Abstract
Rapid and irregular urbanization is an essential issue in terms of environmental assessment and management. The dynamics of landscape patterns should be observed and analyzed by local authorities for a sustainable environment. The aim of this study is to determine which spectral urban index, originated from old Landsat missions, represents impervious area better when new generation Earth observation satellite Landsat 8 data are used. Two datasets of Landsat 8, acquired on 2 September 2013 and 10 September 2016, were utilized to investigate the consistency of the results. In this study, commonly used urban indices namely normalized difference built-up index (NDBI), index-based built-up index (IBI), urban index (UI), and enhanced built-up and bareness index (EBBI) were utilized to extract impervious areas. The accuracy assessment of urban indices was conducted by comparing the results with pan-sharpened images, which were classified using maximum likelihood classification (MLC) method. The kappa values of MLC, IBI, NDBI, EBBI, and UI for 2013 dataset were 0.89, 0.79, 0.71, 0.59, and 0.49, respectively, and the kappa values of MLC, IBI, NDBI, EBBI, and UI for 2016 dataset were 0.90, 0.78, 0.70, 0.56, and 0.47, respectively. In addition, area information was extracted from indices and classified images, and the obtained outcomes showed that IBI presented better results than the other urban indices, and UI extracted impervious areas worse than the other indices in both selected cases. Consequently, Landsat 8 satellite data can be considered as an important source to extract and monitor impervious surfaces for the sustainable development of cities.
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References
Antrop, M. (2004). Landscape change and the urbanization process in Europe. Landscape and Urban Planning, 67(1–4), 9–26. https://doi.org/10.1016/S0169-2046(03)00026-4.
As-syakur, A. R., Adnyana, I. W. S., Arthana, I. W., & Nuarsa, I. W. (2012). Enhanced built-up and bareness index (EBBI) for mapping built-up and bare land in an urban area. Remote Sensing, 4(12), 2957–2970. https://doi.org/10.3390/rs4102957.
Bauer, M., Loffelholz, B., & Wilson, B. (2007). Estimating and mapping impervious surface area by regression analysis of Landsat imagery. In Remote sensing of impervious surfaces (Vol. 8, pp. 3–19). https://doi.org/10.1201/9781420043754.pt1.
Benediktsson, J. A., Swain, P. H., & Ersoy, O. K. (1990). Neural network approaches versus statistical methods in classification of multisource remote sensing data. IEEE Transactions on Geoscience and Remote Sensing, 28(4), 540–551. https://doi.org/10.1109/TGRS.1990.572944
Bhatti, S. S., & Tripathi, N. K. (2014). Built-up area extraction using Landsat 8 OLI imagery. GIScience & Remote Sensing, 51(4), 445–467. https://doi.org/10.1080/15481603.2014.939539.
Blair, R. (2004). The effects of urban sprawl on birds at multiple levels of biological organization. Ecology and Society, 9(5). http://www.ecologyandsociety.org/vol9/iss5/art2/.
Bouhennache, R., Bouden, T., Taleb, A. A., & Chaddad, A. (2015). Extraction of urban land features from TM Landsat image using the land features index and tasseled cap transformation. International Journal of Geological and Environmental Engineering, 2(7), 142–147.
Bouzekri, S., Lasbet, A. A., & Lachehab, A. (2015). A new spectral index for extraction of built-up area using Landsat-8 data. Journal of the Indian Society of Remote Sensing, 43(4), 867–873. https://doi.org/10.1007/s12524-015-0460-6.
Bradley, D., & Roth, G. (2007). Adaptive thresholding using the integral image. Journal of Graphics Tools, 12(2), 13–21. https://doi.org/10.1080/2151237X.2007.10129236.
Chen, J. (2007). Rapid urbanization in China: A real challenge to soil protection and food security. CATENA, 69(1), 1–15. https://doi.org/10.1016/j.catena.2006.04.019.
Delgado, V. C. A., & French, K. (2012). Parasite–bird interactions in urban areas: Current evidence and emerging questions. Landscape and Urban Planning, 105(1–2), 5–14. https://doi.org/10.1016/j.landurbplan.2011.12.019.
