Integrating Geospatial Techniques for Urban Land Use Classification in the Developing Sub-Saharan African City of Lusaka, Zambia
Abstract
:1. Introduction
2. Study Area and Data
2.1. Study Area
2.2. Data
3. Methods
3.1. LUC Classification and Built Area Extraction
3.1.1. Pixel-Based Classification
3.1.2. Object-Based Classification
3.1.3. Post-Classification
3.1.4. LUC Classification Accuracy Assessment
3.2. ULU Classification
3.2.1. Classification Scheme
3.2.2. Creating ULU Parcels
3.2.3. Identification of ULU Classes
3.2.4. Determining Residential Density
3.2.5. Post-Classification Pixel Sorting
3.2.6. Accuracy Assessment and Hypothesis Validation
4. Results
4.1. LUC Classification and Built-Area Extraction
4.2. ULU Classification
4.2.1. Accuracy Assessment and Hypothesis Validation
4.2.2. ULU Maps and Statistics
5. Discussion
5.1. Performance of ULU Classification Approach
5.2. Issues Related to the ULU Classification Approach
6. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Urban Land Use Class | Code | Description |
---|---|---|
Unplanned High Density Residential | UHDR | Residential areas (RD1 > 2,000 du/km2) comprising informal/slum/squatter settlements with many small houses located close to each other showing an enormously unsystematic spatial arrangement. Average RD was estimated at 5000 du/km2 and PD2 at 12,000–28,000 people/km2 |
Unplanned3 Low Density Residential | ULDR | Residential areas (RD ≤ 2000 du/km2) comprising medium sized houses with slightly big lot sizes but showing a relatively unsystematic spatial arrangement. Average RD was estimated at 1500 du/km2 and PD at 400–2000 people/km2 |
Planned4 Medium-High Density Residential | PHMDR | Residential areas (RD > 2000 du/km2) with many small and medium sized houses located close to each other showing a systematic spatial arrangement. Average RD was estimated at 3000 du/km2 and PD at 2000–22,000 people/km2 |
Planned Low Density Residential | PLDR | Residential areas (RD ≤ 2000 du/km2) comprising large houses with big lot sizes and showing a systematic spatial arrangement. Average RD was estimated at 1000 du/km2 and PD at 400–2000 people/km2 |
Commercial and Industrial | CMI | Commercial: General retail, shopping malls, markets, hotels, financial services (banks), roads, rails, etc.Industrial: Manufacturing, warehousing, quarrying, mining facilities, and commercial agriculture facilities |
Public Institutions and Service | PIS | Areas comprising education and health facilities, religious institutions, government and administration houses, municipal utilities, transportation terminals, aviation facilities, etc. |
Non-Built-Up | NBU | All vegetated areas (forest and grassland), bare lands, and agriculture areas |
Water | WT | Rivers, streams, dams, etc. |
ULU Map | Classified Data | Reference Data | Total | UA (%) | |||||
---|---|---|---|---|---|---|---|---|---|
UHDR | ULDR | PMHDR | PLDR | CMI | PIS | ||||
2010 | UHDR | 186 | 0 | 17 | 0 | 0 | 0 | 203 | 91.63 |
ULDR | 4 | 54 | 10 | 4 | 0 | 1 | 73 | 73.97 | |
PMHDR | 21 | 5 | 99 | 11 | 0 | 2 | 138 | 71.74 | |
PLDR | 0 | 8 | 8 | 63 | 1 | 0 | 80 | 78.75 | |
CMI | 0 | 0 | 0 | 3 | 127 | 2 | 132 | 96.21 | |
PIS | 0 | 0 | 0 | 6 | 5 | 42 | 53 | 79.25 | |
Total | 211 | 67 | 134 | 87 | 133 | 47 | 679 | ||
PA (%) | 88.15 | 80.60 | 73.88 | 72.41 | 95.49 | 89.36 | |||
Overall Classification Accuracy = 84.09%, Overall Kappa Coefficient = 80.63% | |||||||||
2000 | UHDR | 151 | 4 | 7 | 1 | 0 | 0 | 163 | 92.64 |
ULDR | 2 | 26 | 0 | 0 | 0 | 0 | 28 | 92.86 | |
PMHDR | 18 | 5 | 112 | 4 | 0 | 0 | 139 | 80.58 | |
PLDR | 0 | 0 | 9 | 37 | 4 | 2 | 52 | 71.15 | |
CMI | 0 | 0 | 5 | 0 | 85 | 1 | 91 | 93.41 | |
PIS | 0 | 0 | 5 | 2 | 3 | 26 | 36 | 72.22 | |
Total | 171 | 35 | 138 | 44 | 92 | 29 | 509 | ||
PA (%) | 88.30 | 74.29 | 81.16 | 84.09 | 92.39 | 89.66 | |||
Overall Classification Accuracy = 85.86%, Overall Kappa Coefficient = 84.02% |
ULU Class | 1990 | 2000 | 2010 | |||
---|---|---|---|---|---|---|
Area (Km2) | % | Area (Km2) | % | Area (Km2) | % | |
UHDR | 13.85 | 3.32 | 25.26 | 6.05 | 46.88 | 11.22 |
ULDR | 0.99 | 0.24 | 2.31 | 0.55 | 15.89 | 3.80 |
PMHDR | 14.18 | 3.40 | 21.96 | 5.26 | 32.17 | 7.70 |
PLDR | 4.77 | 1.14 | 9.00 | 2.16 | 20.61 | 4.93 |
CMI | 11.20 | 2.68 | 19.31 | 4.62 | 33.65 | 8.06 |
PIS | 4.18 | 1.00 | 6.32 | 1.51 | 9.61 | 2.30 |
NBU | 367.36 | 87.95 | 333.11 | 79.75 | 258.47 | 61.88 |
WT | 1.16 | 0.28 | 0.52 | 0.12 | 0.41 | 0.10 |
Total | 418 | 100 | 418 | 100 | 418 | 100 |
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Simwanda, M.; Murayama, Y. Integrating Geospatial Techniques for Urban Land Use Classification in the Developing Sub-Saharan African City of Lusaka, Zambia. ISPRS Int. J. Geo-Inf. 2017, 6, 102. https://doi.org/10.3390/ijgi6040102
Simwanda M, Murayama Y. Integrating Geospatial Techniques for Urban Land Use Classification in the Developing Sub-Saharan African City of Lusaka, Zambia. ISPRS International Journal of Geo-Information. 2017; 6(4):102. https://doi.org/10.3390/ijgi6040102
Chicago/Turabian StyleSimwanda, Matamyo, and Yuji Murayama. 2017. "Integrating Geospatial Techniques for Urban Land Use Classification in the Developing Sub-Saharan African City of Lusaka, Zambia" ISPRS International Journal of Geo-Information 6, no. 4: 102. https://doi.org/10.3390/ijgi6040102
APA StyleSimwanda, M., & Murayama, Y. (2017). Integrating Geospatial Techniques for Urban Land Use Classification in the Developing Sub-Saharan African City of Lusaka, Zambia. ISPRS International Journal of Geo-Information, 6(4), 102. https://doi.org/10.3390/ijgi6040102