Detection of Urban Development in Uyo (Nigeria) Using Remote Sensing
Abstract
:1. Introduction
- (i)
- To classify land cover classes for three years in order to quantify changes.
- (ii)
- To run a time series analysis to detect phases of urban growth, spatially and temporally.
- (iii)
- To link urban changes to the drivers of change.
2. Materials and Methods
2.1. Study Area
2.2. Data Collection
2.3. Remote Sensing Data
2.4. Training Samples and Reference Data
2.5. Land Cover Classification and Change Detection
2.6. Time Series Analysis
3. Results
3.1. Changes in Land Cover Patterns
3.2. NDVI Trends in Uyo
3.3. Changes in Economic Structure
3.4. Population Growth
4. Discussion
Urbanization
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Category | Training Samples | |
---|---|---|
ROI | Pixels | |
Water bodies | 95 | 5321 |
Swamp vegetation | 101 | 8942 |
Sparse built-up | 105 | 10,422 |
Mixed vegetation | 98 | 7356 |
Forest | 97 | 8945 |
Dense built-up | 94 | 8132 |
Cropland | 110 | 12,435 |
Borrow pit | 70 | 4851 |
Built-Up | Cropland | Forest | Mixed Vegetation | Swamp Vegetation | Water Bodies | UA% | |
---|---|---|---|---|---|---|---|
Built-up | 312 | 7 | 0 | 57 | 0 | 0 | 91% |
Cropland | 1 | 439 | 0 | 23 | 0 | 0 | 86% |
Forest | 0 | 0 | 440 | 0 | 40 | 1 | 82% |
Mixed vegetation | 34 | 27 | 1 | 423 | 0 | 0 | 80% |
Swamp vegetation | 0 | 0 | 49 | 0 | 383 | 0 | 92% |
Water bodies | 0 | 0 | 8 | 0 | 0 | 173 | 96% |
Borrow Pit | Cropland | Dense Built-Up | Forest | Mixed Vegetation | Sparse Built-Up | Swamp Vegetation | Water | UA% | |
---|---|---|---|---|---|---|---|---|---|
Borrow Pit | 87 | 0 | 0 | 0 | 1 | 7 | 0 | 0 | 94% |
Cropland | 0 | 404 | 0 | 0 | 6 | 0 | 0 | 0 | 96% |
Dense built-up | 0 | 0 | 345 | 0 | 0 | 1 | 0 | 0 | 98% |
Forest | 0 | 0 | 0 | 391 | 24 | 0 | 19 | 0 | 87% |
Mixed vegetation | 0 | 14 | 0 | 20 | 366 | 0 | 10 | 0 | 89% |
Sparse built-up | 3 | 0 | 0 | 0 | 0 | 438 | 0 | 0 | 98% |
Swamp vegetation | 0 | 0 | 0 | 26 | 18 | 0 | 360 | 0 | 88% |
Water bodies | 0 | 0 | 0 | 3 | 0 | 0 | 2 | 68 | 98% |
Borrow Pit | Cropland | Dense Built-Up | Forest | Mixed Vegetation | Sparse Built-Up | Swamp Vegetation | Water Bodies | UA% | |
---|---|---|---|---|---|---|---|---|---|
Borrow Pit | 154 | 1 | 0 | 0 | 0 | 2 | 0 | 0 | 97% |
Cropland | 0 | 336 | 0 | 0 | 0 | 0 | 0 | 0 | 98% |
Dense built-up | 0 | 0 | 212 | 0 | 0 | 1 | 0 | 0 | 100% |
Forest | 0 | 0 | 0 | 369 | 3 | 0 | 0 | 0 | 93% |
Mixed vegetation | 0 | 0 | 0 | 3 | 270 | 0 | 1 | 0 | 93% |
Sparse built-up | 1 | 0 | 0 | 0 | 0 | 379 | 0 | 0 | 95% |
Swamp vegetation | 0 | 0 | 0 | 0 | 3 | 0 | 313 | 0 | 97% |
Water bodies | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 98 | 100% |
Land Cover Class | 1986 Land Cover (km2) | 2013 Land Cover (km2) | 2017 Land Cover (km2) | Change in Land Cover (km2) 2003–2017 |
---|---|---|---|---|
Built-up | 24.85 | |||
Dense built-up | 15.75 | 26.42 | 10.67 | |
Sparse built-up | 154.23 | 251.51 | 97.26 | |
Borrow pit | 4.85 | 9.79 | 4.94 | |
Swamp vegetation | 48.94 | 28.91 | 18.31 | –10.6 |
Mixed vegetation | 44.2 | 35.38 | 29.53 | –5.85 |
Cropland | 232.73 | 261.6 | 284.41 | –19.81 |
Forest | 414.94 | 249.35 | 219.41 | –29.94 |
Water bodies | 1.01 | 1.04 | 1.01 |
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Essien, E.; Cyrus, S. Detection of Urban Development in Uyo (Nigeria) Using Remote Sensing. Land 2019, 8, 102. https://doi.org/10.3390/land8060102
Essien E, Cyrus S. Detection of Urban Development in Uyo (Nigeria) Using Remote Sensing. Land. 2019; 8(6):102. https://doi.org/10.3390/land8060102
Chicago/Turabian StyleEssien, Etido, and Samimi Cyrus. 2019. "Detection of Urban Development in Uyo (Nigeria) Using Remote Sensing" Land 8, no. 6: 102. https://doi.org/10.3390/land8060102