Basics of Cellular Automata (CA)-Markov Model for Land Use land Cover modelling

1. Modelling and Analyzing the Watershed Dynamics using Cellular Automata (CA) -
Markov Model –A Geoinformation Based Approach
Mid-Semester Seminar
15-10- 2009
Prepared by
SANTOSH BORATE
08WM6002
Under the guidance of
DR. M. D. BEHERA
SCHOOL OF WATER RESORCES
INDIAN INSTITUTE OF TECHNOLOGY KHARAGPUR

2. 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

3. Introduction
Introduction
Definition • Watershed, Land Use/ Land Cover
Review of
Need of Watershed • In order to maintain equilibrium Literature
Modelling between surrounding environment and
climate Aim and
Objectives
Image classification • Prerequisite for Land Use Land Cover Study Area
Change (LULCC) detection
Methodology
• Understand relationships & interactions Model
Change detection with human & natural phenomena to description
better management
Work Done
Use of advanced • Remote sensing & GIS tools provides Work to be
synoptic coverage & repeatability thus is done
spatial technology
tools cost effective Acknowledge-
ment

4. Review of Literature
 Research Papers
Introduction
Anuj Kumar Singh (2003) conducted study of LULCC with Cellular Automata(CA)
which has advantage, that it incorporate the spatial component. Suggest that
Review of
Cellular Automata(CA) Model is highly depend on Spatial variables taken in to Literature
consideration . More variables can increase the accuracy of the model.
Aim and
Daniel G. Brown(2004) Introduced the different type of models for LULCC Objectives
Modeling in relation to the purpose of the model, avaibility of data , drivers
responsible for LULCC. Study Area
Methodology
Antonius B. Wijanarto(2006) Described Markov Change Detection is one
application of change detection that can be used to predict future changes based
Model
on the rates of past change. The method is based on probability that a given piece
description
of land will change from one mutually exclusive state to another. These
probabilities are generated from past changes and then applied to predict future Work Done
change.
Work to be
Thomas HOUET, Laurence HUBERT-MOY(2006) Cellular automata (CA), that done
provide a powerful tool for the dynamic modeling of land use changes, is a
common method to take spatial interactions into account. They have been Acknowledge-
implemented in land use models that are able to simulate multiple land use ment
types.

5. Review of Literature continue……
Soe W. Myint and Le Wang(2006) This study demonstrates the integration of
Markov chain analysis and Cellular Automata (CA) model to predict the Land Use Introduction
Land Cover Change of Norman in 2000 using multicriteria decision making
Review of
approach. This study used the post-classification change detection approach to Literature
identify the land use land cover change in Norman, Oklahoma, between
September 1979 and July 1989 using Landsat Multispectral Scanner (MSS) and Aim and
Thematic Map (TM) images. Objectives
Huiping Liu (2008) Research shows that Land use/land cover change detection Study Area
using multi-temporal images by means of remote sensing and ration research of
model of urban expansion by GIS are good means of research of urban expansion. Methodology
Model
 BOOKS
description
1. Introduction to probability.
- Charles M. Grinstead, J. Laurie Snell Work Done
2. Probability and statistics for Engineers and Scientists.
- Ronald E. Walpole Work to be
3. Markov Chains Gibbs Fields, Monte Carlo Simulation and Queues. done
- J.E. Marrsden
4. Introduction to Geographic Information System(GIS). Acknowledge-
-Kang-tsung Chang ment

6. Aim and Objectives
AIM Introduction
To Model and Analyze the Watershed Dynamics using Cellular Automata
(CA) -Markov Model and predict the change for next 10 years. Review of
Literature
OBJECTIVES
Aim and
 To generate land use / land cover database with uniform classification Objectives
scheme for 1972, 1990, 1999 and 2004 using satellite data
Study Area
 To create database on demographic, socioeconomic, Infrastructure
parameters Methodology
 To derive the Transition Area matrix and suitability images based on Model
description
classification
Work Done
 Analysis of indicators and drivers and their impact on watershed
dynamics Work to be
done
 Projecting future watershed dynamics scenarios using CA-Markov Model
Acknowledge-
ment

7. STUDY AREA River basin
map of India
Introduction
• Drainage Area = 195 sq.km
• latitude- 20 29 33.39 to 20 40 21.09 N Review of
•Longitude- 85 44 59.33 to 85 54 16.62 E Literature
•Growing Industrial Area
Aim and
Objectives
Study Area
Mahanadi Methodology
River Basin
Model
description
Work Done
Work to be
done
Acknowledge-
ment

8. Parameters to be considered
A) Biophysical Parameters: B) Socio-economic Parameters Introduction
Review of
1. Altitude 1. Urban Sprawl Literature
2. Slope 2. Population Density
3. Soil Type 3. Road Network Aim and
4. LU/LC classes 4. Socioeconomic Environment Objectives
a) Wetlands Policies
Study Area
b) Forest 5. Residential development
c) Shrubs 6. Industrial Structure Methodology
d) Agriculture 7. GDPA
e) Urban Area 8. Public Sector Policies Model
5. Extreme Events 9. Literacy description
a) Flood
b) Forest Fire Work Done
6. Drainage Network
Work to be
7. Meteorological done
a) Rainfall
b) Runoff Acknowledge-
ment

