Abstract:
Predicting future conditions of the ecosystems resulting from different human activities
is very important in spatial planning for sustainable resources utilisation. This study has
developed a spatial decision making supporting system (SDSS) or a model for predicting
ecosystems vulnerability under landscape changes in the Livingstone Mountain Ranges
in Mbinga District, Tanzania. Various datasets collected through remote sensing and
cross-section survey were used in this study. Changes in the spatial extent of the habitats
were assessed from remotely sensed data based on land use and cover changes. Variables
extracted from remotely sensed data were used to generate parameters and were
integrated in the GIS environment to develop the prediction model called, Livingstone
Mountain - Conversion of Land Use and Its Effects (LIM-CLUES) Model.
Predicted results from the developed model were based on the business as usual and
policy scenarios used in this study. The results from the business as usual scenario
showed that there would be a continuous decrease in the woodland ecosystem up to the
year 2020 mainly at the expense of agro-ecosystem due to livelihood strategies of the
local communities in deriving goods and services. Predicted results also showed that
there was an increase in the area of cultivated land at the expense of the upland woodland
and upland bushland with scattered cropland from year 2005 to 2020. The results also
showed that upland cultivation would continue to expand towards the south eastern side
up to year 2020. Encroachment would also start to take place in the upland woodlands
located in the southern part between the boundaries of Chiwanda (lowland) and Tingi
(upland) wards. Likewise, simulated results from the policy scenario showed that upland
bushland with scattered cropland category would be extending upwards in Kingerikiti
ward between years 2005 and 2010. Upland woodland started to regenerate in the same
Kingerikiti ward from year 2005. As a result of this study, a LIM-CLUES model helped to understand factors influencing landscape changes and can be used to project near
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future land use trajectories, which are important for targeting spatial management
decisions in the study area.
