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Assessing Combinations of Landsat, Sentinel-2 and Sentinel-1 Time series for Detecting Bark Beetle Infestations

König, Simon und Thonfeld, Frank und Förster, Michael und Dubovyk, Olena und Heurich, Marco (2023) Assessing Combinations of Landsat, Sentinel-2 and Sentinel-1 Time series for Detecting Bark Beetle Infestations. GIScience and Remote Sensing, 60 (1). Taylor & Francis. doi: 10.1080/15481603.2023.2226515. ISSN 1548-1603.

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Offizielle URL: https://dx.doi.org/10.1080/15481603.2023.2226515

Kurzfassung

Bark beetle infestations are among the most substantial forest disturbance agents worldwide. Moreover, as a consequence of global climate change, they have increased in frequency and in the size and number of affected areas. Controlling bark beetle outbreaks requires consistent operational monitoring, as is possible using satellite data. However, while many satellite-based approaches have been developed, the full potential of dense, multi-sensor time series has yet to be fully explored. Here, for the first time, we used all available multispectral data from Landsat and Sentinel-2, Sentinel-1 SAR data, and combinations thereof to detect bark beetle infestations in the Bavarian Forest National Park. Based on a multi-year reference dataset of annual infested areas, we assessed the separability between healthy and infested forests for various vegetation indices calculated from the satellite data. We used two approaches to compute infestation probability time series from the different datasets: Bayesian conditional probabilities, based on the best-separating index from each satellite type, and random forest regression, based on all indices from each satellite type. Five different sensor configurations were tested for their detection capabilities: Landsat alone, Sentinel-1 alone, Sentinel-2 alone, Landsat and Sentinel-2 combined, and data from all satellite types combined. The best overall results in terms of spatial accuracy were achieved with Sentinel-2 (max. overall accuracy: 0.93). The detections of Sentinel-2 also were the closest to the onset of infestation estimated for each year. Sentinel-2 detected infested areas in larger contiguous patches with higher reliability compared to smaller patches. The results achieved with Landsat were somewhat inferior to those of Sentinel-2 (max. accuracy: 0.89). While yielding similar results, the combination of Landsat and Sentinel-2 did not provide any advantages over using Landsat or Sentinel-2 alone (max. accuracy: 0.87), while Sentinel-1 was unable to detect infested areas (max. accuracy: 0.62). The combined data of all three satellite types did not achieve satisfactory results either (max. accuracy: 0.67). Spatial accuracies were typically higher for Bayesian conditional probabilities than for random forest-derived probabilities, but the latter resulted in earlier detections. The approach presented herein provides a flexible disturbance detection pipeline well-suited for the monitoring of bark beetle outbreaks. Furthermore, it can also be applied to other disturbance types.

elib-URL des Eintrags:https://elib.dlr.de/195667/
Dokumentart:Zeitschriftenbeitrag
Titel:Assessing Combinations of Landsat, Sentinel-2 and Sentinel-1 Time series for Detecting Bark Beetle Infestations
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
König, SimonNICHT SPEZIFIZIERThttps://orcid.org/0000-0002-4924-7544NICHT SPEZIFIZIERT
Thonfeld, FrankFrank.Thonfeld (at) dlr.dehttps://orcid.org/0000-0002-3371-7206139590799
Förster, MichaelNICHT SPEZIFIZIERThttps://orcid.org/0000-0001-6689-5714NICHT SPEZIFIZIERT
Dubovyk, OlenaNICHT SPEZIFIZIERThttps://orcid.org/0000-0002-7338-3167NICHT SPEZIFIZIERT
Heurich, MarcoNICHT SPEZIFIZIERThttps://orcid.org/0000-0003-0051-2930NICHT SPEZIFIZIERT
Datum:24 Juni 2023
Erschienen in:GIScience and Remote Sensing
Referierte Publikation:Ja
Open Access:Ja
Gold Open Access:Ja
In SCOPUS:Ja
In ISI Web of Science:Ja
Band:60
DOI:10.1080/15481603.2023.2226515
Verlag:Taylor & Francis
ISSN:1548-1603
Status:veröffentlicht
Stichwörter:forest disturbance, multispectral, SAR, time series, Bayesian probabilities, random forest regression
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 - Fernerkundung u. Geoforschung
Standort: Oberpfaffenhofen
Institute & Einrichtungen:Deutsches Fernerkundungsdatenzentrum > Dynamik der Landoberfläche
Hinterlegt von: Thonfeld, Dr. Frank
Hinterlegt am:31 Jul 2023 11:35
Letzte Änderung:12 Jun 2024 12:28

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