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Please use this identifier to cite or link to this item: http://hdl.handle.net/10204/6309

Title: Herbaceous biomass predication from environmental and remote sensing indicators
Authors: Dudeni-Tlhone, N
Ramoelo, A
Debba, P
Cho, MA
Mathieu, R
Keywords: Herbivore animals
Herbivore animal feeding patterns
Kruger National Park
Grass biomass estimation
Environmental sensing indicators
Remote sensing indicators
Ridge recession
Issue Date: Nov-2012
Citation: Dudeni-Tlhone, N, Ramoelo, A, Debba, P, Cho, MA and Mathieu, R. Herbaceous biomass predication from environmental and remote sensing indicators. Proceedings of the 54th Annual Conference of the South African Statistical Association for 2012 (SASA 2012), Nelson Mandela Metropolitan University (NMMU), Port Elizabeth, South Africa, 7-9 November 2012
Series/Report no.: Workflow;9853
Abstract: Feeding patterns and distribution of herbivores animals are known to be influenced by quality and quantity of forage such as grass. Modelling indicators of grass quality and biomass are critical in understanding such patterns and for decision makers such as park managers and farmers to efficiently plan and manage their rangelands. This study focused on predicting grass biomass using remote sensing and environmental variables. Since some of these variables were highly correlated, multivariate techniques such as partial least squares (PLS) and ridge regression were used to predict grass biomass in the Kruger National Park and the surrounding areas. The results indicated that both the environmental and remote sensing indicators had potential to predict grass biomass. Ridge regression showed better results since it explained about 41% of variation in the grass biomass, compared to the PLS model which explained approximately 33% variation.
Description: Proceedings of the 54th Annual Conference of the South African Statistical Association for 2012 (SASA 2012), Nelson Mandela Metropolitan University (NMMU), Port Elizabeth, South Africa, 7-9 November 20
URI: http://hdl.handle.net/10204/6309
Appears in Collections:Environmental management
Earth observation
Logistics and quantitative methods
Planning support systems
General science, engineering & technology

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