Bitemporal Radiative Transfer Modeling Using Bitemporal 3D-Explicit Forest Reconstruction from Terrestrial Laser Scanning
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area and Data Collection
2.2. 3D-Explicit Reconstruction of 2022 Forest Stand Structure
2.2.1. Tree Extraction
2.2.2. Quantitative Modeling of Tree Structure
2.2.3. Leaf Addition
2.2.4. DTM Generation and Forest Stand Assembling
2.3. Radiative Transfer Modeling
2.3.1. Canopy Light Regimes at the Stand Scale
2.3.2. Canopy Light Availability at Explicit Locations
2.4. Evaluation of Computational Efficiency of Bitemporal 3D-Explicit Radiative Transfer Modeling
2.5. Flowchart of Research Methodology
3. Results
3.1. 3D-Explicit Reconstruction of 2022 Forest Stand
3.2. Bitemporal Radiative Transfer Modeling
3.2.1. Vertically Resolved Radiative Transfer
3.2.2. Forward Modeling of Hemispherical-Directional Reflectance Factor
3.2.3. Impacts of Canopy Gap Dynamics on Light Availability
3.2.4. Computational Efficiency of Bitemporal 3D-Explicit Radiative Transfer Modeling
4. Discussion
4.1. Impacts of Forest Structural Dynamics on Radiative Transfer
4.2. Impacts of Canopy Gap Dynamics on Local Light Availability
4.3. Outlook
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Hardware | 2 × 64-Core AMD EPYC 7773X (Milan-X @ 2.2 GHz) One Node × 18 Processors for Each RT Simulation | ||
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DART simulation settings | Radiative method | Image and sensor simulations | DART-Lux |
Radiative budget simulations | DART-FT | ||
Target sample density per pixel (DART-Lux) | 100 | ||
Cell dimension (DART-FT) | 1 m | ||
Transition: TOA <-> BOA | RT modeling with Earth-atmosphere coupling and diffuse transmittance |
Year | 2015 | 2022 | |
---|---|---|---|
LAI | 3.8 | 4.7 | |
Vegetationcover | Leaf cover | 85% | 85% |
Wood cover | 48% | 43% | |
Canopy cover (leaf + wood) | 88% | 87% | |
FAPAR | Leaf | 0.69 | 0.73 |
Wood | 0.14 | 0.10 | |
Understorey | 0.06 | 0.06 | |
Total | 0.89 | 0.90 |
RT Modeling Type | Spatial Resolution | Time Consumption (min) | RAM Consumption (GB) |
---|---|---|---|
Optical remote sensing observation | 10 m | 7 | 8 |
25 cm | 17 | 10.2 | |
1 cm | 4867.8 | 785.4 | |
Canopy light distribution | 1 m | 216.8 | 41.8 |
Light availability at explicit locations (PAR sensor simulation) | Each sensor | 24.1 | 3.5 |
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Liu, C.; Calders, K.; Origo, N.; Terryn, L.; Adams, J.; Gastellu-Etchegorry, J.-P.; Wang, Y.; Meunier, F.; Armston, J.; Disney, M.; et al. Bitemporal Radiative Transfer Modeling Using Bitemporal 3D-Explicit Forest Reconstruction from Terrestrial Laser Scanning. Remote Sens. 2024, 16, 3639. https://doi.org/10.3390/rs16193639
Liu C, Calders K, Origo N, Terryn L, Adams J, Gastellu-Etchegorry J-P, Wang Y, Meunier F, Armston J, Disney M, et al. Bitemporal Radiative Transfer Modeling Using Bitemporal 3D-Explicit Forest Reconstruction from Terrestrial Laser Scanning. Remote Sensing. 2024; 16(19):3639. https://doi.org/10.3390/rs16193639
Chicago/Turabian StyleLiu, Chang, Kim Calders, Niall Origo, Louise Terryn, Jennifer Adams, Jean-Philippe Gastellu-Etchegorry, Yingjie Wang, Félicien Meunier, John Armston, Mathias Disney, and et al. 2024. "Bitemporal Radiative Transfer Modeling Using Bitemporal 3D-Explicit Forest Reconstruction from Terrestrial Laser Scanning" Remote Sensing 16, no. 19: 3639. https://doi.org/10.3390/rs16193639
APA StyleLiu, C., Calders, K., Origo, N., Terryn, L., Adams, J., Gastellu-Etchegorry, J. -P., Wang, Y., Meunier, F., Armston, J., Disney, M., Woodgate, W., Nightingale, J., & Verbeeck, H. (2024). Bitemporal Radiative Transfer Modeling Using Bitemporal 3D-Explicit Forest Reconstruction from Terrestrial Laser Scanning. Remote Sensing, 16(19), 3639. https://doi.org/10.3390/rs16193639