Mapping submerged aquatic vegetation using RapidEye satellite data: The example of Lake Kummerow (Germany)

Water (Switzerland)

Volumn 9, Issue 7, 2017, Pages

  Fritz, Christine ^a   Dörnhöfer, Katja ^b   Schneider, Thomas ^a   Geist, Juergen ^a   Oppelt, Natascha ^b  

^a TECHNICAL UNIVERSITY OF MUNICH   (Germany)

^b UNIVERSITY OF KIEL   (Germany)

Author keywords

Depth invariant index; Monitoring inland waters; Remote sensing; Submersed macrophyte

Indexed keywords

CLIMATE CHANGE; MAPPING; REMOTE SENSING; SEDIMENTS; SUBSTRATES; VEGETATION; WATER LEVELS;

DEPTH-INVARIANT INDEX; DIFFUSE ATTENUATION COEFFICIENTS; ENVIRONMENTAL CONDITIONS; INLAND WATERS; MACROPHYTES; SEMI-EMPIRICAL METHODS; SUBMERGED AQUATIC VEGETATIONS; TOPOGRAPHIC CORRECTION;

LAKES;

ALGAL BLOOM; BIOINDICATOR; CLIMATE CHANGE; COMMUNITY COMPOSITION; DATA ACQUISITION; ENVIRONMENTAL CONDITIONS; GROWTH; IN SITU MEASUREMENT; LAKE ECOSYSTEM; MACROPHYTE; RAPIDEYE; REMOTE SENSING; SATELLITE DATA; SATELLITE IMAGERY; SUBMERGED VEGETATION; TROPHIC STATUS; VEGETATION INDEX; VEGETATION MAPPING;

GERMANY; LAKE KUMMEROW;

ALGAE;

EID: 85024927300     PISSN: None     EISSN: 20734441     Source Type: Journal    
DOI: 10.3390/w9070510     Document Type: Article

References (69)

