Comparative Evaluation of Various Impervious Surface Indices Using Sentinel-2A MSI and Landsat-9 OLI-2 Images: A case of Samsun

Abstract

The world is experiencing rapid urbanization, and many natural areas are transformed into impervious surfaces through urbanization. The increase in impervious surfaces in urban areas leads to the deterioration of the environment and a decrease in natural resources. Therefore, information about impervious surfaces, a primary indicator of urban construction, is needed in studies on urbanization and its environmental effects. Obtaining spatio-temporal urban impervious surface information in an accurate and cost-effective manner is essential for supporting sustainable urban development. Today, impervious surface indices based on remote sensing technology can effectively extract impervious surface areas. However, the difficulty of the impervious surface extraction complicates the selection of the method to get the optimum result. In this study, the performance of six different impervious surface indices, including Urban Index (UI), Normalized Difference Built-up Index (NDBI), Index-based Built-up Index (IBI), Combinational Biophysical Composition Index (CBCI), Enhanced Normalized Difference Impervious Surfaces Index (ENDISI), and Normalized Impervious Surface Index (NISI), were employed to extract impervious surfaces from Sentinel-2A MSI and Landsat-9 OLI-2 images in an area of Samsun, where has high urbanization potential. The results were evaluated by spectral discrimination index and error matrix approach. Additionally, the effects of indices were investigated using visual assessments. The results showed that ENDISI was the best-performing index in both Sentinel-2A MSI and Landsat-9 OLI-2 images in the study area, but Sentinel-2A MSI gave higher accuracy than Landsat-9 OLI-2. In the extraction of impervious surfaces using the ENDISI index, the overall accuracy for Sentinel-2A MSI is 91.53% and the kappa value is 0.8301, while the overall accuracy for Landsat-9 OLI-2 is 78.29% and the kappa value is 0.5646. The study’s results revealed that Sentinel-2 and Landsat-9 satellite images have a significant potential for impervious surface extraction, and the extraction success can be increased with the optimum result to be determined by comparisons based on different satellite images and indices.

Keywords

• impervious surface index
• Sentinel-2
• Landsat-9
• accuracy
• comparison

Sentinel-2A MSI ve Landsat-9 OLI-2 Görüntüleri Kullanılarak Farklı Geçirimsiz Yüzey İndekslerinin Karşılaştırmalı Değerlendirmesi: Samsun Örneği

Abstract

Dünyada hızlı bir kentleşme yaşanmakta ve kentleşme süreciyle birlikte önemli miktarda doğal alan geçirimsiz yüzeylere dönüşmektedir. Kentsel alanlarda geçirimsiz yüzeylerin artması, çevrenin bozulmasına ve doğal kaynakların azalmasına yol açmaktadır. Bu nedenle, kentleşme ve kentleşmenin çevresel etkileriyle ilgili çalışmalarda kentsel yapılaşmanın temel bir göstergesi olan geçirimsiz yüzeylerle ilgili bilgilere gereksinim duyulmaktadır. Kentsel geçirimsiz yüzey bilgilerinin zamanında, maliyet etkin ve doğru bir şekilde elde edilmesi, sürdürülebilir kentsel gelişimin desteklenmesi için büyük önem taşımaktadır. Günümüzde uzaktan algılama teknolojisine dayalı geçirimsiz yüzey indeksleri, geçirimsiz yüzey alanlarının elde edilmesinde etkin olarak kullanılabilmektedir. Ancak geçirimsiz yüzey çıkarımının karmaşıklığı, optimum sonucu elde etmek için yöntem seçimini zorlaştırmaktadır. Bu çalışmada Samsun’da yüksek kentleşme potansiyeli olan bir alanda Sentinel-2A MSI ve Landsat-9 OLI-2 görüntülerinden geçirimsiz yüzey çıkarımında Kent İndeksi (Urban Index-UI), Normalleştirilmiş Fark Yapay Alan İndeksi (Normalized Difference Built-up Index-NDBI), İndeks Tabanlı Yapay Alan İndeksi (Index-based Built-up index-IBI), Kombinasyonel Biyofiziksel Bileşim İndeksi (Combinational Biophysical Composition Index-CBCI), Geliştirilmiş Normalleştirilmiş Fark Geçirimsiz Yüzey İndeksi (Enhanced Normalized Difference Impervious Surfaces Index-ENDISI) ve Normalleştirilmiş Geçirimsiz Yüzey İndeksi (Normalized Impervious Surface Index-NISI) olmak üzere altı farklı geçirimsiz yüzey indeksinin performansı spektral ayrım indeksi ve hata matrisi yaklaşımıyla karşılaştırılmış, ayrıca görsel incelemeler ile indeks etkileri araştırılmıştır. Çalışmanın sonucunda ENDISI’nin hem Sentinel-2A MSI hem de Landsat-9 OLI-2 görüntülerinde en iyi performans gösteren indeks olduğu ancak Sentinel-2A MSI ile Landsat-9 OLI-2’den daha yüksek doğruluk elde edildiği belirlenmiştir. ENDISI indeksiyle geçirimsiz yüzey çıkarımında Sentinel-2A MSI için toplam doğruluk % 91,53 ve kappa değeri 0,8301 iken Landsat-9 OLI-2 için toplam doğruluk % 78,29 ve kappa değeri 0,5646’dır. Çalışmanın sonuçları Sentinel-2 ve Landsat-9 uydu görüntülerinin geçirimsiz yüzey çıkarımında önemli bir potansiyele sahip olduğunu ve farklı uydu görüntüleri ve indekslere dayalı karşılaştırmalarla belirlenen optimum sonuç ile geçirimsiz yüzey çıkarım başarısının artırılabileceğini ortaya koymuştur.

