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<p>Forest disturbance detection studies in European temperate forests are currently largely based on optical imagery, often using fixed thresholds on vegetation indices (Francini et al. 2020; Thonfeld et al. 2022) to distinguish between disturbed and non-disturbed forest. Such approaches are limited by data availability, (especially in winter due to persistent cloud cover), and do not take natural seasonal variability as a result of forest phenology into account in the signal. Radar-based disturbance monitoring has been successfully applied over wet tropical forests (Reiche et al. 2021), but implementation in Europe is challenging due to seasonal signal variability and heterogenous forest composition. In addition, the detection of low-intensity disturbances has not been widely studied. This study will explore the capability of dense Sentinel-1 C-band time series to track disturbances of varying intensities in temperate European forests, using a set of 14 experimental sites in the Netherlands as a case study. These sites contain homogeneous forest cover (Beech, Douglas Fir, and Scots Pine) and four disturbance intensities per site which were carried out at a known date. They simulated clearcut, shelterwood, high-thinning and control management regimes, with 100%, 80%, 20%, and 0% basal area removed in each regime respectively, see figure. High-resolution Lidar and drone data were used to derive the canopy cover fraction at a 10m resolution pixel level, which were then compared with Sentinel 1 backscatter timeseries. The results indicate that at a canopy cover loss of 30-40% (of total pixel area), 75% (+-15%) of pixels are detectable as ‘disturbed’ on average. In addition, geometric effects related to radar viewing geometry such as layover and shadow affect the detection potential. Shadow effects ‘pull’ backscatter values down, while layover effects ‘push’ backscatter values up, resulting in lower detection potential at equal canopy cover loss values. Finally, it was found that using the information contained in opposing orbit directions can increase detection potential at all canopy cover loss values by mitigating inaccuracies introduced by geometric effects. Overall, these results could be of great importance in the development of a radar-based system for large scale (near-real time) disturbance detection in European temperate forest.</p> <p><strong>References</strong></p> <p>Francini, Saverio et al. 2020. “Near-Real Time Forest Change Detection Using PlanetScope Imagery.” European Journal of Remote Sensing 53(1): 233–44.</p> <p>Reiche, Johannes et al. 2021. “Forest Disturbance Alerts for the Congo Basin Using Sentinel-1.” Environmental Research Letters 16(2).</p> <p>Thonfeld, Frank et al. 2022. “A First Assessment of Canopy Cover Loss in Germany’s Forests after the 2018–2020 Drought Years.” Remote Sensing 14(3): 562.</p>