Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Nile Delta Biography: Challenges and Opportunities Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Estimation of Bathymetry Using High-resolution Satellite Imagery: Case Study El-Burullus Lake, Northern Nile Delta

Authors : Abdelazim M. Negm, Hassan Mohamed, Mohamed Zahran, Sommer Abdel-Fattah

Published in: The Nile Delta

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Collecting and analyzing bathymetric information is essential to coastal and lake areas. This is commonly accomplished through field measurements, which are time consuming and costly. However, remotely sensed imagery provides wide coverage, low cost, and time-saving solutions for bathymetric measurements, especially in shallow areas with high erosion or sediment accumulation, such as onshore coastal areas. This chapter gives a brief description of the bathymetry determination, starting from the ordinary methods using echo sounders and sonars. Then the basics of detecting bathymetry from satellite images will be discussed followed by an illustration of the most common methods for image calibration. Also, the basic algorithms for bathymetry detection and the recently invented methods will be presented. Finally two case studies for bathymetry determination using satellite images will be discussed.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now
previous chapter Protection Methods Against Sea-Level Rise Caused by Climatic Change: Case Study of the Nile Delta Coastal Zones
next chapter Sustainability of Water Bodies of Northern Egyptian Lakes: Case Studies, Burrulus and Manzalla Lakes
Literature
1.
D. L. B. Jupp(1989) Background and extensions to depth of penetration (DOP) mapping in shallow coastal waters. In: Remote sensing of coastal zone, p IV (2)1–IV (2)19
2.
Jupp DLB, Mayo KK, Kuchler DA, Claasen DVR, Kenchington RA, Guerin PR (1985) Remote sensing for planning and managing the great barrier reef of Australia. Photogrammetria 40(1):21–42CrossRef
3.
Storlazzi CD, Logan JB, Field ME (2003) Quantitative morphology of a fringing reef tract from high-resolution laser bathymetry: Southern Molokai, Hawaii. Geol Soc Am Bull 115(11):1344–1355CrossRef
4.
Brock JC, Wright CW, Clayton TD, Nayegandhi A (2004) LIDAR optical rugosity of coral reefs in Biscayne National Park, Florida. Coral Reefs 23(1):48–59CrossRef
5.
Finkl CW, Benedet L, Andrews JL (2005) Submarine geomorphology of the continental shelf off Southeast Florida based on interpretation of airborne laser bathymetry. J Coast Res 21(6):1178–1190CrossRef
6.
7.
Kunzig R (2000) Mapping the deep: the extraordinary story of ocean science, 1st edn. W. W. Norton, New York
8.
Clark RK, Fay TH, Walker CL (1987) Bathymetry calculations with Landsat 4 TM imagery under a generalized ratio assumption. Appl Optics 26(19):4036CrossRef
9.
Kutser T (2007) Mapping bottom type and water depth in shallow coastal waters with satellite remote sensing. J Coast Res 2007(50):185–189
10.
Lyzenga DR (1978) Passive remote sensing techniques for mapping water depth and bottom features. Appl Optics 17(3):379–383CrossRef
11.
Stumpf RP, Holderied K, Sinclair M (2003) Determination of water depth with high-resolution satellite imagery over variable bottom types. Limnol Oceanogr 48(1_part_2):547–556CrossRef
12.
Ceyhun Ö, Yalçın A (2010) Remote sensing of water depths in shallow waters via artificial neural networks. Estuar Coast Shelf Sci 89(1):89–96CrossRef
13.
Su H, Liu H, Heyman WD (2008) Automated derivation of bathymetric information from multi-spectral satellite imagery using a non-linear inversion model. Mar Geod 31(May):281–298CrossRef
14.
Corucci L (2011) Approaching bathymetry estimation from high resolution multispectral satellite images using a neuro-fuzzy technique. J Appl Remote Sens 5(1):053515CrossRef
15.
Mohamed H, Negm A, Zahran M, Saavedra OC (2015) Assessment of artificial neural network for bathymetry estimation using high resolution satellite imagery in shallow lakes: case study el burullus lake. Int Water Technol J 5(3):248–259
16.
17.
18.
19.
20.
Densham M (2005) Bathymetric mapping with Quickbird data. Defense Technical Information Center, Fort Belvoir
21.
