Parameterisation of drivers responsible for changes in Land use Land Cover of watershed in Mahanadi River

1. Modelling and Analyzing the Watershed Dynamics using Cellular Automata (CA) -
Markov Model –A Geoinformation Based Approach
Semester End Seminar
19-11- 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 (MCA)
- Cellular Automata(CA)
- CA-Markov model in IDRISI- Andes
• Work Done
• Work to be done
• Conclusion
• Acknowledgement

3. Introduction
Introduction
Definition • Watershed, Land Use/ Land Cover
Review of
Need of Watershed • Implies the proper use of all land, water Literature
Modelling and natural resources of a watershed 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 Conclusion
Acknowledge-
ment

4. Review of Literature
 Research Papers
Introduction
Gautam (2006) done the watershed modelling for Kundapallam watershed using
remote sensing and GIS by considering the main causes like changing of land use from
Review of
forest into pasture, agriculture and urban, as a result of population growth and general
Literature
scarcity, use of the wood as a source of heat and energy in economically poor area,
also general degradation of forests caused by industrial growth, Environmental
Aim and
pollution, and an increase of consumption. Objectives
Alemayehu et al. (2009) assessed the impact of watershed management on land use Study Area
and land cover dynamics in Eastern Tigray (Ethiopia) and determined the land use and
cover dynamics that it has induced. Methodology
Daniel G. Brown(2004) Introduced the different type of models for LULCC Modeling in Model
relation to the purpose of the model, avaibility of data , drivers responsible for LULCC. description
Soe W. Myint and Le Wang(2006) This study demonstrates the integration of Markov Work Done
chain analysis and Cellular Automata (CA) model to predict the Land Use Land Cover
Change of Norman in 2000 using multicriteria decision making approach. This study Work to be
used the post-classification change detection approach to identify the land use land done
cover change in Norman, Oklahoma, between September 1979 and July 1989 using
Landsat Multispectral Scanner (MSS) and Thematic Map (TM) images. Conclusion
Acknowledge-
ment

5. Review of Literature continue……
Fan et al. (2008) conducted the study of detecting the temporal and spatial change in Introduction
between1998 to 2003 and then predicted land use and land cover in Core corridor of
Pearl River Delta (China) by using Markov and Cellular Automata (CA) model. Review of
Literature
 BOOKS
Aim and
1. Introduction to probability. Objectives
- Charles M. Grinstead, J. Laurie Snell
2. Probability and statistics for Engineers and Scientists. Study Area
- Ronald E. Walpole
3. Markov Chains Gibbs Fields, Monte Carlo Simulation and Queues. Methodology
- J.E. Marrsden
4. Introduction to Geographic Information System(GIS). Model
description
-Kang-tsung Chang
Work Done
Work to be
done
Conclusion
Acknowledge-
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
Analysis of indicators and drivers and their impact on watershed dynamics Model
description
To derive the Transition Area matrix and suitability images based on Work Done
classification
Work to be
To project future watershed dynamics scenarios using CA-Markov Model done
To give the plan of measures for minimize the future watershed dynamics Conclusion
change
Acknowledge-
ment

7. STUDY AREA River basin
map of India
Introduction
• Drainage Area = 195 sq.km
• latitude- 20 29’33 to 20 40’21 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
Conclusion
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 Conclusion
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 Conclusion
Resolution 30m
Acknowledge-
GLCF – Global Land Cover Facility ment

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
Conclusion
Acknowledge-
ment

11. METHODOLOGY

12. Toposheet 1945 MSS 1972 TM 1990 ETM+ 1999 TM 2004
Data download and
Layer stack
Georeferencing and
Reprojection
Area extraction
Multitemporal
Classification of the satellite data
image Classification
Road network Drainage Network Soil Type Altitude
Preparing
Ancillary Data Industrial Population
Urban Sprawl Slope
Structure Density
Statistics
Calculation of LU/LC area statistics for different classes (for different periods)
TAM and Suitability Obtain Transition Area Matrix (TAM) by Markov Chain Analysis and Suitability
Images
Images by MCE
Simulation Run CA- Markov model in IDRISI- Andes by giving -1) Basis land Cover Image ,
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
Markov Chain Analysis
Cellular Automata (CA) Review of
Literature
CA-Markov Model in IDRISI Andes
Aim and
Input files- 1) Basis land Cover Image , Objectives
2) Transition Area Matrix
3) Suitability Image Study Area
Methodology
Model
description
Work Done
Work to be
done
Conclusion
Acknowledge-
ment

