Detection and prediction of biodiversity patterns as a rapid assessment tool in the tropical forest of East Usambara, Eastern Arc Mountains, Tanzania

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dc.contributor.authorSengupta, Ninaen
dc.contributor.committeechairFraser, James D.en
dc.contributor.committeememberStauffer, Dean F.en
dc.contributor.committeememberCampbell, James B. Jr.en
dc.contributor.committeememberWalters, Jeffrey R.en
dc.contributor.committeememberOderwald, Richard G.en
dc.contributor.committeememberWynne, Randolph H.en
dc.contributor.departmentFisheries and Wildlife Sciencesen
dc.date.accessioned2014-03-14T20:21:11Zen
dc.date.adate2004-01-08en
dc.date.available2014-03-14T20:21:11Zen
dc.date.issued2003-11-20en
dc.date.rdate2005-01-08en
dc.date.sdate2003-12-28en
dc.description.abstractAs a strategy to conserve tropical rainforests of the East Usambara block of the Eastern Arc Mountains, Tanzania, I developed a set of models that can identify above-average tree species richness areas within the humid forests. I developed the model based on geo-referenced field data and satellite image-based variables from the Amani Nature Reserve, the largest forest sector in the East Usambara. I then verified the model by applying it to the Nilo Forest Reserve. The field data, part of the Tanzanian National Biodiversity Database, were collected by Frontier-Tanzania between 1999 and 2001, through the East Usambara Conservation Area Management Program, Government of Tanzania. The field data used are rapidly collectible by people with varied backgrounds and education. I gathered spectral reflectance values from pixels in the Landsat Enhanced Thematic Mapper (Landsat ETM) image covering the study area that corresponded to the ground sample points. The spectral information from different bands formed the satellite image-based variables in the dataset. The best satellite image logistic regression and discriminant analysis models were based on a single band, raw Landsat ETM mid-infrared band 7 (RB7). In the Amani forest, the RB7-based model resulted in 65.3% overall accuracy in identifying above average tree species locations. When the logistic and discriminant models were applied to Nilo forest sector, the overall accuracy was 62.3%. Of the rapidly collectible field variables, only tree density (number of trees) was selected in the logistic regression and the discriminant analysis models. Logistic and discriminant models using both RB7 and number of trees recorded 76.3% overall accuracy in Amani, and when applied to Nilo, 76.8% accuracy. It is possible to apply and adapt the current set of models to identify above-average tree species richness areas in East Usambara and other forest blocks of the Eastern Arc Mountains. Potentially, managers and researchers can periodically use the model to rapidly assess, monitor, update, and map the tree species rich areas within the forest. The same or similar models could be applied to check their applicability in other humid tropical forest areas.en
dc.description.degreePh. D.en
dc.identifier.otheretd-12282003-150656en
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-12282003-150656/en
dc.identifier.urihttp://hdl.handle.net/10919/30272en
dc.publisherVirginia Techen
dc.relation.haspartNinaSengupta.pdfen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectrain foresten
dc.subjecthumid tropical foresten
dc.subjectsatellite imageen
dc.subjectremote sensingen
dc.subjectrapidly collectable field dataen
dc.subjectassessmenten
dc.subjectEast Usambaraen
dc.subjectEastern Arcen
dc.subjectAmanien
dc.subjectNiloen
dc.titleDetection and prediction of biodiversity patterns as a rapid assessment tool in the tropical forest of East Usambara, Eastern Arc Mountains, Tanzaniaen
dc.typeDissertationen
thesis.degree.disciplineFisheries and Wildlife Sciencesen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.leveldoctoralen
thesis.degree.namePh. D.en

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