Extraction of Urban Impervious Surface Areas in Samsun-Atakum Using Spectral Indices from Sentinel-2 Satellite Images

Abstract

Impervious surfaces are increasing rapidly and causing many environmental and ecological problems because of rapid urbanization. It is important to monitor impervious surfaces through effective methods, such as remote sensing. In this study, impervious surface areas were extracted from the Sentinel-2 satellite image (July 7, 2022) in Samsun-Atakum district using UI, NDBI, IBI, CBCI, and NISI indices in the GIS environment and the indices performances were compared using the spectral discrimination index and error matrix approach. NISI was the most accurate index with a spectral discrimination index of 1.3605, an overall accuracy of 89.20%, and a kappa value of 0.7850. According to NISI, the impervious surface areas were over 40% in 5 of the 30 neighborhoods and between 30–40% in 2 neighborhoods. The results showed that Sentinel-2 satellite images have considerable potential in the extraction of impervious surfaces, and the success of impervious surface extraction can be increased by using the optimum index determined by comparing different indices.

Keywords

• Urbanization
• Impervious surface index
• Remote sensing
• Sentinel-2
• Atakum

Samsun-Atakum’da Kentsel Geçirimsiz Yüzey Alanlarının Sentinel-2 Uydu Görüntülerinden Spektral İndeksler Kullanılarak Belirlenmesi

Abstract

Günümüzde hızlı kentleşmeyle birlikte geçirimsiz yüzeyler hızla artmakta ve çeşitli çevresel ve ekolojik sorunlara neden olmaktadır. Bu nedenle geçirimsiz yüzeylerin uzaktan algılama gibi etkin yöntemlerle takibi önem kazanmaktadır. Bu çalışmada yüksek kentleşme oranına sahip Samsun-Atakum ilçesinde 07.07.2022 tarihli Sentinel-2 uydu görüntüsünden UI, NDBI, IBI, CBCI ve NISI indeksleri kullanılarak Coğrafi Bilgi Sistemleri (CBS) ortamında geçirimsiz yüzey alanları çıkarılmış, indekslerin performansı spektral ayrım indeksi ve hata matrisi yaklaşımı ile değerlendirilmiştir. Analizlerin sonucunda çalışma alanında en başarılı indeksin NISI olduğu belirlenmiştir. NISI indeksi 1,3605 spektral ayrım indeksi, % 89,20 genel doğruluk ve 0,7850 kappa değeriyle yüksek performans göstermiş, hem binaların hem de yolların çıkarımında başarılı olmuştur. NISI indeksi sonuçlarına göre çalışma alanında incelenen 30 mahallenin 5’inde geçirimsiz yüzey alanlarının %40’ın üzerinde ve 2’sinde % 30–40 arasında olduğu belirlenmiştir. Çalışmadan elde edilen sonuçlar Sentinel-2 uydu görüntülerinin geçirimsiz yüzey çıkarımında önemli bir potansiyel taşıdığını ve farklı indekslerin karşılaştırılması sonucunda belirlenen optimum indeksin kullanılmasıyla geçirimsiz yüzey çıkarım başarısının artırılabileceğini ortaya çıkarmıştır.

Keywords

• Kentleşme
• Geçirimsiz yüzey indeksi
• Uzaktan algılama
• Sentinel-2
• Atakum

References

1. Ansari, T. A., Katpatal, Y. B., Vasudeo, A. D. (2016). Spatial evaluation of impacts of increase in impervious surface area on SCS-CN and runoff in Nagpur urban watersheds, India. Arabian Journal of Geosciences, 9 (18), 1–15. doi:10.1007/s12517-016-2702-5
2. Arnold Jr, C. L., Gibbons, C. J. (1996). Impervious surface coverage: the emergence of a key environmental indicator. Journal of the American planning Association, 62 (2), 243–258. doi:10.1080/01944369608975688
3. Atakum Belediyesi (2014). Atakum Belediyesi 2015–2019 Stratejik Planı. Samsun.
4. Atakum Kaymakamlığı (2013). Değişirken Gelişen Atakum. Samsun.
5. Batra, U., Roy, N. R., Panda, B. (Ed.) (2020). Data science and analytics: 5th international conference on recent developments in science, engineering and technology, REDSET 2019, Gurugram, India, November 15–16, 2019, Revised Selected Papers, Part II.
6. Bauer, M. E., Loffelholz, B. C., Wilson, B. (2007). Estimating and mapping impervious surface area by regression analysis of Landsat imagery. Remote Sensing of Impervious Surfaces içinde (31–48). CRC Press.
7. 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:10.1080/10106049.2018.1497094
8. Carlson, T. N. (2007). Impervious surface area and its effect on water abundance and water quality. Remote Sensing of Impervious Surfaces içinde (381–396). CRC Press.

