Long-term forest structure trends in the peninsular Spain from lidar-optical sensors synergies

Abstract

Information on forest structure is needed for many management aspects, from carbon stock evaluation to fire hazard prediction. Such information is increasingly available from remote sensing data, including light detection and ranging (lidar) sensors. However, continuous forest monitoring is hindered by the limited temporal availability of lidar acquisitions. This study focused on integrating temporally consistent optical acquisitions with temporally sparse lidar acquisitions to provide long term information on forest structural characteristics across the peninsular Spain. We evaluated different modeling approaches including machine learning and advanced deep learning models to ascertain their limitations for long-term forest monitoring. Subsequently, annual estimates of forest structural attributes were generated from 1985 to 2024. Across all forests, height (H), canopy cover (FCC), and above ground biomass (AGB) increased by 34 %, 29 % and respectively 17.5 % from the first (1985–1989) to the last lustrum (2019–2024). By biome, the increase in H, FCC and AGB was larger over the Mediterranean (35 %, 33 %, and 18 % respectively) when compared to the Atlantic (33 %, 10 %, and 15 % respectively) forests. For the model training year, deep learning models improved estimation accuracy by 27 ± 2.6 % (mean value across regions and variables) when compared to machine learning models. However, when applied to data from other years (temporal inference), model precision was similar except for height in the Mediterranean were deep learning models improved RMSE estimates by 7 %. Overall, the deep learning models presented significant computational drawbacks when applied to large geographic extents and extensive temporal ranges, substantially increasing both training and prediction times without providing a clear improvement in accuracy compared to machine learning across years. Therefore, the long-term forest database was generated using machine learning models. This database provides a spatially explicit understanding of forest structural changes over nearly four decades, offering a valuable resource for forest management, ecological assessments, and climate-related policy-making.