An Algorithm Approach for the Analysis of Urban Land-Use/Cover: Logic Filters

Yıl 2014, Cilt: 1 Sayı: 1, 12 - 20, 10.11.2014

Şinasi Kaya Fikret Pekin Dursun Zafer Şeker Ayşegül Tanık

https://doi.org/10.30897/ijegeo.300722

Cited By: 3

Öz

Accurate classification of land-use/cover based on
remotely sensed data is important for interpreters who analyze time or
event-based change on certain areas. Any method that has user flexibility on
area selection provides great simplicity during analysis, since the analyzer
may need to work on a specific area of interest instead of dealing with the
entire remotely sensed data. The objectives of the paper are to develop an
automation algorithm using Matlab & Simulink on user selected areas, to
filter V-I-S (Vegetation, Impervious, Soil) components using the algorithm, to
analyze the components according to upper and lower threshold values based on
each band histogram, and finally to obtain land-use/cover map combining the
V-I-S components. LANDSAT 5TM satellite data covering Istanbul and Izmit
regions are utilized, and 4, 3, 2 (RGB) band combination is selected to fulfill
the aims of the study. These referred bands are normalized, and V-I-S
components of each band are determined. This methodology that uses Matlab &
Simulink program is equally successful like the unsupervised and supervised
methods. Practices with these methods that lead to qualitative and quantitative
assessments of selected urban areas will further provide important spatial
information and data especially to the urban planners and decision-makers.

Anahtar Kelimeler

Istanbul, District, Landsat, Matlab & Simulink

Kaynakça

• Boardman, JW., Kruse, FA. (1995). Mapping target signature via partial unmixing of AVIRIS data. Summaries of the 5th JPL Airborne Earth Science Workshop; JPL Publication 95–1, NASA Jet Propulsion Laboratory, Pasadena, Calif., 1995; pp. 23–26.
• Dimyati, M., Mizuno, K., Shintaro, K., Kitamur, T. (1996). An analysis of land use/cover change using the combination of MSS Landsat and land use map - a case study in Yogyakarta, Indonesia, International Journal of Remote Sensing, 17: 931-944.
• Forster, BC., Jones, C. (1988). Urban density monitoring using high resolution space borne system, Com. VII (Kyoto: ISPRS), pp: 189-195.
• Hung, MC. (2002). Urban land cover analysis from satellite images, Pecora 15/Land Satellite Information IV/ISPRS Commission I/FIEOS 2002, Conference Proceedings, p 1.
• Jensen, JR., Toll, DL. (1982). Detecting residential land-use development at the urban fringe, Photogrammetric Engineering and Remote Sensing, 48: 629-643.
• Kaya S., Pekin F., Seker DZ. (2013). Automation of V-I-S model Using Matlab & Simulink on user selected areas, ACRS 2013, Proceedings in CD, 20-24 October, Bali, Indonesia.
• Kaya, S. (2007). Multitemporal Analysis of Rapid Urban Growth in Istanbul Using Remotely Sensing Data, Environmental Engineering Science, 24(2): 228-233.
• Kaya, S., Curran, PJ. (2006). Monitoring urban growth on the European side of the Istanbul metropolitan area: a case study, International Journal of Applied Earth Observation and Geoinformation, 8(1): 18-25.
• Kaya, S., Llewellyn, G., Curran, PJ. (2004). Displaying earthquake damage on an urban area using a V-I-S model and remotely sensed data, XXth Congress of the International Society for Photogrammetry and Remote Sensing (ISPRS), 2004, 12-25 July, Istanbul, Turkey.
• Kaya, S., Pekin, F. (2011). Use of logic filters in remote sensing: an application on V-I-S model, 13th Turkish Scientific and Technical Mapping Congress, Proceedings in CD, 18-22 April, Ankara. (in Turkish).
• Kaya, S., Seker, DZ., Tanik, A. (2012). Analysis of urbanized areas using V-I-S components model, Fresenius Environmental Bulletin, 21(11): 3243-3248.
• Lee, S.; Lathrop, RG. Jr. (2005). Sub-pixel estimation of urban land cover components with linear mixture model analysis and Landsat Thematic Mapper imagery, International Journal of Remote Sensing, 26(22): 4885-4905.
• Lu, D., Weng, Q. (2006). Use of impervious surface in urban land use classification, Remote Sensing of Environment, 102(1-2):146-160.
• Madhavan, BB., Kubo, S., Kurisaki, NT., Sivakumar, VLN. (2001). Appraising the anatomy and spatial growth of the Bangkok Metropolitan area using a vegetation-impervious-soil model through remote sensing, International Journal of Remote Sensing, 22: 789-806.
• Melesse, AM., Weng Q., Thenkabail PS., Senay GB. (2007). Remote sensing sensors and applications in environmental resources mapping and modeling, Sensors 2007, 7: 3209-3241.
• Pathan, S.K., Sastry, SVC, Dhinwa, PS, Rao, M., Mujumdar, KL., Kumar, DS., Patkar, VN., Phatak, VN. (1993). Urban growth trend analysis using GIS techniques - a case study of the Bombay Metropolitan Region, International Journal of Remote Sensing, 14: 3169-3179.
• Pekin, F., Kaya, S. (2010). Automation of V-I-S model using Matlab & Simulink, Third Remote Sensing and Geographical Information Systems Symposium, UZALCBS, Proceedings in CD, 11-13 October, Gebze-Kocaeli, pp. 428-435. (in Turkish).
• Pekin, FH. (2010). Automation of V-I-S model using Matlab & Simulink, M.Sc. Thesis, ITU Informatics Institute, Maslak-Istanbul.(in Turkish).
• Phinn, S., Stanford, M., Scarth, P., Murray, AT., Shyy, PT. (2002). Monitoring the composition of urban environments based on the vegetation-impervious surface-soil (VIS) model by sub pixel analysis techniques, International Journal of Remote Sensing, 20: 4131-4153.
• Powell, RL., Roberts, DA., Dennison, PE., Hess, LL. (2007). Sub-pixel mapping of urban land cover using multiple end-member spectral mixture analysis: Manaus, Brazil. Remote Sensing of Environment 2007, 106(2): 253-267.
• Ridd, MK. (1995). Exploring a V-I-S model for urban ecosystem analysis through remote sensing: comparative anatomy for cities, International Journal of Remote Sensing, 16: 2165-2185.
• Setiawan, E., Mathieu, R. (2006). Assessing the applicability of the V-I-S model to map urban land use in the developing world: Case study of Yogyakarta, Indonesia, Computers, Environment and Urban Systems, 30(4): 503-522.
• Small, C. (2001): Estimation of urban vegetation abundance by spectral mixture analysis, International Journal of Remote Sensing 2001, 22: 1305-1334.
• Ward, D., Phinn, SR., Murray, AT. (2000). Monitoring growth in rapidly urbanizing areas using remotely sensed data, The Professional Geographer, 53: 371-386.
• Welch, R., and Ehlers, M. (1987). Merging multi-resolution SPOT HRV and Landsat TM data, Photogrammetric Engineering and Remote Sensing, 52: 301-303.
• Weng, Q., Lu, D., Schubring. J., (2004). Estimation of land surface temperature-vegetation abundance relationship for urban heat island studies, Remote Sensing of Environment 2004, 89: 467-483.
• Wu, C., Murray, A.T. (2003). Estimating impervious surface distribution by spectral mixture analysis, Remote Sensing of Environment 2003, 84: 93-505.