Monitoring Land-Use Change in Nakuru (Kenya) Using Multi-Sensor Satellite Data

Kenneth Mubea, Gunter Menz

Center for Remote Sensing of Land surfaces (ZFL), University of Bonn, Bonn, Germany.
Remote Sensing Research Group (RSRG), University of Bonn, Bonn, Germany.

DOI: 10.4236/ars.2012.13008                

Abstract

Recently land-use change has been the main concern for worldwide environment change and is being used by city and regional planners to design sustainable cities. Nakuru in the central Rift Valley of Kenya has undergone rapid urban growth in last decade. This paper focused on urban growth using multi-sensor satellite imageries and explored the potential benefits of combining data from optical sensors (Landsat, Worldview-2) with Radar sensor data from Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) data for urban land-use mapping. Landsat has sufficient spectral bands allowing for better delineation of urban green and impervious surface, Worldview-2 has a higher spatial resolution and facilitates urban growth mapping while PALSAR has higher temporal resolution compared to other operational sensors and has the capability of penetrating clouds irrespective of weather conditions and time of day, a condition prevalent in Nakuru, because it lies in a tropical area. Several classical and modern classifiers namely maximum likelihood (ML) and support vector machine (SVM) were applied for image classification and their performance assessed. The land-use data of the years 1986, 2000 and 2010 were compiled and analyzed using post classification comparison (PCC). The value of combining multi-temporal Landsat imagery and PALSAR was explored and achieved in this research. Our research illustrated that SVM algorithm yielded better results compared to ML. The integration of Landsat and ALOS PALSAR gave good results compared to when ALOS PAL- SAR was classified alone. 19.70 km² of land changed to urban land-use from non-urban land-use between the years 2000 to 2010 indicating rapid urban growth has taken place. Land-use information is useful for the comprehensive land-use planning and an integrated management of resources to ensure sustainability of land and to achieve social Eq- uity, economic efficiency and environmental sustainability.

 

Keywords

Land-Use Monitoring; Nakuru; Urban Growth; Multi-Sensors Satellite Data; Maximum Likelihood; Support Vector Machine; Post Classification Comparison; Sustainability

