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Spatially-Explicit Uncertainty of Remote Sensing Coastal Biodiversity Products using a scalable cloud-based framework in the Google Earth Engine

Christofilakos, Spyridon und Blume, Alina und Pertiwi, Avi Putri und Lee, Chengfa Benjamin und Traganos, Dimosthenis (2022) Spatially-Explicit Uncertainty of Remote Sensing Coastal Biodiversity Products using a scalable cloud-based framework in the Google Earth Engine. 4th ESP Europe Conference, 10-14 Oct 2022, Heraklion, Greece.

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Kurzfassung

Recent advances in remote sensing have enabled the global monitoring of Earth's biodiversity. These developments are providing global information on the extent, structure, function and services of different ecosystem types, and their benefits to the environment and humans. In contradiction with the advances, relevant uncertainty methods and information are missing the understanding of the product biases. In our study, we present a uncertainty quantification framework, developed entirely within the Google Earth Engine, which assesses both thematic (e.g., ecosystem presence/absence) and continuous products (e.g., satellite-derived bathymetry) related to coastal biodiversity using multi-temporal and cloud-free 10-m Sentinel-2, field data collections, and human-annotated data points. By exploiting the cloud-native machine learning classifier and its outputs, we estimate the uncertainty of the procedure per pixel. With that information, our model is able to re-train itself in a data driven way and produce better results. There are three areas of interest in this study. The first is the Archipelago of Bahamas, where we assess a four-class benthic habitat classification product. Our second and third study area is the national scale of Belize and the Quirimbas Archipelago (Mozambique), respectively, in which we generate a satellite-derived bathymetry map. In the case of classification, our model achieved a better overall accuracy in comparison with the initial classification while the producer and user accuracy of the habitat class that we are interested in, seagrass, rose by 13% and 7% respectively. On the regression results, our framework highlights the areas with most uncertainty given the byproducts of the maximum likelihood regression that took place. While still in its alpha version, we think that further developments of the framework could allow better quantification of the data and model uncertainty. By reducing the uncertainties in the coastal biodiversity monitoring, more effective policy making efforts can be achieved and thus, better conservation.

elib-URL des Eintrags:https://elib.dlr.de/190667/
Dokumentart:Konferenzbeitrag (Vortrag)
Titel:Spatially-Explicit Uncertainty of Remote Sensing Coastal Biodiversity Products using a scalable cloud-based framework in the Google Earth Engine
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Christofilakos, Spyridonspyridon.christofilakos (at) dlr.dehttps://orcid.org/0009-0006-4163-5426NICHT SPEZIFIZIERT
Blume, Alinaalina.blume (at) dlr.dehttps://orcid.org/0000-0001-9267-8561NICHT SPEZIFIZIERT
Pertiwi, Avi PutriAvi.Pertiwi (at) dlr.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Lee, Chengfa BenjaminChengfa.Lee (at) dlr.dehttps://orcid.org/0000-0002-2207-5615NICHT SPEZIFIZIERT
Traganos, DimosthenisDimosthenis.Traganos (at) dlr.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Datum:2022
Referierte Publikation:Nein
Open Access:Ja
Gold Open Access:Nein
In SCOPUS:Nein
In ISI Web of Science:Nein
Status:veröffentlicht
Stichwörter:Uncertainty, GEE, Bahamas
Veranstaltungstitel:4th ESP Europe Conference
Veranstaltungsort:Heraklion, Greece
Veranstaltungsart:internationale Konferenz
Veranstaltungsdatum:10-14 Oct 2022
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: Christofilakos, Spyridon
Hinterlegt am:23 Nov 2022 13:37
Letzte Änderung:23 Nov 2022 13:54

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