SCHOOL OF WATER RESOURCES INDIAN INSTITUTE OF TECHNOLOGY KHARAGPUR M.Tech Thesis Presentation Need of Watershed Modelling Impact of change in watershed Dynamics Problems of Choudwar Watershed Transformatio Land Use Land Cover

1. 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

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

3. Introduction
 Watershed Dynamics
Watershed
Resources
Land
Uses
Agricultural
Settlement
Industrial
Development
Artificial Structures
Land
Covers
Wetlands
Forests
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. 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

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

10. 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

11. 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

12. 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, 2002
Census of India
Bhubaneswar
2 Residential Development 1971, 1981, 1991, 2002
Statistical Handbook
data
3 Industrial development 1991, 2001, 2004, 2007
Statistical Handbook
data
4 Road Network 2001 NRIS
5 Railway Network 2001 NRIS
6
Total Area under Winter
Crops
1991, 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

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

14. 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 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
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

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

16. Correlation between different factors
Population Settlement Agriculture
No of
House
hold
Total Area
under Winter
Crops
Number of
Industries and
Mining’s
Forest
Population
1 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

17. 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
Markov Chain Analysis (MCA)

18. 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

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
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.

22. Factors
Slope Population
Road Rail Network
Distance
Settlement
Distance

23. 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

24. Constraints or Limitations
Existing
Settlement
Road Rail Network

25. Suitability Maps

26. CA-Markov Output
Predicted Land Use Land cover
map for year 2004
Actual Land Use Land cover
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
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 and
water conservation structures as per the guidelines given by Integrated Mission for
Sustainable Development (IMSD, 1995), Indian National Committee on Hydrology
(INCOH)

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.

31. 31