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  • 1
    Keywords: Forschungsbericht
    Type of Medium: Online Resource
    Pages: Online-Ressource (PDF-Datei: 33 S., 1.756 KB) , graph. Darst., Kt.
    Language: German
    Note: Unterschiede zwischen dem gedruckten Dokument und der elektronischen Ressource können nicht ausgeschlossen werden , Förderkennzeichen BMBF 50 EE 1020 , Systemvoraussetzungen: Acrobat reader.
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  • 2
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    In:  EPIC3ESA Living Planet Symposium, Edinborough, UK, 2013-09-09-2013-09-13
    Publication Date: 2019-07-16
    Description: Marine macroalgae fulfil an important role in coastal ecosystems providing food and habitat for wildlife. The impacts of climate change and increasing human encroachment exert significant pressures on coastal ecosystems. Monitoring of marine macroalgae communities provides information on the state of habitats and their structural changes. Remote sensing is an acknowledged tool for the monitoring of coastal vegetation at landscape scale. Imaging spectrometers with their narrow bandwidths enable the detection of characteristic absorption features and thus, the development of new classification methods based on hyperspectral data. In this paper, we adapt an easy to use slope-based classification approach to CHRIS/Proba data. The slope approach has been successfully applied to map intertidal habitats at the rocky shores of Helgoland (North Sea, Germany) using airborne AISA Eagle data. The application using satellite remote sensing has significant advantages for an operational use of mapping approaches, while the generally lower spatial and spectral resolution of satellite based data often require adaptation of the original approach. To test the applicability of the slope approach, we compare the classification results of CHRIS/Proba and AISA Eagle data. Furthermore, we examine the applicability of the slope approach in subtidal areas and present its suitability for intertidal and subtidal macroalgae mapping.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Conference , notRev
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  • 3
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    Unknown
    In:  EPIC34th National EnMap User Workshop, 2013-11-14-2013-11-14
    Publication Date: 2019-07-16
    Repository Name: EPIC Alfred Wegener Institut
    Type: Conference , notRev
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  • 4
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    Unknown
    Fuck Druck und Verlag, Koblenz
    In:  EPIC3Geoinformation in der Küste, Fuck Druck und Verlag, Koblenz, 4, pp. 233-245
    Publication Date: 2019-07-16
    Description: Die Erfassung des Umweltzustandes von Küstengewässern bildet die Grundlage für die Entwicklung geeigneter Maßnahmen zum Erhalt natürlicher Lebensräume von Fauna und Flora. Verbreitung, Abundanz, Artenverschiebung und Sensitivität von Makroalgen geben Aufschluss über Zustand und Veränderung ihrer Lebensräume. Die größtenteils schwer zugängliche Küstenzone kann durch die Verwendung hyperspektraler Fernerkundungssensoren auf regionaler Skala erfasst werden. Wir haben einen neuen Ansatz zur Erfassung der Verteilung von Makroalgengemeinschaften im Felswatt von Helgoland anhand von flugzeuggestützten hyperspektralen AISAeagle+ Daten getestet. Anstelle von absoluten Reflexionswerten werden die Steigungen zwischen Wellenlängenbereichen mit pigmentspezifischen Absorptionen als Klassifikationsgrundlage verwendet. Die neu berechneten Steigungsbänder werden durch einen k-Means-Algorithmus klassifiziert. Ein wichtiger Faktor ist dabei die Auswahl geeigneter Wellenlängenbereiche zur Berechnung der Steigungen vor der Durchführung der Klassifikation. Es zeigte sich, dass die Verwendung von fünf Wellenlängenbereichen die besten Ergebnisse erzielte (Cohan`s Kappa Koeffizient = 0.7783). Der Ansatz bedarf nur eines vergleichsweise geringen zeitlichen Aufwandes und liefert dennoch eine im Vergleich zu konventionellen Klassifikationsverfahren hohe Genauigkeit bei der Identifikation von Makroalgen.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , notRev
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  • 5
    Publication Date: 2024-02-07
    Description: Along the Baltic coastline of Germany, drifting vegetation and beach cast create overlays at the otherwise sandy or stony beaches. These overlays influence the morphodynamics and structures of the beaches. To better understand the influence of these patchy habitats on coastal environments, regular monitoring is necessary. Most studies, however, have been conducted on spatially larger and temporally more stable occurrences of aquatic vegetation such as floating fields of Sargassum. Nevertheless, drifting vegetation and beach cast pose a particular challenge, as they exhibit high temporal dynamics and sometimes small spatial extent. Regular surveys and mappings are the traditional methods to record their habitats, but they are time-consuming and cost-intensive. Spaceborne remote sensing can provide frequent recordings of the coastal zone at lower cost. Our study therefore aims at the monitoring of drifting vegetation and beach cast on spatial scales between 3 and 10 m. We developed an automated coastline masking algorithm and tested six supervised classification methods and various classification ensembles for their suitability to detect small-scale assemblages of drifting vegetation and beach cast in a study area at the coastline of the Western Baltic Sea using multispectral data of the sensors Sentinel-2 MSI and PlanetScope. The shoreline masking algorithm shows high accuracies in masking the land area while preserving the sand-covered shoreline. We could achieve best classification results using PlanetScope data with an ensemble of a random forest classifier, cart classifier, support vector machine classifier, naïve bayes classifier and stochastic gradient boosting classifier. This ensemble accomplished a combined f1-score of 0.95. The accuracy of the Sentinel-2 classifications was lower but still achieved a combined f1-score of 0.86 for the same ensemble. The results of this study can be considered as a starting point for the development of time series analysis of the vegetation dynamics along Baltic beaches
    Type: Article , PeerReviewed
    Format: text
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