Spatial Analysis of Urbanization Patterns in Four Rapidly Growing South Asian Cities Using Sentinel-2 Data
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Overall Workflow
2.3. Data and Preparations
2.3.1. Satellite Data
2.3.2. Population Data
2.4. LULC Classification and Accuracy Assessments
2.4.1. Classification Scheme
2.4.2. LULC Classification
2.4.3. Post-Classification
2.4.4. Accuracy Assessment
2.5. Gradient Analysis
2.6. Spatial Metrics
2.7. Grid-based Analysis
3. Results
3.1. Accuracy Assessment Results
3.2. LULC Patterns
3.3. LULC Statistics
3.4. LULC along the Urban–Rural Gradient
3.5. Spatial Metrics
3.6. Grid-based Analysis Results
4. Discussion
4.1. Spatial Patterns of LULC
4.2. LULC along the Urban–Rural Gradient
4.3. Urban Landscape Configurations
4.4. Influence of Population for Urbanization
4.5. Urbanization Related Issues and Future Challenges
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Classified Data | Reference Data | ||||||
---|---|---|---|---|---|---|---|
Open Lands | Built-up | Croplands | Green Spaces | Water | Total | User’s Accuracy (%) | |
Open lands | 121 | 7 | 1 | 1 | 0 | 130 | 93.1 |
Built-up | 6 | 167 | 0 | 1 | 0 | 174 | 96.0 |
Croplands | 4 | 0 | 11 | 0 | 0 | 15 | 73.3 |
Green spaces | 6 | 4 | 2 | 150 | 0 | 162 | 92.6 |
Water | 0 | 1 | 0 | 0 | 18 | 19 | 94.7 |
Total | 137 | 179 | 14 | 152 | 18 | 500 | |
Producer’s Accuracy (%) | 88.3 | 93.3 | 78.6 | 98.7 | 100.0 |
Classified Data | Reference Data | ||||||
---|---|---|---|---|---|---|---|
Open Lands | Built-up | Croplands | Green Spaces | Water | Total | User’s Accuracy (%) | |
Open lands | 12 | 1 | 0 | 2 | 0 | 15 | 80.0 |
Built-up | 1 | 181 | 1 | 2 | 1 | 186 | 97.3 |
Croplands | 2 | 25 | 125 | 20 | 1 | 173 | 72.3 |
Green spaces | 0 | 0 | 11 | 101 | 0 | 112 | 90.2 |
Water | 0 | 0 | 0 | 1 | 13 | 14 | 92.9 |
Total | 15 | 207 | 137 | 126 | 15 | 500 | |
Producer’s Accuracy (%) | 80.0 | 87.4 | 91.2 | 80.2 | 86.7 |
Classified Data | Reference Data | ||||||
---|---|---|---|---|---|---|---|
Open Lands | Built-up | Croplands | Green Spaces | Water | Total | User’s Accuracy (%) | |
Open lands | 205 | 4 | 1 | 0 | 0 | 210 | 97.6 |
Built-up | 5 | 178 | 0 | 0 | 0 | 183 | 97.3 |
Croplands | 14 | 11 | 55 | 1 | 0 | 81 | 67.9 |
Green spaces | 0 | 0 | 1 | 15 | 0 | 16 | 93.8 |
Water | 1 | 0 | 0 | 0 | 9 | 10 | 90.0 |
Total | 225 | 193 | 57 | 16 | 9 | 500 | |
Producer’s Accuracy (%) | 91.1 | 92.2 | 96.5 | 93.8 | 100.0 |
Classified Data | Reference Data | ||||||
---|---|---|---|---|---|---|---|
Open Lands | Built-up | Croplands | Green Spaces | Water | Total | User’s Accuracy (%) | |
Open lands | 28 | 0 | 1 | 1 | 0 | 30 | 93.3 |
Built-up | 1 | 100 | 1 | 2 | 1 | 105 | 95.2 |
Croplands | 6 | 19 | 238 | 9 | 4 | 276 | 86.2 |
Green spaces | 0 | 0 | 9 | 49 | 0 | 58 | 84.5 |
Water | 0 | 0 | 0 | 0 | 31 | 31 | 100.0 |
Total | 35 | 119 | 249 | 61 | 36 | 500 | |
Producer’s Accuracy (%) | 80.0 | 84.0 | 95.6 | 80.3 | 86.1 |
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Spatial Resolution (m) | Bands | Central Wavelength (nm) | Bandwidth (nm) |
---|---|---|---|
10 | Band 2—Blue | 492.4 | 66 |
Band 3—Green | 559.8 | 36 | |
Band 4—Red | 664.6 | 31 | |
Band 8—NIR | 832.8 | 106 | |
20 | Band 6—Red edge | 740.5 | 15 |
Band 7—Red edge | 782.8 | 20 | |
Band 8A—Narrow NIR | 864.7 | 21 | |
Band 11—SWIR | 1613.7 | 91 | |
Band 12—SWIR | 2202.4 | 175 | |
60 | Band 1—Coastal aerosol | 442.7 | 21 |
Band 9—Water vapor | 945.1 | 20 | |
Band 10—SWIR–Cirrus | 1373.5 | 31 |
LULC | Description |
---|---|
Open land | Bareland, bare soil, exposed soils, landfill sites, active excavation, open lands, and other all land not accounted for in the below four categories |
Built-up | Residential, commercial, industrial, mixed urban and other urban, transportation, road, and other construction sites |
Cropland | Agricultural area, paddy land, vegetable land, and mixed lands |
