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• W2226174507 abstract "The continued loss of biological diversity has prompted managers and conservationists tofind ways of monitoring ecosystems for the purposes of protecting and preventing furtherloss of biological diversity. Remote sensing has been suggested as a way of monitoringecosystems because of its ability to provide spatial data which can be updated relativelyfaster and at a relatively cheaper than conventional methods. The relationship betweentree species diversity and satellite derived indices is well documented. However, notmuch work has been done in mapping tree species using the obtained relationships, moreso in savanna woodlands. Thus, the main objective of this thesis was to investigatewhether and to what extent remote sensing can be used to measure tree species diversityin savanna woodlands. In addition the thesis also tested the extent to which linear andspatial regression can be used within a Geographic Information System to predict thespatial distribution of tree species diversity. We based our study on the hypotheses that 1)variance in the reflectance within remotely-sensed images is directly related with treespecies diversity in savanna woodlands, and 2) that a remote sensing index of biomass isrelated to tree species diversity. The former is based on the Spectral VariationHypothesis, which predicts that species richness can be estimated from habitatheterogeneity while the latter is based on the biomass-diversity or productivity-diversityhypothesis. The biomass-diversity hypothesis predicts that optimum biodiversity is foundin ecosystems which have intermediate biomass, while low species diversity ischaracteristic of ecosystems which have low or high biomass. Specifically, we usedregression analysis to test whether and in what way tree species diversity is related to thestandard deviation of the Near Infrared (NIR) radiance (a measure of spectral variation)and tree biomass estimated via the Soil Adjusted Vegetation Index (SAVI) in threeselected savanna woodlands of Zimbabwe. We also tested the wider applicability of thederived regression models by applying them in a study site away from where the modelswere developed. Our results showed that tree species diversity has a significant (p< 0.05)hump-shaped response to variations in the standard deviation of NIR radiance and SAVI.Furthermore, results show that the combination of the standard deviation of NIR andSAVI explained between 59% and 73% of the variance in tree species diversity in thestudy sites. Also, we found that our regression models can be used to spatially predicttree species diversity in landscapes with comparable physical characteristics. Overall, we conclude that remote sensing and GIS can be used to successfully estimate tree speciesdiversity in savanna woodlands." @default.
• W2226174507 created "2016-06-24" @default.
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• W2226174507 date "2014-06-27" @default.
• W2226174507 modified "2023-09-27" @default.
• W2226174507 title "Geographic Information Systems and Remote Sensing modelling of tree species diversity in the woodlands of Zimbabwe" @default.
• W2226174507 hasPublicationYear "2014" @default.
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