Photosynthetic Efficiency and Vegetation Stress

Authored by: Elizabeth M. Middleton , K. Fred Huemmrich , Qingyuan Zhang , Petya K.E. Campbell , David R. Landis

Biophysical and Biochemical Characterization and Plant Species Studies

Print publication date:  December  2018
Online publication date:  December  2018

Print ISBN: 9781138364714
eBook ISBN: 9780429431180
Adobe ISBN:

10.1201/9780429431180-5

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Abstract

Detecting plant stress is a major goal of ecosystem studies, and remote sensing approaches pro-vide the only practical means for repeated and synoptic observations. Plant stress is induced by unfavorable environmental or physiological factors, causing plants to perform below expected or optimal levels in growth, maintenance, and reproduction. Hyperspectral data can be used to de-tect plant stress through “spectral bioindicator” indices that track pigment levels and functional performance. These indices can be obtained from narrow-band proximal remote sensing observa-tions of canopies, as well as from aircraft and satellite sensors. Here, we provide experimental data about two bioindicators of plant stress: (i) chlorophyll fluorescence; and (ii) non-photochemical quenching estimated with the photochemcial reflectance index (PRI). Previously, pigment levels and short-term responses to environmental conditions have been difficult to assess with broad-band data, including satellite imagery (such as Landsat data) which only enable us to generate simple vegetation indices. However, current and upcoming narrow-band sensors and spectrometers will give us potent tools, including new statistical and data mining approaches to capitalize on higher dimensionality, improving detection of plant stress, chlorophyll and other plant pigment contents, as well as other measures of ecosystem function, health, composition, and biodiversity. This new generation of instruments, especially those from low Earth orbits, will allow us to more fully monitor terrestrial ecosystems and directly observe and measure photo-synthetic functions without requiring extensive ancillary data. This will give us a global capability for detecting and monitoring the dynamics of our Earth’s ecosystems in the face of global climate change.

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