Capturing Temporal Dynamics in Large-Scale Canopy Tree Height Estimation
Jan Pauls, Max Zimmer, Berkant Turan, Sassan Saatchi, Philippe Ciais, Sebastian Pokutta, Fabian Gieseke
摘要
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, highresolution 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 10 m 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.
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- Budgeted Training: Rethinking Deep Neural Network Training Under Resource ConstraintsMengtian Li, Ersin Yumer, Deva RamananICLR 2020 · 被引用 58 次
- Estimating Canopy Height at ScaleJan Pauls, Max Zimmer, Una M. Kelly, Martin Schwartz 等ICML 2024 · 被引用 27 次
- How I Learned to Stop Worrying and Love RetrainingMax Zimmer, Christoph Spiegel, Sebastian PokuttaICLR 2023
- ViTs for SITS: Vision Transformers for Satellite Image Time SeriesMichail Tarasiou, Erik Chavez, Stefanos ZafeiriouCVPR 2023
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