Estimating Forest Leaf Area Index Using Hyperspectral Remote Sensing Data Of Hypeion

Forest Leaf Area Index (LAI) which is the efficient parameter of estimating forest biomass and evaluating forest plant diseases and insect pests is one of important forestry indication of the token of growing forest and the forest output forecast. Also, it is the means of making certain standard of forest quantitative remote sensing on the ground. At present, many overseas scholars have estimated Forest Leaf Area Index in big scale area using hyperspectral remote sensing data more than ten years and done a lot of labors. However, in our country , the researches in this aspect began later and mainly were estimating Forest Leaf Area Index with traditional broad bands remote sensing images. So, the research have still not any report using hyperspectral remote sensing data estimating Forest Leaf Area Index.This article took the CuLai mountain forestry centre located in the middle of ShanDong province as experiment area and select 28 forest samples to measure forest LAI on the ground using the LAI-2000 vegetation scanopy analysis instrument. Hyperspectral remote sensing data adopted Hyperion image. Firstly, for hyperspectral data with the specialty of many bands and higher spectrum resolution and two-by-four band width , the article selected bands from Hyperion reflection image by preprocessing and atmosphere correction. And we adopted standard error and correlation coefficient and so on Stat methods to evaluate and analyzed. So we can select optimal bands to combine the artificial color image, then do median filter with 3*3 template to wipe off noises for the artificial color image. Secondly, we constructed three regression model using abstracted doubted bands vegetable index and three bands vegetation indices (NDVI, SR and RSR) from the image and measured forest LAI on the ground to estimate Forest LAI. Thirdly, we can reckon the mean forest LAI for the whole experiment area. Consequently, we may picture the more reasonable forest LAI map. The experimental results indicate that among three vegetation indices, the correlation between RSR and LAI is biggest and theestimating precision is also highest, next is NDVI, SR is lowest. Moreover, RSR and NDVI are nonlinear correlativity with LAI whereas SR is linear correlativity with LAI. The experimental result also indicate that the three vegetation indices are positive correlativity.Although the study result is still primitive and on discussing stage, the research result of quantitative index make people realize profoundly that using hyperspectral remote sensing data estimate the predominance of forest LAI on the ground.