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Monitoring vegetation dynamics using the universal normalized vegetation index (UNVI): An optimized vegetation index-VIUPD

机译:使用通用归一化植被指数(UNVI)监测植被动态:一种优化的植被指数VIUPD

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摘要

This paper propose a universal normalized vegetation index (UNVI), which is an improved vegetation index (VI) based on the universal pattern decomposition method (UPDM), termed VIUPD. We also derive new matrices to facilitate convenient calculation of the UNVI based on data from the MODIS and Landsat-TM, ETM, OLI satellite sensors. We compared the performance of the UNVI to the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) and modified soil adjusted vegetation index 2 (MSAVI2) to estimate the vegetation dynamics (chlorophyll content and leaf area index [LAI]). The results show that the UNVI was more sensitive to vegetation dynamics than the NDVI, EVI and MSAVI2. The UNVI has a higher LAI saturation point than the other three indices. The UNVI can be used to monitor global changes in above ground biomass (AGB) and gross primary production (GPP) with respect to a wide range of vegetation dynamics.
机译:本文提出了一种通用归一化植被指数(UNVI),它是一种基于通用模式分解方法(UPDM)的改进植被指数(VI),称为VIUPD。我们还根据来自MODIS和Landsat-TM,ETM,OLI卫星传感器的数据推导了新的矩阵,以方便方便地计算UNVI。我们将UNVI的性能与归一化差异植被指数(NDVI),增强植被指数(EVI)和改良土壤改良植被指数2(MSAVI2)进行了比较,以估算植被动态(叶绿素含量和叶面积指数[LAI])。结果表明,UNVI比NDVI,EVI和MSAVI2对植被动态更敏感。 UNVI具有比其他三个指数更高的LAI饱和点。相对于广泛的植被动态,UNVI可用于监测地上生物量(AGB)和初级生产总值(GPP)的全球变化。

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  • 来源
    《Remote sensing letters》 |2019年第9期|629-638|共10页
  • 作者单位

    Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Corps, Shihezi University, Shihezi, China,State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China;

    State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China,University of Chinese Academy of Sciences, Beijing, China;

    State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China;

    State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China;

    Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Corps, Shihezi University, Shihezi, China;

    State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China;

    Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Corps, Shihezi University, Shihezi, China;

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