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The use of bands ratio derived from Sentinel-2 imagery to detect built-up area in the dry period (North-East Algeria)

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Abstract

In this research, the band rationing technique was used to expect accurate detection of built-up in a dry period over El-Eulma city (North-East Algeria). In this context, the VNIR Sentinel-2 bands were examined statistically over the study area. Consequently, two bands ratio (BR) which are mainly the blue-near-infrared (B2/B8) and the green-near-infrared (B3/B8), were selected to be used singly as input data, for the binarization process via the use of Otsu method. To evaluate the approach and find the optimal bands ratio for built-up detection in the dry period, the accuracy assessment was done, using the high-resolution Google Earth images as a reference map. Also, the results obtained were compared to the both built-up mapping resulting from the support vector machine (SVM) classification and built-up area index (BAI). The findings showed that the BR (B2/B8) works approximately similar to the SVM classification result. In contrast, the BR (B2/B8) works better than the BR (B3/B8) and BAI index; the overall accuracy (OA) and kappa coefficient of the first BR (B2/B8) are about 92,33% and 80,81%, respectively. In contrast, the (OA) and kappa coefficient of the second BR (B3/B8) are about 90,97% and 76,72% respectively, Meanwhile, the (OA) of the BAI index is about 89.33% and its kappa coefficient is about 74,80%. Therefore, the results present BR (B2/B8) as a simple automatic technique that could be suitable for mapping cities accurately in a dry climate, for better land use planning.

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References

  • Askari G, Pour AB, Pradhan B, Sarfi M, Nazemnejad F (2018) Band ratios matrix transformation (BRMT): a sedimentary lithology mapping approach using ASTER satellite sensor sensors (Basel, Switzerland) 18. https://doi.org/10.3390/s18103213

  • Bouzekri S, Aziz Lasbet A, Lachehab A ( 2015) A new spectral index for extraction of built-up area using Landsat-8 Data J Indian Soc Remote Sens 43. https://doi.org/10.1007/s12524-015-0460-6

  • Bramhe V, Ghosh S, Garg P (2018) Extraction of built-up area by combining textural features and spectral indices from Landsat-8 multispectral image. ISPRS-Int Arch Photogramm, Remote Sens Spatial Inf Sci XLII–5:727–733. https://doi.org/10.5194/isprs-archives-XLII-5-727-2018

    Article  Google Scholar 

  • Chavez P Jr (1984) Image processing techniques for thermatic mapper data proceedings. ASPRS-ACSM Technical Papers 2:728–742

    Google Scholar 

  • Chavez P, GL B, LB S (1982) Statistical method for selecting Landsat MSS ratios

  • Congalton R ( 1991) A review of assessing the accuracy of classifications of remotely sensed data. 37. https://doi.org/10.1016/0034-4257(91)90048-B

  • Congedo L (2016) Semi-automatic classification plugin documentation. Release 6.0.1.1. https://doi.org/10.13140/RG.2.2.29474.02242/1

  • Côte M (1996) L'algerie espace er societe masson. Paris

  • Deventer AP, Ward AD, Gowda PH (1997) Using thematic mapper data to identify contrasting soil plains and tillage practices. In, Lyon, John Grimson

  • Fan J-L, Lei B (2012) A modified valley-emphasis method for automatic thresholding. Pattern Recogn Lett 33:703–708. https://doi.org/10.1016/j.patrec.2011.12.009

    Article  Google Scholar 

  • Faridatul M, Wu B (2018) Automatic classification of major urban land covers based on novel spectral indices. ISPRS Int J Geo-Inform 7:453. https://doi.org/10.3390/ijgi7120453

    Article  Google Scholar 

  • Foody GM (2002) Status of land cover classification accuracy assessment. Remote Sens Environ 80:185–201. https://doi.org/10.1016/S0034-4257(01)00295-4

    Article  Google Scholar 

  • Fu Z, Caelli T, Liu N, Robles-Kelly A (2006) Boosted band ratio feature selection for hyperspectral image classification. Paper Proc 18th IntConf Pattern Recogn 01