9. 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:10.1080/22797254.2020.1820383
10. 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:10.1117/1.JRS.13.016502
11. Congalton, R. G., Green, K. (2019). Assessing the accuracy of remotely sensed data: principles and practices. CRC Press.
12. 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:10.1007/s11356-020-11007-4
13. Deng, C. B., Wu, C. S. (2012). BCI: A biophysical composition index for remote sensing of urban environments. Remote Sensing of Environment, 127, 247–259. doi:10.1016/j.rse.2012.09.009
14. 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:10.1016/j.jag.2015.02.010
15. Du, S., Shi, P., Van Rompaey, A., Wen, J. (2015). Quantifying the impact of impervious surface location on flood peak discharge in urban areas. Natural Hazards, 76 (3), 1457–1471. doi:10.1007/s11069-014-1463-2
16. Earth Resources Observation and Science Center (2022). USGS EROS Archive- Sentinel-2, 16.07.2022 tarihinde https://www.usgs.gov/centers/eros/science/usgs-eros-archive-sentinel-2?qt-science_center_objects=0#qt-science_center_objects adresinden alındı.
17. Ertan, K. (2019). Kentsel yaşam kalitesi ve ütopyalar. Kent ve Çevre Araştırmaları Dergisi, 1(1), 83–103. https://dergipark.org.tr/tr/pub/yykentcevre/issue/50632/643324 adresinden alındı.
18. European Space Agency (2022a). Sentinel Level-1, 15.07.2022 tarihinde https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/processing-levels/level-1 adresinden alındı.
19. European Space Agency (2022b). Copernicus Sentinel-2 Collection 1 MSI Level-1C (L1C), 15.07.2022 tarihinde https://sentinels.copernicus.eu/web/sentinel/sentinel-data-access/sentinel-products/sentinel-2-data-products/collection-1-level-1c adresinden alındı.
20. European Space Agency (2022c). Sen2Cor, 20.07.2022 tarihinde https://step.esa.int/main/snap-supported-plugins/sen2cor/ adresinden alındı.
21. 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:10.1080/07038992.2015.1112730
22. Foody, G. M. (2002). Status of land cover classification accuracy assessment. Remote Sensing of Environment, 80 (1), 185–201. doi:10.1016/S0034-4257(01)00295-4
23. Ghosh, D. K., Mandal, A. C., Majumder, R., Patra, P., Bhunia, G. S. (2018). Analysis for mapping of built-up area using remotely sensed indices–a case study of Rajarhat Block in Barasat Sadar Sub-Division in West Bengal (India). Journal of Landscape Ecology, 11 (2), 67–76. doi:10.2478/jlecol-2018-0007
24. Hidayati, I. N., Suharyadi, R. (2019). A Comparative Study of various Indices for extraction urban impervious surface of Landsat 8 OLI. Forum Geografi, 33 (2), 162–172.
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: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:10.1016/j.envc.2022.100568
28. Li, C., Shao, Z., Zhang, L., Huang, X., Zhang, M. (2021). A comparative analysis of index-based methods for impervious surface mapping using multiseasonal sentinel-2 satellite data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 3682–3694. doi:10.1109/JSTARS.2021.3067325
29. Li, J., Song, C., Cao, L., Zhu, F., Meng, X., Wu, J. (2011). Impacts of landscape structure on surface urban heat islands: A case study of Shanghai, China. Remote Sensing of Environment, 115 (12), 3249–3263. doi:10.1016/j.rse.2011.07.008
30. Li, L., Lu, D., Kuang, W. (2016). Examining urban impervious surface distribution and its dynamic change in Hangzhou metropolis. Remote Sensing, 8 (3), 265. doi:10.3390/rs8030265
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:10.1016/j.catena.2022.106265
32. Liu, Z., Wang, Y., Li, Z., Peng, J. (2013). Impervious surface impact on water quality in the process of rapid urbanization in Shenzhen, China. Environmental Earth Sciences, 68 (8), 2365–2373. doi:10.1007/s12665-012-1918-2
33. Lu, D., Hetrick, S., Moran, E., Li, G. (2010). Detection of urban expansion in an urban-rural landscape with multitemporal QuickBird images. Journal of Applied Remote Sensing, 4 (1), 041880. doi:10.1117/1.3501124
34. Lu, D., Li, G., Kuang, W., Moran, E. (2014). Methods to extract impervious surface areas from satellite images. International Journal of Digital Earth, 7 (2), 93–112. doi:10.1080/17538947.2013.866173
35. Lu, D., Moran, E., Hetrick, S. (2011). Detection of impervious surface change with multitemporal Landsat images in an urban–rural frontier. ISPRS Journal of Photogrammetry and Remote Sensing, 66 (3), 298–306. doi:10.1016/j.isprsjprs.2010.10.010
