Modelled and Analysed the watershed Dynamics in Mahanadi River Basin. Finally came up with watershed Management Plan to minimise the future LUCC in Mahanadi River Basin

1. SCHOOL OF WATER RESOURCES
INDIAN INSTITUTE OF TECHNOLOGY KHARAGPUR
Modelling and Analyzing the Watershed Dynamics using
Cellular Automata (CA) -Markov Model –A Geo-information
Based Approach
M.Tech Thesis Presentation
Supervisor
DR. M. D. BEHERA
Presented By
Mr. SANTOSH NAVNATH BORATE
Date: 04-05-2010 08WM6002

2. Outline of Presentation
• Introduction
• Aim and Objectives
• Study Area
• Methodology
• Model Description
• Results and Discussions
• Watershed Management Plan
• Conclusions

3. Introduction
 Watershed Dynamics
Agricultural
Settlement
Land
Uses Industrial
Development
Artificial Structures
Watershed
Resources
Wetlands
Forests
Land
Covers
Bare soils
Natural streams,
Lakes

4. Drivers affecting LULC
A) Biophysical Drivers B) Socio-economic Drivers
1. Altitude 1. Urban Sprawl
2. Slope 2. Population Density
3. Soil Type 3. Road Network
4. LU/LC classes 4. Socioeconomic Environment
a) Wetlands Policies
b) Forest 5. Residential development
c) Shrubs 6. Industrial Structure
d) Agriculture 7. Public Sector Policies
e) Urban Area 8. Literacy
5. Extreme Events 9. GDP
a) Flood
b) Forest Fire
6. Drainage Network
7. Meteorological
a) Rainfall
b) Runoff

5. 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.

6.  Need of Watershed Modelling
 Improper LU practices
 Drivers complex interaction
 Geo-information based Approach
Remote Sensing- gives spatial and temporal data
GIS- integrate spatial and non spatial data

7. 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

8. Study Area- Choudwar Watershed
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

9. Problems of Choudwar Watershed
Transformation
-wetland is transferring in to Agriculture
-Unavailability of water

10. Land Use Land Cover (LULC) Dynamics
LULC Distribution for year 1972, 1990, 1999 and 2004
1972 1990 1999 2004
Land use and
Land Cover Area Area Area Area Area Area Area Area
Categories (ha) (%) (ha) (%) (ha) (%) (ha) (%)
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

11. Methodology
Toposheet 1945 MSS 1972 TM 1990 ETM+ 1999 TM 2004
Data download
and Layer stack
Geo-referencing and
Reprojection
Area
extraction
Multi-temporal
image Classification of the satellite data
Classification
Population Drainage Network Slope Road and Rail Network
Preparing
Ancillary Data
Settlement Residential Land Use Distance from Road
Distance Development Land Cover and Rail Network
Statistics
Calculation of LU/LC area statistics for different classes (for different periods)
TAM and
Suitability Images Obtain Transition Area Matrix (TAM) by Markov Chain Analysis and Suitability
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 2014 from the obtained trend
Management Preparation of management plan to minimize change in watershed dynamics
Plan

12. Data Required
Period Satellite and data type Resolution (m) Path Row
1972 Landsat, MSS 79 150 46
Acquired
Satellite 1990 Landsat, TM 30 140 46
Data 1999 Landsat, ETM+ 30 140 46
2004 Landsat, TM 30 140 46
Sl. Data Type Date of Production Source
Socioeconomic Census of India
1 Population 1971, 1981, 1991, 2002
data Bhubaneswar
Statistical Handbook
2 Residential Development 1971, 1981, 1991, 2002
data
Statistical Handbook
3 Industrial development 1991, 2001, 2004, 2007
data
4 Road Network 2001 NRIS
5 Railway Network 2001 NRIS
Biophysical Total Area under Winter Statistical Handbook
6 1991, 2001, 2004
Parameters Crops data
Sl. Data Type Date of Production Source
1 Drainage Network 2001 NRIS
2 Slope 2001 NRIS

13. 1990
1972 1990
Legend
road rail network
Agriculture
Land use Land Cover Settlement
2004
Classification Forest
wetland
Marshyland
Fallow and Barren Land
Water Body

14. Accuracy Assessment
Accuracy Assessment of classified LULC of years 1972, 1990, 1999 and 2004.
Class
Name 1972 1990 1999 2004
Producers Users Producers Users Producers Users Producers Users
Accuracy Accuracy Accuracy Accuracy Accuracy Accuracy Accuracy Accuracy
Water Body
100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0
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
Agriculture
80.0 100.0 90.9 90.9 94.7 85.7 95.7 95.7
Fallow and
Barren land
75.0 75.0 100.0 75.0 50.0 100.0 75.0 75.0
Overall Classification Accuracy and Overall Kappa Statistics
1972 1990 1999 2004
Overall Classification Accuracy (%) 92 92 90 92.31
Overall Kappa Statistics 0.8725 0.8723 0.8377 0.8931