Bierwirth RV, Lee PN, Burne TJ (1993) Shallow sea-floor reflectance and water depth derived by unmixing multispectral imagery. Photogramm Eng Remote Sens 59(3):331–338
22.
Durkee PA, Jensen DR, Hindman EE, Haar THV (1986) The relationship between marine aerosol particles and satellite-detected radiance. J Geophys Res 91(D3):4063–4072CrossRef
23.
Richter R (1996) A spatially adaptive fast atmospheric correction algorithm. Int J Remote Sens 17(6):1201–1214CrossRef
24.
Berk A, Bernstein LS, Anderson GP, Acharya PK, Robertson DC, Chetwynd JH, Adler-Golden SM (1998) MODTRAN cloud and multiple scattering upgrades with application to AVIRIS. Remote Sens Environ 65(3):367–375CrossRef
25.
26.
AH B, GJ D (2013) Satellite imagery as an aid to bathymetric charting in the Red Sea. Cartogr J 20(1):5–16
27.
Edmund CDC, Green P, Mumby PJ, Edwards AJ (2000) Remote sensing handbook for tropical coastal management. UNESCO Publishing, Paris
28.
Mishra DR, Narumalani S, Rundquist D, Lawson MP, Perk R (2007) Enhancing the detection and classification of coral reef and associated benthic habitats: a hyperspectral remote sensing approach. J Geophys Res 112(C08014):2–18
29.
Doxani G, Papadopoulou M, Lafazani P, Pikridas C, Tsakiri-Strati M (2012) Shallow-water bathymetry over variable bottom types using multispectral worldview-2 image. Int Arch Photogramm Remote Sens Spat Inf Sci XXXIX-B8(Sept):159–164CrossRef
30.
Lyzenga DR (1981) Remote sensing of bottom reflectance and water attenuation parameters in shallow water using aircraft and Landsat data. Int J Remote Sens 2(1):71–82CrossRef
31.
32.
Wang M, Bailey SW (2001) Correction of sun glint contamination on the SeaWiFS ocean and atmosphere products. Appl Optics 40(27):4790–4798CrossRef
33.
Manager P, Bourg L, Morel A (2000) Sun glint flag algorithm. MERIS ESL
34.
Fukushima H, Toratani M, Murakami H, Deschamps P-Y, Frouin R, Tanaka A Evaluation of ADEOS-II GLI ocean color atmospheric correction using SIMBADA handheld radiometer data. J Oceanogr 63(3):533–543
35.
Ottaviani M, Spurr R, Stamnes K, Li W, Su W, Wiscombe W (2008) Improving the description of sunglint for accurate prediction of remotely sensed radiances. J Quant Spectrosc Radiat Transfer 109(14):2364–2375CrossRef
36.
Steinmetz F, Deschamps P-Y, Ramon D (2011) Atmospheric correction in presence of sun glint: application to MERIS. Opt Express 19(10):9783–9800CrossRef
37.
Doerffer R, Schiller H, Fischer J, Preusker R, Bouvet M (2008) The impact of sun glint on the retrieval of water parameters and possibilities for the correction of MERIS scenes. In: Proceedings of the 2nd MERIS/(A)ATSR User Workshop, no. ESA SP-666
38.
Hochberg EJ, Andrefouet S, Tyler MR (2003) Sea surface correction of high spatial resolution Ikonos images to improve bottom mapping in near-shore environments. IEEE Trans Geosci Remote Sens 41(7):1724–1729CrossRef
39.
Hedley JD, Harborne AR, Mumby PJ (2005) Technical note: simple and robust removal of sun glint for mapping shallow-water benthos. Int J Remote Sens 26(10):2107–2112CrossRef
40.
Lyzenga DR, Malinas NP, Tanis FJ (2006) Multispectral bathymetry using a simple physically based algorithm. IEEE Trans Geosci Remote Sens 44(8):2251–2259CrossRef
41.
Philpot W (2007) Estimating atmospheric transmission and surface reflectance from a glint-contaminated spectral image. IEEE Trans Geosci Remote Sens 45(2):448–457CrossRef
42.
Goodman JA, Lee Z, Ustin SL (2008) Influence of atmospheric and sea-surface corrections on retrieval of bottom depth and reflectance using a semi-analytical model: a case study in Kaneohe Bay, Hawaii. Appl Optics 47(28):F1–F11CrossRef
43.
Kutser T, Vahtmäe E, Praks J (2009) A sun glint correction method for hyperspectral imagery containing areas with non-negligible water leaving NIR signal. Remote Sens Environ 113(10):2267–2274CrossRef
44.
Kay S, Hedley JD, Lavender S (2009) Sun glint correction of high and low spatial resolution images of aquatic scenes: a review of methods for visible and near-infrared wavelengths. Remote Sens 1(4):697–730CrossRef
45.
Lyzenga DR (1985) Shallow-water bathymetry using combined lidar and passive multispectral scanner data. Int J Remote Sens 6(1):115–125CrossRef
46.
