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Assessment of foliar nitrogen as an indicator of vegetation stress using remote sensing : the case study of Waterberg region, Limpopo Province

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dc.contributor.advisor Ramoelo, Abel
dc.contributor.advisor Jordaan, Maarten
dc.contributor.author Manyashi, Enoch Khomotšo
dc.date.accessioned 2016-04-01T08:56:36Z
dc.date.available 2016-04-01T08:56:36Z
dc.date.issued 2015-06
dc.identifier.citation Manyashi, Enoch Khomotšo (2015) Assessment of foliar nitrogen as an indicator of vegetation stress using remote sensing: The case study of Waterberg region, Limpopo Province, University of South Africa, Pretoria, <http://hdl.handle.net/10500/20066> en
dc.identifier.uri http://hdl.handle.net/10500/20066
dc.description.abstract Vegetation status is a key indicator of the ecosystem condition in a particular area. The study objective was about the estimation of leaf nitrogen (N) as an indicator of vegetation water stress using vegetation indices especially the red edge based ones, and how leaf N concentration is influenced by various environmental factors. Leaf nitrogen was estimated using univariate and multivariate regression techniques of stepwise multiple linear regression (SMLR) and random forest. The effects of environmental parameters on leaf nitrogen distribution were tested through univariate regression and analysis of variance (ANOVA). Vegetation indices were evaluated derived from the analytical spectral device (ASD) data, resampled to RapidEye. The multivariate models were also developed to predict leaf N. The best model was chosen based on the lowest root mean square error (RMSE) and higher coefficient of determination (R2) values. Univariate results showed that red edge based vegetation index called MERRIS Terrestrial Chlorophyll Index (MTCI) yielded higher leaf N estimation accuracy as compared to other vegetation indices. Simple ratio (SR) based on the bands red and near-infrared was found to be the best vegetation index for leaf N estimation with exclusion of red edge band for stepwise multiple linear regression (SMLR) method. Simple ratio (SR3) was the best vegetation index when red edge was included for stepwise linear regression (SMLR) method. Random forest prediction model achieved the highest leaf N estimation accuracy, the best vegetation index was Red Green Index (RGI1) based on all bands with red green index when including the red edge band. When red edge band was excluded the best vegetation index for random forest was Difference Vegetation Index (DVI1). The results for univariate and multivariate results indicated that the inclusion of the red edge band provides opportunity to accurately estimate leaf N. Analysis of variance results showed that vegetation and soil types have a significant effect on leaf N distribution with p-values<0.05. Red edge based indices provides opportunity to assess vegetation health using remote sensing techniques. en
dc.format.extent 1 electronic resource (x, 71 leaves) : illustrations, color maps en
dc.language.iso en en
dc.subject Foliar nitrogen en
dc.subject Remote sensing en
dc.subject Red edge en
dc.subject Vegetation index en
dc.subject Leaf N estimation en
dc.subject Univariate regression en
dc.subject Multivariate regression en
dc.subject Indicator en
dc.subject Vegetation stress en
dc.subject Leaf N map en
dc.subject.ddc 581.71450968253
dc.subject.lcsh Foliar diagnosis -- South Africa -- Waterberge (Limpopo) -- Remote sensing -- Case studies en
dc.subject.lcsh Vegetation monitoring -- South Africa -- Waterberge (Limpopo) -- Remote sensing -- Case studies en
dc.subject.lcsh Forest health -- South Africa -- Waterberge (Limpopo) -- Remote sensing -- Case studies en
dc.title Assessment of foliar nitrogen as an indicator of vegetation stress using remote sensing : the case study of Waterberg region, Limpopo Province en
dc.type Dissertation en
dc.description.department Environmental Sciences en
dc.description.degree M. Sc. (Environmental Management) en


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