15. Trends
Population trend line from 1972 to 2004
Area under winter crops trend line from 1972 to 2004

16. Correlation between different factors
No of Total Area Number of
Population Settlement Agriculture House under Winter Industries and Forest
hold Crops Mining’s
1 0.89 0.91 - - - -0.99
Population
Settlement 0.89 1 0.89 0.94
Agriculture 0.91 0.87 1 - 0.95 0.97 -
No of House - 0.94 - 1
hold
Total Area
- - 0.95 - 1 - -
under winter
crops
Number of
0.97
Industries - - - - 1 -
and Mining’s
Forest - - -0.99 - - - 1

17. Markov Chain Analysis (MCA)
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}.
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

18. Transition Probability Matrix of for prediction of LULC in year 2004
Marshy Fallow and Water
Agriculture Settlement Forest Wetland land Barren Land 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
Transition Area Matrix of for prediction of LULC in year 2004 .
Marshy Fallow and Water
Agriculture Settlement Forest Wetland land Barren Land 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

19. 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
• 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

20. Cellular Automata(CA) –MCA in IDRISI -Andes
• Combines cellular automata and the Markov change land cover
prediction.
• Adds knowledge of the likely spatial distribution of transitions
to Markov change analysis.
Input files required- 1) Basis land Cover Image ,
2) Transition Area Matrix
3) Suitability Images

21. Transition Suitability Maps
Transition suitability implies the suitability of a cell for a particular land cover.
Slope
Biophysical
Drainage Network
drivers
Vegetative Cover
Drivers
Considered Population Growth
Residential
Socio- Development
economic Agricultural Expansion
Factors
Distances to road and rail
Proximate network
Factors
Distances to town
River Course
Constraints Existing Settlement
Road and rail network

22. Factors
Slope Population
Road Rail Network Settlement
Distance Distance

23. Weights Applied for Drivers by AHP
Land use and land Relative Land use and
cover classes Factors Weight Constraints land cover Relative
Population 0.1837 River Course classes Factors Weight Constraints
Residential Population 0.1031 River Course
Development 0.206 Settlement Residential
Agriculture
settlement Road and rail Wetland Development 0.1078 Settlement
Distance 0.5668 network Road and rail
slope 0.0435 Slope 0.7891 network
Population 0.1617 River Course Population 0.0744 River Course
Residential Drainage
Development 0.1703 Settlement distance 0.6042 Settlement
Road rail network Road and rail Marshy Land Road and rail
Settlement
distance 0.0908 network Slope 0.2007 network
Slope 0.057 Road rail
Settlement network distance 0.1207
Distance 0.5202 Population 0.2202 River Course
Population 0.1188 River Course Residential
Residential Fallow and Development 0.2169 Settlement
Development 0.1188 Settlement barren land Settlement Road and rail
Forest Road rail network Road and rail Distance 0.494 network
distance 0.0678 network
Slope 0.0689
Slope 0.3897 Agriculture
Population 0.0953 Settlement
Settlement
Road and rail
Distance 0.3049
Water Slope 0.6548 network
Drainage
distance 0.2499

24. Constraints or Limitations
Existing Road Rail Network
Settlement

25. Suitability Maps

26. CA-Markov Output
Predicted Land Use Land cover Actual Land Use Land cover
map for year 2004 map for year 2004

27. CA-Markov Output
Predicted Land Use Land cover
map for year 2014

28. 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
Decision Rules decision rules are formulized for selection of sites for various soil and
water conservation structures as per the guidelines given by Integrated Mission for
Sustainable Development (IMSD, 1995), Indian National Committee on Hydrology
(INCOH)
Structures Area Slope Permeability Run-off Land Use
Potential
Check dam - Gentle to steep Low to Medium Hilly area
slope Medium
Percolation >40 ha Nearly Level to Medium to Low/Medium Near stream
Pond Gentle slope high
Irrigation 2 ha Nearly level to Very Low Low/Medium Agriculture
Tank Gentle slope

29. Management Plan
Map of suitable locations for different water conservation
structures in watershed

30. Conclusions
•This research work demonstrates the ability of GIS and Remote
Sensing in capturing spatial-temporal dynamics of watershed.
•We believe that the study has demonstrated the usefulness of a
holistic model that combines Markov and CA models for watershed
changes.
•The combination of Markov and a simple CA filter was reasonably
accurate for projecting future land use land cover, since it produced
the overall accuracy of 76.22% which is more than US standard
acceptable accuracy 60%.
•We can prepare the future watershed management plan on the basis
of projected land use land cover of watershed dynamics by CA-
Markov Model.

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