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
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
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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
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 .
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.