  • Inzana J, Kusky T, Higgs G, Tucker R (2003) Supervised classifications of Landsat TM band ratio images and Landsat TM band ratio image with radar for geological interpretations of central Madagascar. J Afr Earth Sc 37:59–72. https://doi.org/10.1016/S0899-5362(03)00071-X

    Article  Google Scholar 

  • Jiang W, Ni Y, Pang Z, He G, Fu J, Lu J, Yang K, Long T, Lei T (2020) A new index for identifying water body from Sentinel-2 satellite remote sensing imagery. ISPRS Ann Photogramm, Remote Sens Spat Inf Sci V-3–2020:33–38. https://doi.org/10.5194/isprs-annals-V-3-2020-33-2020

    Article  Google Scholar 

  • Kaufman YJ, Remer LA (1994) Detection of forests using mid-IR reflectance: an application for aerosol studies. IEEE Trans Geosci Remote Sens 32:672–683. https://doi.org/10.1109/36.297984

    Article  Google Scholar 

  • Khalifa A, Çakir Z, Kaya S, Gabr S (2020) ASTER spectral band ratios for lithological mapping: a case study for measuring geological offset along the Erkenek Segment of the East Anatolian Fault Zone, Turkey. https://doi.org/10.1007/s12517-020-05849-y

  • 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 J Sel Top Appl Earth Observ Remote Sens 14:3682–3694

    Article  Google Scholar 

  • Li H, Wang C, Zhong C, Su A, Xiong C, Wang J, Liu J (2017) Mapping urban bare land Automatically from Landsat imagery with a simple index. Remote Sens 9. https://doi.org/10.3390/rs9030249

  • Lu D, Weng Q (2007) A survey of image classification methods and techniques for improving classification performance. Int J Remote Sens 28:823–870. https://doi.org/10.1080/01431160600746456

    Article  Google Scholar 

  • Mhangara P, Odindi J, Kleyn L, Remas H (2011) Road extraction using object oriented classification. Visualisation Tech 45–50

  • Milczarek M, Robak A, Gadawska A (2017) Sentinel Water Mask (SWM) - new index for water detection on Sentinel-2 images

  • Mohy H, El-Magd I, Basta F (2016) Newly improved band ratio of ASTER data for lithological mapping of the Fawakhir Area, Central Eastern Desert. Egypt J Indian Soc Remote Sens 44. https://doi.org/10.1007/s12524-015-0539-0

  • Muniz R, Garcıa S, Gonzalez F, Corrales JA (2003) Use of band rationing for color texture classification. - IbPRIA 606–615

  • Ng H-F (2006) Automatic thresholding for defect detection. Pattern Recogn Lett 27:1644–1649. https://doi.org/10.1016/j.patrec.2006.03.009

    Article  Google Scholar 

  • Qi C, Huang M, Bai K, Lii X (2020) An optimal two bands ratio model to monitor chlorophyll-a in urban lake using Landsat 8 Data. E3S Web Conf 143:02003. https://doi.org/10.1051/e3sconf/202014302003

    Article  Google Scholar 

  • Rahman M, Ullah R, Lan M, Sri Sumantyo J, Kuze H, Tateishi R (2013) Comparison of Landsat image classification methods for detecting mangrove forests in Sundarbans. Int J Remote Sens 34:1041–1056. https://doi.org/10.1080/01431161.2012.717181

    Article  Google Scholar 

  • Rasul A, Balzter H, Ibrahim GRF, Hameed HM, Wheeler J, Adamu B, Ibrahim SA, Najmaddin PM (2018) Applying built-up and bare-soil indices from Landsat 8 to cities in dry climates. Land 7:81. https://doi.org/10.3390/land7030081

  • Rouibah K, Belabbas M (2020) Applying multi-index approach from Sentinel-2 imagery to extract urban area in dry season (semi-arid land in North East Algeria). Rev Teledetec 89–101. https://doi.org/10.4995/raet.2020.13787

