首页 | 本学科首页   官方微博 | 高级检索  
     

帽儿山地区森林冠层叶面积指数的地面观测与遥感反演
引用本文:Zhu GL,Ju WM,Jm C,Fan WY,Zhou YL,Li XF,Li MZ. 帽儿山地区森林冠层叶面积指数的地面观测与遥感反演[J]. 应用生态学报, 2010, 21(8): 2117-2124
作者姓名:Zhu GL  Ju WM  Jm C  Fan WY  Zhou YL  Li XF  Li MZ
基金项目:国家高技术研究发展计划项目
摘    要:叶面积指数(leaf area index,LAI)是陆地生态系统最重要的结构参数之一,遥感和基于冠层孔隙率模型的光学仪器观测是快速获取LAI的有效方法,但由于植被叶片的聚集效应,这些方法通常只能获取有效叶面积指数(effective LAI,LAIe).本文以东北林业大学帽儿山实验林场为研究区,利用LAI2000观测森林冠层LAIe,并结合TRAC观测的叶片聚集度系数估算了森林冠层LAI,并通过分析基于Landsat5-TM数据计算的不同植被指数与LAIe之间的关系,建立了该区森林LAI遥感估算模型.结果表明:研究区阔叶林的LAI和LAIe基本相当,而针叶林的LAI比LAIe大27%;减化比值植被指数(reduced simple ratio,RSR)与该区LAIe的相关性最好(R2=0.763,n=23),最适合该区LAI的遥感提取.当海拔<400 m时,LAI随海拔高度的上升而快速增大;当海拔在400~750 m时,LAI随海拔高度的上升缓慢增大;当海拔>750 m时,LAI呈下降趋势.研究区森林冠层LAI与森林地上生物量存在显著的正相关关系.

关 键 词:叶面积指数  叶片聚集度系数  LAI2000  TRAC

Forest canopy leaf area index in Maoershan Mountain: ground measurement and remote sensing retrieval
Zhu Gao-Long,Ju Wei-Min,Jm Chen,Fan Wen-Yi,Zhou Yan-Lian,Li Xian-Feng,Li Ming-Ze. Forest canopy leaf area index in Maoershan Mountain: ground measurement and remote sensing retrieval[J]. The journal of applied ecology, 2010, 21(8): 2117-2124
Authors:Zhu Gao-Long  Ju Wei-Min  Jm Chen  Fan Wen-Yi  Zhou Yan-Lian  Li Xian-Feng  Li Ming-Ze
Affiliation:International Institute for Earth System Science, Nanjing University, Nanjing 210093, China. Zhugaolong@163.com
Abstract: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.
Keywords:
本文献已被 万方数据 PubMed 等数据库收录!
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号