首页> 外文OA文献 >Correction: Anderson, H.B. et al. Using Ordinary Digital Cameras in Place of Near-Infrared Sensors to Derive Vegetation Indices for Phenology Studies of High Arctic Vegetation. Remote Sens. 2016, 8, 847
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Correction: Anderson, H.B. et al. Using Ordinary Digital Cameras in Place of Near-Infrared Sensors to Derive Vegetation Indices for Phenology Studies of High Arctic Vegetation. Remote Sens. 2016, 8, 847

机译:更正:安德森,H.B。等。使用普通 数码相机取代近红外传感器 获取植被指数的物候学研究 高北极植被。遥感。 2016,8,847

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摘要

After the publication of the research paper by Anderson et al. [1], a reanalysis of the data showedthat mistakes had been introduced in the calculation of the greenness indices and the filtering foroutliers prior to the statistical analysis. The calculation of the 2G_RBi and Channel G% indices werethe most affected, while the filtering of the data for outliers had inadvertently removed too many datapoints which caused poor correlations. Unfortunately, these mistakes affect the conclusions of thepaper. The original paper concluded that GRVI had a good correlation with NDVI in all vegetationtypes, and that 2G_RBi and Channel G% did not. After the reanalysis of the data, however, it becameclear that all three vegetation indices show strong correlations with NDVI. In this correction, we presentthe corrected text and updated versions of Tables 1 and 2 and Figure 2.
机译:在安德森等人发表研究论文后。 [1],对数据的重新分析表明,在统计分析之前,在计算绿色指数和过滤异常值时引入了错误。 2G_RBi和通道G%指数的计算受到的影响最大,而离群值数据的过滤无意间删除了太多数据点,从而导致相关性较差。不幸的是,这些错误影响了论文的结论。原始论文得出结论,在所有植被类型中,GRVI与NDVI都具有良好的相关性,而2G_RBi和通道G%则没有。然而,在对数据进行重新分析之后,很明显所有三个植被指数都显示出与NDVI的强相关性。在此更正中,我们介绍了表1和2以及图2的更正文本和更新版本。

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