1. Melzer, A. Aquatic macrophytes as tools for lake management. Hydrobiologia 1999, 395, 181-190.
2. Penning,W.E., Dudley, B., Mjelde, M., Hellsten, S., Hanganu, J., Kolada, A., van den Berg, M., Poikane, S., Phillips, G., Willby, N., et al. Using aquatic macrophyte community indices to define the ecological status of European lakes. Aquat. Ecol. 2008, 42, 253-264.
3. Skubinna, J.P., Coon, T.G., Batterson, T.R. Increased abundance and depth of submersed macrophytes in response to decreased turbidity in Saginaw Bay, Lake Huron. J. Great Lakes Res. 1995, 21, 476-488.
4. Søndergaard, M., Johansson, L.S., Lauridsen, T.L., Jørgensen, T.B., Liboriussen, L., Jeppesen, E. Submerged macrophytes as indicators of the ecological quality of lakes. Freshw. Biol. 2010, 55, 893-908.
5. Poikane, S., Birk, S., Böhmer, J., Carvalho, L., de Hoyos, C., Gassner, H., Hellsten, S., Kelly, M., Lyche Solheim, A., Olin, M., et al. A hitchhiker's guide to European lake ecological assessment and intercalibration. Ecol. Indic. 2015, 52, 533-544.
6. Silva, T.S.F., Costa, M.P.F., Melack, J.M., Novo, E.M.L.M. Remote sensing of aquatic vegetation: Theory and applications. Environ. Monit. Assess. 2008, 140, 131-145.
7. Short, F.T., Neckles, H.A. The effects of global climate change on seagrasses. Aquat. Bot. 1999, 63, 169-196.
8. Rooney, N., Kalff, J. Inter-annual variation in submerged macrophyte community biomass and distribution: The influence of temperature and lake morphometry. Aquat. Bot. 2000, 68, 321-335.
9. European Commission. The water framework directive (directive 2000/60/EC of the European Parliament and of the Council of 23 October 2000 establishing a framework for Community action in the field of water policy). Off. J. Eur. Commun. Bruss. Belg. 2000, 22, 1-72.
10. Palmer, S.C.J., Kutser, T., Hunter, P.D. Remote sensing of inland waters: Challenges, progress and future directions. Remote Sens. Environ. 2015, 157, 1-8.
11. Dörnhöfer, K., Oppelt, N. Remote sensing for lake research and monitoring-recent advances. Ecol. Indic. 2016, 64, 105-122.
12. George, D.G. The airborne remote sensing of phytoplankton chlorophyll in the lakes and tarns of the English Lake District. Int. J. Remote Sens. 1997, 18, 1961-1975.
13. Dekker, A.G., Vos, R.J., Peters, S.W.M. Analytical algorithms for lake water TSM estimation for retrospective analyses of TM and SPOT sensor data. Int. J. Remote Sens. 2002, 23, 15-35.
14. Malthus, T.J., George, D.G. Airborne remote sensing of macrophytes in Cefni Reservoir, Anglesey, UK. Aquat. Bot. 1997, 58, 317-332.
15. Wolf, P., Rößler, S., Schneider, T., Melzer, A. Collecting in situ remote sensing reflectances of submersed macrophytes to build up a spectral library for lake monitoring. Eur. J. Remote Sens. 2013, 46, 401-416.
16. Giardino, C., Bartoli, M., Candiani, G., Bresciani, M., Pellegrini, L. Recent changes in macrophyte colonisation patterns: An imaging spectrometry-based evaluation of southern Lake Garda (northern Italy). J. Appl. Remote Sens. 2007, 1, 011509-011517.
17. Yuan, L., Zhang, L.-Q. Mapping large-scale distribution of submerged aquatic vegetation coverage using remote sensing. Ecol. Inform. 2008, 3, 245-251.
18. Pinnel, N., Heege, T., Zimmermann, S. Spectral discrimination of submerged macrophytes in lakes using hyperspectral remote sensing data. SPIE Proc. Ocean Optics XVII 2004, 1, 1-16.
19. Roessler, S., Wolf, P., Schneider, T., Melzer, A. Multispectral remote sensing of invasive aquatic plants using RapidEye. In Earth Observation of Global Changes (EOGC); Krisp, J.M., Meng, L., Pail, R., Stilla, U., Eds., Springer: Berlin/Heidelberg, Germany, 2013; pp. 109-123.
20. Malthus, T.J., Karpouzli, E. Integrating field and high spatial resolution satellite-based methods for monitoring shallow submersed aquatic habitats in the Sound of Eriskay, Scotland, UK. Int. J. Remote Sens. 2003, 24, 2585-2593.
21. Williams, D.J., Rybicki, N.B., Lombana, A.V., O'Brien, T.M., Gomez, R.B. Preliminary investigation of submerged aquatic vegetation mapping using hyperspectral remote sensing. Environ. Monit. Assess. 2003, 81, 383-392.
22. Fyfe, S. Spatial and temporal variation in spectral reflectance: Are seagrass species spectrally distinct? Limnol. Oceanogr. 2003, 48, 464-479.
23. Armstrong, R.A. Remote sensing of submerged vegetation canopies for biomass estimation. Int. J. Remote Sens. 1993, 14, 621-627.
24. Gausman, H.W. Evaluation of factors causing reflectance differences between sun and shade leaves. Remote Sens. Environ. 1984, 15, 177-181.
