Improving forest disturbance labels through Sentinel-1 change detection validation

Global forest ecosystems face unprecedented challenges, such as fire, wind, drought, and insect outbreaks, resulting in rapid forest decline. Analyzing these disturbances on a large scale requires the use of remote sensing techniques, but the spatial and temporal uncertainty in forest disturbance reference data poses a significant obstacle.

In this study, we validate and refine existing disturbance labels of the U.S. Forest Service Forest Health Protection [1] Dataset USDA by using a change detection algorithm [2] based on radar data from Sentinel-1. To this end, we analyze the spatio-temporal overlap of disturbed areas from Sentinel-1 with the USDA labels and further explore spatio-temporal fingerprints of remote sensing indices commonly used for disturbance detection. As the analysis of the remote sensing indices shows, this refinement of the accuracy of disturbance labels provides a more reliable basis for ecological research and land management practice.

 

References:

[1] Coleman, T. W., Graves, A. D., Heath, Z., Flowers, R. W., Hanavan, R. P., Cluck, D. R., & Ryerson, D. (2018). Accuracy of aerial detection surveys for mapping insect and disease disturbances in the United States. Forest Ecology and Management, 430, 321–336. https://doi.org/10.1016/j.foreco.2018.08.020

[2] Cremer, F., Gans, F., Cortes, J. & Thiel, C. (2023). Mapping Forest Loss in Europe with Sentinel-1. In European Commission, Joint Research Centre, Soille, P., Lumnitz, S., Albani, S., Proceedings of the 2023 conference on Big Data from Space (BiDS’23) – From foresight to impact – 6-9 November 2023, Austrian Center, Vienna, Soille, P.(editor), Lumnitz, S.(editor), Albani, S.(editor), (pp. 361 - 364) Publications Office of the European Union, 2023, https://data.europa.eu/doi/10.2760/46796