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	UNECE, “Trends in Europe and North America,” The Statistical Yearbook of the Economic Commission for Europe, 2003.
[2]	C. N. Mundia and M. Aniya, “Modeling Urban Growth of Nairobi City Using Cellular Automata and Geographical Information Systems,” Geographical Review of Japan, Vol. 80, No. 12, 2007, pp. 777-788. doi:10.4157/grj.80.777
[3]	C. Lavalle, L. Demichili, M. Turchini, P. Casals Carrsco and M. Niederhuber, “Monitoring Mega-Cities: The MURBANDY/MOLAND Approach,” Development in Practice, Vol. 11, No. 2-3, 2001, pp. 350-357. doi:10.1080/09614520120056478
[4]	J. I. Barredo and L. Demicheli, “Urban Development in Mega Cities: Modeling and Predicting Future Urban Growth,” Cities, Vol. 20, No. 5, 2003, pp. 297-310. doi:10.1016/S0264-2751(03)00047-7
[5]	UN-HABITAT, “State of the World Cities 2010/2011,” 2010. http://www.unhabitat.org/documents/SOWC10/R4.pdf
[6]	C. N. Mundia, M. Aniya and Y. Murayama, “Modeling Spatial Processes of Urban Growth in an African City. A Case Study of Nairobi,” Nova Science Publishers, New York, 2010.
[7]	K. C. Clarke, B. O. Parks and M. P. Crane, “Geographic Information Systems and Environmental Modeling,” Prentice Hall, Upper Saddle River, 2002.
[8]	C. N. Mundia and M. Aniya, “Dynamics of Land-Use/ Cover Changes and Degradation of Nairobi City, Kenya,” Land Degradation and Development, Vol. 17, No. 1, 2006, pp. 97-108. doi:10.1002/ldr.702
[9]	G. Tóth, “Impact of Land-Take on the Land Resource Base for Crop Production in the European Union,” Science of the Total Environment, Vol. 435-436, 2012, pp. 202-214. doi:10.1016/j.scitotenv.2012.06.103
[10]	B. Ghimire, J. Rogan and J. Miller, “Contextural Land- Cover Classification: Incorporating Spatial Dependence in Land-Cover Classification Models Using Random Forests and the Getis Statistic,” Remote Sensing Letters, Vol. 1, No. 1, 2010, pp. 45-54. doi:10.1080/01431160903252327
[11]	M. Pesaresi, “Texture Analysis for Urban Pattern Recognition Using Fine Resolution Panchromaticsatellite Imagery,” Geographical and Environmental Modelling, Vol. 4, No. 1, 2000, pp. 43-63. doi:10.1080/136159300111360
[12]	F. Dell’Acqua and P. Gamba, “Discriminating Urban Environments Using Multiscale Texture and Multiple SAR Images,” International Journal of Remote Sensing, Vol. 27, No. 18, 2006, pp. 3797-3812. doi:10.1080/01431160600557572
[13]	A. Puissant, J. Hirsch and C. Weber, “The Utility of Texture Analysis to Improve Per-Pixel Classifications for High to Very High Spatial Resolution Imagery,” International Journal of Remote Sensing, Vol. 26, No. 4, 2005, pp. 733-745. doi:10.1080/01431160512331316838
[14]	J. Rogan and D. M. Chen, “Remote Sensing Technology for Mapping and Monitoring Land-Cover and Land-Use Change,” Progress in Planning, Vol. 61, No. 4, 2004, pp. 301-325. doi:10.1016/S0305-9006(03)00066-7
[15]	C. T. Chen, K. S. Chen and J. S. Lee, “The Use of Fully Polarimetric Information for the Fuzzy Neural Classification of SAR Image,” Transactions on Geoscience and Remote Sensing, Vol. 41, No. 9, 2003, pp. 352-365.
[16]	R. J. Dekker, “Texture Analysis and Classification of ERS SAR Images for Map Updating of Urban Areas in the Netherland,” Geoscience and Remote Sensing, Vol. 41, No. 9, 2003, pp. 1950-1958. doi:10.1109/TGRS.2003.814628
[17]	T. M. Pellizzeri, P. Gamba, P. Lombardo and F. D. Acqua, “Multitemporal/Multi-Band SAR Classification of Urban Areas Using Spatial Analysis: Statistical versus Neural Kernel-Based Approach,” Geoscience and Remote Sensing, Vol. 41, No. 10, 2003, pp. 2338-2353. doi:10.1109/TGRS.2003.818762
[18]	Z. Zhu, C. E. Woodcock, J. Rogan and J. Kellndorfer, “Assessment of Spectral, Polarimetric, Temporal, and Spatial Dimensions for Urban and Peri-Urban Land Cover Classification Using Landsat and SAR Data,” Remote Sensing of Environment, Vol. 117, 2011, pp. 72-82. doi:10.1016/j.rse.2011.07.020
[19]	D. Amarsaikhan and T. Douglas, “Data Fusion and Multisource Image Classification,” International Journal of Remote Sensing, Vol. 25, No. 14, 2004, pp. 3529-3539. doi:10.1080/0143116031000115111
[20]	C. Corbane, J. Faure, N. Baghdadi, N. Villeneuve and M. Petit, “Rapid Urban Mapping Using SAR/Optical Imagery Synergy,” Sensors, Vol. 8, No. 11, 2008, pp. 7125- 7143. doi:10.3390/s8117125
[21]	B. Waske and S. van der Linden, “Classifying Multilevel Imagery from SAR and Optical Sensors by Decision Fusion,” IEEE Transaction on Geoscience and Remote Sensing, Vol. 46, No. 5, 2008, pp. 1457-1466. doi:10.1109/TGRS.2008.916089
[22]	M. J. Barnsley and S. L. Barr, “A Graph Based Structural Pattern Recognition System to Infer Urban Land-Use from Fine Spatial Resolution Land-Cover Data,” Computer, Environment and Urban Systems, Vol. 21, No. 3-4, 1997, pp. 209-225. doi:10.1016/S0198-9715(97)10001-1
[23]	J. Mas, A. Pérez-Vega and K. C. Clarke, “Assessing Simulated Land-Use/Cover Maps Using Similarity and Fragmentation Indices,” Ecological Complexity, Vol. 11, 2012, pp. 38-45. doi:10.1016/j.ecocom.2012.01.004
