A Comparative Study of various Indices for extraction urban impervious surface of Landsat 8 OLI

Iswari Nur Hidayati(1*), R Suharyadi(2)

(1) Department of Geographic Information Science, Gadjah Mada University, Yogyakarta, Indonesia
(2) 
(*) Corresponding Author

Abstract

Impervious surface is one of the major land cover types of urban and suburban environment. Conversion of rural landscapes and vegetation area to urban and suburban land use is directly related to the increase of the impervious surface area. The impervious surface expansion is straight-lined with decreasing green spaces in urban areas. Impervious surface is one of indicator for detecting urban heat islands. This study compares various indices for mapping impervious surfaces using Landsat 8 OLI imagery by optimizing the different spectral characteristics of Landsat 8 OLI imagery. The research objectives are (1) to apply various indices for impervious surface mapping and (2) identifies impervious surfaces in urban areas based on multiple indices and provide recommendations and find the best index for mapping impervious surface in urban areas. In addition to utilizing the index, land use supervised classification method, maximum likelihood classification used for extracting built-up, and non-built-up areas. Accuracy assessment of this research used field data collection as primary data for calculating kappa coefficient, producer accuracy, and user accuracy. The study can also be extended to find the land surface temperature and correlate the impervious surface extraction data with urban heat islands.

Full Text:

HTML PDF

References

Chen, Y., & Yu, S. (2016). Assessment of urban growth in Guangzhou using multi-temporal, multi-sensor Landsat data to quantify and map impervious surfaces. International Journal of Remote Sensing, 37(24), 5936–5952. https://doi.org/10.1080/01431161.2016.1252473

Deng, C., & Wu, C. (2012). Remote Sensing of Environment BCI : A biophysical composition index for remote sensing of urban environments. Remote Sensing of Environment, 127, 247–259. https://doi.org/10.1016/j.rse.2012.09.009

Deng, C., & Wu, C. (2013). Remote Sensing of Environment Examining the impacts of urban biophysical compositions on surface urban heat island : A spectral unmixing and thermal mixing approach. Remote Sensing of Environment, 131, 262–274. https://doi.org/10.1016/j.rse.2012.12.020

Deng, Y., Wu, C., Li, M., & Chen, R. (2013). RNDSI: A ratio normalized difference soil index for remote sensing of urban/suburban environments. International Journal of Applied Earth Observation and Geoinformation, 39, 40–48. https://doi.org/10.1016/j.jag.2015.02.010

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

Jawak, S. D., & Luis, A. J. (2013). A spectral index ratio-based Antarctic land-cover mapping using hyperspatial 8-band WorldView-2 imagery. Polar Science, 7(1), 18–38. https://doi.org/10.1016/j.polar.2012.12.002

Li, Y., Gong, X., Guo, Z., Xu, K., Hu, D., & Zhou, H. (2016). An index and approach for water extraction using Landsat–OLI data. International Journal of Remote Sensing, 37(16), 3611–3635. https://doi.org/10.1080/01431161.2016.1201228

Lu, D., & Weng, Q. (2006). Use of impervious surface in urban land-use classification. Remote Sensing of Environment, 102(1–2), 146–160. https://doi.org/10.1016/j.rse.2006.02.010

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

McFeeters, S. K. (2013). Using the normalized difference water index (ndwi) within a geographic information system to detect swimming pools for mosquito abatement: A practical approach. Remote Sensing, 5(7), 3544–3561. https://doi.org/10.3390/rs5073544

Parece, T. E., & Campbell, J. B. (2013). Comparing urban impervious surface identification using landsat and high resolution aerial photography. Remote Sensing, 5(10), 4942–4960. https://doi.org/10.3390/rs5104942

Phinn, S., Stanford, M., Scarth, P., Murray, A. T., & Shyy, P. T. (2002). Monitoring the composition of urban environments based on the vegetation-impervious surface-soil (VIS) model by subpixel analysis techniques. International Journal of Remote Sensing, 23(20), 4131–4153. https://doi.org/10.1080/01431160110114998

Sharma, R., Ghosh, A., & Joshi, P. K. (2013). Spatio-temporal footprints of urbanisation in Surat, the Diamond City of India (1990-2009). Environmental Monitoring and Assessment, 185(4), 3313–3325. https://doi.org/10.1007/s10661-012-2792-9

Sinha, P., Verma, N. K., & Ayele, E. (2016). Urban Built-up Area Extraction and Change Detection of Adama Municipal Area using Time-Series Landsat Images, 5(8), 1886–1895.

Sunde, M. G., He, H. S., Zhou, B., Hubbart, J. A., & Spicci, A. (2014). Imperviousness Change Analysis Tool (I-CAT) for simulating pixel-level urban growth. Landscape and Urban Planning, 124, 104–108. https://doi.org/10.1016/j.landurbplan.2014.01.007

Tang, J., Chen, F., & Schwartz, S. S. (2012). Assessing spatiotemporal variations of greenness in the Baltimore-Washington corridor area. Landscape and Urban Planning, 105(3), 296–306. https://doi.org/10.1016/j.landurbplan.2012.01.004

Walker, J. J., Beurs, K. M. De, & Wynne, R. H. (2014). Remote Sensing of Environment Dryland vegetation phenology across an elevation gradient in Arizona , USA , investigated with fused MODIS and Landsat data. Remote Sensing of Environment, 144, 85–97. https://doi.org/10.1016/j.rse.2014.01.007

Weng, Q. (2012). Remote Sensing of Environment Remote sensing of impervious surfaces in the urban areas : Requirements , methods , and trends. Remote Sensing of Environment, 117, 34–49. https://doi.org/10.1016/j.rse.2011.02.030

Weng, Q., & Lu, D. (2008). A sub-pixel analysis of urbanization effect on land surface temperature and its interplay with impervious surface and vegetation coverage in Indianapolis , United States, 10, 68–83. https://doi.org/10.1016/j.jag.2007.05.002

Wu, C. (2004). Normalized spectral mixture analysis for monitoring urban composition using ETM+ imagery. Remote Sensing of Environment, 93(4), 480–492. https://doi.org/10.1016/j.rse.2004.08.003

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

Zhang, H., Lin, H., Li, Y., Zhang, Y., & Fang, C. (2016). Mapping urban impervious surface with dual-polarimetric SAR data : An improved method. Landscape and Urban Planning, 151, 55–63. https://doi.org/10.1016/j.landurbplan.2016.03.009

DOI: 10.23917/forgeo.v33i2.9179

Article Metrics

Abstract view(s): 1212 time(s)
HTML: 1495 time(s) PDF: 535 time(s)

Refbacks

  • There are currently no refbacks.


Bookmark and Share