O. Mutanga, ITC Enschede : Hyperspectral remote sensing of tropical grass quality and quantity

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7 Apr 2004 15:00
Unit: Wageningen University
Location: ITC, Hengelosestraat 99, Enschede
Promotor: prof.dr. A.K. Skidmore (Vegetation and Agricultural Land Use Survey)prof.dr. H.H.T. Prins (Resource ecology)
Co Promotor: dr. H. Huizing (ITC, Enschede)

 Resource distribution is a fundamental factor governing the movement and distribution of herbivores. Specifically, the quality (foliar concentration of nitrogen, phosphorous, calcium, magnesium, potassium and sodium) and quantity (biomass) of vegetation are important factors. In this regard, the development of techniques that can model the distribution of vegetation quality and quantity are critical for an improved understanding of wildlife distribution as well as facilitating an optimal management of wildlife resources. The advent of hyperspectral remote sensing has offered unprecedented opportunities to accomplish this task.
This study aimed to investigate the potential of hyperspectral remote sensing in estimating biomass of tropical grass and to predict and map the quality of tropical grasses. Our results showed that, at full canopy cover, tropical grass biomass is more accurately estimated by vegetation indices based on narrow wavelengths located in the red edge than the standard NDVI. Using continuum-removed absorption features calculated from field spectra, we could reliably predict the quality (N, K, P, Ca, Mg, Na) of in situ grass measured in the Kruger National Park, South Africa. A new integrated approach, involving the red edge position, continuum-removed absorption features as well as a neural network was applied to map foliar nitrogen concentration in the Kruger National Park, South Africa.
Overall, the study has shown the potential of hyperspectral remote sensing to predict the quality as well as the quantity of tropical grasses. The result is important for wildlife habitat modelling.
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