Keywords

• geçirimsiz yüzey indeksi
• Sentinel-2
• Landsat-9
• doğruluk
• karşılaştırma

References

1. Ali, M. I., Hasim, A. H., & Abidin, M. R. (2019). Monitoring the built-up area transformation using urban index and normalized difference built-up index analysis. International Journal of Engineering Transactions B: Applications, 32(5), 647–653.
2. Altman, D. (1999). Practical statistics for medical research. CRC Press.
3. Baranwal, E., Ahmad, S., & Mudassir, S. M. (2022). New independent component-based spectral index for precise extraction of impervious surfaces through Landsat-8 images. Geocarto International. doi: https://doi.org/10.1080/10106049.2022.2102244
4. Bhatti, S. S., & Tripathi, N. K. (2014). Built-up area extraction using Landsat 8 OLI imagery. GIScience & Remote Sensing, 51(4), 445–467. doi: https://doi.org/10.1080/15481603.2014.939539
5. Bouhennache, R., Bouden, T., Taleb-Ahmed, A., & Cheddad, A. (2019). A new spectral index for the extraction of built-up land features from Landsat 8 satellite imagery. Geocarto International, 34(14), 1531–1551. doi: https://doi.org/10.1080/10106049.2018.1497094
6. Capolupo, A., Monterisi, C., Caporusso, G., & Tarantino, E. (2020). Extracting land cover data using GEE: A review of the classification indices. In International Conference on Computational Science and Its Applications (pp. 782–796). Springer.
7. Chen, J., Chen, S., Yang, C., He, L., Hou, M., & Shi, T. (2020). A comparative study of impervious surface extraction using Sentinel-2 imagery. European Journal of Remote Sensing, 53(1), 274–292. doi: https://doi.org/10.1080/22797254.2020.1820383
8. Chen, J., Yang, K., Chen, S., Yang, C., Zhang, S., & He, L. (2019). Enhanced normalized difference index for impervious surface area estimation at the plateau basin scale. Journal of Applied Remote Sensing, 13(1), 016502. doi: https://doi.org/10.1117/1.JRS.13.016502

