Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 425722
Title Remote sensing of forage nutrients: Combining ecological and spectral absorption feature data
Author(s) Knox, N.; Skidmore, A.K.; Prins, H.H.T.; Heitkonig, I.M.A.; Slotow, R.; Waal, C. van der; Boer, W.F. de
Source ISPRS Journal of Photogrammetry and Remote Sensing 72 (2012). - ISSN 0924-2716 - p. 27 - 35.
DOI https://doi.org/10.1016/j.isprsjprs.2012.05.013
Department(s) Resource Ecology
PE&RC
Publication type Refereed Article in a scientific journal
Publication year 2012
Keyword(s) south-african savanna - multiple linear-regression - kruger-national-park - mineral-nutrition - leaf biochemistry - hyperspectral reflectance - nitrogen concentration - imaging spectroscopy - grass - quality
Abstract Forage quality in grassland-savanna ecosystems support high biomass of both wild ungulates and domestic livestock. Forage quality is however variable in both space and time. In this study findings from ecological and laboratory studies, focused on assessing forage quality, are combined to evaluate the feasibility of a remote sensing approach for predicting the spatial and temporal variations in forage quality. Spatially available ecological findings (ancillary data), and physically linked spectral data (absorption data) are evaluated in this study and combined to create models which predict forage quality (nitrogen, phosphorus and fibre concentrations) of grasses collected in the Kruger National Park, South Africa, and analysed in both dry and wet seasons. Models were developed using best subsets regression modelling. Ancillary data alone, could predict forage components, with a higher goodness of fit and predictive capability, than absorption data (Ancillary: R2 adj ¼ 0:42—0:74 compared with absorption: R2 adj ¼ 0:11—0:51, and lower RMSE values for each nutrient produced by the ancillary models). Plant species and soil classes were found to be ecological variables most frequently included in prediction models of ancillary data. Models in which both ancillary and absorption variables were included, had the highest predictive capabilities ( R2 adj ¼ 0:49—0:74 and lowest RMSE values) compared to models where data sources were derived from only one of the two groups. This research provides an important step in the process of creating biochemical models for mapping forage nutrients in savanna systems that can be generalised seasonally over large areas.
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