Investigating and Analyzing the Performance of Spectral Indices in Urban Extraction by Multi-spectral Satellite Images

Document Type : Independent Research Articles

Authors

1 MSc student of Photogrammetry, Faculty of Civil Engineering, Noshirvani University of Technology, Babol Iran.

2 MSc student in photogrammetry, Faculty of Civil Engineering, Babol Noshirvani University of Technology, Babol, Iran.

3 Assistant Professor, Department of Geomatics, Faculty of Civil Engineering, Babol Noshirvani University of Technology, Iran.

Abstract

Population growth has resulted in rapid changes in urban landscapes in many countries, including Iran. Monitoring urban areas can be accelerated by utilizing remote sensing images and spectral indices in urban planning. This requires evaluating the performance of indices according to the application in different conditions, which can be challenging for users. This study was conducted to compare and analyze suitable solutions for analyzing urban indicators using satellite imagery and their effectiveness. For this purpose, Landsat-8 and Sentinel-2 images were first analyzed to extract spectral index; then automated threshold algorithms were applied to separate constructed areas. Results indicate that Sentinel-2 images perform better than Landsat images in general. Among the spectral indices, the UI index for Landsat and Sentinel-2 images in Rafsanjan with overall accuracy of 86.28 and 98.19, the NBI index for Landsat and Sentinel-2 images in Amol city with overall accuracy of 87.21 and 97.48, and the UI index in Landsat and IBI in Sentinel-2 for Isfahan with overall accuracy of 78.73 and 91.69 have the best performance. Furthermore, applying the manual threshold limit to weak indicators has increased accuracy in most cases. Another study used Sentinel-1 images with user-controlled thresholds to identify built-up areas, and the results for Amol, Rafsanjan, and Isfahan were 89.24, 80.03, and 76.10, respectively. The findings of this study revealed that the performance accuracy of indices can vary depending on parameters such as the type of climate, different sensors, and thresholds. Therefore, as a strategic model, the results of this analysis can provide researchers with a better understanding of indicators as a result of considering different parameters.

