Estimating leaf AREA index of qinghai spruce forest in qilian mountain using quickbird satellite data

Leaf area has important influence on the exchange of energy, water vapor and carbon dioxide between terrestrial ecosystems and the atmosphere. So, most distributed ecosystem process models that simulate carbon and hydrologic cycles require Leaf area index (LAI) as an input variable. However, there has been a lack of effective methods for its spatial estimation. Qinghai spruce (Picea crassifolia) is the dominant species in the forest ecosystem of Qilian Mountains and is critical to the eco-hydrological processes of the ecosystem. In this study, our objective is to retrieve the spatial distribution of canopy leaf area index of Qinghai spruce forests in the study area by QuickBird data. Generally, the canopy LAI was underestimated by instruments because of the clumping factor of conifer forests. In order to adjust the LAI measured and obtain the spatial distribution of LAI, the study was conducted as follows: First the clumping index of Qinghai spruce forest was measured by Tracing Radiation and Architecture of Canopies (TRAC). Then the adjusting coefficient was calculated by the clumping index, which was used to adjust LAI value measured by LAI-2000 canopy analyzer. Finally, the relationship between adjusted LAI and vegetation indexes retrieved by high resolution remote sensing data (QuickBird) was built to estimate the spatial distribution of canopy LAI of Qinghai spruce in the study. From the study, the conclusion can be drawn that LAI-2000 canopy analyzer underestimated the canopy LAI of the forest about 3.14-3.86 times. The statistical models between adjusted LAI and vegetation indexes (VIs) were built. Among them the optimal model, that is, relationship between NDVI and LAI, was selected through validating.