  • Sabins FF (2007) Remote sensing: principles and interpretation. Waveland Press

    Google Scholar 

  • Samira D, Souiher N, Djamal B (2022) Extraction of urban areas using spectral indices combination and Google Earth engine in Algerian highlands (case study: cities of Djelfa, Messaad, Ain Oussera). Anu Inst Geocienc 45. https://doi.org/10.11137/1982-3908_2022_45_44537

  • San B, Sumer E, Gurcay B (2004) Comparison of band ratioing and spectral indices methods for detecting alunite and kaolinite minerals using ASTER data in Biga region, Turkey

  • Sekertekin A, Abdikan S, Marangoz A (2018) The acquisition of impervious surface area from LANDSAT 8 satellite sensor data using urban indices: a comparative analysis. Environ Monit Assess 190. https://doi.org/10.1007/s10661-018-6767-3

  • Shnain SK (2008) Effect of band ratios and indices on classification accuracy of multispectral satellite images. M.sc. thesis, The College of Science of Al-Nahrain University, Iraq

  • Simon N, Ali CA, Mohamed KR, Sharir K (2016) Best band ratio combinations for the lithological discrimination of the Dayang Bunting and Tuba Islands, Langkawi, Malaysia. Sains Malaysiana 45:659–667

    Google Scholar 

  • Twumasi Y, Shao Z, Altan O (2019) Mapping built-up areas using two band ratio on landsat imagery of accra in ghana from 1980 TO 2017 Applied Ecology and Environmental Research 6:13147–13168.  https://doi.org/10.15666/aeer/1706_1314713168

  • Valdiviezo-N J, Téllez-Quiñones A, Salazar-Garibay A, López-Caloca A (2018) Built-up index methods and their applications for urban extraction from Sentinel 2A satellite data: discussion. J Opt Soc Am A 35:35. https://doi.org/10.1364/JOSAA.35.000035

    Article  Google Scholar 

  • Vanhellemont Q, Ruddick K (2016) Acolite for Sentinel-2: Aquatic applications of MSI imagery. In: Proceedings of the 2016 ESA Living Planet Symposium, Prague, Czech Republic pp 9–13

  • Vapnik VN (1995) The nature of statistical learning theory. Springer-Verlag

    Book  Google Scholar 

  • Waqar M, Mirza J, Mumtaz R, Hussain E (2012) Development of New Indices for Extraction of Built-Up Area & Bare Soil from Landsat Data Open Access Scientific Reports 1:1–4. https://doi.org/10.4172/scientificreports.136

    Article  Google Scholar 

  • Warner TA, Nerry F (2009) Does single broadband or multispectral thermal data add information for classification of visible, near- and shortwave infrared imagery of urban areas? Int J Remote Sens 30:2155–2171. https://doi.org/10.1080/01431160802549286

    Article  Google Scholar 

  • Wirabumi P, Kamal M, Wicaksono P (2021) Determining effective water depth for total suspended solids (TSS) mapping using PlanetScope imagery. Int J Remote Sens 42:5784–5810. https://doi.org/10.1080/01431161.2021.1931538

    Article  Google Scholar 

  • Xi Y, Xuan Thinh N, Li C (2019) Preliminary comparative assessment of various spectral indices for built-up land derived from Landsat-8 OLI and Sentinel-2A MSI imageries. Eur J Remote Sens 52:240–252. https://doi.org/10.1080/22797254.2019.1584737

    Article  Google Scholar 

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  1. École Normale Supérieure Messaoud Zeghar-Sétif, P B. 556, 19600, El Eulma, Setif, Algeria

    Khaled Rouibah

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  1. Khaled Rouibah
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Rouibah, K. The use of bands ratio derived from Sentinel-2 imagery to detect built-up area in the dry period (North-East Algeria). Appl Geomat 15, 473–482 (2023). https://doi.org/10.1007/s12518-023-00513-y

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