Capturing Temporal Dynamics in Large-Scale Canopy Tree Height Estimation
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915855361638400 |
|---|---|
| author | Pauls, Jan Zimmer, Max Turan, Berkant Saatchi, Sassan Ciais, Philippe Pokutta, Sebastian Gieseke, Fabian |
| author_facet | Pauls, Jan Zimmer, Max Turan, Berkant Saatchi, Sassan Ciais, Philippe Pokutta, Sebastian Gieseke, Fabian |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_19328 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Capturing Temporal Dynamics in Large-Scale Canopy Tree Height Estimation Pauls, Jan Zimmer, Max Turan, Berkant Saatchi, Sassan Ciais, Philippe Pokutta, Sebastian Gieseke, Fabian Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition 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. |
| title | Capturing Temporal Dynamics in Large-Scale Canopy Tree Height Estimation |
| topic | Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2501.19328 |