Deng, C., & Wu, C. (2013). A spatially adaptive spectral mixture analysis for mapping subpixel urban impervious surface distribution. Remote Sensing of Environment, 133, 62–70. https://doi.org/10.1016/j.rse.2013.02.005.
Du, Z., Li, W., Zhou, D., Tian, L., Ling, F., Wang, H., et al. (2014). Analysis of Landsat-8 OLI imagery for land surface water mapping. Remote Sensing Letters, 5(7), 672–681. https://doi.org/10.1080/2150704X.2014.960606.
Du, Z., Linghu, B., Ling, F., Li, W., Tian, W., Wang, H., et al. (2012). Estimating surface water area changes using time-series Landsat data in the Qingjiang River basin, China. Journal of Applied Remote Sensing, 6(1), 063609. https://doi.org/10.1117/1.JRS.6.063609.
Elhag, M., & Boteva, S. (2016). Mediterranean Land Use and Land Cover Classification Assessment Using High Spatial Resolution Data. IOP Conference Series: Earth and Environmental Science, 44(4), 042032. https://doi.org/10.1088/1755-1315/44/4/042032
Estoque, R. C., & Murayama, Y. (2015). Classification and change detection of built-up lands from Landsat-7 ETM+ and Landsat-8 OLI/TIRS imageries: A comparative assessment of various spectral indices. Ecological Indicators, 56, 205–217. https://doi.org/10.1016/j.ecolind.2015.03.037.
Ettehadi Osgouei, P., & Kaya, S. (2017). Analysis of land cover/use changes using Landsat 5 TM data and indices. Environmental Monitoring and Assessment, 189(4), 136. https://doi.org/10.1007/s10661-017-5818-5.
Hoornweg, D., Sugar, L., & Trejos Gómez, C. L. (2011). Cities and greenhouse gas emissions: Moving forward. Environment and Urbanization, 23(1), 207–227. https://doi.org/10.1177/0956247810392270.
Kapur, J. N., Sahoo, P. K., & Wong, A. K. C. (1980). A new method for grey-level picture thresholding using the entropy of the histogram. Signal Processing, 2(3), 223–237. https://doi.org/10.1016/0165-1684(80)90020-1.
Kaushal, S. S., Groffman, P. M., Band, L. E., Shields, C. A., Morgan, R. P., Palmer, M. A., Belt, K. T., Swan, C. M., Findlay, S. E. G., & Fisher, G. T. (2008). Interaction between urbanization and climate variability amplifies watershed nitrate export in Maryland. Environmental Science & Technology, 42(16), 5872–5878. https://doi.org/10.1021/es800264f.
Kawamura, M., Jayamanna, S., & Tsujiko, Y. (1996). Relation between social and environmental conditions in Colombo Sri Lanka and the urban index estimated by satellite remote sensing data. In International Archives of Photogrammetry and Remote Sensing (Vol. 31, p. 321–326.).
Kaya, S. (2007). Multitemporal analysis of rapid urban growth in Istanbul using remotely sensed data. Environmental Engineering Science, 24(2), 228–233. https://doi.org/10.1089/ees.2005.0040.
Kaya, S., Basar, U. G., Karaca, M., & Seker, D. Z. (2012a). Assessment of urban Heat Islands using remotely sensed data. Ekoloji, 21(84), 107–113. https://doi.org/10.5053/ekoloji.2012.8412.
Kaya, S., Seker, D., & Tanik, A. (2012b). Analysis Of Urbanized Areas Using V-I-S Components Model. Fresenius Environmental Bulletin, 21(11), 3243–3248.
Li, W., Du, Z., Ling, F., Zhou, D., Wang, H., Gui, Y., et al. (2013). A comparison of land surface water mapping using the normalized difference water index from TM, ETM+ and ALI. Remote Sensing, 5(11), 5530–5549. https://doi.org/10.3390/rs5115530.
Mwakapuja, F., Liwa, E., & Kashaigili, J. (2013). Usage of indices for extraction of built-up areas and vegetation features from Landsat TM image: A case of Dar Es Salaam and Kisarawe Peri-urban areas, Tanzania. International Journal of Agriculture and Forestry, 3(7), 273–283. https://doi.org/10.5923/j.ijaf.20130307.04.
Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1), 62–66. https://doi.org/10.1109/TSMC.1979.4310076.