Keywords

Main Subjects

References

Ali, Ahmed, & Nayyar, Zeeshan Alam. (2021). A Modified Built-up Index (MBI) for automatic urban area extraction from Landsat 8 Imagery. Infrared Physics & Technology, 116, 103769.
Attarchi, Sara, Poorrahimi, Mojtaba, & Isazade, Vahid. (2020). Comparison of spectral indices and object- based classification for built-up area extraction in different urban areas. Geographical Urban Planning Research (GUPR), 8(1), 23-43. https://doi.org/10.22059/jurbangeo.2020.299492.1249
Deliry, Sayed Ishaq, Avdan, Zehra Yiğit, & Avdan, Uğur. (2021). Extracting urban impervious surfaces from Sentinel-2 and Landsat-8 satellite data for urban planning and environmental management. Environmental Science and Pollution Research, 28(6), 6572-6586.
Ezimand, Keyvan, Abdollahi Kakroodi, Ataallah, & Kiavarz Moghaddam, Majid. (2017). Classification and Change Detection of Urban Built-up Lands Using Remote Sensing Images. Geographical Urban Planning Research (GUPR), 5(3), 445-468. https://doi.org/10.22059/jurbangeo.2018.229640.687
Ezimand, Keyvan, Kakroodi, AA, & Kiavarz, Majid. (2018). The development of spectral indices for detecting built-up land areas and their relationship with land-surface temperature. International journal of remote sensing, 39(23), 8428-8449.
Foody, Giles M. (2004). Thematic map comparison: Evaluating the statistical significance of differences in classification accuracy. Photogrammetric Engineering and Remote Sensing, 70(5), 627-634.
Jafari, Shahin, & Attarchi, Sara. (2021). Detection of urban built-up areas by using Sentinel-1images from different orbits, Case study: Isfahan. Geographical Urban Planning Research (GUPR), 9(3), 709-734. https://doi.org/10.22059/jurbangeo.2021.317204.1439
Jieli, Chen, Manchun, LI, et al. (2010). Extract residential areas automatically by new built-up index. The 18th International Conference on Geoinformatics: GIScience in Change, Geoinformatics 2010, Peking University, Beijing, China, June, 18-20, 2010
Kawamura, Makoto. (1996). Relation between social and environmental conditions in Colombo Sri Lanka and the urban index estimated by satellite remote sensing data. Proc. 51st Annual Conference of the Japan Society of Civil Engineers,
Liu, Yongxue, Chen, Jieli, et al. (2014a). Spatiotemporal dynamics of the urban sprawl in a typical urban agglomeration: a case study on Southern Jiangsu, China (1983–2007). Frontiers of earth science, 8, 490-504.
Liu, Yongxue, Chen, Jieli, et al. (2014b). Spatiotemporal dynamics of the urban sprawl in a typical urban agglomeration: a case study on Southern Jiangsu, China (1983–2007). Frontiers of earth science, 8(4), 490-504.
Lynch, Philip, Blesius, Leonhard, & Hines, Ellen. (2020). Classification of urban area using multispectral indices for urban planning. Remote Sensing, 12(15), 2503.
Masek, JG, Lindsay, FE, & Goward, SN. (2000). Dynamics of urban growth in the Washington DC metropolitan area, 1973-1996, from Landsat observations. International journal of remote sensing, 21(18), 3473-3486.
McNemar, Quinn. (1947). Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika, 12(2), 153-157.
Muttitanon, Wutjanun. (2021). ANALYZING OF VARIOUS INDICES OF BUILT-UP AND BARE LAND IN YANGON, MYANMAR. การ ประชุม วิชาการ วิศวกรรม โยธา แห่ง ชาติ ครั้ง ที่ 26, 26.
Otsu, Nobuyuki. (1979). A threshold selection method from gray-level histograms. IEEE Transactions on systems, man, and cybernetics, 9(1), 62-66.
Pandey, Dwijendra, & Tiwari, KC. (2020). Extraction of urban built-up surfaces and its subclasses using existing built-up indices with separability analysis of spectrally mixed classes in AVIRIS-NG imagery. Advances in Space Research, 66(8), 1829-1845.
Risky, Yanuar S, Aulia, Yogi H, & Widayani, Prima. (2017). Spatiotemporal Built-up Land Density Mapping Using Various Spectral Indices in Landsat-7 ETM+ and Landsat-8 OLI/TIRS (Case Study: Surakarta City). IOP Conference Series: Earth and Environmental Science,
Sun, Zhongchang, Xu, Ru, et al. (2019). High-resolution urban land mapping in China from sentinel 1A/2 imagery based on Google Earth Engine. Remote Sensing, 11(7), 752.
Valdiviezo-N, Juan C, Téllez-Quiñones, Alejandro, Salazar-Garibay, Adan, & López-Caloca, Alejandra A. (2018). Built-up index methods and their applications for urban extraction from Sentinel 2A satellite data: discussion. JOSA A, 35(1), 35-44.
Xi, Yantao, Thinh, Nguyen Xuan, & Li, Cheng. (2019). Preliminary comparative assessment of various spectral indices for built-up land derived from Landsat-8 OLI and Sentinel-2A MSI imageries. European Journal of Remote Sensing, 52(1), 240-252.
Xu, Hanqiu. (2007). Extraction of urban built-up land features from Landsat imagery using a thematicoriented index combination technique. Photogrammetric Engineering & Remote Sensing, 73(12), 1381-1391.
Xu, Hanqiu. (2008). A new index for delineating built‐up land features in satellite imagery. International journal of remote sensing, 29(14), 4269-4276.
Zha, Yong, Gao, Jay, & Ni, Shaoxiang. (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.

Files

History

  • Receive Date: 19 March 2023
  • Revise Date: 29 May 2023
  • Accept Date: 01 October 2023

Share

How to cite

Statistics