9. Congalton, R. G., & Green, K. (2019). Assessing the accuracy of remotely sensed data: principles and practices (3rd ed.) CRC Press.
10. Daramola, M. T., Eresanya, E. O., & Ishola, K. A. (2018). Assessment of the thermal response of variations in land surface around an urban area. Modeling Earth Systems and Environment, 4(2), 535–553. doi: https://doi.org/10.1007/s40808-018-0463-8
11. Deliry, S. I., Avdan, Z. Y., & Avdan, U. (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. doi: https://doi.org/10.1007/s11356-020-11007-4
12. Deng, Y., Wu, C., Li, M., & Chen, R. (2015). 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. doi: https://doi.org/10.1016/j.jag.2015.02.010
13. Dixit, M., Chaurasia, K., Mishra, V. K., Singh, D., & Lee, H. N. (2022). 6+: A novel approach for building extraction from a medium resolution multi-spectral satellite. Sustainability, 14(3), 1615. doi: https://doi.org/10.3390/su14031615
14. Earth Resources Observation and Science Center (2022, July 16). USGS EROS Archive - Sentinel-2: https://www.usgs.gov/centers/eros/science/usgs-eros-archive-sentinel-2?qt-science_center_objects=0#qt-science_center_objects
15. European Space Agency (2022a, July 15). Sentinel Level-1: https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/processing-levels/level-1
16. European Space Agency (2022b, July 20). Sen2Cor: https://step.esa.int/main/snap-supported-plugins/sen2cor/
17. Fan, F., Fan, W., & Weng, Q. (2015). Improving urban impervious surface mapping by linear spectral mixture analysis and using spectral indices. Canadian Journal of Remote Sensing, 41(6), 577–586. doi: https://doi.org/10.1080/07038992.2015.1112730
18. Feizizadeh, B., Darabi, S., Blaschke, T., & Lakes, T. (2022). QADI as a new method and alternative to kappa for accuracy assessment of remote sensing-based image classification. Sensors, 22(12), 4506. doi: https://doi.org/10.3390/s22124506
19. Foody, G. M. (2002). Status of land cover classification accuracy assessment. Remote Sensing of Environment, 80(1), 185–201. doi: https://doi.org/10.1016/S0034-4257(01)00295-4
20. Friedl, M. A., Sulla-Menashe, D., Tan, B., Schneider, A., Ramankutty, N., Sibley, A., & Huang, X. (2010). MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets. Remote Sensing of Environment, 114(1), 168–182. doi: https://doi.org/10.1016/j.rse.2009.08.016
21. Harita Genel Müdürlüğü (2022, Eylül 12). İl ve ilçe yüzölçümleri: https://www.harita.gov.tr/il-ve-ilce-yuzolcumleri
22. Hekimoğlu, B., Altındeğer, M., & Demirbaş, A. R. (2007). Samsun ilinin fiziki durumu ve avantajları. Samsun Valiliği Tarım İl Müdürlüğü, 38 s.
23. Hidayati, I. N., & Suharyadi, R. (2019, December). A comparative study of various indices for extraction urban impervious surface of Landsat 8 OLI. Forum Geografi, 33(2), 162–172. doi: https://doi.org/10.23917/forgeo.v33i2.9179
24. Huete, A. R. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3), 295–309.
25. Kaur, R., & Pandey, P. (2022). A review on spectral indices for built-up area extraction using remote sensing technology. Arabian Journal of Geosciences, 15(5), 1–22. doi: https://doi.org/10.1007/s12517-022-09688-x
26. Kawamura, M., Jayamana, S., & Tsujiko, Y. (1996). Relation between social and environmental conditions in Colombo Sri Lanka and the Urban Index estimated by satellite remote sensing data. The International Archives of Photogrammetry and Remote Sensing, 31(PART B7), 321–326.
27. Kebede, T. A., Hailu, B. T., & Suryabhagavan, K. V. (2022). Evaluation of spectral built-up indices for impervious surface extraction using Sentinel-2A MSI imageries: A case of Addis Ababa city, Ethiopia. Environmental Challenges, 8, 100568. doi: https://doi.org/10.1016/j.envc.2022.100568
28. Li, W. (2019). Mapping urban impervious surfaces by using spectral mixture analysis and spectral indices. Remote Sensing, 12(1), 94. doi: https://doi.org/10.3390/rs12010094
