.2169 Settlement

Settlement

Distance 0.494

Road and rail

network

Slope 0.0689

Water

Population 0.0953 Settlement

Slope 0.6548

Road and rail

network

Drainage

distance 0.2499

Constraints or Limitations

Existing Settlement

Road Rail Network

Suitability Maps

CA-Markov Output

Predicted Land Use Land covermap for year 2004

Actual Land Use Land covermap for year 2004

CA-Markov Output

Predicted Land Use Land covermap for year 2014

Management Plan

Objectives considered• To construct the small water and soil conservation structures at gullies.• To participate rural peoples and stakeholder for prevent land degradation and

watershed management activities. • Improvement of agriculture production.

• Use of Remote Sensing and GIS

Structures Area Slope Permeability Run-off

Potential

Land Use

Check dam - Gentle to steep

slope

Low to

Medium

Medium Hilly area

Percolation

Pond

>40 ha Nearly Level to

Gentle slope

Medium to

high

Low/Medium Near stream

Irrigation

Tank

2 ha Nearly level to

Gentle slope

Very Low Low/Medium Agriculture

Decision Rules decision rules are formulized for selection of sites for various soil andwater conservation structures as per the guidelines given by Integrated Mission forSustainable Development (IMSD, 1995), Indian National Committee on Hydrology(INCOH)

Management Plan

Map of suitable locations for different water conservation structures in watershed

Conclusions

•This research work demonstrates the ability of GIS and RemoteSensing in capturing spatial-temporal dynamics of watershed.

•We believe that the study has demonstrated the usefulness of aholistic model that combines Markov and CA models for watershedchanges.

•The combination of Markov and a simple CA filter was reasonablyaccurate for projecting future land use land cover, since it producedthe overall accuracy of 76.22% which is more than US standardacceptable accuracy 60%.

•We can prepare the future watershed management plan on the basisof projected land use land cover of watershed dynamics by CA-Markov Model.

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