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Scientia Silvae Sinicae ›› 2013, Vol. 49 ›› Issue (8): 58-64.doi: 10.11707/j.1001-7488.20130809

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Seasonality and Spatial Pattern of Leaf Area Index of a Spruce-Fir Forest at the Valley in Xiaoxing’an Mountains

Liu Zhili1, Qi Yujiao2, Jin Guangze1   

  1. 1. Center for Ecological Research, Northeast Forestry University Harbin 150040;
    2. School of Forestry, Northeast Forestry University Harbin 150040
  • Received:2012-08-06 Revised:2013-03-25 Online:2013-08-25 Published:2013-08-17

Abstract:

Leaf area index (LAI) is one of the most frequently used parameters for analysis of canopy structure. We monitored the seasonality of effective leaf area index (LAIe) for a Spruce-fir forest at the valley in Xiaoxing'an Mountains by using Digital Hemispherical Photography (DHP) during deciduous season (from July to November). In order to estimate LAI more accurate for the stand, the optically-based effective leaf area index (LAIe) values of early November were adjusted to eliminate wood elements and clumping effects (including both beyond and within shoots). The adjusted value was regarded as true leaf area index (LAIt) of evergreen species. The seasonal dynamics of LAIt were obtained by incorporation of litterfall data for each observation, and we analyzed spatial pattern of LAIt during the maximum and minimum LAIt period. Results showed that the mean woody-to-total area ratio (α) was 0.10±0.06 and the mean clumping effect (ΩE) was 0.90±0.04 in early November for Spruce-fir forest at the valley, and the order of mean needle-to-shoot area ratio (γE) for evergreen coniferous species was Pinus koraiensis (1.77)>Picea spp. (1.28)>Abies nephrolepis (1.10). LAIt decreased with season from July to November, and LAIt was highest with a value of 3.97 in July, and lowest in November with a value of 2.71. In comparison with LAIt , LAIe was underestimated by 32.98% (from 28.81% to 43.24%); The spatial heterogeneity of the LAIt was greater with the higher LAIt, and the spatial heterogeneity of LAIt for July and November resulted mainly from the spatial autocorrelation that accounted for 99.8% and 66.9% of the total space heterogeneity, respectively.

Key words: Xiaoxing’an Mountains, spruce-fir forest at valley, effective leaf area index, leaf area index, calibration, litterfall method

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