29. Liu, Q., & Trinder, J. C. (2018). Sub-pixel technique for time series analysis of shoreline changes based on multispectral satellite imagery. In M. Marghany (Ed.), Advanced Remote Sensing Technology for Synthetic Aperture Radar Applications, Tsunami Disasters, and Infrastructure. IntechOpen. doi: http://dx.doi.org/10.5772/intechopen.81789
30. Liu, F., Zhao, Y., Muhammad, R., Liu, X., & Chen, M. (2020). Impervious surface expansion: A key indicator for environment and urban agglomeration—A case study of Guangdong-Hong Kong-Macao greater bay area by using Landsat data. Journal of Sensors, 3896589. doi: https://doi.org/10.1155/2020/3896589
31. Liu, Y., Meng, Q., Zhang, L., & Wu, C. (2022). NDBSI: A normalized difference bare soil index for remote sensing to improve bare soil mapping accuracy in urban and rural areas. Catena, 214, 106265. doi: https://doi.org/10.1016/j.catena.2022.106265
32. Ma, Y., & Wang, J. (2021). Comparison of impervious surface extraction index based on two kinds of satellite sensors. Spacecraft Recovery & Remote Sensing, 42(2), 139–151. doi: https://doi.org/10.3969/j.issn.1009-8518.2021.02.016
33. Mekânsal Planlama Genel Müdürlüğü (2012). Samsun bütünleşik kıyı alanları yönetim ve planlama projesi-Mekânsal strateji planı, 208 s.
34. Mourya, M., Kumari, B., Tayyab, M., Paarcha, A., & Rahman, A. (2021). Indices based assessment of built-up density and urban expansion of fast growing Surat city using multi-temporal Landsat data sets. GeoJournal, 86, 1607–1623. doi: https://doi.org/10.1007/s10708-020-10148-w
35. Navulur, K. (2006). Multispectral image analysis using the object-oriented paradigm (1st ed.). CRC Press.
36. Nguyen, C. T., Chidthaisong, A., Kieu Diem, P., & Huo, L. Z. (2021). A modified bare soil index to identify bare land features during agricultural fallow-period in southeast Asia using Landsat 8. Land, 10(3), 231. doi: https://doi.org/10.3390/land10030231
37. Olofsson, P., Foody, G. M., Herold, M., Stehman, S. V., Woodcock, C. E., & Wulder, M. A. (2014). Good practices for estimating area and assessing accuracy of land change. Remote Sensing of Environment, 148, 42–57. doi: https://doi.org/10.1016/j.rse.2014.02.015
38. Ozturk, D. (2017a). Assessment of urban sprawl using Shannon’s entropy and fractal analysis: a case study of Atakum, Ilkadim and Canik (Samsun, Turkey). Journal of Environmental Engineering and Landscape Management, 25(3), 264–276. doi: https://doi.org/10.3846/16486897.2016.1233881
39. Ozturk, D. (2017b). Modelling spatial changes in coastal areas of Samsun (Turkey) using a cellular automata-markov chain method. Tehnički Vjesnik, 24(1), 99–107. doi: https://doi.org/10.17559/TV-20141110125014
40. Öztürk, D., & Gündüz, U. (2019). Samsun ili arazi kullanımı/örtüsünün mekânsal-zamansal değişimlerinin fraktal analiz kullanılarak belirlenmesi. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, 24(2), 643–660. doi: https://doi.org/10.17482/uumfd.553486
41. Öztürk, D., & Gündüz, U. (2020). Samsun ilçelerinde kentsel doku morfolojisindeki zamansal değişimlerin fraktal analiz ile belirlenmesi. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi, 22(64), 81–95. doi: https://doi.org/10.21205/deufmd.2020226409
42. Roth, K. L., Roberts, D. A., Dennison, P. E., Alonzo, M., Peterson, S. H., & Beland, M. (2015). Differentiating plant species within and across diverse ecosystems with imaging spectroscopy. Remote Sensing of Environment, 167, 135–151. doi: https://doi.org/10.1016/j.rse.2015.05.007
43. Shrestha, B., Stephen, H., & Ahmad, S. (2021). Impervious surfaces mapping at city scale by fusion of radar and optical data through a random forest classifier. Remote Sensing, 13(15), 3040. doi: https://doi.org/10.3390/rs13153040
44. 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. International Journal of Advanced Remote Sensing and GIS, 5(8), 1886–1895.
45. Stehman, S. V. (2013). Estimating area from an accuracy assessment error matrix. Remote Sensing of Environment, 132, 202–211. doi: https://doi.org/10.1016/j.rse.2013.01.016
46. Su, S., Tian, J., Dong, X., Tian, Q., Wang, N., & Xi, Y. (2022). An impervious surface spectral index on multispectral imagery using visible and near-infrared bands. Remote Sensing, 14(14), 3391. doi: https://doi.org/10.3390/rs14143391
