DEVELOPING URBAN BUILT-UP EXTRACTION METHOD BASED ON REMOTE SENSING IMAGERY INDEX TRANSFORMATION

DOI: https://doi.org/10.14710/geoplanning.0.0.%25p
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Published: 25-04-2018
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Urban areas became a challenge to be studied, both visually and digitally. Supervised classification using remote sensing imagery to differentiate between built up and non-built-up area used to be leading in digital studies of urban land area. The next generation used index transformation for automatic urban data extraction. The extraction of urban built land can be automatically done with NDBI and also ups and downs experienced. The weakness of NDBI is not separating the built up and bare land. The previous research provide opportunities for continued research in terms of increasing accuracy particularly using index transformation. The study objective is to obtain the maximum accuracy of the merging of NDBI, NDVI, MNDWI, NDWI, and SAVI that involved in urban area. The merging of the index is using four formulas, the merging of two indexes, then three indexes, then four indexes and then five indexes. The merging of the five indexes is either by addition, subtraction, or multiplication in the experiment in this study. The result showed merger NDBI and MNDWI produce the highest accuracy of 90.30% either by multiplication (overlay) or reduction. Application of SAVI, NDBI, and NDWI also gives worthy effect for extracting urban built up areas and has 85.72% mapping accuracy.

Keywords

built up extraction, remote sensing, index transformation

  1. Iswari Nur Hidayati  Scopus Sinta
    Student of Doctorate Program in Faculty of Geography, Gadjah Mada University, Indonesia Faculty of Geography, Gadjah Mada University, Indonesia
  2. R Suharyadi 
  3. Projo Danoedoro  Scopus Sinta
  1. Bagan, H., & Yamagata, Y. (2012). Landsat analysis of urban growth: How Tokyo became the world’s largest megacity during the last 40years. Remote Sensing of Environment, 127, 210–222. https://doi.org/10.1016/j.rse.2012.09.011
  2. Caroline, A. H., & Hidayati, I. N. (2016). Pemanfaatan Citra Quickbird dan SIG untuk Pemetaan Tingkat Kenyamanan Permukiman di Kecamatan Semarang Barat dan Kecamatan Semarang Utara. Majalah Geografi Indonesia, 30(1), 1–8.
  3. Couturier, S., Ricárdez, M., Osorno, J., & López-Martínez, R. (2011). Morpho-spatial extraction of urban nuclei in diffusely urbanized metropolitan areas. Landscape and Urban Planning, 101(4), 338–348. https://doi.org/10.1016/j.landurbplan.2011.02.039
  4. Deng, C., & Wu, C. (2013). ISPRS Journal of Photogrammetry and Remote Sensing The use of single-date MODIS imagery for estimating large-scale urban impervious surface fraction with spectral mixture analysis and machine learning techniques. ISPRS Journal of Photogrammetry and Remote Sensing, 86, 100–110. https://doi.org/10.1016/j.isprsjprs.2013.09.010
  5. Forzieri, G., Tanteri, L., Moser, G., & Catani, F. (2013). Mapping natural and urban environments using airborne multi-sensor ADS40-MIVIS-LiDAR synergies. International Journal of Applied Earth Observation and Geoinformation, 23(1), 313–323. https://doi.org/10.1016/j.jag.2012.10.004
  6. Gao, B. (1996). NDWI - A Normalized Difference Water Index for Remote Sensing of Vegetation Liquid Water From Space. Remote Sensing of Environment, 266(April), 257–266.
