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Article

Using Sentinel-1 and Google Earth Engine cloud computing for detecting historical flood hazards in tropical urban regions: a case of Dar es Salaam

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Article: 2202296 | Received 29 Nov 2022, Accepted 06 Apr 2023, Published online: 20 Apr 2023

References

  • Adedeji O, Olusola A, Babamaaji R, Adelabu S. 2021. An assessment of flood event along Lower Niger using Sentinel-1 imagery. Environ Monit Assess. 193(12):1–17.
  • Adiri Z, El Harti A, Jellouli A, Lhissou R, Maacha L, Azmi M, Zouhair M, Bachaoui EM. 2017. Comparison of Landsat-8, ASTER and Sentinel 1 satellite remote sensing data in automatic lineaments extraction: a case study of Sidi Flah-Bouskour inlier, Moroccan Anti Atlas. Adv Sp Res. 60(11):2355–2367.
  • Ahmed T. 2021. Monitoring and mapping of flash flood of Patna city using Sentinel-1 images: a case of India’s most flood prone state. Acad Lett. 2. https://doi.org/10.20935/AL1349
  • Anande DM, Luhunga PM. 2019. Assessment of socio-economic impacts of the December 2011 flood event in Dar es Salaam, Tanzania. Atmos Clim Sci. 9(03):421.
  • Andaya AE, Arboleda ER, Andilab AA, Dellosa RM. 2019. Meat marbling scoring using image processing with fuzzy logic based classifier. Int J Sci Technol Res. 8(08):1442–1445.
  • Arboleda ER, De Jesus CLT, Tia LMS. 2021. Pineapple maturity classifier using image processing and fuzzy logic. IJ-AI. 10(4):830.
  • Baghermanesh SS, Jabari S, McGrath H. 2021. Urban flood detection using Sentinel1-A images. International Geoscience and Remote Sensing Symposium. IEEE; p. 527–530.
  • Bauer-Marschallinger B, Freeman V, Cao S, Paulik C, Schaufler S, Stachl T, Modanesi S, Massari C, Ciabatta L, Brocca L, et al. 2018. Toward global soil moisture monitoring with Sentinel-1: Harnessing assets and overcoming obstacles. IEEE Trans Geosci Remote Sens. 57(1):520–539.
  • Bergler E, Hertel V, Mrak V. 2021. Space-based technologies for effective flood management in urban Africa: benefits, challenges and potential solutions. Austria: Regional Academy on the United Nations.
  • Bovolo F, Bruzzone L. 2007. A split-based approach to unsupervised change detection in large-size multitemporal images: application to tsunami-damage assessment. IEEE Trans Geosci Remote Sens. 45(6):1658–1670.
  • Brown R, Chanson H. 2012. Suspended sediment properties and suspended sediment flux estimates in an inundated urban environment during a major flood event. Water Resour Res. 48(11). https://doi.org/10.1029/2012WR012381
  • Callaghan TV, Kulikova O, Rakhmanova L, Topp-Jørgensen E, Labba N, Kuhmanen L-A, Kirpotin S, Shaduyko O, Burgess H, Rautio A, et al. 2020. Improving dialogue among researchers, local and indigenous peoples and decision-makers to address issues of climate change in the North. Ambio. 49(6):1161–1178.
  • Cherif I, Ovakoglou G, Alexandridis TK, Mensah F, Garba I. 2021. Near real time high resolution mapping of flood extent in west African sites. EGU General Assembly Conference Abstracts. Viena; p. EGU21–15170.
  • Chini M, Hostache R, Giustarini L, Matgen P. 2017. A hierarchical split-based approach for parametric thresholding of SAR images: flood inundation as a test case. IEEE Trans Geosci Remote Sens. 55(12):6975–6988.
  • Congalton RG, Green K. 2019. Assessing the accuracy of remotely sensed data: principles and practices. Boca Raton: CRC Press.
  • Curran PJ, Atkinson PM. 1999. Issues of scale and optimal pixel size. In Spatial statistics for remote sensing. Dordrecht: Springer; p. 115–133.
