农业工程学报Issue(13)：124-129,6.DOI:10.3969/j.issn.1002-6819.2013.13.017
基于混合像元分解模型的森林叶面积指数反演
Reversion of leaf area index in forest based on linear mixture model
摘要
Abstract
Leaf area index (LAI) is not only an important parameter of biomass estimation, but also one of the most important structural parameters for the quantitative analysis of the land ecological system’s energy exchange. This paper was designed to find a method to estimate LAI, which was accurate, rapid, large scale, and not damaging. In the remote sensing estimation of leaf area index (LAI), the most commonly used methods were based on the statistics. However, it has significant limitations and had difficulty dealing with the problem of“the same thing with different spectrum, and the same spectrum but different thing”for those models. Based on the physical structure of the ground component, this study developed the linear mixture model for forest LAI estimation. It can not only deal with the difficulty of spectral discrimination, but also was simple, feasible, and general. The minimum noise fraction (MNF) method, which can eliminate the correlation between the bands of TM images and increase the quality of endmembers, was employed to convert the TM image into its principal components. After that, endmenbers were obtained from the image itself and the endmembers were regarded as the extremes in the triangles of an image scattergram. An unconstrained least-squares solution was used to un-mix the spectral image into fractions, and the vegetation cover percent was obtained from it. Then, according to the relationship between vegetation cover percent and the LAI, we were able to extract LAI from the remote sensing imagery successfully. Moreover, the canopy model of multiple scattering was applied to estimate the accurate LAI. Finally, four endmembers (green vegetation, soil, water, and non-photosynthetic vegetation) were selected, and an unconstrained least-squares solution was used to un-mix the spectral image into fractions. The average error was 0.0028, and the quality of fraction images was better. The results shows that the method that combined the linear mixture model with the canopy model could estimate the forest LAI accurately. In the study area, there was a strong correlation between the observed value and the predicted value. The coincidence degree of the model was 82.19%, and the RMSE was 0.368.关键词
遥感/森林/估算/叶面积指数/混和像元分解模型/多次散射Key words
remote sensing/forestry/estimation/leaf area index (LAI)/spectral mixture analysis/multiple scattering分类
信息技术与安全科学引用本文复制引用
陈丽,张晓丽※,焦志敏..基于混合像元分解模型的森林叶面积指数反演[J].农业工程学报,2013,(13):124-129,6.基金项目
国家“863”计划课题“数字化森林资源监测关键技术研究” （）
