Annual canopy height mapping in Italy from Landsat and multi-source LiDAR for disturbance and recovery monitoring

Su, Y., Besic, N., Francini, S., Zhang, X., Xu, Y., D'Amico, G., Chirici, G., Schwartz, M., Fayad, I., Brood, S., Pellissier-Tanon, A., Yu, K., Chen, H., Li, H., Chen, S., d'Aspremont, A., & Ciais, P. (2026). Annual canopy height mapping in Italy from Landsat and multi-source LiDAR for disturbance and recovery monitoring. Ecological Informatics 99 e104036. 10.1016/j.ecoinf.2026.104036.

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Abstract

Accurate and continuous monitoring of forest canopy height is essential for assessing forest structure, biomass, disturbance, and recovery. However, airborne laser scanning (ALS) has incomplete national coverage, is updated infrequently, and is costly to acquire. Here, we present an annual 30 m canopy height dataset for Italy spanning 2004–2024, developed by integrating Landsat imagery with multi-source LiDAR observations in a time-series deep learning framework. Two U-Net models were trained independently using canopy height reference data from ALS and NASA's Global Ecosystem Dynamics Investigation (GEDI) spaceborne LiDAR mission. Annual canopy height predictions from the two models were then fused using Bayesian Model Averaging (BMA) to improve spatial consistency and temporal continuity. Validation against field measurements from the Italian National Forest Inventory (NFI) showed high predictive accuracy, with a mean absolute error of 3.98 m. Despite its coarser spatial resolution, our 30 m product outperformed canopy height products currently available in Italy, including a Europe-scale PlanetScope-based dataset at 3 m resolution and a global Sentinel-based canopy height product at 10 m resolution, while also providing a harmonized annual record over two decades rather than only one or a few years. In contrast, an existing Europe-scale Landsat-based product yielded a low R2 in this mountainous region, highlighting the limited transferability of broad-scale models in complex terrain. To demonstrate the utility of the dataset for forest dynamics monitoring, we derived a canopy height change-based disturbance product and evaluated it against independent observed events, obtaining a mean precision of 0.64 for 2005–2016. We also quantified post-disturbance recovery by tracking canopy height regrowth in areas disturbed during 2004–2005 across major Italian biomes through 2024. These results show that combining Landsat time series, multi-source LiDAR, deep learning, and Bayesian fusion provides an effective framework for annual canopy height mapping and for monitoring forest disturbance and recovery. The final dataset is publicly available via Zenodo and offers a reproducible resource for forest monitoring, ecological analysis, and climate-related applications.

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