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A coastal band spectral combination for water body extraction using Landsat 8 images

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Abstract

The explosive rate of population growth demands a revision of existing protective measures to address water scarcity that urges water body monitoring models to be developed using satellite image-based change detection approaches. And the main objective of this proposed work was to investigate the spectral indices for spatial object extraction in satellite images with respect to water body extraction. The Landsat 8 coastal/aerosol band (0.433–0.453 μm) is still an unexplored band with spectral signatures that favor water extraction, and it has been used in the proposed deep blue normalized difference water index (DBNDWI). The multi-temporal Landsat 8 data products of three lakes with distinct geographical importance from India and Iran are chosen for assessment. Widely used spectral indices for water body extraction such as the normalized difference water index (NDWI), modified normalized difference water index, and automated water extraction index are the benchmark results used for comparative assessment. Along with these conventional water indices, the most recent water indices weighted normalized difference water index (WNDWI) and Wavelet-based normalized difference water index (WAWI) are also compared for extended validation. The use of standard image quality analysis and statistical histogram distance measures justifies the significance of coastal band in extracting water bodies. The experimental results tabulated show that proposed DBNDWI outperforms the most recent WNDWI and WAWI with higher accuracy, even in moderate-resolution images.

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References

  • Acharya TD, Subedi A, Lee DH (2018) Evaluation of water indices for surface water extraction in a Landsat 8 scene of Nepal. Sensors 18(8):1–15

    Article  Google Scholar 

  • Aroma RJ, Raimond K (2016) An overview of technological revolution in satellite image analysis. J Eng Sci Tech Rev 9(4):1–5

    Article  Google Scholar 

  • Aroma J, Raimond K (2019) A wavelet transform applied spectral index for effective water body extraction from moderate-resolution satellite images. Artif Intell Tech Satell Image Anal 24:255–274

    Article  Google Scholar 

  • Aroma J, Raimond K (2021) Investigation on spectral indices and soft classifiers-based water body segmentation approaches for satellite image analysis. J Ind Soc Remote Sens 49:341–356

    Article  Google Scholar 

  • Aroma RJ, Raimond K, Razmjooy N, Estrela VV, Hemanth J (2020) Multispectral vs. hyperspectral imaging for unmanned aerial vehicles: current and prospective state of affairs. IET Imaging Sensing Unmanned Aircr Syst Deploy Appl 2:133–155

    Google Scholar 

  • Asokan A, Anitha J (2019) Change detection techniques for remote sensing applications: a survey. Earth Sci Inform 12:143–160

    Article  Google Scholar 

  • Bhardwaj A, Singh MK, Joshi PK, Snehmani SS, Sam L, Gupta RD, Kumar R (2015) A lake detection algorithm (LDA) using Landsat 8 data: a comparative approach in glacial environment. Int J Appl Earth Obs Geoinfo 38:150–163

    Google Scholar 

  • Campos GFC, Mastelini SM, Aguiar GJ, Mantovani RG, de Melo LF, Barbon S (2019) Machine learning hyperparameter selection for contrast limited adaptive histogram equalization. EURASIP J Image Video Process. https://doi.org/10.1186/s13640-019-0445-4

    Article  Google Scholar 

  • Cetin M (2019) The effect of urban planning on urban formations determining bioclimatic comfort area’s effect using satellitia imagines on air quality: a case study of Bursa city. Air Qual Atmos Health 12:1237–1249

    Article  CAS  Google Scholar 

  • Cetin M, Agacsapan B, Cabuk SN, Kurkcuoglu MAS, Pekkan OI, Argun EB, Dabanlı A, Kucukpehlivan T, Yilmazel B, Cabuk A (2021) Assessment of the ecological footprint of Eskisehir technical University-Iki Eylul campus. J Ind Soc Remote Sens. 49:2311–2327

    Article  Google Scholar 

  • Chaudhary MD, Pithadia PV (2014) Multi-feature histogram intersection for efficient content based image retrieval. In: 2014 international conference on circuits, power and computing technologies [ICCPCT-2014], pp 1366–1371

  • Chen Y, Tang L, Kan Z, Bilal M, Li Q (2020) A novel water body extraction neural network (WBE-NN) for optical high-resolution multispectral imagery. J Hydrol 588:125092

    Article  Google Scholar 

  • Chen T, He H, Li D, An P, Hui Z (2020) Damage signature generation of revetment surface along urban rivers using UAV-based mapping. ISPRS Int J Geo-Inf 9:283

    Article  Google Scholar 

  • Cui Z, Kerekes JP (2018) Potential of red edge spectral bands in future landsat satellites on agroecosystem canopy green leaf area index retrieval. Remote Sens 10(9):1458

    Article  Google Scholar 

  • Delegido J, Verrelst J, Meza CM, Rivera JP, Alonso L, Moreno J (2013) A red-edge spectral index for remote sensing estimation of green LAI over agroecosystems. Eur J Agron 46:42–52