Pat, S., & Chavez Jr., S. C. S. (1991). Comparison of three different methods to merge multiresolution and multispectral data: LANDSAT TM and SPOT panchromatic: ABSTRACT. Photogrammetric Engineering and Remote Sensing, 57(3), 295–303. https://doi.org/10.1306/44B4C288-170A-11D7-8645000102C1865D.
Paudel, S., & Yuan, F. (2012). Assessing landscape changes and dynamics using patch analysis and GIS modeling. International Journal of Applied Earth Observation and Geoinformation, 16(1), 66–76. https://doi.org/10.1016/j.jag.2011.12.003.
Ridler, T. W., & Calvard, S. (1978). Picture thresholding using an iterative selection method. IEEE Transactions on Systems, Man, and Cybernetics, 8(8), 630–632. https://doi.org/10.1109/TSMC.1978.4310039.
Rosin, P. L. (2001). Unimodal thresholding. Pattern Recognition, 34(11), 2083–2096. https://doi.org/10.1016/S0031-3203(00)00136-9.
Sarp, G. (2014). Spectral and spatial quality analysis of pan-sharpening algorithms: A case study in Istanbul. European Journal of Remote Sensing, 47(1), 19–28. https://doi.org/10.5721/EuJRS20144702.
Sekertekin, A., Kutoglu, S. H., & Kaya, S. (2016). Evaluation of spatio-temporal variability in land surface temperature: A case study of Zonguldak, Turkey. Environmental Monitoring and Assessment, 188(1), 30. https://doi.org/10.1007/s10661-015-5032-2.
Sekertekin, A., & Marangoz, A. M. (2017). An Erdas imagine model to extract urban indices using Landsat 8 satellite imagery. International Journal of Scientific & Technology Research, 6(01), 62–67.
Sharma, R., & Joshi, P. K. (2016). Mapping environmental impacts of rapid urbanization in the National Capital Region of India using remote sensing inputs. Urban Climate, 15, 70–82. https://doi.org/10.1016/j.uclim.2016.01.004.
Sinha, P., & Verma, N. K. (2016). Urban built-up area extraction and change detection of Adama municipal area using time-series Landsat images. International Journal of Advanced Remote Sensing and GIS, 5(8), 1886–1895.
Tsai, W.-H. (1985). Moment-preserving thresolding: A new approach. Computer Vision, Graphics, and Image Processing, 29(3), 377–393. https://doi.org/10.1016/0734-189X(85)90133-1.
Xu, H. (2007). Extraction of urban built-up land features from Landsat imagery using a thematic-oriented index combination technique. Photogrammetric Engineering and Remote Sensing, 73(12), 1381–1391. https://doi.org/10.14358/PERS.73.12.1381.
Xu, H. (2008). A new index for delineating built-up land features in satellite imagery. International Journal of Remote Sensing, 29(14), 4269–4276. https://doi.org/10.1080/01431160802039957.
Zha, Y., Gao, J., & Ni, S. (2003). Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. International Journal of Remote Sensing, 24(3), 583–594. https://doi.org/10.1080/01431160304987.
Zhang, J., Li, P., & Wang, J. (2014). Urban built-up area extraction from Landsat TM/ETM+ images using spectral information and multivariate texture. Remote Sensing, 6(8), 7339–7359. https://doi.org/10.3390/rs6087339.
Zhang, R., Lei, W., Tie, X., & Hess, P. (2004). Industrial emissions cause extreme urban ozone diurnal variability. Proceedings of the National Academy of Sciences, 101(17), 6346–6350. https://doi.org/10.1073/pnas.0401484101.
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The Worldview-2 image presented in Fig. 1 was provided by BEUN Scientific Research Project: 2015-47912266-02.
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Sekertekin, A., Abdikan, S. & Marangoz, A.M. The acquisition of impervious surface area from LANDSAT 8 satellite sensor data using urban indices: a comparative analysis. Environ Monit Assess 190, 381 (2018). https://doi.org/10.1007/s10661-018-6767-3
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DOI: https://doi.org/10.1007/s10661-018-6767-3
Keywords
- Spectral urban indices
- NDBI
- IBI
- UI
- EBBI
- Landsat 8