47. Sun, Z., Guo, H., Li, X., Lu, L., & Du, X. (2011). Estimating urban impervious surfaces from Landsat-5 TM imagery using multilayer perceptron neural network and support vector machine. Journal of Applied Remote Sensing, 5(1), 053501. doi: https://doi.org/10.1117/1.3539767
48. Teixeira Pinto, C., Jing, X., & Leigh, L. (2020). Evaluation analysis of Landsat level-1 and level-2 data products using in situ measurements. Remote Sensing, 12(16), 2597. doi: https://doi.org/10.3390/rs12162597
49. Türkiye İstatistik Kurumu (2022, Eylül 5). İstatistik Veri Portalı: Nüfus ve Demografi: https://data.tuik.gov.tr/Kategori/GetKategori?p=Nufus-ve-Demografi-109
50. U.S. Geological Survey (2022a, July 7). Earth Explorer: https://earthexplorer.usgs.gov
51. U.S. Geological Survey (2022b, September 12). Landsat Collection 2 Level-2 Science Products: https://www.usgs.gov/landsat-missions/landsat-collection-2-level-2-science-products
52. U.S. Geological Survey (2022c, September 9). Landsat 9 Data Users Handbook: http://www.gisandbeers.com/GeoBazar/Libros/Teledeteccion/Manual-Landsat-9-Handbook.pdf
53. 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. doi: https://doi.org/10.1007/s12524-013-0333-9
54. Vermote, E., Justice, C., Claverie, M., & Franch, B. (2016). Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. Remote Sensing of Environment, 185, 46–56. doi: https://doi.org/10.1016/j.rse.2016.04.008
55. Wang, Z., Gang, C., Li, X., Chen, Y., & Li, J. (2015). Application of a normalized difference impervious index (NDII) to extract urban impervious surface features based on Landsat TM images. International Journal of Remote Sensing, 36(4), 1055–1069. doi: https://doi.org/10.1080/01431161.2015.1007250
56. Weng, Q. (2012). Remote sensing of impervious surfaces in the urban areas: Requirements, methods, and trends. Remote Sensing of Environment, 117, 34–49. doi: https://doi.org/10.1016/j.rse.2011.02.030
57. Xi, Y., Thinh, N. X., & Li, C. (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. doi: https://doi.org/10.1080/22797254.2019.1584737
58. Xia, C., Zhang, A., & Yeh, A. G. O. (2020). Shape-weighted landscape evolution index: An improved approach for simultaneously analyzing urban land expansion and redevelopment. Journal of Cleaner Production, 244, 118836. doi: https://doi.org/10.1016/j.jclepro.2019.118836
59. Xu, H. Q. (2008). A new index for delineating built-up land features in satellite imagery. International Journal of Remote Sensing, 29(14), 4269–4276. doi: https://doi.org/10.1080/01431160802039957
60. Xu, H. (2010). Analysis of impervious surface and its impact on urban heat environment using the normalized difference impervious surface index (NDISI). Photogrammetric Engineering and Remote Sensing, 76(5), 557–565. doi: https://doi.org/10.14358/pers.76.5.557
61. 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. doi: https://doi.org/10.1080/01431160304987
62. Zhang, L., Tian, Y., & Liu, Q. (2020). A novel urban composition index based on water-impervious surface-pervious surface (WIP) model for urban compositions mapping using Landsat imagery. Remote Sensing, 13(1), 3. doi: https://doi.org/10.3390/rs13010003
63. Zhang, S., Yang, K., Li, M., Ma, Y., & Sun, M. (2018). Combinational biophysical composition index (CBCI) for effective mapping biophysical composition in urban areas. IEEE Access, 6, 41224–41237. doi: https://doi.org/10.1109/ACCESS.2018.2857405
64. Zhang, S., Yang, K., Ma, Y., & Li, M. (2021). The Expansion Dynamics and Modes of Impervious Surfaces in the Guangdong-Hong Kong-Macau Bay Area, China. Land, 10(11), 1167. doi: https://doi.org/10.3390/land10111167
65. Zhang, Y., Odeh, I. O., & Han, C. (2009). Bi-temporal characterization of land surface temperature in relation to impervious surface area, NDVI and NDBI, using a sub-pixel image analysis. International Journal of Applied Earth Observation and Geoinformation, 11(4), 256–264. doi: https://doi.org/10.1016/j.jag.2009.03.001