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Flooding Identification by Vegetation Index

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Environmental Remote Sensing in Flooding Areas

Abstract

In this chapter, flooding identification by vegetation index was conducted by using SAR images and multispectral images in order to obtain the flooding period of the study area.

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References

  • Badji M, Dautrebande S (1997) Characterization of flood inundated areas and delineation of poor drainage soil using ERS-1 SAR imagery. Hydrol Process 11(10):1441–1450

    Article  Google Scholar 

  • Baldassarre D, Schumann G, Brandimarte L, Bates P (2011) Timely low resolution SAR imagery to support floodplain modelling: a case study review. Surveys in Geophysics 32:255–269

    Google Scholar 

  • Brakenridge R, Anderson E (2006) Modis-based flood detection, mapping and measurement: the potential for operational hydrological applications. Transbound Floods: Reduc Risks Through Flood Manage 72:1–12

    Google Scholar 

  • Brivio PA, Colombo R, Maggi M, Tomasoni R (2002) Integration of remote sensing data and GIS for accurate mapping of flooded areas. Int J Remote Sen 23(3):429–441

    Article  Google Scholar 

  • Butenuth M, Frey D, Nielsen AA, Skriver H (2011) Infrastructure assessment for disaster management using multi-sensor and multi-temporal remote sensing imagery. Int J Remote Sen 32(23):8575–8594

    Article  Google Scholar 

  • Clandillon S, De Fraipont P, Yesou H (1995) Assessment of the future SPOT 4 MIR for wetland monitoring and soil moisture analysis. A simulation case over the Grand Ried d’Alsace (France). In: European Symposium on Remote Sensing II.

    Google Scholar 

  • Du ZQ, Bin LH, Ling F, Li WB, Tian WD, Wang HL, Gui YM, Sun BY, Zhang XM (2012) Estimating surface water area changes using time-series Landsat data in the Qingjiang River Basin, China. J Appl Remote Sen 6

    Google Scholar 

  • Gao BC (1996) NDWI—a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sen Environ 58:257–266

    Google Scholar 

  • Gupta KK, Gupta R (2007) Despeckle and geographical feature extraction in SAR images by wavelet transform. Isprs J Photogram Remote Sen 62(6):473–484

    Article  Google Scholar 

  • Hardisky MA, Klemas V, Smart RM (1983) The influences of soil salinity, growth form, and leaf moisture on the spectral reflectance of Spartina alterniflora canopies. Photogrammetric Engineering and Remote Sensing 49:77-83

    Google Scholar 

  • Hoque R, Nakayama D, Matsuyama H, Matsumoto J (2011) Flood monitoring, mapping and assessing capabilities using RADARSAT remote sensing, GIS and ground data for Bangladesh. Nat Hazards 57(2):525–548

    Article  Google Scholar 

  • Horritt MS, Bates PD (2001) Effects of spatial resolution on a raster based model of flood flow. J Hydrology 253(1–4):239–249

    Article  Google Scholar 

  • Hostache R, Matgen P, Schumann G, Puech C, Hoffmann L, Pfister L (2009) Water level estimation and reduction of hydraulic model calibration uncertainties using satellite SAR images of floods. IEEE Trans Geosci Remote Sen 47(2):431–441

    Article  Google Scholar 

  • Huete AR, Liu HQ, Batchily K, vanLeeuwen W (1997) A comparison of vegetation indices global set of TM images for EOS-MODIS. Remote Sen Environ 59(3):440–451

    Article  Google Scholar 

  • Huete A, Didan K, Miura T, Rodriguez EP, Gao X, Ferreira LG (2002) Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sen Environ 83:195-213

    Google Scholar 

  • Irons JR, Petersen GW (1981) Texture transforms of remote sensing data. Remote Sen Environ 11(5):359–370

    Google Scholar 

  • Ji L, Zhang L, Wylie B (2009) Analysis of Dynamic Thresholds for the Normalized Difference Water Index. Photogram Eng Remote Sen 75(11):1307–1317

    Article  Google Scholar 

  • Kuan DT, Sawchuk AA, Strand TC, Chavel P (1985) Adaptive noise smoothing filter for images with signal-dependent noise. IEEE Trans Pattern Anal Mach Intell 7(2):165–177

    Article  Google Scholar 

  • Lacaux JP, Tourre YM, Vignolles C, Ndione JA, Lafaye M (2007) Classification of ponds from high-spatial resolution remote sensing: application to rift valley fever epidemics in Senegal. Remote Sen Environ 106:66-74

    Google Scholar 

  • Li WB, Du ZQ, Ling F, Zhou DB, Wang HL, Gui YM, Sun BY, Zhang XM (2013) A comparison of land surface water mapping using the normalized difference water index from TM, ETM plus and ALI. Remote Sen 5(11):5530–5549

    Article  Google Scholar 

  • Lu SL, Wu BF, Yan NN, Wang H (2011) Water body mapping method with HJ-1A/B satellite imagery. Int J Appl Earth Obs Geoinf 13(3):428–434

    Article  Google Scholar 

  • Matgen P, Schumann G, Henry JB, Hoffmann L, Pfister L (2007) Integration of SAR-derived river inundation areas, high-precision topographic data and a river flow model toward near real-time flood management. Int J Appl Earth Obs Geoinf 9(3):247–263

