Developing an Extraction Method of Urban Built-Up Area Based on Remote Sensing Imagery Transformation Index

Iswari Nur Hidayati, R Suharyadi, Projo Danoedoro

DOI: https://doi.org/10.23917/forgeo.v32i1.5907

Abstract

Studying urban areas using remote sensing imagery has become a challenge, both visually and digitally. Supervised classification, one of the digital classification approaches to differentiate between built-up and non-built-up area, used to be leading in digital studies of urban area. Then the next generation uses index transformation for automatic urban data extraction. The extraction of urban built-up land can be automatically done with NDBI although it has one limitation on separating built-up land and bare land. The previous studies provide opportunities for further research to increase the accuracy of the extraction, particularly using index transformation. This study aims to obtain the maximum accuracy of the extraction by merging several indices including NDBI, NDVI, MNDWI, NDWI, and SAVI. The merging of the indices is using four stages: merging of two indices, three indices, four indexes and five indices. Several operations were experimented to merge the indices, either by addition, subtraction, or multiplication. The results show that merging NDBI and MNDWI produce the highest accuracy of 90.30% either by multiplication (overlay) or reduction. Application of SAVI, NDBI, and NDWI also gives a good effect for extracting urban built-up areas and has 85.72% mapping accuracy.

Keywords

built-up area extraction; remote sensing; index transformation; Landsat 8 OLI

Full Text:

HTML

References

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

Campbell, J. B., & Wynne, R. H. (2011). Introduction to remote sensing. Guilford Press.

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.

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

Deng, C., & Wu, C. (2013). 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

Exelesis Visual Information Solutions. (2014). ENVI Classic Tutorial : Classification Methods. In ENVI classic Tutorial (hal. 1–26). North America. Diambil dari http://www.harrisgeospatial.com/portals/0/pdfs/envi/Classification_Methods.pdf

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, 313–323. https://doi.org/10.1016/j.jag.2012.10.004

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.

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

Hai, P. M., & Yamaguchi, Y. (2007). Characterizing the Urban Growth From 1975 To 2003 of Hanoi City Using Remote Sensing and a Spatial Metric. Forum Geografi, 21(2), 104–110.

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

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, pp. 19–28.

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

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

Li, K., & Chen, Y. (2018). A Genetic Algorithm-based urban cluster automatic threshold method by combining VIIRS DNB, NDVI, and NDBI to monitor urbanization. Remote Sensing, 10(2), 1–22. https://doi.org/10.3390/rs10020277

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

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

Luo, X., Peng, Y., & Gao, Y. (2017). An Improved Optimal Segmentation Threshold Algorithm and Its Application in the Built-up Quick Mapping. Journal of the Indian Society of Remote Sensing, 45(6), 953–964. https://doi.org/10.1007/s12524-016-0656-4

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.

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.

Sari, N. M., Chulafak, G. A., Zylshal, Z., & Kushardono, D. (2017). The Relationship between the Mixed Pixel Spectral Value of Landsat 8 OLI Data and LAPAN Surveillance Aircraft (LSA) Aerial-Photo Data. Forum Geografi, 31(1), 83–98.

Suarez-rubio, M., Lookingbill, T. R., & Elmore, A. J. (2012). Remote Sensing of Environment 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

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

Varshney, A., & Rajesh, E. (2014). A Comparative Study of Built-up Index Approaches for Automated Extraction of Built-up Regions From Remote Sensing Data. Journal of the Indian Society of Remote Sensing, 42(3), 659–663. https://doi.org/10.1007/s12524-013-0333-9

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

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.

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 di ff erence built-up index in automatically mapping urban areas from TM imagery. International Journal of Remote Sensing, 24(3), 583–594.

Article Level Metrics

Refbacks

  • There are currently no refbacks.