Zhu Gao-Long, Ju Wei-Min, Jm Chen, Fan Wen-Yi, Zhou Yan-Lian, Li Xian-Feng, Li Ming-Ze
International Institute for Earth System Science, Nanjing University, Nanjing 210093, China.
Ying Yong Sheng Tai Xue Bao. 2010 Aug;21(8):2117-24.
Leaf area index (LAI) is one of the most important structural parameters of terrestrial ecosystem, while the remote sensing retrieval and the ground optical instrument measurement and based on canopy gap model are the effective approaches to rapidly obtain LAI. However, these two approaches can only acquire effective LAI (LAI(e)), due to the clumping of vegetation canopy. Taking the experimental forest farm of Northeast Forestry University at Maoershan Mountain in Heilongjiang Province of Northeast China as study site, this paper measured the forest canopy LAI(e) by LAI2000, and estimated the LAI by the combination of TRAC (tracing radiation and architecture of canopies) measurement of foliage clumping index. A LAI remote sensing retrieval model was constructed through the analysis of the relationships between different vegetation indices calculated from Landsat5-TM and measured LAI(e). The results showed that at the study site, the LAI of broad leaved forests was close to the LAI(e), but the LAI of needle leaved forests was 27% larger than the LAI(e). Reduced simple ratio index (RSR) had the highest relationship with measured LAI(e) (R2 = 0.763, n = 23), which could be used as the best predictor of LAI. The LAI at study site increased rapidly with increasing elevation when the elevation was below 400 m, but had a slow increase when the elevation was from 400 m to 750 m. When the elevation was above 750 m, the LAI decreased. There was a significant correlation between the forest canopy LAI and aboveground biomass.
叶面积指数（LAI）是陆地生态系统最重要的结构参数之一，而基于冠层间隙模型的遥感反演和地面光学仪器测量是快速获取叶面积指数的有效方法。然而，由于植被冠层的聚集性，这两种方法只能获取有效叶面积指数（LAI(e)）。以中国东北黑龙江省帽儿山的东北林业大学实验林场为研究地点，本文利用LAI2000测量了森林冠层的LAI(e)，并结合TRAC（追踪辐射和冠层结构）测量的叶聚集指数估算了叶面积指数。通过分析Landsat5-TM计算的不同植被指数与实测LAI(e)之间的关系，构建了叶面积指数遥感反演模型。结果表明，在研究地点，阔叶林的叶面积指数接近LAI(e)，但针叶林的叶面积指数比LAI(e)大27%。归一化简化比值指数（RSR）与实测LAI(e)的相关性最高（R2 = 0.763，n = 23），可作为叶面积指数的最佳预测指标。当海拔低于400 m时，研究地点的叶面积指数随海拔升高迅速增加，但当海拔从400 m升高到750 m时增加缓慢。当海拔高于750 m时，叶面积指数下降。森林冠层叶面积指数与地上生物量之间存在显著相关性。