  7. Glenn, E. P., Huete, A. R., Nagler, P. L., & Nelson, S. G. (2008). Relationship between remotely-sensed vegetation indices, canopy attributes and plant physiological processes: What vegetation indices can and cannot tell us about the landscape. Sensors, 8(4), 2136–2160. https://doi.org/10.3390/s8042136
  8. He, C., Shi, P., Xie, D., & Zhao, Y. (2010). Improving the normalized difference built- up index to map urban built-up areas using a semiautomatic segmentation approach. Remote Sensing Letters, 1(December 2010), 213–221. https://doi.org/10.1080/01431161.2010.481681
  9. Hidayati, I.N, Suharyadi;, & Danoedoro, P. (2017). Pemetaan Lahan Terbangun Perkotaan Menggunakan Pendekatan NDBI dan Segmentasi Semi-Automatik. In Prosiding Seminar Nasional Geografi UMS 2017 (hal. 19–28). Diambil dari https://publikasiilmiah.ums.ac.id/bitstream/handle/11617/8998/semnasgeo2017_2.pdf?sequence=1
  10. Huete, A. . (1988). A soil-adjusted vegetation index ( SAVI ). Remote Sensing of Environment, 25(March 2014), 295–309. https://doi.org/10.1016/0034-4257(88)90106-X
  11. Kaspersen, P., Fensholt, R., & Drews, M. (2015). Using Landsat Vegetation Indices to Estimate Impervious Surface Fractions for European Cities. Remote Sensing, 7(6), 8224–8249. https://doi.org/10.3390/rs70608224
  12. Kong, F., Yin, H., Nakagoshi, N., & James, P. (2012). Simulating urban growth processes incorporating a potential model with spatial metrics. Ecological Indicators, 20, 82–91. https://doi.org/10.1016/j.ecolind.2012.02.003
  13. Li, L., Lu, D., & Kuang, W. (2016). Examining Urban Impervious Surface Distribution and Its Dynamic Change in Hangzhou Metropolis, 19–24. https://doi.org/10.3390/rs8030265
  14. Liu, T., & Yang, X. (2013). Mapping vegetation in an urban area with stratified classification and multiple endmember spectral mixture analysis. Remote Sensing of Environment, 133, 251–264. https://doi.org/10.1016/j.rse.2013.02.020
  15. McInerney, M., & Lozar, R. (2007). Comparison of methodologies to derive a Normalized Difference Thermal Index (NDTI) from ATLAS imagery. American Society for Photogrammetry and Remote Sensing - ASPRS Annual Conference 2007: Identifying Geospatial Solutions, 1(Figure 1), 411–420. Diambil dari http://www.scopus.com/inward/record.url?eid=2-s2.0-84868623223&partnerID=40&md5=7b8d5e26afde67d9c057136b8aeb59be
  16. Purwanto, A. (2015). Pemanfaatan Citra Landsat 8 Untuk Identifikasi Normalized Difference Vegetation Index ( Ndvi ) Di Kecamatan Silat Hilir Kabupaten Kapuas Hulu. Edukasi, 13(1), 27–36.
  17. Suarez-Rubio, M., Lookingbill, T. R., & Elmore, A. J. (2012). Exurban development derived from Landsat from 1986 to 2009 surrounding the District of Columbia, USA. Remote Sensing of Environment, 124, 360–370. https://doi.org/10.1016/j.rse.2012.03.029
  18. Varshney, A., & Rajesh, E. (2013). A Comparative Study of Built-up Index Approaches for Automated Extraction of Built-up Regions From Remote Sensing Data. Indian SocietyRemote Sensing, 42(3), 659–663. https://doi.org/10.1007/s12524-013-0333-9
  19. Xu, H. (2006). Modification of Normalized Difference Water Index ( NDWI ) to Enhance Open Water Features in Remotely Sensed Imagery. International Journal of Remote Sensing, 27(No.14), 3025–3033. https://doi.org/10.1080/01431160600589179
  20. Xu, H. (2007). Extraction of Urban Built-up Land Features from Landsat Imagery Using a Thematic- oriented Index Combination Technique. Photogrammetric Engineering & Remote Sensing, 73(12), 1381–1391.
  21. Zha, Y., Gao, J., & Ni, S. (2003). Use of normalized di ff erence built-up index in automatically mapping urban areas from TM imagery. International Journal of Remote Sensing, 24(3), 583–594.