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Water 2017, 9(7), 510; https://doi.org/10.3390/w9070510

Mapping Submerged Aquatic Vegetation Using RapidEye Satellite Data: The Example of Lake Kummerow (Germany)

1
Aquatic Systems Biology Unit, Limnological Research Station Iffeldorf, Department of Ecology and Ecosystem Management, Technical University of Munich, Hofmark 1–3, 82393 Iffeldorf, Germany
2
Earth Observation and Modelling, Department of Geography, Christian-Albrechts-Universität zu Kiel, Ludewig-Meyn-Str. 14, 24098 Kiel, Germany
*
Author to whom correspondence should be addressed.
Received: 20 March 2017 / Revised: 13 June 2017 / Accepted: 5 July 2017 / Published: 12 July 2017
(This article belongs to the Special Issue Water Quality Monitoring and Modeling in Lakes)
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Abstract

Submersed aquatic vegetation (SAV) is sensitive to changes in environmental conditions and plays an important role as a long-term indictor for the trophic state of freshwater lakes. Variations in water level height, nutrient condition, light availability and water temperature affect the growth and species composition of SAV. Detailed information about seasonal variations in littoral bottom coverage are still unknown, although these effects are expected to mask climate change-related long-term changes, as derived by snapshots of standard monitoring methods included in the European Water Framework Directive. Remote sensing offers concepts to map SAV quickly, within large areas, and at short intervals. This study analyses the potential of a semi-empirical method to map littoral bottom coverage by a multi-seasonal approach. Depth-invariant indices were calculated for four Atmospheric & Topographic Correction (ATCOR2) atmospheric corrected RapidEye data sets acquired at Lake Kummerow, Germany, between June and August 2015. RapidEye data evaluation was supported by in situ measurements of the diffuse attenuation coefficient of the water column and bottom reflectance. The processing chain was able to differentiate between SAV and sandy sediment. The successive increase of SAV coverage from June to August was correctly monitored. Comparisons with in situ and Google Earth imagery revealed medium accuracies (kappa coefficient = 0.61, overall accuracy = 72.2%). The analysed time series further revealed how water constituents and temporary surface phenomena such as sun glint or algal blooms influence the identification success of lake bottom substrates. An abundant algal bloom biased the interpretability of shallow water substrate such that a differentiation of sediments and SAV patches failed completely. Despite the documented limitations, mapping of SAV using RapidEye seems possible, even in eutrophic lakes. View Full-Text
Keywords: Remote sensing; submersed macrophyte; monitoring inland waters; depth-invariant index Remote sensing; submersed macrophyte; monitoring inland waters; depth-invariant index
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Fritz, C.; Dörnhöfer, K.; Schneider, T.; Geist, J.; Oppelt, N. Mapping Submerged Aquatic Vegetation Using RapidEye Satellite Data: The Example of Lake Kummerow (Germany). Water 2017, 9, 510.

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