IJE TRANSACTIONS B: Applications Vol. 32, No. 5 (May 2019) 647-653   

downloaded Downloaded: 71   viewed Viewed: 626

M. Ichsan Ali, A. Hafid Hasim and M. Raiz Abidin
( Received: November 02, 2018 – Accepted in Revised Form: May 02, 2019 )

Abstract    Makassar is one of the metropolitan cities located in Indonesia which recently experiences massive an increased construction because of population growth. Mapping the spatial distribution and development of the built-up region is the best method that can use as an indicator to set the urban planning policy. The purpose of this study is to identify changes in land use and density in Makassar City that occurred in 2013 and 2017 primarily for built areas, including settlements using optical data, especially Landsat data. The data analyzed by using multi-temporal Landsat OLI 8 data taken from 2013 to 2017. Normalized Difference Built-Up Index (NDBI), Urban Index (UI) and Normalized Difference Vegetation Index (NDVI) are the spectral indices produced from Landsat OLI band covering Short Wave Infrared (SWIR) wavelength, visible Red (R) and Near Infrared (NIR) areas that can be revealed by examining changes in land use and area cover. The result shows that both spectral indices namely NDBI and UI indicate an increased built-up area approximately 18 and 6%, respectively over four years. Also, based on NDBI reveals that most an increased built-up area distributes in the north of Makassar (Biringkanaya sub-district), meanwhile UI shows that Biringkanaya and Manggala sub-districts experience an increased built-up area. The development of the city will also never be separated from the history of city growth, current conditions, and the growth of the town to come. The phenomenon of the development of the town will include the development of city elements in detail, aspects of the shape of the town and the development of city regulations.


Keywords    Geographic Information System; Landsat OLI 8; Land Use; Remote Sensing



مك كاسر يكي از شهرهاي بزرگ اندونزي است كه به دليل رشد جمعيت، به دليل افزايش جمعيت، ساختمان هاي بزرگي را به وجود آورده است. نقشه برداری توزیع فضایی و توسعه منطقه ساخته شده بهترین روش است که می تواند به عنوان شاخص برای تعیین سیاست های برنامه ریزی شهری استفاده شود. هدف از این مطالعه شناسایی تغییرات در استفاده و تراکم زمین در شهر ماکاسار است که در سالهای 2013 و 2017 رخ داده است که عمدتا برای مناطق ساخته شده است، از جمله شهرک سازی با استفاده از داده های نوری، به ویژه داده های لندست. داده های مورد تجزیه و تحلیل داده ها با استفاده از داده های ماهواره ای Landsat OLI 8 چند ساله از 2013 تا 2017 گرفته شده است. شاخص NDBI، شاخص شهری (UI) و شاخص پوشش گیاهی (Normalized Difference Vegetation Index) (NDVI) شاخص های طیفی تولید شده از گروه Landsat OLI پوشش موج کوتاه موج مادون قرمز (SWIR) مناطق سرخ و مناطق مادون قرمز نزدیک ((NIR را می توان با بررسی تغییرات در استفاده از زمین و پوشش منطقه نشان داد. نتیجه نشان می دهد که هر دو شاخص طیفی یعنی NDBI و UI نشان دهنده افزایش تقاضای مسکن در حدود 18 و 6 درصد به ترتیب بیش از چهار سال است. همچنین براساس NDBI نشان می دهد که اکثر منطقه افزایش یافته در شمال مازار بخش فرعی (Biringkanaya) توزیع می شود، در عین حال UI نشان می دهد که مناطق فرعی Biringkanaya و Manggala منطقه افزایش یافته را تجربه می کنند. توسعه شهر نیز هرگز از تاریخ رشد شهر، شرایط فعلی و رشد شهر پیشی نخواهد گرفت. پدیده توسعه شهر شامل توسعه عناصر شهر به تفصیل، جنبه های شکل شهر و توسعه مقررات شهرستان است.


