Determination and monitoring of vegetation stress using hyperspectral remote sensing

Sani, Yahaya (2013) Determination and monitoring of vegetation stress using hyperspectral remote sensing. PhD thesis, University of Nottingham.

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Abstract

Stress causes crops to grow below their potential and this affects the vitality and physiological functioning of the plants at all levels leading to reduction in yield. Remote sensing of vegetation is regarded as a valuable tool for the detection and discrimination of stress, especially over large or sensitive regions.

The main aim of the research carried out is to assess the potential of remote sensing to detect CO2 leakage from CCS repositories. Further to this, the capability of remote sensing to discriminate between stresses with similar mode of action is explored. Two stress factors were selected for study: (1) elevated concentrations of soil CO2 in the plant root zone and; (2) herbicide, applied at sub-lethal levels. To understand the effects of soil CO2 and herbicide stress on vegetation reflectance, field experiments were carried out on maize (2009) and barley (2010) to investigate the effects of elevated soil CO2 concentrations and of different levels of herbicide treatments on vegetation growth and canopy reflectance using hyperspectral remote sensing techniques.

The findings from this study shows that the average canopy reflectance response of maize and barley to CO2 and herbicide stress were increased reflectance in the visible and decrease in near infra-red region as well as changes in the position and shape of the red-edge. The red-edge first-derivative for barley treated with CO2 were composed of maximum peaks between 716 and 730nm and smaller peaks at 699 and 759nm, the control had peaks at 727 and 730 nm, with similar smaller peaks. Barley treated with herbicide had early peaks (a day after treatment) at 697, 715 and 717nm with a shoulder at 759nm, as the experiment progressed (16 days after treatment) the stress became apparent and the peak remained stationary at 730nm, the magnitude decreased to 712nm at late treatment period (35 days after treatment). The control had single peak at 726nm.

CO2 treated maize had double peaks at 718 and 730nm, with secondary peaks at 707 and 794nm. Maize treated with herbicide had maximum peaks at 716 and 723nm, with the shoulder at 759 nm; the peaks were similar with the control plots but decreased in magnitude. The main differences between the treatments were in the shape and positions of the peaks that identify the red-edge. The canopy reflectances of the plants were further analysed using the blue (400-550nm) and red (550-750nm). In these regions the main feature of concern is chlorophyll content. The analysis showed that the band depths of controls plants were deeper compared to the stressed plants which is dependent on the stress and crop type.

Other vegetation indices used in this study were the Chlorophyll Normalized Difference Index (Chl NDI), the Pigment Specific Simple Ratio for chlorophyll a and b (PSSRa and PSSRb) and the Physiological Reflectance Index (PRI). The results show that they were promising indicators of early stress detection, some indices performed better than others depending on the stress type, species and duration of stress. Chl NDI was sensitive to high soil CO2 concentration in maize and barley, sub-lethal herbicide treatment at 10% - 40% level in barley and was insensitive to both low CO2 in the barley and maize as well as 10% herbicide treatment in maize. PSSRa was a good indicator of early CO2 stress in maize and high CO2 in barley as well as 10- 40% herbicide treatments. PSSRb could detect high CO2 level in maize and barley and all levels (5-40%) of herbicide treatments. PRI was insensitive to 5% herbicide treatment in barley but sensitive to high CO2 in maize at early stage of the experiment.

This study has demonstrated that remote sensing approach could be deployed for discriminating between different stressors using their red-edge first-derivative peaks, band depths and vegetation indices.

Item Type: Thesis (University of Nottingham only) (PhD)
Supervisors: Foody, G.M.
Steven, M.D.
Subjects: Q Science > QK Botany > QK Botany (General), including geographical distribution
G Geography. Anthropology. Recreation > G Geography (General)
Faculties/Schools: UK Campuses > Faculty of Social Sciences, Law and Education > School of Geography
Item ID: 13740
Depositing User: EP, Services
Date Deposited: 26 Feb 2014 12:07
Last Modified: 13 Sep 2016 23:18
URI: http://eprints.nottingham.ac.uk/id/eprint/13740

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