Title page for ETD etd-12282003-150656

Type of Document Dissertation
Author Sengupta, Nina
URN etd-12282003-150656
Title Detection and prediction of biodiversity patterns as a rapid assessment tool in the tropical forest of East Usambara, Eastern Arc Mountains, Tanzania
Degree PhD
Department Fisheries and Wildlife Sciences
Advisory Committee
Advisor Name Title
Fraser, James D. Committee Chair
Campbell, James B. Jr. Committee Member
Oderwald, Richard G. Committee Member
Stauffer, Dean F. Committee Member
Walters, Jeffery R. Committee Member
Wynne, Randolph H. Committee Member
  • rain forest
  • humid tropical forest
  • satellite image
  • remote sensing
  • rapidly collectable field data
  • assessment
  • East Usambara
  • Eastern Arc
  • Amani
  • Nilo
Date of Defense 2003-11-20
Availability unrestricted
As 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.
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