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Proceedings Paper

Canopy BRF simulation of forest with different crown shape and height in larger scale based on radiosity method
Author(s): Jinling Song; Yonghua Qu; Jindi Wang; Huawei Wan; Xiaoqing Liu
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Paper Abstract

Radiosity method is based on the computer simulation of 3D real structures of vegetations, such as leaves, branches and stems, which are composed by many facets. Using this method we can simulate the canopy reflectance and its bidirectional distribution of the vegetation canopy in visible and NIR regions. But with vegetations are more complex, more facets to compose them, so large memory and lots of time to calculate view factors are required, which are the choke points of using Radiosity method to calculate canopy BRF of lager scale vegetation scenes. We derived a new method to solve the problem, and the main idea is to abstract vegetation crown shapes and to simplify their structures, which can lessen the number of facets. The facets are given optical properties according to the reflectance, transmission and absorption of the real structure canopy. Based on the above work, we can simulate the canopy BRF of the mix scenes with different species vegetation in the large scale. In this study, taking broadleaf trees as an example, based on their structure characteristics, we abstracted their crowns as ellipsoid shells, and simulated the canopy BRF in visible and NIR regions of the large scale scene with different crown shape and different height ellipsoids. Form this study, we can conclude: LAI, LAD the probability gap, the sunlit and shaded surfaces are more important parameter to simulate the simplified vegetation canopy BRF. And the Radiosity method can apply us canopy BRF data in any conditions for our research.

Paper Details

Date Published: 26 July 2007
PDF: 10 pages
Proc. SPIE 6752, Geoinformatics 2007: Remotely Sensed Data and Information, 67521U (26 July 2007); doi: 10.1117/12.760709
Show Author Affiliations
Jinling Song, Beijing Normal Univ. (China)
Yonghua Qu, Beijing Normal Univ. (China)
Jindi Wang, Beijing Normal Univ. (China)
Huawei Wan, Beijing Normal Univ. (China)
Xiaoqing Liu, Beijing Normal Univ. (China)


Published in SPIE Proceedings Vol. 6752:
Geoinformatics 2007: Remotely Sensed Data and Information

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