Application of Remote Sensing and GIS for Forest Cover Change Detection. (A Case Study of Owabi Catchment in Kumasi, Ghana)
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Date
April, 2011
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Abstract
Farming activities, continued sand winning operations and the allocation of plots of land to prospective developers in and around the catchments of the Owabi Dam pose a serious threat to the forest covers and the lifespan of the dam. The aim of this study was to analyzing the Change detection of forest cover in Owabi catchment area in Kumasi, using multi-temporal Remote Sensing (RS) data and Geographic Information System (GIS) based techniques. For this study, Landsat TM image of 11th January 1986, aster image of 15th January 2002 and Landsat ETM image of 24th February 2007 were analyzed using Erdas Imagine and ArcGIS software. After performing supervised classification on these images, a total of eight land use and land cover (LULC) classes were identified and mapped. These were water, bare soil/sand, grassland, built-up, sparse forest, high density forest, croplands and wetlands. An NDVI analysis was performed on these images and vegetation covers were identified. Using Fragstats software, changes in the landscape structure were analyzed and some fragmantation statistics of the LULC types were computed. Gain and loss to persistence ratio as well as the net change to persistence ratio of the various LULC classes were also computed. Topographic map of 1974 were used to identify the spatial distribution of the reserved forest.
The results of the analysis showed that from 1986 to 2002 and 2002 to 2007 the forest covers‟ has decreased by 2136.6 ha and 1231.56 ha respectively representing 24.7% and 14.2%. It emerged that from 1986 to 2007, forest covers‟ reduced by 3368.16 ha, representing 38.9%. These changes were as a result of an increase in human activities and population explosion within the catchment area. There was no significant difference between the NDVI classification and the supervised classification of the images. Overlay of the reserved forest of the 1974 and the classified maps of 1986, 2002 and 2007 shows that the reserved forest had been highly depleted over the past 33 years. The use of Satellite image data, GIS and RS technique is a valuable tool for detection and prediction of forest cover change and the identification of areas under risk of invasion.
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Thesis submitted to the Kwame Nkrumah University of Science and Technology (KNUST), Kumasi, Ghana in Partial Fulfilment of the Requirements for the Degree of Master of Science in Geomatic Engineering,