基于GF-1与Landsat-8的康保县叶面积指数遥感反演研究

Modeling LAI of Kangbao county using GF-1 and Landsat-8 image

以GF-1和Landsat8遥感影像为数据源,采用逐步回归、非线性Logistic回归和基于空间位置的地理加权回归3种方法,结合134个野外样地调查数据,在河北省康保县开展叶面积指数反演研究,并对结果进行精度检验。结果表明:(1)在荒漠化地区,GF-1和Landsat-8遥感影像提取的植被指数因子与LAI均有较高的相关性。运用主成分分析方法对植被指数因子进行处理,可以有效消除各影响因子间的共线性。(2)基于GF-1和Landsat-8影像分别建立的3种模型,均以地理加权回归决定系数最大,均方根误差最小,反演精度最高。(3)国产GF-1数据反演LAI效果优于Landsat-8,可以代替Landsat-8数据进行叶面积指数的估测。

Leaf area index（LAI）is an important indicator of forest structural parameter.In this study,a novel method that combined PCA with a linear stepwise regression,a logistic-model and GWR regression was developed to derive an integrated regression model of LAI.A total of 134 sample plots were systematically selected in the study area-Kangbao County,Hebei province and LAI data were collected.Landsat-8 and GF-1 image were acquired.The results were validated using the observations of sample plots and showed that：（1）In the desertification area,the vegetation index and LAI extracted by GF-1 and Landsat-8 had a high correlation.The PCA method can be used to eliminate the collinearity of the vegetation index factors.（2）The estimation accuracy of GWR regression was the highest for both GF-1 and Landsat-8 data with the greatest determination coefficient and smallest root mean square error（RMSE）.（3）Inversion of LAI by domestically produced GF-1 data in the study area is better than that of Landsat-8,and can be used as a substitute for Landsat-8 data for estimation of LAI.