Loomis MJ (2009) Depth derivation from the Worldview-2 satellite using hyperspectral imagery. Naval Postgraduate School, Monterey, pp 1–69
47.
Mas JF (2004) Mapping land use/cover in a tropical coastal area using satellite sensor data, GIS and artificial neural networks. Estuar Coast Shelf Sci 59(2):219–230CrossRef
48.
Rumelhart DE, Hinton GE, Williams RJ (1986) Learning internal representations by error propagation. In: Rumelhart DE, McClelland JL, PDP Research Group (eds) Parallel distributed processing: explorations in the microstructure of cognition, vol 1. MIT Press, Cambridge, pp. 318–362
49.
50.
Razavi B (2014) Predicting the trend of land use changes using artificial neural network and markov chain model (case study: Kermanshah City). Res J Environ Earth Sci 6(4):215–226
51.
Gardner MW, Dorling SR (1998) Artificial neural networks (the multilayer perceptron) – a review of applications in the atmospheric sciences. Atmos Environ 32(14–15):2627–2636CrossRef
52.
Ranganathan R (2004) The Levenberg-Marquardt algorithm. Tutoral LM Algorithm 11(1):101–110
53.
Hagan MT, Menhaj MB (1994) Training feedforward networks with the Marquardt algorithm. IEEE Trans Neural Networks 5(6):989–993CrossRef
54.
Hengel WVAN, Spitzer D (1991) Multi-temporal water depth mapping by means of Landsat TM. Int J Remote Sens 12(4):703–712CrossRef
55.
Pacheco A, Horta J, Loureiro C, Ferreira Ó (2015) Retrieval of nearshore bathymetry from Landsat 8 images: a tool for coastal monitoring in shallow waters. Remote Sens Environ 159:102–116CrossRef
56.
Lyons M, Phinn S, Roelfsema C (2011) Integrating Quickbird multi-spectral satellite and field data: mapping bathymetry, seagrass cover, seagrass species and change in Moreton Bay, Australia in 2004 and 2007. Remote Sens 3(12):42–64CrossRef
57.
Bramante JF, Raju DK, Sin TM (2013) Multispectral derivation of bathymetry in Singapore’s shallow, turbid waters. Int J Remote Sens 34(6):2070–2088CrossRef
58.
Gholamalifard M, Kutser T, Esmaili-Sari A, Abkar A, Naimi B (2013) Remotely sensed empirical modeling of bathymetry in the southeastern Caspian Sea. Remote Sens 5(6):2746–2762CrossRef
59.
Moses SA, Janaki L, Joseph S, Gomathi JP, Joseph J (2013) Lake bathymetry from Indian Remote Sensing (P6-LISS III) satellite imagery using artificial neural network model. Lakes Reserv Res Manag 18(2):145–153CrossRef
60.
El-Adawy A, Negm AM, Elzeir MA, Saavedra OC, El-Shinnawy IA, Nadaoka K (2013) Modeling the hydrodynamics and salinity of El-Burullus Lake (Nile Delta, Northern Egypt). J Clean Energy Technol 1(2):157–163CrossRef
61.
Ali EM (2011) Impact of drain water on water quality and eutrophication status of Lake Burullus, Egypt, a southern Mediterranean lagoon. African J Aquat Sci 36(3):267–277CrossRef
62.
Sánchez-Carnero N, Ojeda-Zujar J, Rodríguez-Pérez D, Marquez-Perez J (2014) Assessment of different models for bathymetry calculation using SPOT multispectral images in a high-turbidity area: the mouth of the Guadiana Estuary. Int J Remote Sens 35(2):493–514CrossRef
63.
Mohamed H, Negm A, Zahran M, Saavedra OC (2016) Bathymetry determination from high resolution satellite imagery using Ensemble Learning algorithms in shallow lakes: case study El-Burullus Lake. Int J Environ Sci Dev 7(4):295CrossRef
64.
Ihler A (2012) Machine learning and data mining, ensembles of learners. California
65.
Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning data mining, inference, and prediction, vol 27, 2nd edn. Springer Series in Statistics, Stanford
66.
Quinlan JR (2006) Bagging, boosting, and C4.5. In: Proc Thirteen Natl Conf Artif Intell, vol. 5, no. Quinlan 1993. pp 725–730
67.
68.
Casale P, Pujol O, Radeva P (2011) Embedding random projections in regularized. In: Ensembles in machine learning applications. Springer, Berlin, Heidelberg, pp. 201–216CrossRef
69.
Bühlmann P (2006) Boosting for high-dimensional linear models. Ann Stat 34(2):559–583CrossRef
Metadata
Title
Estimation of Bathymetry Using High-resolution Satellite Imagery: Case Study El-Burullus Lake, Northern Nile Delta
Authors
Abdelazim M. Negm
Hassan Mohamed
Mohamed Zahran
Sommer Abdel-Fattah
Copyright Year
2017
Book
The Nile Delta

Print ISBN: 978-3-319-56122-6

Electronic ISBN: 978-3-319-56124-0

Copyright Year: 2017

DOI
https://doi.org/10.1007/698_2016_89