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

ABSTRACT

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.

KEYWORDS:

• forest disturbance
• multispectral
• SAR
• time series
• Bayesian probabilities
• random forest regression

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The data that support the findings of this study are available from the corresponding author, S.K., upon reasonable request.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/15481603.2023.2226515

Additional information

Funding

This research was funded by the German Federal Ministry of Transport and Digital Infrastructure through the FirSt 2.0 project under grant number 19F2127D.