    Article  Google Scholar 

  • Extent of Amazon Forest Fire, [Online, accessed on 25.07.2020]: https://news.mongabay.com/2020/04/satellite-data-show-amazon-rainforest-likely-drier-more-fire-prone-this-year/

  • Feyisa GL, Meilby H, Fensholt R, Proud SR (2014) Automated water extraction index: a new technique for surface water mapping using landsat imagery. Remote Sens Environ 140:23–35

    Article  Google Scholar 

  • Frey H, Huggel C, Paul F, Haeberli W (2010) Automated detection of glacier lakes based on remote sensing in view of assessing associated hazard potentials. In: 10th international symposium on high mountain remote sensing cartography, 8–18 September 2008, ICIMOD, Kathmandu, Nepal, pp 261–272

  • Ghamisi P, Rasti B, Yokoya N, Wang Q, Hofle B, Bruzzone L, Bovolo F, Chi M, Anders K, Gloaguen R, Atkinson P, Benediktsson J (2019) Multisource and multitemporal data fusion in remote sensing: a comprehensive review of the state of the art. IEEE Geosci Remote Sens Mag 7:6–39

    Article  Google Scholar 

  • Gilmore S, Saleem A, Dewan AM (2015) Effectiveness of DOS (Dark-Object Subtraction) method and water index techniques to map wetlands in a rapidly urbanising megacity with Landsat 8 data. Proc Annu Conf Spatial Info in Aus N Z 1323:100–108

    Google Scholar 

  • Guo Q, Ruiliang Pu, Li J, Cheng J (2017) A weighted normalized difference water index for water extraction using Landsat imagery. Int Jour of Rem Sens 38:5430–5445

    Article  Google Scholar 

  • Guo H, He G, Jiang W, Yin R, Yan L, Leng W (2020) A Multi-Scale Water Extraction Convolutional Neural Network (MWEN) Method for GaoFen-1 Remote Sensing Images. ISPRS Int J Geo-Inf 9:189

    Article  Google Scholar 

  • Huyan N, Zhang X, Zhou H, Jiao L (2019) Hyperspectral anomaly detection via background and potential anomaly dictionaries construction. IEEE Trans Geo Remote Sens 57:2263–2276

    Article  Google Scholar 

  • ImageJ, [Online-accessed on 29-10-21]: https://imagej.nih.gov/ij/

  • Imani M (2020) Nonparametric spectral-spatial anomaly detection. J AI Data Min 8:95–103

    Google Scholar 

  • Ji L, Zhang L, Wylie B (2009) Analysis of dynamic thresholds for the normalized difference water index. Photogramm Eng Remote Sens 75:1307–1317

    Article  Google Scholar 

  • Jiang H, Feng M, Zhu Y, Lu N, Huang J, Xiao T (2014) An automated method for extracting Rivers and Lakes from Landsat Imagery. Remote Sens 6:5067–5089

    Article  Google Scholar 

  • Kaya E, Agca M, Adiguzel F, Cetin M (2018) Spatial data analysis with R programming for environment. Hum Ecol Risk Assess Int J 25:1521–1530

    Article  Google Scholar 

  • Sambhar Lake - [Online, accessed on 02–10–2020]:https://en.wikipedia.org/wiki/Sambhar_Salt_Lake.

  • Landsat 8 Band Specifications, [Online, accessed on 10–08–2020]: https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/

  • Landsat 8 L2 C1 product, [Online- accessed on 28-10-21]: https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C02_T1_L2

  • Li X, Jiang G (2017) A photograph based approach for visual simulation of wrapped Jacquardtronic lace. Text Res J 88:2654–2664

    Article  Google Scholar 

  • Li W, Du Z, Ling F, Zhou D, Wang H, Gui Y, Sun B, Zhang X (2013) A comparison of land surface water mapping using the normalized difference water index from TM, ETM+ and ALI. Remote Sens 5:5530–5549

    Article  Google Scholar 

  • Liu Z, Chen W, Zou Y (2012) Regions of interest extraction based on HSV color space. IEEE Xplore. https://doi.org/10.1109/INDIN.2012.6301214

    Article  Google Scholar 

  • Long Yu, Zhang R, Tian S, Yang L, Lv Y (2018) Deep multi-feature learning for water body extraction from landsat imagery. Automatic Control Comp Sci 52(6):517–527

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Lu S, Wu B, Yan N, Wang H (2011) Water body mapping method with HJ-1A/B satellite imagery. Int J Appl Earth Observ Geoinform 13:428–434

    Article  Google Scholar 

  • McFeeters SK (1996) The use of normalized difference water index (NDWI) in the delineation of open water features. Int J Remote Sens 17(7):1425–1432

    Article  Google Scholar 

  • Prasath V, Alfeilat HA, Lasassmeh O, Hassanat AB (2017) Distance and similarity measures effect on the performance of k-nearest neighbor classifier-a review. ArXiv, abs/1708.04321. https://arxiv.org/abs/1708.04321

