Dudeni-Tlhone, NRamoelo, AbelDebba, PraveshCho, Moses AMathieu, Renaud SA2012-11-142012-11-142012-11Dudeni-Tlhone, N., Ramoelo, A., Debba, P., Cho, M.A. 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 2012http://hdl.handle.net/10204/6309Proceedings 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 20Feeding 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.enHerbivore animalsHerbivore animal feeding patternsKruger National ParkGrass biomass estimationEnvironmental sensing indicatorsRemote sensing indicatorsRidge recessionConference PresentationDudeni-Tlhone, N., Ramoelo, A., Debba, P., Cho, M. A., & Mathieu, R. S. (2012). Herbaceous biomass predication from environmental and remote sensing indicators. http://hdl.handle.net/10204/6309Dudeni-Tlhone, N, Abel Ramoelo, Pravesh Debba, Moses A Cho, and Renaud SA Mathieu. "Herbaceous biomass predication from environmental and remote sensing indicators." (2012): http://hdl.handle.net/10204/6309Dudeni-Tlhone N, Ramoelo A, Debba P, Cho MA, Mathieu RS, Herbaceous biomass predication from environmental and remote sensing indicators; 2012. http://hdl.handle.net/10204/6309 .TY - Conference Presentation AU - Dudeni-Tlhone, N AU - Ramoelo, Abel AU - Debba, Pravesh AU - Cho, Moses A AU - Mathieu, Renaud SA AB - 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. DA - 2012-11 DB - ResearchSpace DP - CSIR KW - Herbivore animals KW - Herbivore animal feeding patterns KW - Kruger National Park KW - Grass biomass estimation KW - Environmental sensing indicators KW - Remote sensing indicators KW - Ridge recession LK - https://researchspace.csir.co.za PY - 2012 T1 - Herbaceous biomass predication from environmental and remote sensing indicators TI - Herbaceous biomass predication from environmental and remote sensing indicators UR - http://hdl.handle.net/10204/6309 ER -