基于EO—1Hyperion高光谱影像的福建森林叶面积指数反演  

Forest Leaf Area Index Retrieval in Fujian Province Based on EO—1 Hyperion Hyper-spectral Imagery

作　　者：张兆明[1] 何国金[1] 江洪[2] 

机构地区：[1]中国科学院对地观测与数字地球科学中心,北京100094 [2]福州大学福建省空间信息工程研究中心,福州350002

出　　处：《科学技术与工程》2013年第31期9159-9162,9176,共5页Science Technology and Engineering

基　　金：国家自然科学基金(20972142);CEODE主任基金资助

摘　　要：森林生态系统碳循环是目前全球变化研究中的一个热点问题,叶面积指数(leaf area index,LAI)是森林生态系统碳循环模型中的一个重要的输入参数。准确地获取LAI的空间分布对提高碳循环模型的模拟精度具有重要意义。高光谱影像反演LAI比多光谱影像具有明显的优势。以福建永安重点林区为研究区,以EO—1 Hyperion高光谱影像为数据源开展森林LAI反演模型研究,在对不同类型植被指数以及不同近红外/红波段组合构建的植被指数与实测LAI相关性做综合分析比较的基础上,最终建立研究区高精度LAI反演模型。该研究对于提高福建乃至全国森林LAI反演精度和碳循环的模拟能力、增强国际竞争力具有重要的意义。Carbon cycling of forest ecosystem is an important issue in global change studies. Leaf area index （LAI） is a key parameter in carbon cycling models of forest ecosystem and acquiring leaf area index with higher temporal and spatial accuracy is important to improve the performance of carbon cycling models. Hyper-spectral sat- ellite imagery is superior to multi-spectral imagery in retrieving LAI. The study aims to develop high accuracy LAI retrieval models based on EO--1 Hyperion hyper-spectral remotely sensed satellite imagery in forested area of yon- gan county, Fujian province. Correlations between ground measured LAI and various hyper-spectral vegetation in- dex constructed with different NIR/red band combinations were comprehensively compared, then LAI retrieval mod- els with the highest accuracy were given. This study is of great value to improve the accuracy of LAI retrieval and carbon cycling models in the forested area of Fujian province, even in the whole China.

关 键 词：高光谱 叶面积指数 遥感 

分 类 号：P237[天文地球—摄影测量与遥感]