欢迎访问《应用生态学报》官方网站,今天是 分享到:

应用生态学报 ›› 2010, Vol. 21 ›› Issue (08): 2117-2124.

• 研究报告 • 上一篇    下一篇

帽儿山地区森林冠层叶面积指数的地面观测与遥感反演

朱高龙1,2,居为民1**,Jing M. Chen3,范文义4,周艳莲5,李显风1,李明泽4   

  1. 1南京大学国际地球系统科学研究所,南京 210093;2闽江学院地理科学系,福州 350108;3Department of Geography, University of Toronto, Ontario M5S 3G3, Canada;4东北林业大学林学院;哈尔滨 150040;5南京大学地理与海洋科学学院;南京 210093
  • 出版日期:2010-08-18 发布日期:2010-08-18

Forest canopy leaf area index in Maoershan Mountain: Ground measurement and remote sensing retrieval.

ZHU Gao-long1,2, JU Wei-min1, Chen JM3, FAN Wen-yi4, ZHOU Yan-lian5, LI Xian-feng1, LI Ming-ze4   

  1. 1International Institute for Earth System Science, Nanjing University, Nanjing 210093, China|2Department of Geography, Minjiang University, Fuzhou 350108, China|3Department of Geography, University of Toronto, Ontario M5S 3G3, Canada|4College of Forestry, Northeast Forestry University, Harbin 150040, China|5School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210093, China
  • Online:2010-08-18 Published:2010-08-18

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LAILAIe基本相当,而针叶林的LAILAIe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, 大兴安岭, 火烧迹地, 恢复, 碳储量

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 modelare the effective approaches to rapidly obtain LAI. However, these two approaches can only acquire effective LAI(LAIe), 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 LAIe 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 LAIe. The results showed that at the study site, the LAIof broad leaved forests was close to the LAIe, but the LAIof needle leaved forests was 27% larger than the LAIe. Reduced simple ratio index (RSR) had the highest relationship with measured LAIe (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.

Key words: leaf area index, foliage clumping index, LAI2000, TRAC, Greater Xing’an Mountains, burned area, restoration, carbon storage.