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University of Twente (2012)

Savanna grass quality : remote sensing estimation from local to regional scale.

Ramoelo, A.,

Titre : Savanna grass quality : remote sensing estimation from local to regional scale.

Auteur : Ramoelo, A., (Abel)

Etablissement de soutenance : University of Twente

Grade : Doctor University of Twente 2012

Résumé partiel
Information about the distribution of grass foliar nitrogen (N) and phosphorus (P) is important to understand rangeland vitality and to facilitate effective management of wildlife and livestock. Grass N and P concentrations are direct indicators of rangeland quality, and they vary over space. Foliar N concentration is known to relate to the protein content. Protein is major nutrient requirement for the herbivores. On the other hand, foliar P concentration is a crucial requirement for reproduction and lactating animals. Successful estimation of foliar N and P concentrations could facilitate the computation of the key indicator of nutrient limitation, known as N : P ratio. Understanding the nutrient limitation could equally help the ecologists, farmers and resource manager to understand the feeding patterns, distribution and densities of herbivores both in protected and communal areas. Landscape view of the nutrient distribution and limitation as an interest to planners and managers could be achieved through remote sensing measurement and analysis. This study was undertaken in the north-eastern part of South Africa, in the savanna ecosystem. The study area was purposively selected as it covers the rangelands in the communal areas, Sabi Sands private game reserve and Kruger National Park (KNP). This study area offers experimental sites for foliar biochemical estimation, because of a pronounced contrast in soil fertility induced by various geological types, i.e. basalt and gabbro associated with high soil fertility and granite associated with low soil fertility. The main aim of the study was to develop and improve estimation of grass quality using remote sensing measurements from local to regional scale. The objectives were (1) to test water removed spectra for foliar N and P estimation as compared to the existing spectral techniques, (2) To estimate foliar N : P ratio using field spectroscopy or in situ hyperspectral data, (3) to investigate the applicability of the non-linear partial least square regression in integrating in situ hyperspectral remote sensing and environmental variables to estimate foliar N and P concentrations, and (4) to investigate the utility of the red edge band in RapidEye data for estimating foliar and canopy N at regional scale. The success in remote sensing estimation of foliar biochemical is faced with various challenges which are yet to be addressed. The noted success is mainly based on the use of hyperspectral remote sensing. One of the challenges is the water absorption effects in short-wave infrared which mask weak or subtle foliar biochemical concentrations. At laboratory level using in situ hyperspectral, there were several attempts to address this challenge by drying leaf samples and measure the reflectance. The main problem associated with this approach was upscaling from laboratory (leaf) to canopy level. In Chapter 2, we proposed a technique that could be applied to minimize water absorption effects when estimating foliar biochemical using hyperspectral remote sensing data. This study was based on the greenhouse experiment. The aim of this study was to test the utility of water removed (WR) spectra in combination with partial least square regression (PLSR) and stepwise multiple linear regression (SMLR) to estimate foliar N and P concentrations, compared to spectral transformation techniques such as first derivative, continuum removal and log transformed spectra (Log(1/R)). The savanna grass species (Digitaria eriantha) was sown in the greenhouse. Spectral measurements were made using a spectrometer. D. eriantha was cut, dried and chemically analyzed for foliar N and P concentrations. WR spectra were determined by calculating the residual from the modelled leaf water spectra using the non-linear spectral matching technique and observed leaf spectra. It was concluded that the water removal technique could be a promising technique to minimize the perturbing effect of foliar water content when estimating grass nutrient concentrations. The performance of WR spectra was also evident in Chapter 3 on estimation of foliar N : P using in situ hyperspectral remote sensing data. The objective of Chapter 3 study was to investigate the utility of in situ hyperspectral remote sensing to estimate foliar N : P, in combination with PLSR. The results showed that foliar N : P can be highly estimated by water removed and continuum removed spectra. This was undertaken at field level using ASD FieldSpec 3®, which shows a potential of this technique across various remote sensing measurement levels. The crucial level at which this technique could be tested is at airborne hyperspectral level. Generally, Chapter 3 demonstrated that foliar N : P ratio could be estimated using remote sensing.


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