  • DeVries B, Huang C, Armston J, Huang W, Jones JW, Lang MW. 2020. Rapid and robust monitoring of flood events using Sentinel-1 and Landsat data on the Google Earth Engine. Remote Sens Environ. 240:111664.
  • Donchyts G, Baart F, Winsemius H, Gorelick N, Kwadijk J, Van De Giesen N. 2016. Earth’s surface water change over the past 30 years. Nature Clim Change. 6(9):810–813.
  • Dooley CA, Leasure DR, Tatem AJ. 2020. Gridded maps of building patterns throughout sub-Saharan Africa, version 1.1. Southampton, UK: University of Southampton.
  • Dubey AK, Arora R, Ahmed A. 2017. A comparative review of various segmentation methods and its application. International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions). IEEE; p. 645–650.
  • ESA. 2021. Sentinel-1 observation scenario. Available online [Internet]; [accessed 2021 Nov 16]. https://sentinels.copernicus.eu/web/sentinel/missions/sentinel-1/.
  • Filgueiras R, Mantovani EC, Althoff D, Fernandes Filho EI, Cunha Fd. 2019. Crop NDVI monitoring based on sentinel 1. Remote Sens. 11(12):1441.
  • Frolking S, Milliman T, Mahtta R, Paget A, Long DG, Seto KC. 2022. a global urban microwave backscatter time series data set for 1993–2020 using ERS, QuikSCAT, and ASCAT data. Sci Data. 9(1):1–12.
  • Gagnon L, Jouan A. 1997. Speckle filtering of SAR images: a comparative study between complex-wavelet-based and standard filters. In Wavelet Applications in Signal and Image Processing V. Vol. 3169. SPIE; p. 80–91.
  • Giustarini L, Hostache R, Matgen P, Schumann GJ-P, Bates PD, Mason DC. 2013. A change detection approach to flood mapping in urban areas using TerraSAR-X. IEEE Trans Geosci Remote Sens. 51(4):2417–2430.
  • Gomez L, Ospina R, Frery AC. 2019. Statistical properties of an unassisted image quality index for SAR imagery. Remote Sens. 11(4):385–400.
  • Hambati H, Gaston G. 2015. Revealing the vulnerability of urban communities to flood hazard in Tanzania: a case of the Dar es Salaam city ecosystem. Int J Geospat Environ Res. 2(1):3–25.
  • Hill A, Hühner T, Kreibich V, Lindner C. 2014. Dar es Salaam, megacity of tomorrow: informal urban expansion and the provision of technical infrastructure. In: Megacities. Dordrecht: Springer; p. 165–177.
  • Huang D, Xu S, Sun J, Liang S, Song W, Wang Z. 2017. Accuracy assessment model for classification result of remote sensing image based on spatial sampling. J Appl Remote Sens. 11(04):1.
  • Iqbal MA, Talukder KH. 2020. Detection of potato disease using image segmentation and machine learning. 2020 International Conference on Wireless Communications Signal Processing and Networking. IEEE; p. 43–47.
  • Kabanda T. 2020. GIS modeling of flooding exposure in Dar es Salaam coastal areas. Afr Geogr Rev. 39(2):134–143.
  • Kaplan G, Avdan U. 2018. Monthly analysis of wetlands dynamics using remote sensing data. IJGI. 7(10):411.
  • Kebede AS, Nicholls RJ. 2012. Exposure and vulnerability to climate extremes: population and asset exposure to coastal flooding in Dar es Salaam, Tanzania. Reg Environ Change. 12(1):81–94.
  • Kikwasi G, Mbuya E. 2019. Vulnerability analysis of building structures to floods: the case of flooding informal settlements in Dar es salaam, Tanzania. Int J Build Pathol Adapt. 37(5):629–656.
  • Kittler J, Illingworth J. 1986. Minimum error thresholding. Pattern Recognit. 19(1):41–47.
  • Krishan Kumar A, Kaushal Kumar A, Guo S. 2020. Two viewpoints based real‐time recognition for hand gestures. IET Image Process. 14(17):4606–4613.
