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

• Published in: GIScience and remote sensing Vol. 60; no. 1
• Main Authors: König, Simon, Thonfeld, Frank, Förster, Michael, Dubovyk, Olena, Heurich, Marco
• Format: Journal Article
• Language: English
• Published: Taylor & Francis 31.12.2023; Taylor & Francis Group
• Subjects: algorithms; bark beetles; Bayesian probabilities; Bayesian theory; climate change; data collection; forest damage; forest disturbance; forests; Landsat; multispectral; national parks; probability; random forest regression; remote sensing; SAR; time series; time series analysis
• ISSN: 1548-1603; 1943-7226; 1943-7226
• DOI: 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.