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Mcginnis DF, Rango A (1975) Earth resources satellite systems for flood monitoring. Geophys Res Lett 2(4):132–135

    Article  Google Scholar 

  • Oberstadler R, Honsch H, Huth D (1997) Assessment of the mapping capabilities of ERS-1 SAR data for flood mapping: a case study in Germany. Hydrol Process 11(10):1415–1425

    Article  Google Scholar 

  • Oguro Y, Takeuchi S, Suga Y, Tsuchiya K (2003) Higher resolution images for visible and near infrared Bands of Landsat-7 ETM + by using panchromatic Band. Calibration, Characterization of Satellite Sensors, Physical Parameters Derived from Satellite Data 32(11):2269–2274

    Google Scholar 

  • Otsu N (1979) Threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9(1):62–66

    Article  Google Scholar 

  • Papa F, Prigent C, Rossow WB (2007) Ob’ River flood inundations from satellite observations: A relationship with winter snow parameters and river runoff. J Geophys Res-Atmos 112(D18)

    Google Scholar 

  • Pappas G, Papadimitriou P, Siozopoulou V, Christou L, Akritidis N (2008) The globalization of leptospirosis: worldwide incidence trends. Int J Infect Dis 12(4):351-357

    Google Scholar 

  • Prigent C, Papa F, Aires F, Rossow WB, Matthews E (2007) Global inundation dynamics inferred from multiple satellite observations, 1993–2000. J Geophys Res-Atmos 112(D12)

    Google Scholar 

  • Rakwatin P, Sansena T, Marjang N, Rungsipanich A (2013) Using multi-temporal remote-sensing data to estimate 2011 flood area and volume over Chao Phraya River basin Thailand. Remote Sen Lett 4(3):243–250

    Article  Google Scholar 

  • Rouse JW, Haas RH, Schell JA, Deering DW (1973) Monitoring vegetation systems in the great plains with ERTS. 3rd ERTS Symposium 48-62

    Google Scholar 

  • Rundquist DC, Lawson MP, Queen LP, Ceverny RS (1987) The relationship between summer-season rainfall events and lake-surface area. Water Resources Bulletin 23:493-508

    Google Scholar 

  • Schumann G, Bates PD, Horritt MS, Matgen P, Pappenberger F (2009) Progress in Integration of Remote Sensing-Derived Flood Extent and Stage Data and Hydraulic Models. Rev Geophys 47

    Google Scholar 

  • Shi ZG, Fung KB (1994) A comparison of digital speckle filters. In: IGARSS ‘94—1994 international geoscience and remote sensing symposium, vol 1–4, pp 2129–2133

    Google Scholar 

  • Tran A, Goutard F, Chamaille L, Baghdadi N, Lo Seen D (2010) Remote sensing and avian influenza: a review of image processing methods for extracting key variables affecting avian influenza virus survival in water from Earth Observation satellites. Int J Appl Earth Obs Geoinf 12(1):1–8

    Article  Google Scholar 

  • Waisurasingha C, Aniya M, Hirano A, Sommut W (2008) Use of RADARSAT-1 data and a digital elevation model to assess flood damage and improve rice production in the lower part of the Chi River Basin Thailand. Int J Remote Sen 29(20):5837–5850

    Article  Google Scholar 

  • White ME (1978) Reservoir surface area from Landsat imagery. Photogrammetric Engineering and Remote Sensing 44:1421-1426

    Google Scholar 

  • Wilson EH, Sader SA (2002) Detection of forest harvest type using multiple dates of Landsat TM imagery. Remote Sen Environ 80:385-396

    Google Scholar 

  • Work EA, Gilmer DS (1976) Utilization of satellite data for inventorying prairie ponds and lakes. Photogrammetric Engineering and Remote Sensing 42:685-694

    Google Scholar 

  • Xiao XM, Boles S, Liu JY, Zhuang DF, Frolking S, Li CS, Salas W, Moore B (2005) Mapping paddy rice agriculture in southern China using multi-temporal MODIS images. Remote Sen Environ 95:480-492

    Google Scholar 

  • Xu HQ (2006) Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. Int J Remote Sen 27(14):3025–3033

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China

    Chunxiang Cao

  2. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China

    Min Xu

  3. International Academy of Aviation Industry, King Mongkut’s Institute of Technology Ladkrabang, Bangkok, Thailand

    Patcharin Kamsing

  4. Department of Geography, Faculty of Social Sciences, Kasetsart University, Bangkok, Thailand

    Sornkitja Boonprong

  5. Department of Lands, Ministry of Interior, Bangkok, Thailand

    Peera Yomwan

  6. Royal Thai Survey Department, Bangkok, Thailand

    Apitach Saokarn

Authors
  1. Chunxiang Cao
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  2. Min Xu
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  3. Patcharin Kamsing
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  4. Sornkitja Boonprong
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  5. Peera Yomwan
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  6. Apitach Saokarn
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Corresponding author

Correspondence to Chunxiang Cao .

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Cao, C., Xu, M., Kamsing, P., Boonprong, S., Yomwan, P., Saokarn, A. (2021). Flooding Identification by Vegetation Index. In: Environmental Remote Sensing in Flooding Areas. Springer, Singapore. https://doi.org/10.1007/978-981-15-8202-8_3

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