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

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

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


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.

Item URL in elib:https://elib.dlr.de/195667/
Document Type:Article
Title:Assessing Combinations of Landsat, Sentinel-2 and Sentinel-1 Time series for Detecting Bark Beetle Infestations
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
König, SimonUNSPECIFIEDhttps://orcid.org/0000-0002-4924-7544UNSPECIFIED
Thonfeld, FrankUNSPECIFIEDhttps://orcid.org/0000-0002-3371-7206139590799
Förster, MichaelUNSPECIFIEDhttps://orcid.org/0000-0001-6689-5714UNSPECIFIED
Dubovyk, OlenaUNSPECIFIEDhttps://orcid.org/0000-0002-7338-3167UNSPECIFIED
Heurich, MarcoUNSPECIFIEDhttps://orcid.org/0000-0003-0051-2930UNSPECIFIED
Date:24 June 2023
Journal or Publication Title:Giscience & Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In ISI Web of Science:Yes
Publisher:Taylor & Francis
Keywords:forest disturbance, multispectral, SAR, time series, Bayesian probabilities, random forest regression
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Remote Sensing and Geo Research
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center > Land Surface Dynamics
Deposited By: Thonfeld, Dr. Frank
Deposited On:31 Jul 2023 11:35
Last Modified:19 Oct 2023 14:59

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