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The use of high-resolution satellite imagery in forest inventory : a case of Hans Kanyinga Community Forest - Namibia

dc.contributor.advisorKatsch, C.
dc.contributor.advisorBredenkamp, B.
dc.contributor.authorKamwi, Jonathan Mutauen_ZA
dc.contributor.otherUniversity of Stellenbosch. Faculty of Agrisciences. Dept. of Forest and Wood Science.
dc.date.accessioned2008-03-27T13:12:42Zen_ZA
dc.date.accessioned2010-06-01T08:59:51Z
dc.date.available2008-03-27T13:12:42Zen_ZA
dc.date.available2010-06-01T08:59:51Z
dc.date.issued2007-12en_ZA
dc.identifier.urihttp://hdl.handle.net/10019.1/2845
dc.descriptionThesis (MSc (Forest and Wood Science))—University of Stellenbosch, 2007.
dc.description.abstractThe present study investigated double sampling with regression estimators as a quest for efficiency and effectiveness in forest inventory in Namibian woodlands. Auxiliary data used were obtained from Standard QuickBird satellite scenes (phase 1) for Hans Kanyinga Community Forest from October and November 2004 covering an area of 12,107 hectares, amplified with terrestric data (phase 2) of 2002. The relationships between auxiliary and terrestric variables are described and prediction models were constructed. According to the results of the stepwise procedure with the Mallow’s Cp statistic as the selection criteria, photogrammetric stand density and a combination of the photogrammetric crown area with photogrammetric stand density were the best candidates for predicting the stand volume. The resulting volume model explains 56% of the variation. Photogrammetric stand density was found to be highly correlated to the terrestric stand density with the resulting model explaining 81% of the variation. Photogrammetric crown diameter was found to be correlated with the diameter at breast height measured from the plots which were assessed for spatial tree positions, which enabled the derivation of the diameter distribution. The diameter distribution model explains 43% of the variation. In addition, the actual tree positions were determined using the GPS and surveying techniques (polar positions) involving distance and bearings. GPS tree positions showed a considerable shift of up to 8.67 m. However, only the distance measurements of tress from the plot centre using the infield surveying methods were more reliable. Nevertheless, the influences of the tree positional errors are not of high concern for temporary based sample plots which are normally used in Namibian forest inventories. A reduction in inventory cost was found to be 24% i.e. N$25.79 to N$19.67 per hectare. The results of this study are valid for Kavango region or any other region with similar set of physical and climatic conditions, but caution must be exercised in implementing these results elsewhere under different physical and environmental conditions.en_ZA
dc.format.extent1981846 bytesen_ZA
dc.format.mimetypeapplication/pdfen_ZA
dc.language.isoenen_ZA
dc.publisherStellenbosch : University of Stellenbosch
dc.subjectForest surveys -- Namibia -- Hans Kanyinga Community Foresten
dc.subjectForests and forestry -- Namibia -- Remote sensingen
dc.subjectArtificial satellites in forestry -- Namibiaen
dc.subjectDissertations -- Forest and wood scienceen
dc.subjectTheses -- Forest and wood scienceen
dc.titleThe use of high-resolution satellite imagery in forest inventory : a case of Hans Kanyinga Community Forest - Namibiaen_ZA
dc.typeThesisen_ZA
dc.rights.holderUniversity of Stellenbosch


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