9. Acquired Satellite Data
Satellite data for time period 1972 – procured from GLCF site Introduction
Landsat PATH 150
Review of
MSS ROW 46 Literature
Resolution 79m
Aim and
Satellite data for time period 1990 – procured from GLCF site Objectives
Landsat PATH 140 Study Area
TM ROW 46
Resolution 30m Methodology
Satellite data for time period 1999 – procured from GLCF site Model
description
Landsat PATH 140
ETM+ ROW 46 Work Done
Resolution 30m
Work to be
Satellite data for time period 2004 – procured from GLCF site done
Landsat PATH 140
TM ROW 46 Acknowledge-
ment
Resolution 30m
GLCF – Global Land Cover Facility

10. Data Collection
Introduction
1. Population Density
2. Land Use Land Cover Review of
3. Soil Map Literature
4. Rainfall Aim and
5. Road Network Objectives
6. Urban Sprawl
7. GDPA Study Area
8. Literacy
9. Residential development Methodology
Model
description
Work Done
Work to be
done
Acknowledge-
ment

11. METHODOLOGY

12. METHODOLOGY
Toposheet 1945 MSS 1972 TM 1990 ETM+ 1999 TM 2004
Data download
and Layer stack
Georeferencing and
Reprojection
Area
extraction
Multitemporal
image Classification of the satellite data
Classification
Preparing Road network Drainage Network Soil Type Altitude
Ancillary
Data
Industrial Urban Sprawl Slope Population
Structure Density
Statistics Calculation of LU/LC area statistics for different classes (for different periods)
TAM and Obtain Transition Area Matrix (TAM) by Markov Chain Analysis and Suitability
Suitability Images Images by MCE
Run CA- Markov model in IDRISI- Andes by giving -1) Basis land Cover Image ,
Simulation 2) TAM and 3) Suitability Image as inputs
Analysis Analysis of drivers responsible for watershed change
Prediction Predict future watershed dynamics for coming 10 years from the obtained trend

13. CA-Markov Model Description
Introduction
IDRISI Software
Markov Chain Analysis Review of
Literature
Cellular Automata (CA)
Aim and
CA-Markov Model in IDRISI Andes Objectives
Input files- 1) Basis land Cover Image ,
2) Transition Area Matrix Study Area
3) Suitability Image from MCE Methodology
Model
description
Work Done
Work to be
done
Acknowledge-
ment

14. Work Done
Introduction
Review of Literature
a) Research papers Review of
Literature
b) Books
Aim and
Formulation of Methodology Objectives
Analysis of parameters which to be consider
Study Area
Acquisition, Georeferencing, Reprojection of Remote Sensing
Data Methodology
Collection of data like DEM data, road network, drainage Model
network, LULCC, Population, Rainfall etc. description
Extraction of Study Area.
Work Done
Unsupervised Classification of reprojected images
Introduction with Geoinfomatics software's ERDAS IMAGINE 9.1, Work to be
done
ArcGIS 9.1 , IDRISI Andes.
Acknowledge-
ment

15. Work to be done
Introduction
Prepare the spatial layers of socio-economic parameters
considered. Review of
Literature
Obtain Transition Area Matrix by Markov Chain Analysis and
Suitability Images by MCE Aim and
Objectives
Run CA- Markov model in IDRISI- Andes
Analysis of drivers responsible for land use land cover change in Study Area
watershed Methodology
Predict the watershed dynamics for next future 10 years
Model
description
Work done
Work to be
Done
Acknowledge-
ment

16. Acknowledgement
Introduction
Prof. S.N Panda gave the guidance on Modelling of watershed.
Prof. C Chatterjee guided in selection of watershed Review of
Literature
Prof. M.D. Behera guided in developing overall methodology and
Aim and
gave ancillary data. Objectives
Christina Connolly who gave the trial version of IDRISI Software
from Clark Lab. Study Area
SAL (Spatial Analytical Lab) of CORAL Department and JRF and Methodology
SRF in Lab.
Model
GLCF (Global Land Cover Facility) – RS data download. description
SRTM (Shuttle Radar Topography Mission )- DEM data download.
Work done
NRSC (National Remote Sensing Centre)- LULC data
Work to be
done
Acknowledge-
ment

17. 17

18. Markov Chain Analysis
Introduction
Subdivide area into a number of cells
On the basis of observed data between time periods, MCA Review of
Literature
computes the probability that a cell will change from one land
use type (state) to another within a specified period of time. Aim and
Objectives
The probability of moving from one state to another state is
called a transition probability. Study Area
Let set of states, S = { S1,S2, ……., Sr }. Methodology
Model
description
Work Done
where P = Markov transition probability matrix P Work to be
i, j = the land type of the first and second time period done
Pij = the probability from land type i to land type j
Acknowledge-
ment

