Development of a methodology for monitoring changes in Ghanaian forest reserves
Abstract
The Ghanaian Forests are a significant component of the country’s development.
Occasioned by the rapid population growth of the country, increasing phenomena
such as shifting agriculture, logging, fuelwood harvesting and fire outbreaks have
claimed over 70% of the original forests. The reduction of forests has stimulated the
development of management tools to control forest depletion. In order to focus the
intervention of forest managers and environmental planners, the rate and impact of
forest depletion must be monitored and well documented. Financial constraints and
the lack of adequate maps have hindered the setting up of effective monitoring
mechanisms. This study illustrated the feasibility for using Landsat data and GIS to
map changes in the Ghanaian forest reserves. GIS was used to create the initial
database for the study. Three image analysis change detection methods namely
image algebra (image differencing), spectral temporal and spectral temporal principal
component analysis were employed. The results of the analysis showed that spatial
distributions of the changed areas produced by all three methods were similar,
varying only in the extent. The remote sensing image analysis required the
information stored in the GIS database for rectification and for the assessment of the
classification procedure. A quantitative accuracy assessment was not possible for
the procedures due to limited ground truthing. The use of GPS in field data collection
was demonstrated by its use in delineating the boundary of a selected reserve. The
GPS data was able to adequately display the reserve boundary, the spatial
distribution of Taungya and farms along the boundary as well as relocated boundary
pillars. All new layers of information generated from the research were displayed and
stored in the GIS. Finally, the importance of the outlined procedures in the
monitoring of Ghanaian forest and the limitations of the study were discussed.
Collections
- Retrospective theses [1604]