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Simulation of leaf inclination angle distribution of main tree species in Daxing’an Mountains of China based on the Campbell ellipsoid distribution function.

WANG Xu-peng, FAN Wen-yi, WEN Yi-bo   

  1. (College of Forestry, Northeast Forestry University, Harbin 150040, China)
  • Online:2013-11-18 Published:2013-11-18

Abstract: Leaf inclination angle distribution directly decides the amount of radiation interception by vegetation canopy, and also, decides the size and direction of the incident radiation, being the key parameter in quantitative remote sensing. This paper simulated the leaf inclination angle distribution of the main tree species in Daxing’an Mountains forest region based on the Campbell ellipsoid distribution model and iterative method, and quantitatively analyzed the fitting results of canopy with and without leaf stratification as well as the effects of tree age group on the leaf inclination angle distribution. For the test 6 main tree species, the leaf inclination angle distribution was in planophile shape, and the mean leaf inclination angle was smaller for coniferous tree than for broadleaved tree. Whether with or without stratify, the fitting result and the measured result were basically identical. For Betula platyphylla and Larix gmelinii, the correlation coefficient between the simulated and measured values was 0.8268 and 0.8192, and the root mean square error was 3.7% and 4.3% respectively, indicating that the Campbell model was reliable applied for forest canopy. Considering the effects of tree age group, though the leaf inclination angle distribution trend with leaf stratification had no correlation with age group, the mean leaf inclination angle of young L. gmelinii was relatively smaller than that mature one, suggesting that age group had positive effects on the numerical design of leaf inclination angle distribution and negative effects on the numerical design of extinction coefficient.

Key words: leaf inclination angle distribution, ellipsoid distribution, Daxing’an Mountains, quantitative remote sensing.