Declaration 2- Publication and manuscripts
1.7 Thesis outline
To achieve the main objectives of this study, the thesis is organized as a collection of 6 research papers that have been submitted to peer reviewed international journals. Of these 6 papers, 3 papers have already been published and 2 papers are still in review and the remaining in preparation. Each paper has been written as a stand-alone article that can be read separately from the rest of the thesis but that draws separate conclusions that link to the overall research objectives and questions. As a result, a number of overlaps and replications occur in the sections
“Introduction” and “Method” in the different chapters. This problem is deemed to be of little significance when one considers the critical peer review process and the fact that the different chapters are papers that can be read separately without losing the overall context. The thesis consists of 8 chapters:
Chapter 2 contains a detailed literature review of the relevant application of multispectral and hyperspectral remote sensing in discriminating and estimating some of the biophysical and biochemical parameters of wetland vegetation. Specific relevance to the objectives of this study is highlighted in Section 2.6 (spectral discrimination of wetland species using hyperspectral data) and Section 2.7 (estimating biophysical and biochemical parameters of wetland species). The research gaps and challenges in the application of hyperspectral remote sensing in wetland species are introduced.
Chapter 3 contains an investigation into the ability of hyperspectral data to discriminate between papyrus vegetation and its co-existing species. The study determines if there is a significant difference in the mean of reflectance between the pairs of papyrus and each one of the co-existing species (binary class) at each measured wavelength from 350 nm to 2500nm. For the wavelengths that are significantly different (p < 0.001), it was tested whether some wavelengths have more discriminating power than others and which band combinations can yield the lowest misclassification rate.
Chapter 4 contains the findings of an investigation into the potential use of machine learning algorithms (RF) and resampled HYMAP data to accurately discriminate between papyrus and its co-existing species at canopy level. In this chapter, the work presented in Chapter 3 is extended from binary class classification to multi-class classification to assess the use of spectroscopic data in discriminating between papyrus and its co-existing species at canopy level under natural field conditions using RF algorithms and variables selection methods.
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Chapter 5 investigates the potential of several vegetation indices derived from hyperspectral data to better improve the discriminating accuracy between papyrus and other species using RF ensembles. Specifically, the study examined the ability of widely used indices (NDVI and SR) calculated from hyperspectral bands to identify the most important portions of the electromagnetic spectrum that could yield high accuracy in discriminating between papyrus and its co-existing species at canopy level. Some vegetation indices published in the literature were also investigated and new indices were proposed.
Chapter 6 is based on the observations and conclusions drawn from Chapter 3 to Chapter 5 to develop the best approach for discriminating between papyrus and its co-existing species using airborne hyperspectral imagery (AISA eagle).
Chapter 7 evaluates the utility of the widely used indices (NDVI and SR) derived from hyperspectral bands to identify the most sensitive regions of the electromagnetic spectrum that could be used to estimate papyrus biomass at high canopy density. The RF regression algorithm was implemented to test whether narrow band vegetation indices could predict papyrus biomass under field conditions.
Finally, a synthesis of the study is provided in Chapter 8. The findings are summarized and conclusions are derived from the preceding chapters. Some relevant recommendations for future research on the applications of remote sensing in wetland vegetation mapping are outlined. A special focus is directed towards the operational use of remote sensing techniques in mapping and monitoring of papyrus swamps.
A single reference list is provided at the end of the thesis.
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CHAPTER TWO
Literature review
This chapter is based on:
Adam, E., Mutanga, O. and Rugege, D. (2009). Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation. Wetland Ecology and Management, 18,281-296.
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Abstract
Wetland vegetation plays a key role in the ecological functions of wetland environments.
Remote sensing techniques offer timely, up-to-date, and relatively accurate information for sustainable and effective management of wetland vegetation. This article provides an overview on the status of remote sensing applications in discriminating and mapping wetland vegetation, and estimating some of the biochemical and biophysical parameters of wetland vegetation.
Research needs for successful applications of remote sensing in wetland vegetation mapping and the major challenges are also discussed. The review focuses on providing fundamental information relating to the spectral characteristics of wetland vegetation, discriminating wetland vegetation using broad and narrow bands, as well as estimating water content, biomass, and leaf area index. It can be concluded that the remote sensing of wetland vegetation has some particular challenges that require careful consideration in order to obtain successful results. These include an in-depth understanding of the factors affecting the interaction between electromagnetic radiation and wetland vegetation in a particular environment, selecting appropriate spatial and spectral resolution as well as suitable processing techniques for extracting spectral information of wetland vegetation
Keywords: Biomass. Leaf area index. Mapping. Remote sensing. Water content. Wetland vegetation.
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