Literature DB >> 12511301

Chlorophyll content in eucalypt vegetation at the leaf and canopy scales as derived from high resolution spectral data.

Nicholas C Coops1, Christine Stone, Darius S Culvenor, Laurie A Chisholm, Ray N Merton.   

Abstract

The physiological status of forest canopy foliage is influenced by a range of factors that affect leaf pigment content and function. Recently, several indices have been developed from remotely sensed data that attempt to provide robust estimates of leaf chlorophyll content. These indices have been developed from either hand-held spectroradiometer spectra or high spectral resolution (or hyperspectral) imagery. We determined if two previously published indices (Datt 1999), which were specifically developed to predict chlorophyll content in eucalypt vegetation by remote sensing at the leaf scale, can be extrapolated accurately to the canopy. We derived the two indices from hand-held spectroradiometer data of eucalypt leaves exhibiting a range of insect damage symptoms. We also derived the indices from spectra obtained from high spectral and spatial resolution Compact Airborne Spectrographic Imager 2 (CASI-2) imagery to determine if reasonable estimates at a scale of < 1 m can be achieved. One of the indices (R 850/R 710 index, where R is reflectance) derived from hand-held spectroradiometer data showed a moderate correlation with relative leaf chlorophyll content (r = 0.59, P < 0.05) for all dominant eucalypt species in the study area. The R (850)/R (710) index derived from CASI-2 imagery yielded slightly lower correlations over the entire data set (r = 0.42, P < 0.05), but correlations for individual species were high (r = 0.77, P < 0.05). A scaling analysis indicated that the R (850)/R (710) index was strongly affected by soil and water cover types when pixels were mixed, but appeared to be invariant to changes in proportions of understory, which may limit its application.

Entities:  

Mesh:

Substances:

Year:  2003        PMID: 12511301     DOI: 10.1093/treephys/23.1.23

Source DB:  PubMed          Journal:  Tree Physiol        ISSN: 0829-318X            Impact factor:   4.196


  3 in total

1.  A leaf reflectance-based crop yield modeling in Northwest Ethiopia.

Authors:  Gizachew Ayalew Tiruneh; Derege Tsegaye Meshesha; Enyew Adgo; Atsushi Tsunekawa; Nigussie Haregeweyn; Ayele Almaw Fenta; José Miguel Reichert
Journal:  PLoS One       Date:  2022-06-16       Impact factor: 3.752

2.  Meta-Analysis of the Detection of Plant Pigment Concentrations Using Hyperspectral Remotely Sensed Data.

Authors:  Jingfeng Huang; Chen Wei; Yao Zhang; George Alan Blackburn; Xiuzhen Wang; Chuanwen Wei; Jing Wang
Journal:  PLoS One       Date:  2015-09-10       Impact factor: 3.240

3.  Remotely monitoring change in vegetation cover on the Montebello Islands, Western Australia, in response to introduced rodent eradication.

Authors:  Cheryl Lohr; Ricky Van Dongen; Bart Huntley; Lesley Gibson; Keith Morris
Journal:  PLoS One       Date:  2014-12-01       Impact factor: 3.240

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.