Detecting Forest Degradation in Old Oyo National Park in Nigeria Using Remote Sensing and GIS
Abundant fauna and flora resources in Nigeria are being threatened due to the increasing rate of anthropogenic activities across the protected areas in the country. This study examined anthropogenic activities threatening the natural resources considered to be of ecotourism value in Old Oyo National...
| Published in: | Greener Journal of Environmental Management and Public Safety |
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| Main Authors: | , , , |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
2016
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| Subjects: | |
| Online Access: | https://zenodo.org/record/3457113 https://doi.org/10.15580/GJEMPS.2016.2.011416007 |
| Summary: | Abundant fauna and flora resources in Nigeria are being threatened due to the increasing rate of anthropogenic activities across the protected areas in the country. This study examined anthropogenic activities threatening the natural resources considered to be of ecotourism value in Old Oyo National Park. The research also aimed at detecting the level of forest degradation from 1990 to 2014. A time series Landsat data from 1990 to 2014 for Old Oyo National park was used, a maximum likelihood classification algorithm was employed. In order to obtain the area extent (in hectares) of the resulting land use / land cover type for each study year and for subsequent comparison, the GIS analysis in database query (AREA) of Idrisi Taiga was carried out. Tabulation and area calculations provided a comprehensive dataset in terms of the overall land scope and the type and the amount of changes that have occurred. Remote sensing technology in combination with Geographic Information System can render reliable information on landuse dynamics. This study will examine the integration of geospatial technique in examining the level of forest degradation around Old Oyo National park. Landsat TM for 1990, TM 2000 and Landsat 8 of 2014 would be used to determine the level of forest degradation. Markov chain prediction for a period of twenty years would be performed. This is to predict the state of the forest in the next twenty (20) years, from 2000 to 2020. Other analysis will also be performed, such as area calculation, gain and loss using Land change modeller, overlay and image differencing. |
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