19. Markov Chain Analysis
Example: Forest in 2000 is change into two major classes in 2001, Introduction
paddy field and residential; 33 % of forest is changing to residential,
Review of
while 20 % changing to paddy field. Literature
Aim and
Forest Objectives
Study Area
Residential
Methodology
Paddy
Model
2000 2001 description
F R P
Work Done
F .47 .33 .20
P= R PRF PRR PRP transition probability matrix Work to be
P PPF PPR PPP done
Acknowledge-
ment

20. Markov Chain Analysis
Introduction
Transition Area Matrix: is produced by multiplication of each column in
Transition Probability Matrix (P) by no. of pixels of corresponding class in Review of
Literature
later image
F R P Aim and
F 94 66 40 Objectives
A= R ARF ARR ARP Study Area
P APF APR APP
Methodology
Disadvantages: Model
description
Markov analysis does not account the causes of land use change.
An even more serious problem of Markov analysis is that it is insensitive Work Done
to space: it provides no sense of geography.
- Although the transition probabilities may be accurate for a Work to be
done
particular class as a whole, there is no spatial element to the
modeling process. Acknowledge-
- Using cellular automata adds a spatial dimension to the model. ment

21. Cellular Automata (CA) Model
Introduction
Review of
 Spatial component is incorporated Literature
 Powerful tool for Dynamic modelling
Aim and
 Each row represents a single time step Objectives
of the automaton’s evolution.
Study Area
Methodology
St+1 = f (St,N,T)
where St+1 = State at time t+1 Model
description
St = State at time t
N = Neighbourhood Work Done
T = Transition Rule
Work to be
done
Acknowledge-
ment

22. Cellular Automata (CA) Model
Introduction
Transition Rules
 Heart of Cellular Automata Review of
 Each cell’s evolution is affected by its own state and the state of its Literature
immediate neighbours to the left and right.
Aim and
Objectives
Study Area
Methodology
Fig. Von Neumann’s Neighbor and Moore’s Neighbor Model
description
Suitability Maps:
Ex- To check the suitability of pixel for Settlement or Agriculture Work Done
It depends on various Factors : biophysical and Proximity Factor like
altitude, rainfall, distance from road etc Work to be
Sc = Su + N……………………(1) done
Su = (∑Wi * fi) ……………………………………(2) Acknowledge-
ment
∑ Wi

23. Cellular Automata (CA) Model
Classes Settlement Agriculture Introduction
Biophysical (Weights) (Weights)
Factors Review of
Literature
Rainfall 4 8 Aim and
Objectives
Slope 8 2
Altitude 5 1 Study Area
Classes Settlement Agriculture Methodology
Proximate (Weights) (Weights)
Model
Factors description
Distance From Road 10 6
Work Done
Distance From City 5 7
Distance From Industry 3 3 Work to be
done
Table.1. Allotment of Weights for Settlement and agricultural class
Acknowledge-
If SSet ≥ SAg ………then state = Settlement ment
If SSet ≤ SAg ………then state = Agriculture

24. Cellular Automata(CA) –MCA in IDRISI -Andes
Introduction
• Combines cellular automata and the Markov change land Review of
Literature
cover prediction.
Aim and
• Adds knowledge of the likely spatial distribution of Objectives
transitions to Markov change analysis.
Study Area
• The CA process creates a suitability map for each class
based on the factors (Biophysical and Proximate) and Methodology
ensuring that landuse change occurs in proximity to existing Model
like landuse classes, and not in a wholly random manner. description
• In each iteration of the simulation each class will normally
Work Done
gain land from one or more of the other classes or it may
lose some to one or more of the other classes. Work to be
done
Acknowledge-
ment

25. Conclusion
Introduction
 Morkov Model does not incorporate the spatial component in
modelling Land Use and Land Cover prediction Integration of Markov Review of
Literature
chain analysis and Cellular Automata (CA) model adds knowledge of
the likely spatial distribution of transitions to Markov change analysis. Aim and
Objectives
 Integration of Markov chain analysis and Cellular Automata (CA) Study Area
model to predict the Land Use Land Cover Change is reasonably
accurate , since it produces overall accuracy above the 85% when Methodology
comparing predicted map to the original satellite image Model
description
Work Done
Work to be
done
Acknowledge-
ment

26. 1. First (earlier) land cover image
2. Second (Last) land cover image
3. Prefix for output Conditional Probability Image
4. No. of time period between first and last land cover image
5. No. of time period to project forward from second image

27. Markov Spacelessness
Introduction
Review of
Literature
Aim and
Objectives
Study Area
Methodology
Model
description
Work Done
Work to be
done
Acknowledge-
ment

29. 1. Basic Land Cover image
2. Markov Area Transition File
3. Transition Suitability Image Collection
4. Out Put Land Cover Projection
5. No. of Cellular Automata iterations

31. 1972 1990 1999 2004
Forest
Agriculture
Settlement
Wetland
Water Body