Remote Sensing of Plant Pigments in Coastal Aquatic Systems
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Title: | Main Title: Remote Sensing of Plant Pigments in Coastal Aquatic Systems |
Description: | Abstract: In coastal aquatic systems, marine macroalgae provide food and habitat for wildlife. Analysis of their occurrence and socialization therefore enables an estimate the state of coastal marine environment and provides evidence for environmental changes. To identify different macroalgae at family or species level, we have to identify their specific pigment composition. Hyperspectral sensors with their narrow band widths enable the detection of local absorption features of pigments and increased the number of possibilities to determine these features. This led to growing research interest to identify and monitor submerged and emerged coastal vegetation using airborne hyperspectral sensors. A precondition for a successful mapping of macroalgal habitats, however, is that their spectral features are spectrally resolvable. Besides the problems of identifying overlapping pigments features in terrestrial plants, the analysis of aquatic plants is difficult due to the dampening effect of water on the spectral signal. Emergent species usually have a higher average reflectance than submerged plants due to the absence of water attenuation. Moreover, the presence of flooding introduces variability in reflectance values due to the mixing of plant and water signals. This mixing usually results in a decrease in total reïflected radiation, especially in the Near to Mid Infrared. This paper discusses the performance of different approaches to determine the distribution of macroalgae communities in the rocky intertidal and sublitoral of Helgoland (Germany) using airborne AISAeagle data. We used standard supervised classification approaches such as the maximum likelihood classifier; to better cope with the varying reflectance levels we also introduced a new approach, which is based on the measurement of the slope between major algae pigments. The slope approach turned out as time effective possibility to identify the dominating macroalgae species via their pigment assemblage in the intertidal and upper sublitoral zone, even in the heterogeneous and patchy coverage of the study area. With increasing water depths (> 2 m), a water column correction is compulsory for macroalgae mapping. In this study, the bio-optical model MIP was applied to identify different types of brown algae in the sublitoral zone of the study area. |
Identifier: | 10.5880/TR32DB.KGA94.16 (DOI) |
Responsible Party
Creator: | Natascha Oppelt (Author) |
Contributors: | Juliane Bendig (Editor), Georg Bareth (Editor), Transregional Collaborative Research Centre 32 (Meteorological Institute, University of Bonn) (Data Manager), University of Cologne (Regional Computing Centre (RRZK)) (Hosting Institution) |
Publisher: | Geographisches Institut der Universität zu Köln - Kölner Geographische Arbeiten |
Publication Year: | 2014 |
Topic
Subjects: | Keywords: Hyperspectral, Remote Sensing, Water, Hyperspectral Sensor |
File Details
Filename: | Oppelt_2013_KGA94.pdf |
Data Type: | Text - Book Section |
Sizes: | 9 Pages 4089 Kilobytes |
File Size: | 4 MB |
Date: | Issued: 10.06.2014 |
Mime Type: | application/pdf |
Language: | English |
Constraints
Download Permission: | Free |
Licence: | [TR32DB] Data policy agreement |
Geographic
Metadata Details
Metadata Creator: | Constanze Curdt |
Metadata Created: | 10.06.2014 |
Metadata Last Updated: | 10.06.2014 |
Subproject: | Z1 |
Funding Phase: | 2 |
Metadata Language: | English |
Metadata Version: | V50 |
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Metadata Downloads: | 0 |
Dataset Downloads: | 25 |
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