Non-linear partial least square regression increases the estimation accuracy of grass nitrogen and phosphorus using in situ hyperspectral and environmental data

Grass nitrogen (N) and phosphorus (P) concentrations are direct indicators of rangeland quality and provide imperative information for sound management of wildlife and livestock. It is challenging to estimate grass N and P concentrations using remote sensing in the savanna ecosystems. These areas ar...

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Vydáno v:	ISPRS journal of photogrammetry and remote sensing Ročník 82; s. 27 - 40
Hlavní autoři:	Ramoelo, A., Skidmore, A.K., Cho, M.A., Mathieu, R., Heitkönig, I.M.A., Dudeni-Tlhone, N., Schlerf, M., Prins, H.H.T.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Amsterdam Elsevier B.V 01.08.2013 Elsevier
Témata:	absorption absorption features Animal, plant and microbial ecology Applied geophysics band-depth analysis biochemical content Biological and medical sciences chlorophyll estimation Earth sciences Earth, ocean, space Ecosystem Ecosystems environmental factors Exact sciences and technology Fundamental and applied biological sciences. Psychology General aspects. Techniques Grasses grazing intensity humans imaging spectroscopy In situ hyperspectral remote sensing Internal geophysics kruger-national-park land use least squares Least squares method livestock Mathematical models mineral-nutrition multiple linear-regression national parks nitrogen Nitrogen concentrations Nonlinearity normalized difference vegetation index Partial least square regression phosphorus Phosphorus concentrations planning prediction Radial basis neural network rangelands Regression Remote sensing savannas soil soil nutrients south-africa Spectra starch Teledetection and vegetation maps vegetation indexes wildlife management Southern Africa Africa In situ hyperspectral remote sensing Nitrogen concentrations Partial least square regression accuracy human activity nutrients Ecosystem savanna humidity soils performances Anthropogenic factor phosphorus Radial basis neural network nitrogen Browsing remote sensing least-squares concentration Wild life indicators in situ management ecosystems Livestock Phosphorus concentrations
ISSN:	0924-2716, 1872-8235
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Shrnutí:	Grass nitrogen (N) and phosphorus (P) concentrations are direct indicators of rangeland quality and provide imperative information for sound management of wildlife and livestock. It is challenging to estimate grass N and P concentrations using remote sensing in the savanna ecosystems. These areas are diverse and heterogeneous in soil and plant moisture, soil nutrients, grazing pressures, and human activities. The objective of the study is to test the performance of non-linear partial least squares regression (PLSR) for predicting grass N and P concentrations through integrating in situ hyperspectral remote sensing and environmental variables (climatic, edaphic and topographic). Data were collected along a land use gradient in the greater Kruger National Park region. The data consisted of: (i) in situ-measured hyperspectral spectra, (ii) environmental variables and measured grass N and P concentrations. The hyperspectral variables included published starch, N and protein spectral absorption features, red edge position, narrow-band indices such as simple ratio (SR) and normalized difference vegetation index (NDVI). The results of the non-linear PLSR were compared to those of conventional linear PLSR. Using non-linear PLSR, integrating in situ hyperspectral and environmental variables yielded the highest grass N and P estimation accuracy (R2=0.81, root mean square error (RMSE)=0.08, and R2=0.80, RMSE=0.03, respectively) as compared to using remote sensing variables only, and conventional PLSR. The study demonstrates the importance of an integrated modeling approach for estimating grass quality which is a crucial effort towards effective management and planning of protected and communal savanna ecosystems.
Bibliografie:	http://dx.doi.org/10.1016/j.isprsjprs.2013.04.012 ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	0924-2716 1872-8235
DOI:	10.1016/j.isprsjprs.2013.04.012