Browsing by Author "Pienaar, Louis Otto"

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    Modeling above-ground biomass of selected tree species within a Mistbelt forest in KwaZulu Natal
    (Stellenbosch : Stellenbosch University, 2016-03) Pienaar, Louis Otto; Seifert, Thomas; Drew, David M.; Stellenbosch University. Faculty of Agrisciences. Dept. of Forest and Wood Science.
    ENGLISH ABSTRACT: The objective of this study was to develop species-specific allometric models for selected natural forest species within a forest forming part of the Southern Mistbelt Forest Group, close to the town of Richmond in KwaZulu Natal. The objective was met by determining the tree dimensions (diameter at breast height (DBH) and tree height) of the forest. The collected variables were used and the most dominant species in terms of their basal area coverage: Xymalos monospora and Celtis africana were selected for biomass modeling. The allometric models were developed from a two-step stratified sampling approach. Population dimensions were determined from sample plots, where-after trees were sampled for biomass representing the collected dimensions. The dry mass of the sampled components were used in a regression modeling approach to develop a set of species-specific and combined species linear models. The best models were selected based on goodness-of-fit model evaluation criteria (GOF) and parsimony principles and a two-step upscaling process was used to upscale samples to tree level and from tree to stand level. DBH and basic density were significant predictors of total above-ground biomass (AGB) and diameter as single predictor produced consistently good results. Diameter was used throughout the upscaling process to determine the biomass per ha. The estimated AGB for X. monospora, C. africana and all the species were 62.98, 93.56 and 230.86 Mg haˉ¹ respectively. Estimated AGB for all species compared well with results from other biomass studies. Future research can investigate remote sensing applications in combination with the field sampling to estimate forest biomass more cost effectively over larger areas.