14. Work Done
Introduction
Review of Literature
Acquisition, Georeferencing, Reprojection of Remote Sensing Data Review of
Collection of demographic, socioeconomic, Infrastructure parameters data Literature
like DEM data, road network, drainage network, LULCC, Population, Rainfall
etc. Aim and
Objectives
Generation of spatial layers of demographic, socioeconomic and
Infrastructure parameters Study Area
Generation of database of land use land cover in uniform classification
scheme Methodology
Analysis of Land Use Land Cover Change
Model
Introduction with Geo-informatics software's ERDAS IMAGINE 9.1, ArcGIS description
9.1, IDRISI Andes.
Work Done
Work to be
done
Conclusion
Acknowledge-
ment

15. Work to be done
Introduction
To develop the criteria for model construction
To run CA- Markov model through IDRISI- Andes software Review of
Literature
Analysis of drivers responsible for land use land cover change in
watershed Aim and
Objectives
To predict the watershed dynamics scenarios for next future 10 years
To give the plan of measures for minimize the future watershed Study Area
dynamics change
Methodology
Model
description
Work done
Work to be
Done
Conclusion
Acknowledge-
ment

16. Conclusion
Introduction
Watershed modeling implies the proper use of all land, water
and natural resources of a watershed for optimum production Review of
with minimum hazard to eco-system and natural resources. Literature
Helps to policymaker and decision maker. Aim and
Need of implementation of measure plan Objectives
Study Area
Methodology
Model
description
Work done
Work to be
Done
Conclusion
Acknowledge-
ment

17. 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
SAL (Spatial Analytical Lab) of CORAL Department and JRF and
SRF in Lab. Study Area
GLCF (Global Land Cover Facility) – RS data download. Methodology
SRTM (Shuttle Radar Topography Mission )- DEM data download.
Model
NRSC (National Remote Sensing Centre)- LULC data description
Work done
Work to be
done
Conlclusion
Acknowledge-
ment

18. 18

19. 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, ……., Sn}. 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
Conclusion
Acknowledge-
ment

20. Markov Chain Analysis
Example: Wetland class in 2000 changes into two major classes in Introduction
2004, agriculture class and settlement; 33 % of wetland is changing to
Review of
agriculture, while 20 % changing to settlement. Literature
Aim and
Wetland Objectives
Study Area
Settlement
Methodology
Agriculture
Model
2000 2004 description
W A S
Work Done
W .47 .33 .20
P= A PRF PRR PRP transition probability matrix Work to be
S PPF PPR PPP done
Conclusion
Acknowledge-
ment

21. 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
W A S Aim and
W 94 66 40 Objectives
A= A ARF ARR ARP Study Area
S 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.
Work to be
done
Conclusion
Acknowledge-
ment

22. Cellular Automata (CA) Model
Introduction
 Spatial component is incorporated
Review of
 Powerful tool for Dynamic modelling Literature
St+1 = f (St,N,T)
Aim and
where St+1 = State at time t+1 Objectives
St = State at time t Study Area
N = Neighbourhood
Methodology
T = Transition Rule
Model
Transition Rules description
 Heart of Cellular Automata
 Each cell’s evolution is affected by its own state and the state of its Work Done
immediate neighbours to the left and right.
Work to be
done
Conclusion
Acknowledge-
Fig. Von Neumann’s Neighbor and Moore’s Neighbor ment

23. 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 land use change occurs in proximity to Model
existing like land use classes, and not in a wholly random description
manner.
Work Done
Work to be
done
Conclusion
Acknowledge-
ment

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

25. Fig. Spatial layer of Road network, Drainage Network

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

27. Fig. Spatial layer of Soil classes in watershed

28. 1972 1990 1999 2004
Dense forest
Fig. Unsupervised classification of Land use land cover Open forest
Agriculture
Wet Marshy Dense Open Fallow Water Body
Water land land forest forest settlement agriculture land
1972 493.5231 898.9983 1426.311 8597.823 5276.701 584.82 2266.1775 833.6934 Wetland
1990 507.0877 959.9171 1156.969 7398.054 4156.04 780.7347 3633.84405 661.1715
Settlement
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
Marshy land
Fallow land

29. Fig. Land Use Land Cover Trend

32. Agriculture
Settlement
Forest
Wetland
Marshy land
Fallow and Barren Land
Water Body
Legend
road rail network
Agriculture
Settlement
Forest
wetland
Marshyland
Fallow and Barren Land
Water Body