arXiv Computer Vision By Martin Schwartz, Fajwel Fogel, Nikola Besic, Damien Robert, Louis Geist, Jean-Pierre Renaud, Jean-Matthieu Monnet, Clemens Mosig, C\'edric Vega, Alexandre d'Aspremont, Loic Landrieu, Philippe Ciais

FORMSpoT: Revealing Fine-Scale Forest Disturbances from Nation-Wide 1.5 m Forest Canopy Height Time Series

Read the original on arXiv Computer Vision →

FORMSpoT presents a decade-long, 1.5 m resolution mapping of forest canopy height across France using SPOT‑6/7 data and a transformer model trained on airborne laser scanning. The derived FORMSpoT‑Δ disturbance polygons reveal that most French forest disturbances are small (<0.1 ha), a scale largely missed by coarser Sentinel‑1/2 and Landsat products, and that the method achieves high detection accuracy (F1 > 0.8) for events larger than 100 m² while still capturing finer events. Nationally, the approach distinguishes contrasting disturbance regimes—clear‑cut dominated in maritime pine plantations versus diffuse events in mountain forests—and tracks temporal dynamics such as the 2017‑2022 bark beetle crisis.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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