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A Simple Cloud-native Spectral Transformation Method to Disentangle Optically Shallow and Deep Waters

Lee, Chengfa Benjamin und Traganos, Dimosthenis und Reinartz, Peter (2021) A Simple Cloud-native Spectral Transformation Method to Disentangle Optically Shallow and Deep Waters. AGU Fall Meeting 2021, 2021-12-06 - 2021-12-17, New Orleans, LA & Virtuelle.

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Offizielle URL: https://agu.confex.com/agu/fm21/meetingapp.cgi/Paper/860414

Kurzfassung

Seagrasses—one of the world’s most productive ecosystems—provide many valuable ecosystem services such as habitat provisioning, biodiversity maintenance, food security, coastal protection, and carbon sequestration. With the projected temperature extremes and sea level rise due to climate change, these important ecosystems are highly threatened. Conserving these important ecosystems requires accurate and efficient mapping of its distribution and trajectories of change, which naturally includes seagrass meadows across their full depth range of growth. Unfortunately, the spectral similarities between the detectable deep seagrass and optically deep water pixels in the satellite images, the so-called dark pixel confusion, causes potential classification errors. Within the context of the Global Seagrass Watch project, funded by DLR and supported by the Group on Earth Observations-Google Earth Engine program, we have developed a novel coastal aerosol-blue-green false colour HSV approach within the Google Earth Engine platform to identify and mask out these optically deep water pixels on open Sentinel-2 satellite imagery, at both Top-of-Atmosphere (L1C) and Bottom-of-Atmosphere level (L2A). We have also explored comparisons with the commonly used band ratio approach. Both approaches are able to separate optically deep and shallow waters using supervised thresholding. While the band ratios as a group feature a slightly better quantitative performance, the performance of the individual band ratios are highly reliant on water quality and thus not always better. Comparatively, the false colour hue and saturation bands are more consistent in performance for the L1C and L2A images, respectively, and therefore easily transferable to another region. By removing the optically deep water pixels, this method helps reducing potential dark pixel confusion to improve seagrass detection pushing the limits of seagrass remote sensing. This could benefit scientists focused on seagrass-related mapping, protection, conservation, and conservation.

elib-URL des Eintrags:https://elib.dlr.de/188095/
Dokumentart:Konferenzbeitrag (Vortrag)
Titel:A Simple Cloud-native Spectral Transformation Method to Disentangle Optically Shallow and Deep Waters
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Lee, Chengfa BenjaminChengfa.Lee (at) dlr.dehttps://orcid.org/0000-0002-2207-5615NICHT SPEZIFIZIERT
Traganos, DimosthenisDimosthenis.Traganos (at) dlr.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Reinartz, Peterpeter.reinartz (at) dlr.dehttps://orcid.org/0000-0002-8122-1475NICHT SPEZIFIZIERT
Datum:17 Dezember 2021
Referierte Publikation:Nein
Open Access:Nein
Gold Open Access:Nein
In SCOPUS:Nein
In ISI Web of Science:Nein
Status:veröffentlicht
Stichwörter:Ocean colour, deep water, Sentinel-2
Veranstaltungstitel:AGU Fall Meeting 2021
Veranstaltungsort:New Orleans, LA & Virtuelle
Veranstaltungsart:internationale Konferenz
Veranstaltungsbeginn:6 Dezember 2021
Veranstaltungsende:17 Dezember 2021
HGF - Forschungsbereich:Luftfahrt, Raumfahrt und Verkehr
HGF - Programm:Raumfahrt
HGF - Programmthema:Erdbeobachtung
DLR - Schwerpunkt:Raumfahrt
DLR - Forschungsgebiet:R EO - Erdbeobachtung
DLR - Teilgebiet (Projekt, Vorhaben):R - Optische Fernerkundung
Standort: Berlin-Adlershof , Oberpfaffenhofen
Institute & Einrichtungen:Institut für Methodik der Fernerkundung > Photogrammetrie und Bildanalyse
Hinterlegt von: Lee, Chengfa Benjamin
Hinterlegt am:08 Sep 2022 18:08
Letzte Änderung:24 Apr 2024 20:49

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