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Research Article | Open Access |

Monte Carlo Ray Tracing based Method for Investigation of Multiple Reflection among Trees

Author 1: Kohei Arai
International Journal of Advanced Computer Science and Applications (IJACSA) · Vol. 12, No. 7 · Published 2021

DOI: https://doi.org/10.14569/IJACSA.2021.0120720

Abstract

Monte Carlo Ray Tracing (MCRT) method for investigation of the multiple reflection among trees is proposed. For the forest research (Leaf Area Index: LAI, Normalized Difference Vegetation Index: NDVI, forest type, tree age, etc.) with spaceborne based optical sensor data, some errors due to influences of the multiple reflection among trees on the estimation of at sensor radiance have to be considered. The influence is difficult to formulate in a radiative transfer equation. The proposed method allows estimating the influence. Through experiment with miniature sized forest, it is found that the proposed method is validated. It is also found that a few to more than 10% of influence due to multiple reflections among trees are anticipated. Furthermore, the influence on the estimation of at sensor radiance is clarified. The potentialities of the code are then depicted over different types of forests including coniferous and broadleaf canopies.

Keywords

How to Cite this Article

Arai, K. (2021). Monte Carlo Ray Tracing based Method for Investigation of Multiple Reflection among Trees. International Journal of Advanced Computer Science and Applications, 12(7). https://doi.org/10.14569/IJACSA.2021.0120720

Arai, Kohei. "Monte Carlo Ray Tracing based Method for Investigation of Multiple Reflection among Trees." International Journal of Advanced Computer Science and Applications, vol. 12, no. 7, 2021, https://doi.org/10.14569/IJACSA.2021.0120720.

@article{Arai2021,
  title     = {Monte Carlo Ray Tracing based Method for Investigation of Multiple Reflection among Trees},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {12},
  number    = {7},
  year      = {2021},
  publisher = {The Science and Information Organization},
  author    = {Kohei Arai},
  doi       = {10.14569/IJACSA.2021.0120720},
  url       = {https://doi.org/10.14569/IJACSA.2021.0120720}
}

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