基于影像纹理特征的川西南山地常绿阔叶林有效叶面积指数的空间分析  

Spatial analysis of LAIe of montane evergreen broad-leaved forest in southwest Sichuan,Northwest China,based on image texture

作　　者：赵安玖[1] 杨长青[2] 廖成云[2] 

机构地区：[1]四川农业大学水土保持与荒漠化防治省级重点实验室,四川雅安625014 [2]四川省林业调查规划院,成都610081

出　　处：《应用生态学报》2014年第11期3237-3246,共10页Chinese Journal of Applied Ecology

基　　金：国家重点科技攻关项目(2011BAC09B05)资助

摘　　要：遥感是获取叶面积指数(LAI)信息的最有吸引力的选择之一,但目前基于遥感数据的叶面积指数估测精度有限.本文以川西南山地常绿阔叶林为研究对象,基于地面调查的83个20 m×20 m样地和SPOT5数据,运用灰度共生矩阵法提取影像单波段、简单波段比图和主成分图的纹理信息,以不同图像处理方式的纹理参数作为辅助变量进行地统计分析估算有效LAI(LAIe).结果表明:LAIe与不同方式处理图像的纹理参数存在不同程度的相关性,其中,与B1波段、B1/B4和PC1的均质性呈极显著相关关系.与以归一化植被指数(NDVI)为辅助变量相比,以纹理参数B1波段、B1/B4和PC1的均质性作为辅助变量估测LAIe的精度均有所提高,分别提高5.3%、11.0%、14.5%,还能在一定程度上降低统计误差.以NDVI、PC1均质性作为辅助变量的LAIe空间地统计估测模型最优(R2=0.840,RMSE=0.212).本研究结果为合理地选择除植被指数外的其他辅助变量估测区域LAI的空间分布提供了一种新的思路和方法.Optical remote sensing is still one of the most attractive choices for obtaining leaf area in- dex （LAI） information, but currently may be derived from remotely sensed data with limited accuracy. Effective leaf area index （LAIe） of montane evergreen broad-leaved forest in southwest Sichuan was inventoried and assessed in 83 sample field plots of 20 m×20 m using different types of image processing techniques, including simple spectral band, simple spectral band ratios and principal component. Texture information was extracted by gray level co-occurrence matrices （GLCM） from different types of processing image. The results showed that there were correlations of different degrees between LAIe and texture parameters, and highly significant correlations were observed between LAIe with the homogeneity of the Bl band, B1/B4 band ratio or principal component PC1. Using texture information of remotely sensed data as auxiliary variables, we developed geostatistics models. Compared with the model based on NDVI auxiliary variable, the accuracy of LAIe were improved, presenting an increase by 5.3% with the homogeneity of the B1 band, 11.0% with the homogeneity B1/B4 band ratio, and 14.5% with the homogeneity principal component PC1, and the statistical errors were also reduced to some extent. The optimal LAIe model of spatial geostatistics was obtained when taking NDVI and homogeneity principal component PC1 as auxiliary variables （R2 =0. 840,RMSE=0. 212）. Our results provided a new way to estimate regional spatial distribution of LAI using other auxiliary variables besides the vegetation index.

关 键 词：有效叶面积指数 SPOT5 纹理 地统计 山地常绿阔叶林 

分 类 号：S718.55[农业科学—林学]