25. Gitelson, A.A., Zur, Y., Chivkunova, O.B., Merzlyak, M.N. Assessing carotenoid content in plant leaves with reflectance spectroscopy. Photochem. Photobiol. 2002, 75, 272-281.
26. Dekker, A.G., Phinn, S.R., Anstee, J., Bissett, P., Brando, V.E., Casey, B., Fearns, P., Hedley, J., Klonowski, W., Lee, Z.P. Intercomparison of shallow water bathymetry, hydro-optics, and benthos mapping techniques in Australian and Caribbean coastal environments. Limnol. Oceanogr. Methods 2011, 9, 396-425.
27. Giardino, C., Bresciani, M., Valentini, E., Gasperini, L., Bolpagni, R., Brando, V.E. Airborne hyperspectral data to assess suspended particulate matter and aquatic vegetation in a shallow and turbid lake. Remote Sens. Environ. 2015, 157, 48-57.
28. Heege, T., Bogner, A., Pinnel, N. Mapping of Submerged Aquatic Vegetation with a Physically Based Processing Chain; Kramer, E., Ed., SPIE-The International Society for Optical Engineering: Barcelona, Spain, 2003; pp. 43-50.
29. Richter, R., Schläpfer, D. Atmospheric/Topographic Correction for Satellite Imagery: Atcor-2/3 User Guide, Version 9.1.0, dlr/rese, wessling, dlr-ib 565-01/16. October 2016. Available online: http://www.Rese-apps. Com/pdf/atcor3_manual.pdf (accessed on 30 November 2016).
30. Gege, P. A case study at starnberger see for hyperspectral bathymetry mapping using inverse modeling. In Proceedings of the WHISPERS 2014, Lausanne, Switzerland, 25-27 July 2014; pp. 1-4.
31. Villa, P., Bresciani, M., Bolpagni, R., Pinardi, M., Giardino, C. A rule-based approach for mapping macrophyte communities using multi-temporal aquatic vegetation indices. Remote Sens. Environ. 2015, 171, 218-233.
32. Giardino, C., Candiani, G., Bresciani, M., Lee, Z., Gagliano, S., Pepe, M. Bomber: A tool for estimating water quality and bottom properties from remote sensing images. Comput. Geosci. 2012, 45, 313-318.
33. Lyzenga, D.R. Passive remote sensing techniques for mapping water depth and bottom features. Appl. Opt. 1978, 17, 379-383.
34. Gege, P. WASI-2D: A software tool for regionally optimized analysis of imaging spectrometer data from deep and shallow waters. Comput. Geosci. 2013, 62, 208-215.
35. Lyzenga, D.R. Remote sensing of bottom reflectance and water attenuation parameters in shallow water using aircraft and Landsat data. Int. J. Remote Sens. 1981, 2, 71-82.
36. Manessa, M.D.M., Kanno, A., Sekine, M., Ampou, E.E., Widagti, N., As-syakur, A.R. Shallow-water benthic identification using multispectral satellite imagery: Investigation on the effects of improving noise correction method and spectral cover. Rem. Sens. 2014, 6, 4454-4472.
37. Ciraolo, G., Cox, E., La Loggia, G., Maltese, A. The classification of submerged vegetation using hyperspectral MIVIS data. Ann. Geophys. 2006, 49, 287-294.
38. Brooks, C., Grimm, A., Shuchman, R., Sayers, M., Jessee, N. A satellite-based multi-temporal assessment of the extent of nuisance Cladophora and related submerged aquatic vegetation for the Laurentian Great Lakes. Remote Sens. Environ. 2015, 157, 58-71.
39. Shuchman, R.A., Sayers, M.J., Brooks, C.N. Mapping and monitoring the extent of submerged aquatic vegetation in the Laurentian Great Lakes with multi-scale satellite remote sensing. J. Great Lakes Res. 2013, 39, 78-89.
40. Wöbbecke, K., Klett, G., Rechenberg, B. Wasserbeschaffenheit der Wichtigsten seen in der Bundesrepublik Deutschland: Datensammlung 1981-2000; Umweltbundesamt: Dessau-Roßlau, Germany, 2003.
41. LU-MV. Investigation of Macrophytes in Selected Lakes Mecklenburg-Western Pomerania in the Year 2013 (Data Set). Lake Kummerow (200010). MLUV-MV 2015. Data Set Request at MLUV-MV. Available online: http://www.regierung-mv.de/Landesregierung/lm/Umwelt/Wasser/ (accessed on 11 July 2017).
42. Schaumburg, J., Schranz, C., Stelzer, D. Bewertung von Seen mit Makrophyten & Phytobenthos gemäß EG-WRRL-Anpassung des Verfahrens für Natürliche und Künstliche Gewässer sowie Unterstützung der Interkalibrierung; Bayerisches Landesamt für Umwelt: Augsburg/Wielenbach, Germany, 2011.
43. TriOS. Ramses Radiometer. Available online: http://www.Trios.De/en/products/sensors/ramses.Html (accessed on 29 November 2016).
44. Trimble. Datasheet. Trimble Juno SD Handheld GPS Device. Available online: http://trl.Trimble.Com/ docushare/dsweb/get/document-504948/022501-244b_juno%20sd_ds_0712_mgis_hr_nc.Pdf (accessed on 30 November 2016).
45. Mobley, C.D. Estimation of the remote-sensing reflectance from above-surface measurements. Appl. Opt. 1999, 38, 7442-7455.
