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

Yıl 2022, Cilt: 31 Sayı: 2, 401 - 423, 18.12.2022
https://doi.org/10.51800/ecd.1175827

Öz

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.

Kaynakça

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

Yıl 2022, Cilt: 31 Sayı: 2, 401 - 423, 18.12.2022
https://doi.org/10.51800/ecd.1175827

Öz

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.

Kaynakça

  • 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.
  • Altman, D. (1999). Practical statistics for medical research. CRC Press.
  • 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
  • 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
  • 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
  • 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.
  • 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
  • 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
  • Congalton, R. G., & Green, K. (2019). Assessing the accuracy of remotely sensed data: principles and practices (3rd ed.) CRC Press.
  • 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
  • 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
  • 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
  • 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
  • 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
  • European Space Agency (2022a, July 15). Sentinel Level-1: https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/processing-levels/level-1
  • European Space Agency (2022b, July 20). Sen2Cor: https://step.esa.int/main/snap-supported-plugins/sen2cor/
  • 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
  • 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
  • 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
  • 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
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  • 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
  • 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
  • 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
  • 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
  • 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
  • Mekânsal Planlama Genel Müdürlüğü (2012). Samsun bütünleşik kıyı alanları yönetim ve planlama projesi-Mekânsal strateji planı, 208 s.
  • 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
  • Navulur, K. (2006). Multispectral image analysis using the object-oriented paradigm (1st ed.). CRC Press.
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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.
  • 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
  • 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
  • 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
  • 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
  • Türkiye İstatistik Kurumu (2022, Eylül 5). İstatistik Veri Portalı: Nüfus ve Demografi: https://data.tuik.gov.tr/Kategori/GetKategori?p=Nufus-ve-Demografi-109
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  • 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
  • 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
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  • 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
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  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
Toplam 65 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Beşeri Coğrafya
Bölüm Araştırma Makaleleri
Yazarlar

Derya Öztürk 0000-0002-0684-3127

Yayımlanma Tarihi 18 Aralık 2022
Gönderilme Tarihi 15 Eylül 2022
Kabul Tarihi 6 Aralık 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 31 Sayı: 2

Kaynak Göster

APA Öztürk, D. (2022). Sentinel-2A MSI ve Landsat-9 OLI-2 Görüntüleri Kullanılarak Farklı Geçirimsiz Yüzey İndekslerinin Karşılaştırmalı Değerlendirmesi: Samsun Örneği. Ege Coğrafya Dergisi, 31(2), 401-423. https://doi.org/10.51800/ecd.1175827