Predicting Future Land Use Utilizing Economic and Land Surface Parameters with ANN and Markov Chain Models
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data and Software Used
2.3. Preprocessing of Data
2.4. Classification
2.4.1. Maximum Likelihood Classifier
2.4.2. Support Vector Machine Classifier
2.5. Accuracy Assessment
2.6. CA–Markov Model for Land Use Change Prediction
3. Results
3.1. Predicting the Best Classifier
3.2. Land Use and Land Cover Change 1990–2020
3.3. Land Use Transformation
3.3.1. Barren Land to Other Classes
3.3.2. Vegetation to Other Classes
3.3.3. Agriculture to Other Classes
3.3.4. Waterbody to Other Classes
3.3.5. Wetland to Other Classes
3.3.6. Build Up to Other Classes
3.3.7. Fallow Land to Other Classes
3.4. Variables Map
3.5. Predicting of Future LULC
3.6. Model Validation
4. Discussion
Study Limitations and Future Work
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Area of MLC Classified LULC Classes | |||||||||
---|---|---|---|---|---|---|---|---|---|
1990 LULC | 2000 LULC | 2010 LULC | 2020 LULC | ||||||
Classes | Area sq km | Area (%) | Area sq km | Area (%) | Area sq km | Area (%) | Area sq km | Area (%) | |
1 | Barren land | 29.5 | 0.87% | 27.5 | 0.81% | 19.4 | 0.57% | 21.7 | 0.64% |
2 | Vegetation | 215.3 | 6.30% | 276.1 | 8.10% | 178 | 5.25% | 168.7 | 4.90% |
3 | Agriculture | 2115.7 | 62% | 2477.6 | 73.10% | 2587 | 76.40% | 2783.4 | 82.20% |
4 | Waterbody | 7.4 | 0.21% | 6.1 | 0.18% | 7.6 | 0.22% | 6.3 | 0.18% |
5 | Wetland | 10.7 | 0.31% | 17.7 | 0.55% | 19.2 | 0.56% | 19.1 | 0.58% |
6 | Build up | 107 | 3.10% | 114.9 | 3.30% | 122.4 | 3.60% | 224.8 | 6.60% |
7 | Fallow land | 899.4 | 26.50% | 464.5 | 13.70% | 451.7 | 13.30% | 161 | 4.70% |
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Aspect | CA–Markov Methodology | Similar Methodologies |
---|---|---|
Advantages | ||
Spatially Explicit Modeling | Effectively captures how places change and connect. | Shows how places change but might miss some connections. |
Integration of Remote Sensing and GIS | Uses real-time land data and maps for analysis. | Relies on maps but might not include the latest data. |
Capturing Historical Patterns | Looks at how places changed before to predict the future. | Might not use past changes, leading to less accurate predictions. |
Decision-Making Support | Helps with making choices, checking impacts, and watching. | Provides decision-making insights but might not offer long-term projections. |
Disadvantages | ||
Complexity | Requires skilled expertise in both cellular automata and Markov modeling. | Some simpler methods might lack the predictive power of CA–Markov. |
Data Intensiveness | Demands reliable input data, including socio-economic and land cover data. | Simpler models might be less data-intensive but provide less accurate results. |
Transition Probabilities | Relies on accurate transition probabilities, which can be challenging to estimate. | Some methods might assume equal transition probabilities, leading to oversimplification. |
Comparison | ||
