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Potential of recurrence quantification analysis of Sentinel-1 time series for deforestation mapping

Cremer, Felix und Urbazaev, Mikhail und Schmullius, Christiane und Thiel, Christian (2019) Potential of recurrence quantification analysis of Sentinel-1 time series for deforestation mapping. Living Planet Symposium 2019, 2019-05-13 - 2019-05-17, Milan, Italy.

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Kurzfassung

The UNFCCC REDD+ framework increases the need for highly accurate maps of deforestation and degradation in the tropics. Operational forest/non-forest maps are commonly based on optical imagery. However, especially in the tropics optical images are frequently degraded by the presence of clouds. Therefore, we investigated the potential of hyper-temporal Sentinel-1 synthetic aperture radar (SAR) data to derive forest/non-forest and deforestation maps. Feature selection has been used, to decrease the amount of data and to enhance the signal to noise ratio. This is especially relevant for the use of machine learning, because it is one way to deal with the curse of dimensionality. In this study we compared the use of recurrence quantification analysis (RQA) with traditional multi-temporal metrics for feature extraction from dense Sentinel-1 time series. Recurrence quantification analysis (RQA) is a non-linear time series analysis technique. It quantifies the patterns of recurrences in time series. By means of RQA a number of metrics can be calculated (e.g., determinism, recurrence Rate, laminarity), which describe the complex behaviour of dynamic systems. In contrast to traditional multi-temporal metrics (e.g., mean, median, quartiles, standard deviation), RQA considers the temporal order of the images of the time series. After calculating RQA and traditional multi-temporal metrics from the Sentinel-1 image time stacks, we performed a signature analysis. For this, we selected forested and deforested areas based on visual interpretation of annual very high resolution (1 m) optical imagery over temperate and tropical forests of Mexico. The signature analysis of the traditional and RQA metrics showed promising results for the classification of deforestation. Obviously the consideration of the temporal order of time series provides additional information compared to traditional multi-temporal statistics. Therefore RQA can enhance the accuracies of forest/non-forest and deforestation maps. In the future we plan to combine RQA metrics and multi-temporal metrics in order to further improve the map accuracies.

elib-URL des Eintrags:https://elib.dlr.de/133270/
Dokumentart:Konferenzbeitrag (Poster)
Titel:Potential of recurrence quantification analysis of Sentinel-1 time series for deforestation mapping
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Cremer, FelixFelix.Cremer (at) dlr.dehttps://orcid.org/0000-0001-8659-4361NICHT SPEZIFIZIERT
Urbazaev, MikhailFriedrich-Schiller-Universität Jenahttps://orcid.org/0000-0002-0327-6278NICHT SPEZIFIZIERT
Schmullius, ChristianeFSU Jena, Institut für Geographie Lehrstuhl Fernerkundung, c.schmullius (at) uni-jena.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Thiel, ChristianChristian.Thiel (at) dlr.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Datum:Mai 2019
Referierte Publikation:Nein
Open Access:Nein
Gold Open Access:Nein
In SCOPUS:Nein
In ISI Web of Science:Nein
Status:veröffentlicht
Stichwörter:RQA, RADAR, SAR, Sentinel-1, time series, deforestation, recurrence
Veranstaltungstitel:Living Planet Symposium 2019
Veranstaltungsort:Milan, Italy
Veranstaltungsart:internationale Konferenz
Veranstaltungsbeginn:13 Mai 2019
Veranstaltungsende:17 Mai 2019
Veranstalter :ESA
HGF - Forschungsbereich:Luftfahrt, Raumfahrt und Verkehr
HGF - Programm:Raumfahrt
HGF - Programmthema:keine Zuordnung
DLR - Schwerpunkt:Raumfahrt
DLR - Forschungsgebiet:R - keine Zuordnung
DLR - Teilgebiet (Projekt, Vorhaben):R - keine Zuordnung
Standort: Jena
Institute & Einrichtungen:Institut für Datenwissenschaften > Bürgerwissenschaften
Hinterlegt von: Cremer, Felix
Hinterlegt am:07 Jan 2020 13:04
Letzte Änderung:24 Apr 2024 20:36

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