  • Landuyt L, Van Wesemael A, Schumann GJ-P, Hostache R, Verhoest NEC, Van Coillie FMB. 2019. Flood mapping based on synthetic aperture radar: an assessment of established approaches. IEEE Trans Geosci Remote Sens. 57(2):722–739.
  • Lee J-S. 1980. Digital image enhancement and noise filtering by use of local statistics. IEEE Trans Pattern Anal Mach Intell. 2(2):165–168.
  • Lee J-S, Grunes MR, De Grandi G. 1999. Polarimetric SAR speckle filtering and its implication for classification. IEEE Trans. Geosci. Remote Sens. 37(5):2363–2373.
  • Lee J-S, Wen J-H, Ainsworth TL, Chen K-S, Chen AJ. 2008. Improved sigma filter for speckle filtering of SAR imagery. IEEE Trans Geosci Remote Sens. 47(1):202–213.
  • Li C, Dash J, Asamoah M, Sheffield J, Dzodzomenyo M, Gebrechorkos SH, Anghileri D, Wright J. 2022. Increased flooded area and exposure in the White Volta river basin in Western Africa, identified from multi-source remote sensing data. Sci Rep. 12(1):1–13.
  • Li H-C, Celik T, Longbotham N, Emery WJ. 2015. Gabor feature based unsupervised change detection of multitemporal SAR images based on two-level clustering. IEEE Geosci. Remote Sens Lett. 12(12):2458–2462.
  • Li Y, Martinis S, Wieland M, Schlaffer S, Natsuaki R. 2019. Urban flood mapping using SAR intensity and interferometric coherence via Bayesian network fusion. Remote Sens. 11(19):2231.
  • Lin Y-T, Yang M-D, Han J-Y, Su Y-F, Jang J-H. 2020. Quantifying flood water levels using image-based volunteered geographic information. Remote Sens. 12(4):706.
  • Liu J, Freudenberger D, Lim S. 2022. Mapping burned areas and land-uses in Kangaroo Island using an object-based image classification framework and Landsat 8 Imagery from Google Earth Engine. Geomat Nat Hazards Risk. 13(1):1867–1897.
  • Long S, Fatoyinbo TE, Policelli F. 2014. Flood extent mapping for Namibia using change detection and thresholding with SAR. Environ Res Lett. 9(3):035002.
  • Lopes A, Nezry E, Touzi R, Laur H. 1990. Maximum a posteriori speckle filtering and first order texture models in SAR images. 10th Annual International Symposium on Geoscience and Remote Sensing. IEEE; p. 2409–2412.
  • Lu C-H, Ni C-F, Chang C-P, Yen J-Y, Chuang RY. 2018. Coherence difference analysis of sentinel-1 SAR interferogram to identify earthquake-induced disasters in urban areas. Remote Sens. 10(8):1318.
  • Lu J, Giustarini L, Xiong B, Zhao L, Jiang Y, Kuang G. 2014. Automated flood detection with improved robustness and efficiency using multi-temporal SAR data. Remote Sens Lett. 5(3):240–248.
  • Martinis S, Rieke C. 2015. Backscatter analysis using multi-temporal and multi-frequency SAR data in the context of flood mapping at River Saale, Germany. Remote Sens. 7(6):7732–7752.
  • Martinis S, Twele A, Voigt S. 2009. Towards operational near real-time flood detection using a split-based automatic thresholding procedure on high resolution TerraSAR-X data. Nat Hazards Earth Syst Sci. 9(2):303–314.
  • Mason DC, Dance SL, Cloke HL. 2021. Floodwater detection in urban areas using Sentinel-1 and WorldDEM data. J Appl Remote Sens. 15(3):32003.
  • Mason DC, Giustarini L, Garcia-Pintado J, Cloke HL. 2014. Detection of flooded urban areas in high resolution Synthetic Aperture Radar images using double scattering. Int J Appl Earth Obs Geoinf. 28:150–159.
  • Matgen P, Hostache R, Schumann G, Pfister L, Hoffmann L, Savenije HHG. 2011. Towards an automated SAR-based flood monitoring system: lessons learned from two case studies. Phys Chem Earth, Parts A/B/C. 36(7–8):241–252.