Hybrid Model | Mixes cellular automata and Markov chain for details. | Similar methods might lean towards one approach, missing the holistic view of CA–Markov. |
Spatial-Temporal Dynamics | Accounts for historical patterns and spatial interactions for future projections. | Other methods might not consider both spatial and temporal dynamics comprehensively. |
Prediction Accuracy | High predictive power due to data integration and complex modeling. | Other methods might provide accurate predictions but lack the complexity of CA–Markov. |
Data Integration | Incorporates remote sensing, GIS, and socio-economic data for comprehensive modeling. | Similar methods might not effectively integrate multiple data sources. |
Satellite Data | |||||
---|---|---|---|---|---|
Satellite Image | Sensor | Path/Row | Cloud Cover | Spatial Resolution | Acquisition Date |
Landsat 5 | TM | 148/39 | 0.00 | 30 m | 9 March 1990 |
Landsat 5 | TM | 148/39 | 0.00 | 30 m | 17 February 2000 |
Landsat 5 | TM | 148/39 | 0.03 | 30 m | 12 February 2010 |
Landsat 8 | OLI-TIRS | 148/39 | 0.26 | 30 m, Pan–15 m | 24 February 2020 |
Software/Package | Purpose |
---|---|
ArcGIS 10.5 | Geographic Information System (GIS) for spatial data analysis |
QGIS 3.2 | Geographic Information System (GIS) with Molusce plugin for CA Markov analysis |
Google Earth Engine | A platform for geospatial data analysis and visualization |
Remote Sensing Data | Utilized satellite imagery for land cover and change detection |
Maximum Likelihood Classifier (MLC) | Employed for land cover classification |
Support Vector Machine (SVM) | Used for land cover classification and analysis |
Cellular Automaton–Markov Model | Framework for simulating and predicting land use changes |
Multi-Criteria Decision-Making | Applied for land suitability analysis |
Python | Calculating F1 Score and Significant Issue |
Variables Map Data | |||
---|---|---|---|
Data | Source | Map | |
1 | Road Layer | Open Street Map | Distance to Road |
2 | Railway layer | Diva GIS | Distance to Railway |
3 | DEM ASTER | NASA Earth Data | Distance to Stream |
4 | DEM SRTM | NASA Earth Data | Slope Map |
5 | DEM SRTM | NASA Earth Data | Aspect Map |
6 | DEM SRTM | NASA Earth Data | Elevation Map |
Reference Data | ||||||||
---|---|---|---|---|---|---|---|---|
1990 LULC | 2000 LULC | 2010 LULC | 2020 LULC | |||||
LULC Classes | User Accuracy (%) | Producer Accuracy (%) | User Accuracy (%) | Producer Accuracy (%) | User Accuracy (%) | Producer Accuracy (%) | User Accuracy (%) | Producer Accuracy (%) |
Barren land | 80 | 80 | 70 | 78 | 70 | 63 | 80 | 80 |
Vegetation, class | 60 | 60 | 60 | 50 | 70 | 78 | 60 | 67 |
Agriculture | 83 | 83 | 86 | 82 | 76 | 88 | 84 | 75 |
Waterbody | 60 | 100 | 80 | 89 | 90 | 90 | 100 | 100 |
Wetland | 60 | 75 | 40 | 100 | 40 | 100 | 90 | 100 |
build-up area | 90 | 65 | 80 | 100 | 70 | 39 | 80 | 100 |
Fallow land | 77 | 67 | 80 | 50 | 70 | 54 | 80 | 73 |
Kappa index | 0.69 | 0.68 | 0.64 | 0.78 | ||||
Overall Accuracy | 75.5% | 75.2% | 71% | 82.3% |
Reference Data | ||||||||
---|---|---|---|---|---|---|---|---|
1990 LULC | 2000 LULC | 2010 LULC | 2020 LULC | |||||