  • Pulicat lake, [ Online- accessed on 28–10–21]: https://en.wikipedia.org/wiki/Pulicat_Lake

  • Quantum GIS - Official Website, [Online: https://download.qgis.org/, accessed on 10 Sep 2020 ]

  • Ramsar conventions, [Online-accessed on 28–10–21]: https://rsis.ramsar.org/ris-search/?f%5B0%5D=regionCountry_en_ss%3AAsia

  • Reka v (xxxx) Case study: mapping under water terrain using bathymetric LiDAR – [Online, accessed on 29–10–2021] : https://leica-geosystems.com/case-studies/natural-resources/mapping-underwater-terrain-with-bathymetric-lidar

  • Roy PS, Behera MD, Srivastav SK (2017) Satellite remote sensing: sensors, applications and techniques. Proc Natl Acad Sci India Sect A Phys Sci 87(4):465–472

    Article  Google Scholar 

  • Sahin G, Cabuk SN, Cetin M (2022) The change detection in coastal settlements using image processing techniques: a case study of Korfez. Environ Sci Pollution Res 29:15172–15187

    Article  Google Scholar 

  • Schauerte B, Fink GA (2010) Web-based learning of naturalized color model for human-machine interaction, In DICTA

  • Sharifi L, Kamel S, Feizizadeh B (2015) Monitoring bioenvironmental impacts of dam construction on land use/cover changes in sattarkhan basin using multi temporal satellite imagery. Iran J Energy Environ. https://doi.org/10.5829/idosi.ijee.2015.06.01.08

    Article  Google Scholar 

  • United Nations (2015) Transforming our world: the 2030 Agenda for sustainable development. United Nations, Washington, DC

    Google Scholar 

  • Urmia lake, [Online- accessed on 28–10–21]: https://en.wikipedia.org/wiki/Lake_Urmia

  • USGS Earth explorer, [Online, accessed on 01-06-2020]: http://earthexplorer.usgs.gov/

  • Vázquez-Jiménez R, Ramos-Bernal RN, Romero-Calcerrada R, Arrogante-Funes P, Tizapa SS, Novillo CJ (2018) Thresholding Algorithm Optimization for Change Detection to Satellite Imagery. In: Travieso-Gonzalez CM (ed) Colorimetry and Image Processing. InTech. https://doi.org/10.5772/intechopen.71002

    Chapter  Google Scholar 

  • Vijay R, Pinto SM, Kushwaha VK, Pal S, Nandy T (2016) A multi-temporal analysis of change assessment and estimation of algal bloom in Sambhar lake. Env Mon. and Assmt, Rajasthan, India, p 188

    Google Scholar 

  • Wang S, Baig MHA, Zhang L, Jiang H, Ji Y, Zhao H, Tian J (2015) A simple enhanced water index (EWI) for percent surface water estimation using Landsat data, IEEE J. Sel Top Appl Earth Observ Remote Sens 8:90–97

    Article  CAS  Google Scholar 

  • Wang Y, Huang F, Wei Y (2013) Water body extraction from Landsat ETM+ image using MNDWI and K-T transformation. In: IEEE – International conference on Geoinformatics, 1–5

  • Wu C, Du B, Zhang L (2018) Hyperspectral anomalous change detection based on joint sparse representation. ISPRS J Photogramm Remote Sens 146:137–150

    Article  Google Scholar 

  • Xu H (2006) Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. Int J Rem Sens 27:3025–3033

    Article  Google Scholar 

  • Xu YB, Lai XJ, Zhou CG (2010) Water surface change detection and analysis of bottomland submersion-emersion of wetlands in Poyang Lake reserve using ENVISAT ASAR data. China Environ Sci 30:57–63

    CAS  Google Scholar 

  • Yan P, Zhang YJ, Zhang Y (2007) A study on information extraction of water system in semi-arid regions with the enhanced water index (EWI) and GIS-based noise removal techniques. Remote Sensing Inf. https://doi.org/10.3969/j.issn.1000-3177.2007.06.015

    Article  Google Scholar 

  • Zhai Ke, Xiaoquing Wu, Qin Y, Peipei Du (2015) Comparison of surface water extraction performances of different classic water indices using OLI and TM imageries in different situations. Geo-Spatial Info Sci 18:32–42

    Article  Google Scholar 

  • Zhang W, Tang P, Zhao L (2019) Remote sensing image scene classification using CNN-CapsNet. Remote Sens 11:494

    Article  Google Scholar 

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Correspondence to R. J. Aroma.

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Aroma, R.J., Raimond, K., Estrela, V.V. et al. A coastal band spectral combination for water body extraction using Landsat 8 images. Int. J. Environ. Sci. Technol. 21, 1767–1784 (2024). https://doi.org/10.1007/s13762-023-05027-z

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