Preprints
https://doi.org/10.5194/essd-2025-378
https://doi.org/10.5194/essd-2025-378
08 Sep 2025
 | 08 Sep 2025
Status: this preprint is currently under review for the journal ESSD.

A fused canopy height map of Italy (2004–2024) from spaceborne and airborne LiDAR, and Landsat via deep learning and Bayesian averaging

Yang Su, Nikola Besic, Xianglin Zhang, Yidi Xu, Saverio Francini, Giovanni D'Amico, Gherardo Chirici, Martin Schwartz, Ibrahim Fayad, Sarah Brood, Agnes Pellissier-tanon, Ke Yu, Haotian Chen, Songchao Chen, Alexandre d'Aspremont, and Philippe Ciais

Abstract. Forests are vital for the carbon sequestration, biodiversity conservation, and climate regulation, making the precise and continuous monitoring of forest structure attributes such as canopy height essential. Here we present a two decades long (2004–2024), 30 m resolution annual canopy height dataset for Italy, developed using a time-series deep learning framework that integrates Landsat optical imagery with LiDAR observations. Two UNET models were independently trained using canopy height reference data from airborne laser scanning (ALS) and NASA's Global Ecosystem Dynamics Investigation (GEDI) spaceborne LiDAR mission. Annual canopy height predictions from each model were fused using Bayesian Model Averaging (BMA) to enhance spatial consistency and temporal continuity. Validation against ground-based measurements from the Italian National Forest Inventory (NFI) demonstrated high predictive accuracy (mean absolute error = 3.98 m). To further evaluate the utility of our dataset, we derived a canopy height change-based disturbance product and validated it against observed events (mean precision = 0.64 for 2005–2016). In addition, we assessed post-disturbance recovery by monitoring canopy height regrowth in areas affected during 2004–2005, tracking changes annually through 2024 across various Italian biomes. Our results highlight the importance of integrating multi-source remote sensing data with deep learning and Bayesian data fusion for monitoring forest structural dynamics. The final dataset is publicly available via Zenodo and provides a reproducible and scalable resource to support forest research, ecological monitoring, and climate-related policy-making.

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Yang Su, Nikola Besic, Xianglin Zhang, Yidi Xu, Saverio Francini, Giovanni D'Amico, Gherardo Chirici, Martin Schwartz, Ibrahim Fayad, Sarah Brood, Agnes Pellissier-tanon, Ke Yu, Haotian Chen, Songchao Chen, Alexandre d'Aspremont, and Philippe Ciais

Status: open (until 15 Oct 2025)

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Yang Su, Nikola Besic, Xianglin Zhang, Yidi Xu, Saverio Francini, Giovanni D'Amico, Gherardo Chirici, Martin Schwartz, Ibrahim Fayad, Sarah Brood, Agnes Pellissier-tanon, Ke Yu, Haotian Chen, Songchao Chen, Alexandre d'Aspremont, and Philippe Ciais

Data sets

Canopy height map in Italy - 2004-2024 Yang Su https://doi.org/10.5281/zenodo.15627897

Canopy height change derived disturbance map in Italy - 2005-2023 Yang Su https://doi.org/10.5281/zenodo.15627927

Model code and software

Code used in the study - "A fused canopy height map of Italy (2004–2024) from spaceborne and airborne LiDAR, and Landsat via deep learning and Bayesian averaging " Yang Su https://doi.org/10.6084/m9.figshare.29416658

Yang Su, Nikola Besic, Xianglin Zhang, Yidi Xu, Saverio Francini, Giovanni D'Amico, Gherardo Chirici, Martin Schwartz, Ibrahim Fayad, Sarah Brood, Agnes Pellissier-tanon, Ke Yu, Haotian Chen, Songchao Chen, Alexandre d'Aspremont, and Philippe Ciais
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Latest update: 08 Sep 2025
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Short summary
We created the first long-term map of tree height in Italy, showing yearly changes from 2004 to 2024 at a 30-meter resolution. Using satellite images and laser data from both aircraft and space, we applied deep learning and statistical fusion to produce accurate estimates. This map helps reveal where forests have been disturbed and how they recover over time, offering a valuable tool to support forest protection and climate policy.
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