1. Congalton, R.G., “A review of assessing the accuracy of classifications of remotely sensed data.” Remote Sensing of Environment, Vol. 37, No. 1, (1991), 35–46.
2. Weng, Q., “Land use change analysis in the Zhujiang Delta of China using satellite remote sensing, GIS and stochastic modelling.” Journal of Environmental Management, Vol. 64, No. 3, (2002), 273–284.
3. Millette, T.L., Tuladhar, A.R., Kasperson, R.E., and Turner II, B.L., “The use and limits of remote sensing for analyzing environmental and social change in the Himalayan Middle Mountains of Nepal.” Global Environmental Change, Vol. 5, No. 4, (1995), 367–380.
4. Masek, J.G., Lindsay, F.E., and Goward, S.N., “Dynamics of urban growth in the Washington DC metropolitan area, 1973-1996, from Landsat observations.” International Journal of Remote Sensing, Vol. 21, No. 18, (2000), 3473–3486
5. Maru, R., Baharuddin, I.I., Zhiddiq, S., Arfan, A., and Bayudin, B., “Trend Analysis of Urban Heat Island Phenomenon in the City of Makassar, South Sulawesi, Indonesia using Landsat.” Asian Journal of Applied Sciences, Vol. 3, No. 5, (2015), 477–484.
6. Barnes, K.B., Morgan III, J.M., Roberge, M.C., and Lowe, S., “Sprawl development: its patterns, consequences, and measurement.” Towson University, Towson, (2001), 1–24.
7. Weng, Q., “Remote sensing of impervious surfaces.” Boca Raton, Florida, USA: CRC Press, Taylor & Francis Group, 2007.
8. Xu, H., “A new index for delineating built‐up land features in satellite imagery.” International Journal of Remote Sensing, Vol. 29, No. 14, (2008), 4269–4276.
9. Chen, X.L., Zhao, H.M., Li, P.X., and Yin, Z.Y., “Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes.” Remote Sensing of Environment, Vol. 104, No. 2, (2006), 133–146.
10. Zha, Y., Gao, J., and Ni, S., “Use of normalized difference built-up index in automatically mapping urban areas from TM imagery.” International Journal of Remote Sensing, Vol. 24, No. 3, (2003), 583–594.
11. Zhao, H., and Chen, X., “Use of normalized difference bareness index in quickly mapping bare areas from TM/ETM+.” In Geoscience and Remote Sensing Symposium, 2005. IGARSS’05. Proceedings. 2005 IEEE International IEEE, 2005, 1666–1668
12. Rikimaru, A., and Miyatake, S., “Development of forest canopy density mapping and monitoring model using indices of vegetation, bare soil and shadow.” In Presented paper for the 18th ACRS Kuala Lumpur, Malaysia, 1997.
13. As-syakur, A.R., Adnyana, I.W.S., Arthana, I.W., and Nuarsa, I.W., “Enhanced built-UP and bareness index (EBBI) for mapping built-up and bare land in an urban area.” Remote Sensing, Vol. 4, No. 10, (2012), 2957–2970.
14. He, C., Shi, P., Xie, D., and Zhao, Y., “Improving the normalized difference built-up index to map urban built-up areas using a semiautomatic segmentation approach.” Remote Sensing Letters, Vol. 1, No. 4, (2010), 213–221.
15. Wicaksono, P., Danoedoro, P., Hartonom., and Nehren, U., “Mangrove biomass carbon stock mapping of the Karimunjawa Islands using multispectral remote sensing.” International Journal of Remote Sensing, Vol. 37, No. 1, (2016), 26–52.
16. Irons, J.R.; Dwyer, J.L.; and Barsi, J.A., “The next Landsat satellite: The Landsat Data Continuity Mission.” Remote Sensing of Environment, (2012), 1–11.
17. Guo, G., Wu, Z., Xiao, R., Chen, Y., Liu, X., and Zhang, X., “Impacts of urban biophysical composition on land surface temperature in urban heat island clusters.” Landscape and Urban Planning, Vol. 135, (2015), 1–10.
18. Kawamura, M., Jayamana, S., and Tsujiko, Y., “Relation between social and environmental conditions in Colombo Sri Lanka and the urban index estimated by satellite remote sensing data.” International Archives of the Photogrammetry, Remote Sensing, Vol. 31, (1996), 321–326.
19. U.S. Geological Survey, “Product Guide: Landsat Surface Reflectance-Derived Spectral Indices.” Reston, Virginia: Department of the Interior, 2017.
20. Lillesand, T.M., Kiefer, R.W., and Chipman, J.W., “Remote sensing and image interpretation.” John Wiley & Sons Inc.: New York, (2004).
21. Yüksel, A.; Akay, A.E.; and Gundogan, R., “Using ASTER imagery in land use/cover classification of eastern Mediterranean landscapes according to Corine land cover project.” Sensors, Vol. 8, No. 2, (2008), 1237–1251.
22. Tran, T.D.-B., Puissant, A.; Badariotti, D., and Weber, C., “Optimizing spatial resolution of imagery for urban form detection: the cases of France and Vietnam.” Remote Sensing, Vol. 3, No. 10, (2011), 2128–2147.
23. Soegaard, H., and Møller-Jensen, L., “Towards a spatial CO2 budget of a metropolitan region based on textural image classification and flux measurements.” Remote Sensing of Environment, Vol. 87, No. 2–3, (2003), 283–294.
24. Huete, A., Didan, K., Miura, T., Rodriguez, E.P., Gao, X., and Ferreira, L.G., “Overview of the radiometric and biophysical performance of the MODIS vegetation indices.” Remote Sensing of Environment, Vol. 83, No. 1–2, (2002), 195–213
25. Weng, Q., Lu, D., and Schubring, J., “Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies.” Remote sensing of Environment, Vol. 89, No. 4, (2004), 467–483.
26. Mortezaiea, Z., Hassanpour, H., and Amirib, S.A., “Image Enhancement Using an Adaptive Un-sharp Masking Method Considering the Gradient Variation.” International Journal of Engineering, Transactions B: Applications, Vol. 30, No. 8, (2017), 1118–1125.
27. DolatiAsl, K., and Bakhshan, Y., “Estimating solar radiation and developing Iran’s atlas map of optimum monthly tilt angle.” International Journal of Engineering, Transactions B: Applications, Vol. 30, No. 8, (2017), 1197–1204.
28. Sujith, S.M.S., and Selvathi, D., “Fusion of Panchromatic and Multispectral Images Using Non-Subsampled Contourlet Transform and FFT Based Spectral Histogram (Research Note).” International Journal of Engineering-Transactions A: Basics, Vol. 28, No. 10, (2015), 1455–1462.

International Journal of Engineering
E-mail: office@ije.ir
Web Site: http://www.ije.ir