  • Meyer F. 2019. Spaceborne Synthetic Aperture Radar: principles, data access, and basic processing techniques. In: SAR handbook: comprehensive methodologies for forest monitoring and biomass estimation. Huntsville: SERVIR Global Science Coordination Office National Space Science and Technology Center. p. 21–64.
  • Mguni P, Herslund L, Jensen MB. 2016. Sustainable urban drainage systems: examining the potential for green infrastructure-based stormwater management for Sub-Saharan cities. Nat Hazards. 82(S2):241–257.
  • Mitchell SA. 2013. The status of wetlands, threats and the predicted effect of global climate change: the situation in Sub-Saharan Africa. Aquat Sci. 75(1):95–112.
  • Montalti R, Solari L, Bianchini S, Del Soldato M, Raspini F, Casagli N. 2019. A Sentinel-1-based clustering analysis for geo-hazards mitigation at regional scale: a case study in Central Italy. Geomatics, Nat Hazards Risk. 10(1):2257–2275.
  • Moser G, Angiati E, Serpico SB. 2007. Multiscale unsupervised change detection by Markov random fields and wavelet transforms. In: Image and signal processing for remote sensing XIII. Vol. 6748. Florence, Italy: SPIE; p. 31–39.
  • Mullissa A, Vollrath A, Odongo-Braun C, Slagter B, Balling J, Gou Y, Gorelick N, Reiche J. 2021. Sentinel-1 sar backscatter analysis ready data preparation in google earth engine. Remote Sens. 13(10):1954.
  • Munawar HS, Hammad AWA, Waller ST. 2022. Remote sensing methods for flood prediction: a review. Sensors. 22(3):960.
  • Myint SW, Gober P, Brazel A, Grossman-Clarke S, Weng Q. 2011. Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery. Remote Sens Environ. 115(5):1145–1161.
  • Nikaein T, Iannini L, Molijn RA, Lopez-Dekker P. 2021. On the value of sentinel-1 insar coherence time-series for vegetation classification. Remote Sens. 13(16):3300.
  • Notti D, Giordan D, Caló F, Pepe A, Zucca F, Galve JP. 2018. Potential and limitations of open satellite data for flood mapping. Remote Sens. 10(11):1673.
  • O’Donnell EC, Thorne CR, Yeakley JA, Chan FKS. 2020. Sustainable flood risk and stormwater management in blue‐green cities; an interdisciplinary case study in Portland, Oregon. J Am Water Resour Assoc. 56(5):757–775.
  • Otsu N. 1979. A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern. 9(1):62–66.
  • Pelich R, Chini M, Hostache R, Matgen P, Pulvirenti L, Pierdicca N. 2022. Mapping floods in urban areas from dual-polarization InSAR coherence data. IEEE Geosci Remote Sens Lett. 19:1–5.
  • Petersson L, Ten Veldhuis M-C, Verhoeven G, Kapelan Z, Maholi I, Winsemius HC. 2020. Community mapping supports comprehensive urban flood modeling for flood risk management in a data-scarce environment. Front Earth Sci. 8:304.
  • Pradhan B, Moneir AAA, Jena R. 2018. Sand dune risk assessment in Sabha region, Libya using Landsat 8, MODIS, and Google Earth Engine images. Geomatics, Nat Hazards Risk. 9(1):1280–1305.
  • Psomiadis E. 2016. Flash flood area mapping utilising SENTINEL-1 radar data. In: Earth resources and environmental remote sensing/GIS applications VII. Vol. 10005. Edinburgh: SPIE; p. 100051G.
  • Pulvirenti L, Pierdicca N, Chini M, Guerriero L. 2011. An algorithm for operational flood mapping from Synthetic Aperture Radar (SAR) data using fuzzy logic. Nat Hazards Earth Syst Sci. 11(2):529–540.
  • Ramiaramanana FN, Teller J. 2021. Urbanization and floods in sub-Saharan Africa: spatiotemporal study and analysis of vulnerability factors—case of antananarivo agglomeration (Madagascar). Water. 13(2):149.
