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Accueil du site → Master → Afrique du Sud → 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 (2015)

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

Manyashi, Enoch Khomotšo

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

Auteur : Manyashi, Enoch Khomotšo

Université de soutenance : University of South Africa

Grade : Master of Science (MS) in Environmental Management 2015

Résumé
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.

Présentation -> http://uir.unisa.ac.za/handle/10500...

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