文献类型：期刊文献

中文题名：基于GF-1 WFV数据森林叶面积指数估算

英文题名：Estimation of forest leaf area index based on GF-1 WFV data

作者：李晓彤[1] 覃先林[1] 刘树超[1] 孙桂芬[1] 刘倩[1]

第一作者：李晓彤

机构：[1]中国林业科学研究院资源信息研究所国家林业局林业遥感与信息技术实验室

年份：2019

卷号：31

期号：3

起止页码：80-86

中文期刊名：国土资源遥感

外文期刊名：Remote Sensing for Land & Resources

收录：CSTPCD;;北大核心:【北大核心2017】；CSCD:【CSCD2019_2020】；

基金：中国林业科学研究院科研专项资金项目“机载光学全谱段数据处理及林火预警技术研究”(编号:CAFYBB2018SZ009);国家高分专项项目“高分森林灾害监测应用示范子系统(二期)”共同资助

语种：中文

中文关键词：GF-1WFV数据;SiB2模型;LAI;EVI线性模型

外文关键词：GF-1 WFV data;SiB2 model;LAI;EVI linear model

分类号：TP79

摘要：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R 2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,R 2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 ( R 2) of the LAI estimated by the EVI linear model is 0.582, and its root mean square error (RMSE) is 0.701. The R 2 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 R 2 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.