Green spaces | Forest, marshlands, mangrove, and grassland |
Water | All water bodies |
Metric | Description | Unit | Range | Measure |
---|---|---|---|---|
Landscape-Level Metrics | ||||
Contagion (CONTAG) | The overall probability that a cell of a patch type is adjacent to cells of the same type. | Percent | 0 < CONTAG ≤ 100 | Fragmentation/aggregation |
Landscape shape index (LSI) | Patch perimeter divided by the minimum perimeter possible for a maximally compact patch of the corresponding patch area. | None | LSI ≥ 1 | Shape and complexity |
Shannon’s diversity index (SHDI) | The minus sum, across all patch types, of the proportional abundance of each patch type multiplied by the logarithm of that proportion. | None | SHDI ≥ 0 | Diversity |
Class-Level Metrics | ||||
Percentage of landscape (PLAND) | The sum of the built-up class divided by the total landscape area, multiplied by 100. | Percent | 0<PLAND≤100 | Dominance/abundance |
Path density (PD) | Number of built-up patches divided by the total landscape area. | Numbers per 100 hectares | PD ≥ 1, no limit | Fragmentation |
Mean patch size (Area_MN) | The average size of the built-up patches. | Hectares | AREA_MN ≥ 0, no limit. | Composition |
Area-weighted mean patch fractal dimension (Frac_AM) | Area weighted mean value of the fractal dimension values of all built-up patches. Note: the fractal dimension of a patch equals 2 times the logarithm of the patch perimeter (m) divided by the logarithm of the patch area (m2); the perimeter is adjusted to correct for the raster bias in the perimeter. | None | 1≤ FRAC_AM≤ 2 | Shape and complexity |
Mean Euclidean nearest neighbor distance (ENN_MN) | The distance (m) mean value over all built-up patches to the nearest neighboring patch is based on the shortest edge-to-edge distance from the cell center to the cell center. | Meters | ENN_MN > 0, no limit | Complexity |
Accuracy | LULC Category | Mumbai | Colombo | Karachi | Dhaka |
---|---|---|---|---|---|
User’s accuracy (%) | Open land | 93.1 | 80.0 | 97.6 | 93.3 |
Built-up | 96.0 | 97.3 | 97.3 | 95.2 | |
Croplands | 73.3 | 72.3 | 67.9 | 86.2 | |
Green spaces | 92.6 | 90.2 | 93.8 | 84.5 | |
Water | 94.7 | 92.9 | 90.0 | 100.0 | |
Producer’s accuracy (%) | Open land | 88.3 | 80.0 | 91.1 | 80.0 |
Built-up | 93.3 | 87.4 | 92.2 | 84.0 | |
Croplands | 78.6 | 91.2 | 96.5 | 95.6 | |
Green spaces | 98.7 | 80.2 | 93.8 | 80.3 | |
Water | 100.0 | 86.7 | 100.0 | 86.1 | |
Overall Accuracy (%) | 93.0 | 86.0 | 92.0 | 89.0 |
LULC | Area (ha) | LULC Composition (%) | ||||||
---|---|---|---|---|---|---|---|---|
Mumbai | Colombo | Karachi | Dhaka | Mumbai | Colombo | Karachi | Dhaka | |
Open land | 22,978.8 | 2,067.7 | 43,474.1 | 9,674.2 | 25.4 | 2.8 | 42.0 | 6.0 |
Built-up | 32,688.0 | 27,200.9 | 38,433.9 | 33,483.6 | 36.1 | 37.2 | 37.1 | 21.0 |
Croplands | 2,457.1 | 25,309.8 | 16,861.2 | 88,186.3 | 2.7 | 34.7 | 16.3 | 55.1 |
Green spaces | 28,900.6 | 16,360.3 | 3,354.3 | 18,671.1 | 32.0 | 22.4 | 3.2 | 11.7 |
Water | 3,471.2 | 2,102.4 | 1,469.9 | 9,984.8 | 3.8 | 2.9 | 1.4 | 6.2 |
Total | 90,495.9 | 73,041.1 | 103,593.5 | 160,000.0 | 100.0 | 100.0 | 100.0 | 100.0 |
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Ranagalage, M.; Morimoto, T.; Simwanda, M.; Murayama, Y. Spatial Analysis of Urbanization Patterns in Four Rapidly Growing South Asian Cities Using Sentinel-2 Data. Remote Sens. 2021, 13, 1531. https://doi.org/10.3390/rs13081531
Ranagalage M, Morimoto T, Simwanda M, Murayama Y. Spatial Analysis of Urbanization Patterns in Four Rapidly Growing South Asian Cities Using Sentinel-2 Data. Remote Sensing. 2021; 13(8):1531. https://doi.org/10.3390/rs13081531
Chicago/Turabian StyleRanagalage, Manjula, Takehiro Morimoto, Matamyo Simwanda, and Yuji Murayama. 2021. "Spatial Analysis of Urbanization Patterns in Four Rapidly Growing South Asian Cities Using Sentinel-2 Data" Remote Sensing 13, no. 8: 1531. https://doi.org/10.3390/rs13081531