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叶面积指数(Leaf Area Index,LAI)是表征植被冠层结构的核心参数。在地面对LAI的间接测量是遥感反演算法验证和改进的重要手段,而目前基于Beer-Lambert定律的森林LAI地面间接测量方法存在着严重的低估问题。本文通过理论分析,指出Beer-Lambert定律在应用到森林叶面积指数测量时,LAI低估的根本原因来源于叶面积体密度、消光路径及叶倾角投影G函数在空间上的不均匀性,并定量评估了冠层非随机分布对LAI测量结果的影响,发现植被冠层的非随机分布会对LAI的测量带来20%～40%的误差。这一结论,对于Beer-Lambert定律的简单修正应用于森林LAI间接测量时仍存在着较大的局限性,尚未能根本上解决LAI的低估问题,故间接测量LAI的理论和方法需进一步深入研究。
Leaf Area Index (LAI) is the core parameter of vegetation canopy structure. The indirect measurement of LAI on the ground is an important means of verification and improvement of remote sensing inversion algorithm. However, there is a serious underestimation problem in the indirect LAI ground measurement method based on the Beer-Lambert law. The theoretical analysis shows that the root cause of LAI under Beer-Lambert law is the spatial heterogeneity of leaf area density, extinction path and leaf tilt projection G function, The effect of non-random distribution of canopy on LAI measurements was evaluated and it was found that non-random distribution of vegetation canopy caused 20% -40% error in LAI measurements. This conclusion shows that the simple modification of Beer-Lambert’s law still has some limitations when it is applied to the indirect measurement of forest LAI, so the theory and method of indirectly measuring LAI need to be further deepened the study.