不同方法提取的快鸟影像信息估算刺槐林有效叶面积指数的精度比较  

A Comparison of Different Quickbird Image Information for Estimating the Effective Leaf Area Index of Robinia pseudoacacia Plantations

作　　者：周靖靖[1,2] 赵忠[1] 刘金良[1] 赵君[1] 赵青侠 刘俊[3] 

机构地区：[1]西北农林科技大学林学院西部环境与生态教育部重点实验室,杨凌712100 [2]华中农业大学园艺林学学院,武汉430070 [3]国家林业局华东林业调查规划设计院,杭州310019

出　　处：《林业科学》2015年第9期24-34,共11页Scientia Silvae Sinicae

基　　金：国家"十二五"农村领域国家科技支撑计划项目(2012BAD22B0302);森林培育学教学团队项目(Z105021003);中央高校科研业务费专项资金资助项目(2662015QC048)

摘　　要：【目的】合理利用高分辨率影像空间信息可提高森林参数的估算精度,本研究在前人基础上进一步细化,探索高分辨率影像的光谱与空间信息在提高森林有效叶面积指数(LAIe)估算精度上的组合规律,以期为高分辨率影像对森林参数的估算和森林健康评价研究提供参考和基础数据。【方法】以黄土高原渭北地区刺槐人工林为研究对象,野外测定76块刺槐人工林样地的LAIe,并分别提取高分辨率快鸟影像全色数据的7种纹理指数(角二阶矩阵ASM、同质性HOM、相关性COR、对比度CON、非相似度DIS、变化量VAR、熵ENT)和多光谱数据的7种光谱信息(近红外波段b4、土壤调节植被指数SAVI、修正的土壤植被指数MSAVI、非线性植被指数NLI、改进型土壤大气修正植被指数EVI、差值植被指数DVI、归一化植被指数NDVI),通过栅格运算得到光谱-纹理组合参数,利用一元线性回归模型、二次多项式模型、乘幂模型和指数模型分别建立光谱-纹理组合参数、纹理参数与刺槐人工林LAIe的关系方程,计算比较光谱-纹理与纹理参数对刺槐人工林LAIe的估算精度和均方根误差(RMSE),揭示Quickbird影像光谱信息与纹理信息在提高森林LAIe估算精度上的组合规律。【结果】ASM,HOM,COR与任意植被指数结合后,估算精度均比相应纹理指数高;CON,DIS和VAR与部分植被指数结合后,估算精度比相应纹理指数高;相反,ENT与任意植被指数结合,光谱-纹理组合参数的估算精度均小于纹理指数。二次多项式模型和指数模型对LAIe估算的决定系数略高于一元线性回归模型和乘幂模型。【结论】利用高分辨率影像的纹理信息和光谱信息估算刺槐人工林LAIe时,将空间信息加入光谱信息,可有效估算森林LAIe且能够得到较高的森林LAIe估算精度;但并非任意纹理指数与植被指数结合对森林LAIe的估算精度均高于纹理指数,且估算模型对精度有�[Objective]The spatial information of high resolution remote sensing image can improve the estimation accuracy of forestry parameters. This study precisely explored the combinational rule of spectral and spatial information with high resolution remote sensing in order to improve the effective leaf area index （ LAIe） based on the existing research. Obtained results can be provide evidence and data for estimation of forestry parameters and assessments of forestry health.[Method]The black locust （ Robinia pseudoacacia） plantations located in Weibei area of Loess Plateau were chosen as research objects. The LAIe values of 76 plots were measured. We also extracted seven textural parameters of panchromatic data including ASM,HOM,COR,CON,DIS,VAR,ENT and seven spectral parameters of multi-spectral image＆amp;nbsp;including b4,SAVI,MSAVI,NLI,EVI,DVI,NDVI from Quickbird imagey with high resolution. The combined spectral-textural indices of Quickbird imagery were obtained using method of raster operation. Four different techniques, including simple linear regression model, quadratic regression model, power model and exponential model, were developed to describe the relationship between image parameters and field measurements of LAIe. The predicted accuracy of combined spectral-textural index and sole texture parameter was compared to reveal the role of combined spectral index and texture parameters used for LAIe retrieval. [Result]The LAIe estimation accuracy was improved when ASM,COR and HOM were combined with SVIs. To a certain extent,the accuracy of SVIs to estimate LAIe was improved with the combination of CON,DIS,VAR and SVIs. The combination of HOM,ASM and COR with SVIs gained the higher r2 than those achieved using HOM,ASM or COR alone. The performances of CON,DIS and VAR were improved when combining with partly SVIs. The combination of Entropy data with SVIs invariably yielded adjusted r2 values that were lower than those achieved using ENT alone. Quadratic regression model and exponential model exhibited h

关 键 词：有效叶面积指数 光谱-纹理 纹理 高分辨率影像 刺槐林 

分 类 号：S771.8[农业科学—森林工程]