Built-up Area Change Analysis in Hanoi Using Support Vector Machine Classification of Landsat Multi-Temporal Image Stacks and Population Data
Abstract
:1. Introduction
2. Historical Overview of the Study Area
3. Methods
3.1. Remote Sensing of Built-up Areas
3.2. Rural-Urban Gradient Analysis
4. Results and Discussion
4.1. Land Cover Classification
Land Cover Classes | Producer’s Accuracy | User’s Accuracy |
---|---|---|
Agriculture | 95.60% | 95.11% |
Built–up | 95.34% | 95.12% |
Change 1993–2001 | 94.10% | 93.86% |
Change 2001–2006 | 93.01% | 93.15% |
Change 2006–2010 | 92.86% | 92.85% |
Forest | 94.61% | 95.61% |
Water | 94.90% | 95.38% |
Overall Accuracy | 94.5% | |
Kappa Coefficient | 0.93 |
4.2. Gradient Analysis of Newly Built-Up Areas in Hanoi
4.3. Policy Implication
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Nong, D.H.; Fox, J.; Miura, T.; Saksena, S. Built-up Area Change Analysis in Hanoi Using Support Vector Machine Classification of Landsat Multi-Temporal Image Stacks and Population Data. Land 2015, 4, 1213-1231. https://doi.org/10.3390/land4041213
Nong DH, Fox J, Miura T, Saksena S. Built-up Area Change Analysis in Hanoi Using Support Vector Machine Classification of Landsat Multi-Temporal Image Stacks and Population Data. Land. 2015; 4(4):1213-1231. https://doi.org/10.3390/land4041213
Chicago/Turabian StyleNong, Duong H., Jefferson Fox, Tomoaki Miura, and Sumeet Saksena. 2015. "Built-up Area Change Analysis in Hanoi Using Support Vector Machine Classification of Landsat Multi-Temporal Image Stacks and Population Data" Land 4, no. 4: 1213-1231. https://doi.org/10.3390/land4041213