36. Lu, D., Weng, Q. (2004). Spectral mixture analysis of the urban landscape in Indianapolis with Landsat ETM+ imagery. Photogrammetric Engineering & Remote Sensing, 70 (9), 1053–1062. doi:10.14358/PERS.70.9.1053
37. 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. doi:10.1007/s12524-016-0656-4
38. 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.
39. McClung, T., Ibáñez, I. (2018). Quantifying the synergistic effects of impervious surface and drought on radial tree growth. Urban Ecosystems, 21 (1), 147–155. doi:10.1007/s11252-017-0699-5
40. Navulur, K. (2006). Multispectral image analysis using the object-oriented paradigm. CRC Press.
41. 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:10.3390/land10030231
42. Öztürk, D. (2015). Urban growth simulation of Atakum (Samsun, Turkey) using cellular automata-Markov chain and multi-layer perceptron-Markov chain models. Remote Sensing, 7 (5), 5918–5950. doi:10.3390/rs70505918
43. Öztürk, D. (2017). 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:10.3846/16486897.2016.1233881
44. Parekh, J. R., Poortinga, A., Bhandari, B., Mayer, T., Saah, D., Chishtie, F. (2021). Automatic detection of impervious surfaces from remotely sensed data using deep learning. Remote Sensing, 13 (16), 3166. doi:10.3390/rs13163166
45. Patra, S., Sahoo, S., Mishra, P., Mahapatra, S. C. (2018). Impacts of urbanization on land use/cover changes and its probable implications on local climate and groundwater level. Journal of Urban Management, 7 (2), 70–84. doi:10.1016/j.jum.2018.04.006
46. Santra, A., Mitra, S. S., Sinha, S., Routh, S. (2020). Performance testing of selected spectral indices in automated extraction of impervious built-up surface features using Resourcesat LISS-III image. Arabian Journal of Geosciences, 13 (22), 1–11. doi:10.1007/s12517-020-06183-z
47. Sekertekin, A., Zadbagher, E. (2021). Simulation of future land surface temperature distribution and evaluating surface urban heat island based on impervious surface area. Ecological Indicators, 122, 107230. doi:10.1016/j.ecolind.2020.107230
48. 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:10.3390/rs13153040
49. Slonecker, E. T., Jennings, D. B., Garofalo, D. (2001). Remote sensing of impervious surfaces: A review. Remote Sensing Reviews, 20 (3), 227–255. doi:10.1080/02757250109532436
50. Su, S., Tian, J., Dong, X., Tian, Q., Wang, N., Xi, Y. (2022). An impervious surface spectral index on multispectral imagery using nisible and near-infrared bands. Remote Sensing, 14 (14), 3391. doi:10.3390/rs14143391
51. Sun, G., Chen, X., Jia, X., Yao, Y., Wang, Z. (2016). Combinational build-up index (CBI) for effective impervious surface mapping in urban areas. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9 (5), 2081–2092. doi:10.1109/jstars.2015.2478914
52. 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:10.1117/1.3539767
53. Tian, Y., Chen, H., Song, Q., Zheng, K. (2018). A novel index for impervious surface area mapping: Development and validation. Remote Sensing, 10 (10), 1521. doi:10.3390/rs10101521
54. Türkiye İstatistik Kurumu (2022). İstatistik Veri Portalı: Nüfus ve Demografi, 09.09.2022 tarihinde https://data.tuik.gov.tr/Kategori/GetKategori?p=Nufus-ve-Demografi-109 adresinden alındı.
55. United Nations (2014). World Urbanization Prospects: The 2014 Revision, Highlights (ST/ESA/SER.A/352), United Nations Publication.
56. Valdiviezo-N, J. C., Téllez-Quiñones, A., Salazar-Garibay, A., López-Caloca, A. A. (2018). Built-up index methods and their applications for urban extraction from Sentinel 2A satellite data: discussion. JOSA A, 35 (1), 35–44. doi:10.1364/JOSAA.35.000035
57. 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:10.1080/01431161.2015.1007250
58. Weng, Q. (2012). Remote sensing of impervious surfaces in the urban areas: Requirements, methods, and trends. Remote Sensing of Environment, 117, 34–49. doi:10.1016/j.rse.2011.02.030
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:10.1080/01431160802039957
60. 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:10.1080/01431160304987
61. 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:10.1109/ACCESS.2018.2857405
62. Zheng, H. W., Shen, G. Q., Wang, H., Hong, J. (2015). Simulating land use change in urban renewal areas: A case study in Hong Kong. Habitat International, 46, 23–34. doi:10.1016/j.habitatint.2014.10.008