Geographic Information Systems and Remote Sensing modelling of tree species diversity in the woodlands of Zimbabwe
Abstract
The continued loss of biological diversity has prompted managers and conservationists to
find ways of monitoring ecosystems for the purposes of protecting and preventing further
loss of biological diversity. Remote sensing has been suggested as a way of monitoring
ecosystems because of its ability to provide spatial data which can be updated relatively
faster and at a relatively cheaper than conventional methods. The relationship between
tree species diversity and satellite derived indices is well documented. However, not
much work has been done in mapping tree species using the obtained relationships, more
so in savanna woodlands. Thus, the main objective of this thesis was to investigate
whether and to what extent remote sensing can be used to measure tree species diversity
in savanna woodlands. In addition the thesis also tested the extent to which linear and
spatial regression can be used within a Geographic Information System to predict the
spatial 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 tree
species diversity in savanna woodlands, and 2) that a remote sensing index of biomass is
related to tree species diversity. The former is based on the Spectral Variation
Hypothesis, which predicts that species richness can be estimated from habitat
heterogeneity while the latter is based on the biomass-diversity or productivity-diversity
hypothesis. The biomass-diversity hypothesis predicts that optimum biodiversity is found
in ecosystems which have intermediate biomass, while low species diversity is
characteristic of ecosystems which have low or high biomass. Specifically, we used
regression analysis to test whether and in what way tree species diversity is related to the
standard deviation of the Near Infrared (NIR) radiance (a measure of spectral variation)
and tree biomass estimated via the Soil Adjusted Vegetation Index (SAVI) in three
selected savanna woodlands of Zimbabwe. We also tested the wider applicability of the
derived regression models by applying them in a study site away from where the models
were 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 and
SAVI explained between 59% and 73% of the variance in tree species diversity in the
study sites. Also, we found that our regression models can be used to spatially predict
tree species diversity in landscapes with comparable physical characteristics. Overall, we conclude that remote sensing and GIS can be used to successfully estimate tree species
diversity in savanna woodlands.
Subject
remote sensingspatial resolution
biodiversity conservation
ecological patterns
remote sensed indices
biological diversity