多源多角度遥感数据反演森林叶面积指数方法  

Inversion of Forest Leaf Area Index Calculated from Multi-source and Multi-angle Remote Sensing Data

作　　者：杨贵军[1] 黄文江[1] 王纪华 邢著荣[1,2] 

机构地区：[1]国家农业信息化工程技术研究中心,北京100097 [2]山东科技大学摄影测量与遥感系,青岛266510

出　　处：《植物学报》2010年第5期566-578,共13页Chinese Bulletin of Botany

基　　金：国家自然科学基金(No.40901173);863计划(No.2007AA10Z201;2006AA10Z201);北京市自然科学基金(No.4102021);中国科学院遥感应用研究所遥感科学国家重点实验室开放基金(No.2009KFJJ020)

摘　　要：利用北京1号和Landsat多源数据组合成4个角度多波段数据集,在考虑森林三维垂直分布特点的基础上,结合INFORM几何光学与辐射传输混合模型,通过聚类+神经元网络方式,建立相应的多源多角度LAI反演模型。最后利用实地LAI测量数据和MODISLAI产品,对不同角度组合和噪声水平下的LAI反演结果进行验证。结果表明:在保证数据质量的条件下,通过增加角度可以提高森林的LAI反演精度,最终R2=0.713,RMSE=0.957,比单个角度的反演精度平均提高约20%。Beijing small satellite,named BJ-1,and Landsat TM data were used to construct multi-source and four-angle datasets for inversion of forest leaf area index （LAI）.Taking into account the vertical and 3-D distribution of forests,the hybrid model,INFORM,combining the geometric optical model and radiative transfer model,was used to support the retrieval model of LAI.The clustered method of ANN was utilized to obtain the information from forward INFORM-model simulated data under different groups of input parameters.After these steps,the inversion model was applied in different combinations of multi-angle under different levels of noise.The accuracy of inversion of forest LAI can be improved by adding observations of angle data if the quality of data is considered.Our data analysis resulted in an accuracy of R2=0.713,RMSE=0.957,which was 20% greater than the average accuracy of mono-angle data for inversion of LAI.

关 键 词：聚类+神经元网络 森林叶面积指数 混合模型 多源多角度 

分 类 号：S771.8[农业科学—森林工程]