  • Refice A, Zingaro M, D’Addabbo A, Chini M. 2020. Integrating C-and L-band SAR imagery for detailed flood monitoring of remote vegetated areas. Water. 12(10):2745.
  • Rentschler J, Braese J, Jones N, Avner P. 2019. Three feet under: the impact of floods on urban jobs, connectivity, and infrastructure.
  • Sadek M, Li X. 2019. Low-cost solution for assessment of urban flash flood impacts using sentinel-2 satellite images and fuzzy analytic hierarchy process: a case study of ras ghareb city, Egypt. Adv Civ Eng. 2019:1–15.
  • Sakijege T, Lupala J, Sheuya S. 2012. Flooding, flood risks and coping strategies in urban informal residential areas: the case of Keko Machungwa, Dar es Salaam, Tanzania. Jàmbá J Disaster Risk Stud. 4(1):1–10.
  • Sarnacchiaro P, D’ambra A. 2007. Explorative data analysis and Catanova for ordinal variables: an integrated approach. J Appl Stat. 34(9):1035–1050.
  • Schmidt K, Schwerdt M, Miranda N, Reimann J. 2020. Radiometric comparison within the Sentinel-1 SAR constellation over a wide backscatter range. Remote Sens. 12(5):854.
  • Senthilnath J, Shenoy HV, Rajendra R, Omkar SN, Mani V, Diwakar PG. 2013. Integration of speckle de-noising and image segmentation using Synthetic Aperture Radar image for flood extent extraction. J Earth Syst Sci. 122(3):559–572.
  • Series T, Bangira T, Iannini L, Menenti M, van Niekerk A, Vekerdy Z. 2021. Flood extent mapping in the Caprivi floodplain using Sentinel-1 time series.
  • Shen X, Wang D, Mao K, Anagnostou E, Hong Y. 2019. Inundation extent mapping by synthetic aperture radar: a review. Remote Sens. 11(7):879.
  • Tanguy M, Chokmani K, Bernier M, Poulin J, Raymond S. 2017. River flood mapping in urban areas combining Radarsat-2 data and flood return period data. Remote Sens Environ. 198:442–459.
  • Tobias OJ, Seara R. 2002. Image segmentation by histogram thresholding using fuzzy sets. IEEE Trans Image Process. 11(12):1457–1465.
  • Torres R, Snoeij P, Geudtner D, Bibby D, Davidson M, Attema E, Potin P, Rommen B, Floury N, Brown M, et al. 2012. GMES Sentinel-1 mission. Remote Sens Environ. 120:9–24.
  • Twele A, Cao W, Plank S, Martinis S. 2016. Sentinel-1-based flood mapping: a fully automated processing chain. Int J Remote Sens. 37(13):2990–3004.
  • Veloso A, Mermoz S, Bouvet A, Le Toan T, Planells M, Dejoux J-F, Ceschia E. 2017. Understanding the temporal behavior of crops using Sentinel-1 and Sentinel-2-like data for agricultural applications. Remote Sens Environ. 199:415–426.
  • Vollrath A, Mullissa A, Reiche J. 2020. Angular-based radiometric slope correction for Sentinel-1 on google earth engine. Remote Sens. 12(11):1867.
  • Watkiss P, Downing T, Dyszynski J, Pye S, Savage M, Goodwin J, Longanecker M, Lynn S. 2011. The economics of climate change in the United Republic of Tanzania. Global Climate Adaptation Partnership (GCAP).
  • Woldai T. 2020. The status of Earth Observation (EO) & Geo-Information Sciences in Africa–trends and challenges. Geo Spatial Inf Sci. 23(1):107–123.
  • Zhang H, Qi Z, Li X, Chen Y, Wang X, He Y. 2021. An urban flooding index for unsupervised inundated urban area detection using Sentinel-1 polarimetric SAR images. Remote Sens. 13(22):4511.
  • Zhou S, Kan P, Silbernagel J, Jin J. 2020. Application of image segmentation in surface water extraction of freshwater lakes using radar data. IJGI. 9(7):424.