46. Maritorena, S. Remote sensing of the water attenuation in coral reefs: A case study in French Polynesia. Int. J. Remote Sens. 1996, 17, 155-166.
47. Richter, R. Correction of atmospheric and topographic effects for high spatial resolution satellite imagery. Int. J. Remote Sens. 1997, 18, 1099-1111.
48. Mobley, C.D., Boss, E., Roesler, C. Ocean Optics Web Book. 2015. Available online: http://www. oceanopticsbook.info/ (accessed on 10 July 2017).
49. Kaufman, Y., Tanré, D., Gordon, H., Nakajima, T., Lenoble, J., Frouin, R., Grassl, H., Herman, B., King, M., Teillet, P. Passive remote sensing of tropospheric aerosol and atmospheric correction for the aerosol effect. J. Geophysis. Res-Atmos. 1997, 102, 16815-16830.
50. Levy, R., Hsu, C. MODIS Atmosphere L2 Aerosol Product (MYD04_L2). NASA MODIS Adaptive Processing System, Goddard Space Flight Center, USA. 2015a. Available online: http://dx.doi.org/10.5067/MODIS/ MYD04_L2.006 (accessed on 10 July 2017).
51. Levy, R., Hsu, C. MODIS Atmosphere L2 Aerosol Product (MOD04_L2). NASA MODIS Adaptive Processing System, Goddard Space Flight Center, USA. 2015b. Available online: http://dx.doi.org/10.5067/MODIS/ MOD04_L2.006 (accessed on 10 July 2017).
52. Zambrano-Bigiarini, M. Hydrogof: Goodness-of-fit Functions for Comparison of Simulated and Observed Hydrological: R Package Version 0.3-8. Available online: https://cran.r-project.org/web/packages/ hydroGOF/index.html (accessed on 10 July 2017).
53. Spitzer, D., Dirks, R.W.J. Bottom influence on the reflectance of the sea. Int. J. Remote Sens. 1987, 8, 279-308.
54. Foody, G.M. Status of land cover classification accuracy assessment. Remote Sens. Environ. 2002, 80, 185-201.
55. Congalton, R.G. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens. Environ. 1991, 37, 35-46.
56. Kirk, J.T. Light and Photosynthesis in Aquatic Ecosystems; Cambridge University Press: Cambridge, UK, 1994.
57. Koch, E.W. Beyond light: Physical, geological, and geochemical parameters as possible submersed aquatic vegetation habitat requirements. Estuaries 2001, 24, 1-17.
58. Dörnhöfer, K., Klinger, P., Heege, T., Oppelt, N. Multi-sensor satellite and in situ monitoring of phytoplankton development in a eutrophic-mesotrophic lake. Sci. Total Environ. In review.
59. Heblinski, J., Schmieder, K., Heege, T., Agyemang, T.K., Sayadyan, H., Vardanyan, L. High-resolution satellite remote sensing of littoral vegetation of Lake Sevan (Armenia) as a basis for monitoring and assessment. Hydrobiologia 2011, 661, 97-111.
60. Hunter, P.D., Gilvear, D.J., Tyler, A.N.;Willby, N.J., Kelly, A. Mapping macrophytic vegetation in shallow lakes using the Compact Airborne Spectrographic Imager (CASI). Aquat. Conserv. Mar. Freshw. Ecosyst. 2010, 20, 717-727.
61. Dogan, O.K., Akyurek, Z., Beklioglu, M. Identification and mapping of submerged plants in a shallow lake using quickbird satellite data. J. Environ. Manag. 2009, 90, 2138-2143.
62. Bolpagni, R., Bresciani, M., Laini, A., Pinardi, M., Matta, E., Ampe, E.M., Giardino, C., Viaroli, P., Bartoli, M. Remote sensing of phytoplankton-macrophyte coexistence in shallow hypereutrophic fluvial lakes. Hydrobiologia 2014, 737, 67-76.
63. Wang, P., Huang, C., Brown de Colstoun, E.C. Mapping 2000-2010 impervious surface change in India using global land survey landsat data. Remote Sens. 2017, 9, 366.
64. Manakos, I., Karakizi, C., Gkinis, I., Karantzalos, K. Validation and inter-comparison of spaceborne derived global and continental land cover products for the Mediterranean region: The case of Thessaly. Land 2017, 6, 34.
65. Bey, A., Sánchez-Paus Díaz, A., Maniatis, D., Marchi, G., Mollicone, D., Ricci, S., Bastin, J.-F., Moore, R., Federici, S., Rezende, M. Collect earth: Land use and land cover assessment through augmented visual interpretation. Remote Sens. 2016, 8, 807.
66. Smith, G.M., Milton, E.J. The use of the empirical line method to calibrate remotely sensed data to reflectance. Int. J. Remote Sens. 1999, 20, 2653-2662.
67. Sterckx, S., Knaeps, E., Ruddick, K. Detection and correction of adjacency effects in hyperspectral airborne data of coastal and inland waters: The use of the near infrared similarity spectrum. Int. J. Remote Sens. 2011, 32, 6479-6505.
68. Santer, R., Schmechtig, C. Adjacency effects on water surfaces: Primary scattering approximation and sensitivity study. Appl. Opt. 2000, 39, 361-375.
69. Kay, S., Hedley, J.D., Lavender, S. 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. 2009, 1, 697-730.