摘要
叶面倾角分布（ＬＡＤ）和叶面积指数（ＬＡＩ）是植被冠层结构的重要参量。由于植被结构的复杂和千变万化，这些参量的野外直接测量，是十分费力和费时的，特别是对针叶树种就更难进行。底视广角照片已用于ＬＡＩ的估值，但由于ＬＡＤ和叶丛、枝的丛集情况未知而导致了估值的误差。Ｊ．Ｃｈｅｎ等近期采用最小平方反演技术从鱼眼象片中同时获取ＬＡＩ和ＬＡＤ，他们注意到提取的ＬＡＤ的精度不高，误差产生的原因可能在于假定对不同的观察方向叶丛集指数为常量。从典型的鱼眼象片中可见，对森林群落，接近水平方向的光间隙是随机的，但接近天顶却明显呈丛集状，Ｎｉｌｓｏｎ（１９７１）曾指出这一丛集指数可能是与观察方向有关的，但近期的工作通常将其视作常量，也有一些例外，如Ｊ．Ｃｈｅｎ基于加拿大北部森林的叶丛结构，指出了叶丛集指数随观察方向的变化。我们将树冠间间隙的概念引入Ｃｈｅｎ的方法，并将叶丛集指数明确地表示作观察方向天顶角的函数，同样应用最小平方反演，我们提取了满意的ＬＡＩ和ＬＡＤ的估计值。我们已用两种针叶树的广角图象进行了验证实验。数字化的底视广角图象用树冠断层象数据采集系统（ＴＣＴＳ）摄取，１９９５年我们在园柏苗圃中摄取广角图象并同期手工测量的５?
The Leaf Angle Distribution (LAD) and Leaf Area Index (LAI) are important descriptors of the vegetation canopy. But direct field measurement of these variables is very labor-intensive and timeconsuming because of the complexity and variation in canopy architecture within and between species, especially for conifers. Upward-looking fisheye photos have been used to provide LAI estimates, but such estimates are subject to errors caused by unknown LAD and clumping of leaves and branches. In this paper, we present our new method on LAD and LAI estimation. The main approach is on how to introduce our intercrown gap probability model into LAI and LAD inversion algorithm, to express the effect of viewing direction on clumping index in normal gap probability model. Two validation experiments have been done. In one of them the estimated LAD (10-degree grouping) fits the directly measured LAD with a correlation coefficient 0.97. Another result is similar. Although the primary results are encouraging, more validation, especially under natural forest, are still needed to incorporate the method into our tree tomography procedure.
出处
《遥感学报》
EI
CSCD
1997年第S1期62-69,共8页
NATIONAL REMOTE SENSING BULLETIN
关键词
针叶树冠层
方向间隙率
叶倾角分布
反演
Coniferous canopy, Directional gap probability, LAD, Inversion