LULC Classes | User Accuracy (%) | Producer Accuracy (%) | User Accuracy (%) | Producer Accuracy (%) | User Accuracy (%) | Producer Accuracy (%) | User Accuracy (%) | Producer Accuracy (%) |
Vegetation | 80 | 89 | 90 | 100 | 90 | 100 | 100 | 83 |
Agriculture | 90 | 100 | 100 | 100 | 100 | 83 | 90 | 100 |
Waterbody | 92 | 81 | 92 | 92 | 92 | 96 | 95 | 95 |
Wetland | 80 | 100 | 90 | 100 | 100 | 100 | 100 | 100 |
build-up area | 100 | 100 | 90 | 100 | 70 | 100 | 90 | 90 |
Fallow land | 80 | 100 | 80 | 73 | 90 | 75 | 70 | 100 |
Kappa | 90 | 75 | 90 | 75 | 90 | 82 | 100 | 83 |
index | 0.85 | 0.88 | 0.88 | 0.91 | ||||
Overall | 88.5% | 91% | 91% | 93% |
Class Label | F1 Score (MLC) | F1 Score (SVM) | Significant Issue (MLC) | Significant Issue (MLC) |
---|---|---|---|---|
Barren land | 0.8000 | 0.9071 | No | No |
Vegetation | 0.6331 | 0.9474 | Yes | No |
Agriculture | 0.7925 | 0.9500 | No | No |
Waterbody | 1.0000 | 1.0000 | No | No |
Wetland | 0.9474 | 0.9000 | No | No |
Build-up area | 0.8889 | 0.8235 | No | No |
Fallow land | 0.7634 | 0.9071 | No | No |
Changes LULC 1990 to 2020 | ||||||||
---|---|---|---|---|---|---|---|---|
Classs Name | LULC 1990 Area | LULC 2000 Area | LULC 2010 Area | LULC 2020 Area | ||||
Km2 | % | Km2 | % | Km2 | % | Km2 | % | |
Barren land | 55.2 | 1.63 | 45 | 1.32 | 32.5 | 0.96 | 17.7 | 0.52 |
Vegetation | 81.3 | 2.40 | 91.4 | 2.7 | 93 | 2.7 | 123.2 | 3.6 |
Agriculture | 2597.4 | 76.7 | 2872.1 | 84.8 | 2886.6 | 85.2 | 2859.6 | 84.4 |
Waterbody | 13.4 | 0.39 | 11.4 | 0.33 | 9.8 | 0.28 | 8.1 | 0.23 |
Wetland | 2.9 | 0.08 | 5.2 | 0.15 | 5 | 0.14 | 2.3 | 0.06 |
Build up | 136.4 | 4.0 | 155.8 | 4.6 | 170.3 | 5 | 203.2 | 6.0 |
Fallow land | 498.2 | 14.7 | 204 | 6.0 | 187.6 | 5.5 | 171 | 5 |
LULC Classes | LULC 2030 Predicted Area | LULC 2050 Predicted Area | |||
---|---|---|---|---|---|
Km2 | % | Km2 | % | ||
1 | Barren land | 15.8 | 0.46 | 11.3 | 0.33 |
2 | Vegetation | 132.2 | 3.90 | 158.5 | 4.6 |
3 | Agriculture | 2870.3 | 84.7 | 2898.4 | 85.6 |
4 | Waterbody | 7.7 | 0.22 | 5.6 | 0.16 |
5 | Wetland | 2.1 | 0.06 | 0.68 | 0.02 |
6 | Build up | 215.5 | 6.56 | 235.3 | 6.95 |
7 | Fallow land | 141 | 4.1 | 74.3 | 2.19 |
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Rani, A.; Gupta, S.K.; Singh, S.K.; Meraj, G.; Kumar, P.; Kanga, S.; Đurin, B.; Dogančić, D. Predicting Future Land Use Utilizing Economic and Land Surface Parameters with ANN and Markov Chain Models. Earth 2023, 4, 728-751. https://doi.org/10.3390/earth4030039
Rani A, Gupta SK, Singh SK, Meraj G, Kumar P, Kanga S, Đurin B, Dogančić D. Predicting Future Land Use Utilizing Economic and Land Surface Parameters with ANN and Markov Chain Models. Earth. 2023; 4(3):728-751. https://doi.org/10.3390/earth4030039
Chicago/Turabian StyleRani, Ankush, Saurabh Kumar Gupta, Suraj Kumar Singh, Gowhar Meraj, Pankaj Kumar, Shruti Kanga, Bojan Đurin, and Dragana Dogančić. 2023. "Predicting Future Land Use Utilizing Economic and Land Surface Parameters with ANN and Markov Chain Models" Earth 4, no. 3: 728-751. https://doi.org/10.3390/earth4030039