[24]	Republic of Kenya, “Kenya Population Census 1969,” Nairobi, 1970.
[25]	Republic of Kenya, “Kenya Population Census 1979,” Nairobi, 1981.
[26]	Republic of Kenya, “Kenya Population Census 1989,” Nairobi, 1994.
[27]	Republic of Kenya, “Economic Survey 2000,” Nairobi, 2000.
[28]	W. Mwangi, “Kenya Safaris-Nakuru Travel,” University of Amsterdam, Amsterdam, 2007.
[29]	C. O. Obura, “Towards an Environmental Planning Approach in Urban Industrial Sitting and Operations in Kenya: The Case of Eldoret Town,” Netherlands Geographical Studies, Utrecht, 1996.
[30]	M. Herold, D. A. Roberts, M. E. Gardner and P. E. Dennison, “Spectrometry for Urban Area Remote Sensing Development and Analysis of a Spectral Library from 350 to 2400 nm,” Remote Sensing of Environment, Vol. 91, No. 3-4, 2004, pp. 304-319. doi:10.1016/j.rse.2004.02.013
[31]	C. Small, “A global Analysis of Urban Reflectance,” International Journal of Remote Sensing, Vol. 26, No. 4, 2005, pp. 661-681. doi:10.1080/01431160310001654950
[32]	P. Griffiths, P. Hostert, O. Gruebner and S. Linden, “Mapping Megacity Growth with Multi-Sensor Data,” Remote Sensing of Environment, Vol. 114, No. 2, 2010, pp. 426- 439. doi:10.1016/j.rse.2009.09.012
[33]	S. Phinn, M. Stanford, P. Scarth, A. T. Murray and P. T. Shyy, “Monitoring the Composition of Urban Environments Based on the Vegetation-Impervious Surface-Soil (VIS) Model by Subpixel Analysis Techniques,” International Journal of Remote Sensing, Vol. 23, No. 22, 2002, pp. 4131-4153. doi:10.1080/01431160110114998
[34]	F. Pacifici, F. Del Frate, W. J. Emery, P. Gamba and J. Chanussot, “Urban Mapping Using Coarsesar and Optical Data,” IEEE Geoscience and Remote Sensing Letters, Vol. 5, No. 3, 2008, pp. 331-335. doi:10.1109/LGRS.2008.915939
[35]	L. Gomez-Chova, et al., “Urban Monitoring Using Multi- Temporal SAR and Multi-Spectral Data,” Pattern Recognition Letters, Vol. 27, No. 4, 2006, pp. 234-243. doi:10.1016/j.patrec.2005.08.004
[36]	NASA, “Landsat Data Continuing Mission,” 2008. http://ldcm.nasa.gov/index.html
[37]	M. A. Wulder, et al., “Landsat Continuity: Issues and Opportunities for Land Cover Monitoring,” Remote Sensing of Environment, Vol. 112, No. 3, 2008, pp. 955-969. doi:10.1016/j.rse.2007.07.004
[38]	F. Garestier, P. Dubois-Fernandez, X. Dupuis, P. Paillou and I. Hajnsek, “PolInSAR Analysis of X-Band Data over Vegetated and Urban Areas,” IEEE Transactions on Geoscience and Remote Sensing, Vol. 44, No. 2, 2006, pp. 356-364. doi:10.1109/TGRS.2005.862525
[39]	F. M. Henderson and A. J. Lewis, “Principles and Applications of Imaging Radar,” 3rd Edition, John Wiley & Sons, New York, 1998.
[40]	Y. Dong, B. Forster and C. Ticehurst, “Radar Backscatter Analysis for Urban Environments,” International Journal of Remote Sensing, Vol. 18, No. 6, 1997, pp. 1351-1364. doi:10.1080/014311697218467
[41]	R. Z. Schneider, K. P. Papathanassiou, I. Hajnsek and A. Moreira, “Polarimetric and Interferometric Characterization of Coherent Scatterers in Urban Areas,” IEEE Transactions on Geoscience and Remote Sensing, Vol. 44, No. 4, 2006, pp. 971-983. doi:10.1109/TGRS.2005.860950
[42]	J. F. Anderson, E. E. Hardy, J. T. Roach and R. E. Witmer, “A Land-Use and Land Cover Classification System for Use with Remote Sensor Data,” U.S. Geological Survey, Washington, 1976.
[43]	N. Lam and D. A. Quattrochi, “On the Issues of Scale, Resolution, and Fractal Analysis in the Mapping Sciences,” Professional Geographer, Vol. 44, 1992, pp. 88-98. doi:10.1111/j.0033-0124.1992.00088.x
[44]	D. P. Ming, J. Y. Yang, L. X. Li and Z. Q. Song, “Modified ALV for Selecting the Optimal Spatial Resolutionand Its Scale Effect on Image Classification Accuracy,” Mathematical and Computer Modelling, Vol. 54, No. 3-4, 2011, pp. 1061-1068. doi:10.1016/j.mcm.2010.11.036
[45]	T. Idol, B. Haack, S. Sawaya, and A. Sheoran, “Land Cover/Use Mapping with Quad Polarization Radar and Derived Texture Measures,” American Society of Photogrammetry and Remote Sensing, Oregon, 2008.
[46]	H. Z. M. Shafri and F. S. H. Ramle, “A Comparison of Support Vector Machine and Decision Tree Classifications Using Satellite Data of Langkawi Island,” Journal of Information Technology, Vol. 8, No. 1, 2009, pp. 64-70. doi:10.3923/itj.2009.64.70
[47]	B. Waske and J. A. Benediktsson, “Fusion of Support Vector Machines for Classification of Multisensor Data,” IEEE Transaction on Geoscience and Remote Sensing, Vol. 45, No. 12, 2007, pp. 3858-3866. doi:10.1109/TGRS.2007.898446
[48]	F. Melgani and L. Bruzzone, “Classification of Hyperspectral Remote Sensing Images with Support Vector Machines,” IEEE Transactions on Geoscience and Remote Sensing, Vol. 42, No. 8, 2004, pp. 1778-1790. doi:10.1109/TGRS.2004.831865
[49]	C. Huang, L. S. Davis and J. R. G. Townshend, “An Assessment of Support Vector Machines for Land Cover Classification,” International Journal of Remote Sensing, Vol. 23, No. 4, 2002, pp. 725-749. doi:10.1080/01431160110040323
[50]	R. G. Congalton and K. Green, “Assessing the Accuracy of Remotely Sensed Data: Principles and Practices,” Lewis Publishers, Boca Raton, 1999.