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基于近红外肩部波长的新型光谱植被指数用于草地植物量的遥感探测。

New spectral vegetation indices based on the near-infrared shoulder wavelengths for remote detection of grassland phytomass.

作者信息

Vescovo Loris, Wohlfahrt Georg, Balzarolo Manuela, Pilloni Sebastian, Sottocornola Matteo, Rodeghiero Mirco, Gianelle Damiano

机构信息

IASMA Research and Innovation Centre, Fondazione Edmund Mach, 38010 San Michele all'Adige, Trento, Italy.

Institut für Ökologie, Universität Innsbruck, 6020 Innsbruck, Austria.

出版信息

Int J Remote Sens. 2012 Apr 10;33(7). doi: 10.1080/01431161.2011.607195.

Abstract

This article examines the possibility of exploiting ground reflectance in the near-infrared (NIR) for monitoring grassland phytomass on a temporal basis. Three new spectral vegetation indices (infrared slope index, ISI; normalized infrared difference index, NIDI; and normalized difference structural index, NDSI), which are based on the reflectance values in the H25 (863-881 nm) and the H18 (745-751 nm) Chris Proba (mode 5) bands, are proposed. Ground measurements of hyperspectral reflectance and phytomass were made at six grassland sites in the Italian and Austrian mountains using a hand-held spectroradiometer. At full canopy cover, strong saturation was observed for many traditional vegetation indices (normalized difference vegetation index (NDVI), modified simple ratio (MSR), enhanced vegetation index (EVI), enhanced vegetation index 2 (EVI 2), renormalized difference vegetation index (RDVI), wide dynamic range vegetation index (WDRVI)). Conversely, ISI and NDSI were linearly related to grassland phytomass with negligible inter-annual variability. The relationships between both ISI and NDSI and phytomass were however site specific. The WinSail model indicated that this was mostly due to grassland species composition and background reflectance. Further studies are needed to confirm the usefulness of these indices (e.g. using multispectral specific sensors) for monitoring vegetation structural biophysical variables in other ecosystem types and to test these relationships with aircraft and satellite sensors data. For grassland ecosystems, we conclude that ISI and NDSI hold great promise for non-destructively monitoring the temporal variability of grassland phytomass.

摘要

本文探讨了利用近红外(NIR)波段的地面反射率对草地植物量进行时间序列监测的可能性。基于克里斯普洛巴(模式5)传感器H25(863 - 881纳米)和H18(745 - 751纳米)波段的反射率值,提出了三个新的光谱植被指数(红外斜率指数,ISI;归一化红外差异指数,NIDI;归一化差异结构指数,NDSI)。利用手持式光谱辐射计,在意大利和奥地利山区的六个草地站点进行了高光谱反射率和植物量的地面测量。在植被完全覆盖时,许多传统植被指数(归一化差异植被指数(NDVI)、修正简单比值(MSR)、增强植被指数(EVI)、增强植被指数2(EVI 2)、重新归一化差异植被指数(RDVI)、宽动态范围植被指数(WDRVI))出现了强烈的饱和现象。相反,ISI和NDSI与草地植物量呈线性相关,年际变异性可忽略不计。然而,ISI和NDSI与植物量之间的关系因站点而异。WinSail模型表明,这主要是由于草地物种组成和背景反射率所致。需要进一步研究以确认这些指数(例如使用多光谱特定传感器)在监测其他生态系统类型中植被结构生物物理变量方面的有用性,并利用飞机和卫星传感器数据检验这些关系。对于草地生态系统,我们得出结论,ISI和NDSI在无损监测草地植物量的时间变异性方面具有很大潜力。

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本文引用的文献

1
Asymptotic nature of grass canopy spectral reflectance.
Appl Opt. 1977 May 1;16(5):1151-6. doi: 10.1364/AO.16.001151.

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