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arXiv AI

[2501.19328] Capturing Temporal Dynamics in Large-Scale Canopy Tree Height Estimation

By Advanced AI EditorJune 16, 2025No Comments2 Mins Read
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[Submitted on 31 Jan 2025 (v1), last revised 12 Jun 2025 (this version, v2)]

View a PDF of the paper titled Capturing Temporal Dynamics in Large-Scale Canopy Tree Height Estimation, by Jan Pauls and 6 other authors

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Abstract:With the rise in global greenhouse gas emissions, accurate large-scale tree canopy height maps are essential for understanding forest structure, estimating above-ground biomass, and monitoring ecological disruptions. To this end, we present a novel approach to generate large-scale, high-resolution canopy height maps over time. Our model accurately predicts canopy height over multiple years given Sentinel-1 composite and Sentinel~2 time series satellite data. Using GEDI LiDAR data as the ground truth for training the model, we present the first 10m resolution temporal canopy height map of the European continent for the period 2019-2022. As part of this product, we also offer a detailed canopy height map for 2020, providing more precise estimates than previous studies. Our pipeline and the resulting temporal height map are publicly available, enabling comprehensive large-scale monitoring of forests and, hence, facilitating future research and ecological analyses.

Submission history

From: Max Zimmer [view email]
[v1]
Fri, 31 Jan 2025 17:26:06 UTC (16,590 KB)
[v2]
Thu, 12 Jun 2025 21:27:36 UTC (15,767 KB)



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