> News & Events > Bridging spatio-temporal gaps in ALS data using Landsat time series and forest disturbance-recovery metrics via multi-task neural networks

Bridging spatio-temporal gaps in ALS data using Landsat time series and forest disturbance-recovery metrics via multi-task neural networks

European forests help mitigate climate change, but increasing disturbances like fires and droughts call for better monitoring. Current forest data from National Forest Inventories (NFIs) are inconsistent across countries, and satellite or laser data alone have limitations. This study developed a neural network model combining Landsat satellite data with airborne laser scanning (ALS) metrics to […]

European forests help mitigate climate change, but increasing disturbances like fires and droughts call for better monitoring. Current forest data from National Forest Inventories (NFIs) are inconsistent across countries, and satellite or laser data alone have limitations. This study developed a neural network model combining Landsat satellite data with airborne laser scanning (ALS) metrics to predict forest height, growing stock volume, and basal area. Tested across five European regions, the method showed strong accuracy (R² up to 0.78). By predicting ALS-based metrics from satellite data, this approach enables scalable, affordable, and frequent forest monitoring to support climate-smart forest management.

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