[Forest canopy leaf area index in Maoershan Mountain: ground measurement and remote sensing retrieval].
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.
International Institute for Earth System Science, Nanjing University, Nanjing 210093, China. Zhugaolong@163.com
Gao-Long
GL
Zhu
Wei-Min
WM
Ju
Chen
C
Jm
Wen-Yi
WY
Fan
Yan-Lian
YL
Zhou
Xian-Feng
XF
Li
Ming-Ze
MZ
Li
D018533:Biomass; D002681:China; D003247:Conservation of Natural Resources; D017753:Ecosystem; D004784:Environmental Monitoring / Q000379:methods; D008962:Models, Theoretical; D018515:Plant Leaves / Q000254:growth & development*; D058998:Remote Sensing Technology; D017215:Satellite Communications; D014197:Trees / Q000254:growth & development*