Estimation of forest leaf area index based on GF-1 WFV data
Xiaotong LI, Xianlin QIN(), Shuchao LIU, Guifen SUN, Qian LIU
Key Laboratory of Forestry Remote Sensing and Information Techniques, State Forestry Administration, Research Institute of Forest Resources Information Technique, Chinese Academy of Forestry, Beijing 100091, China
以国产高分一号(GF-1)宽幅数据(wide field of view,WFV)为数据源,采用简单生物圈模型2(simple biosphere model2,SiB2)对黑龙江省漠河县森林植被叶面积指数(leaf area index,LAI)进行估算,并与增强植被指数(enhanced vegetation index,EVI)线性模型的估算结果进行对比,结合地面实测LAI数据分别对这2种模型估算结果进行精度评价。结果表明,采用EVI线性模型估算LAI,决定系数R2为0.582,均方根误差(root mean square error,RMSE)为0.701; 而采用SiB2模型估算LAI,R2为0.798,RMSE为0.358,均比EVI线性模型有所改善。该研究发现,结合中高空间分辨率的GF-1 WFV数据,SiB2模型更适宜于该研究区森林植被的LAI反演。
In this study, domestic GF-1 WFV data were used as the data source, SiB2 model was used to estimate the LAI of forest vegetation in Mohe County of Heilongjiang Province and the value was compared with the estimation result of the enhanced vegetation index (EVI) linear model. Estimation results of the two models were combined with the synchronous ground LAI data for accuracy evaluation. The results show that the coefficient of determination (R2) of the LAI estimated by the EVI linear model is 0.582, and its root mean square error (RMSE) is 0.701. The R2 of the LAI estimated by the SiB2 model is 0.798, and its RMSE is 0.358. Compared with the performance of the EVI linear model, the results estimated by the SiB2 model are improved on both R2 and RMSE. The results show that the SiB2 model is more suitable for LAI inversion of forest vegetation in the study area, in combination with the high spatial resolution GF-1 WFV data.
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2019, Vol. 31 
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