The spatial prediction of tree species diversity in savanna woodlands of Southern Africa |
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| Authors: | Godfrey Mutowo Amon Murwira |
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| Institution: | 1. Department of Geography and Environmental Science , University of Zimbabwe , Mount Pleasant , Harare , P.O. Box, MP167 , Zimbabwe gmutowar@arts.uz.ac.zw gdfmtw6@gmail.com;3. Department of Geography and Environmental Science , University of Zimbabwe , Mount Pleasant , Harare , P.O. Box, MP167 , Zimbabwe |
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| Abstract: | In this study, we tested the utility of remotely sensed data in predicting tree species diversity in savanna woodlands. Specifically, we developed linear regression functions based on a combination of the coefficient of variation of near infrared (NIR) radiance and the soil-adjusted vegetation index (SAVI), both derived from advanced space-borne thermal emission and reflection radiometer satellite imagery. Using the regression functions in a Geographic Information System (GIS), we predicted the spatial variations in tree species diversity. Our results showed that tree species diversity can be predicted using a combination of the coefficient of variation of NIR radiance and SAVI. We conclude that remotely sensed data can be used to spatially predict tree species diversity in savanna woodlands. |
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| Keywords: | near infrared spatial prediction tree species diversity savanna |
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