Hybrid forest disturbance classification using Sentinel-1 and inventory data: a case-study for Southeastern USA

Müller, Franziska; Eifler, Laura; Cremer, Felix; Beck, Pieter; Camps-Valls, Gustau; Bastos, Ana

doi:10.5194/nhess-26-2785-2026

Articles | Volume 26, issue 6

https://doi.org/10.5194/nhess-26-2785-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/nhess-26-2785-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 26, issue 6

Research article

|

12 Jun 2026

Research article |

| 12 Jun 2026

Hybrid forest disturbance classification using Sentinel-1 and inventory data: a case-study for Southeastern USA

Franziska Müller, Laura Eifler, Felix Cremer, Pieter Beck, Gustau Camps-Valls, and Ana Bastos

Data sets

Satellite-Based Forest Disturbance Dataset from Sentinel-1 SAR (2016–2021) (1.0.0) F. Cremer and F. Gans https://doi.org/10.5281/zenodo.16903909

Sentinel-1 Disturbance Map [Data set]. In Natural Hazards and Earth System Sciences (NHESS) (1.0.0). European Geosciences Union General Assembly 2024 (EGU 2024), Vienna F. Müller et al. https://doi.org/10.5281/zenodo.15672487

Short summary

Forest health is increasingly threatened, but disturbances like wind damage and insect outbreaks are hard to track. Our Sentinel-1 Disturbance Mapping (S1DM) approach combines satellite radar with survey data, improving detection for wind and bark beetle impacts and often spotting them earlier. Defoliators remain difficult to capture, but this method strengthens monitoring and supports better forest management.