The spatial
variation of the temporal response of NDVI to precipitation in the Serengeti
Maasai Mara ecosystem, south Africa
By Junyan
Ding
Abstract
Natural ecosystems have evolved to adjust to physical
environments to maximize resource exploitation while maintaining a
well-balanced material cycle. We fail to keep the balance of the material cycle
when we maximize the use of natural resources, and this has caused huge
environmental problems. To achieve sustainable management, it is important for
us to understand how the productivity of a natural plant community varies with
climate based on physical environmental factors. In this project, I use a
quadratic regression model to describe the correlation between monthly
precipitation and NDVI, and to identify the biological meaning of this model.
Next, I apply geographically weighted regression analysis to identify physical
factors that have a great impact on each parameter and the R2 of the
quadratic regression, and to understand their significance and the way by which
the impact of the physical factors vary across the Serengeti ecosystem.
The ‘R2’ and regression parameters of the
quadratic regression model of the correlation between monthly precipitation and
NDVI show certain spatial patterns. Annual precipitation and vegetation type
are the two factors that are identified as having the greatest impact on the ‘R2’
and parameter ‘a’ of the correlation between monthly precipitation and on NDVI
over the whole region. Soil nitrogen, TCI and WHC are identified as having an
impact on ‘R2’ and parameter ‘a’ only within the park. To fully
explore the impact of environmental factors on the response of NDVI to
precipitation, finer spatial resolution data and the temporal variation or
temperature and soil nutrient content will be required.