FACULTY OF RENEWABLE NATURAL RESOURCES

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    Geospatial assessment of climate sensitivity in Ibarapa North, Oyo State, Nigeria
    (Faculty of Agriculture, Usmanu Danfodiyo University, Sokoto, Nigeria, 2024) Agbor, C.F.; Akintunde-Alo, D. A.; Ogunwale, O. R.
    This study employed remote sensing and geographic information system (GIS) to evaluate the spatial pattern of carbon(iv)oxide (CO2) concentration and the resulting climate sensitivity in Ibarapa North local government areas of Oyo State, Nigeria. The evaluation was carried out using Landsat images of 2003 and 2023, digital elevation model, as well as CO2 data collected with CO2. meter. Surface temperature and radiative forcing were generated from the satellite images using random forest algorithm in 𝑅 software environment, while the climate sensitivity was evaluated using Drakes’ Sensitivity Linear Model. The results revealed mean air temperature of 31.5oC and 32.7oC in 2003 and 2023 respectively. The area experienced positive radiative forcing mean value of about 2.69𝑊 𝑚−2, which indicates more energy being trapped on the earth’s surface that could cause warming. The climate sensitivity in 2023 was 0.4oC 𝑚−2s-2 which falls below global average of about 3oC 𝑚−2s-1. The CO2 concentration was extrapolated based on the mathematical function derived from the regression function between the variable and elevation. The results revealed positive radiative forcing and low climate sensitivity value. This may seem positive, but that doesn’t negate the need for action to mitigate adverse effects of climate change.
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    Modelling forest cover dynamics in Shasha forest reserve, Osun state, Nigeria
    (Faculty of Agriculture, Usmanu Danfodiyo, Sokoto, Nigeria, 2020) Alo A.A.; Adetola, A.A.; Agbor, C.F.
    Understanding the dynamics of forest cover change is vital to forest manager for planning, formulation of policies and decision making. Nigeria’s forest reserves have witnessed significant changes over the years due to various anthropogenic activities. Incessant activities of poachers, illegal fellers and other farming activities in Shasha Forest Reserve have adverse effects on the ecosystem with consequence for global warming. However, there is no up-to-date information on the dynamics of forest cover in Shasha Forest Reserve. Therefore, this study aimed at assessing forest cover changes using remote sensing in Shasha Forest Reserve. Landsat Thematic mapper (TM), Enhanced Thematic Mapper (ETM+) and Operational Land Imager (OLI) data for the periods of 1984, 2000 and 2017 were obtained. The Landsat images were pre-processed and classified using maximum likelihood classification algorithm. The Classification was based on Anderson scheme of land use/cover for change detection between 1984 and 2017. Kappa coefficient was used for accuracy assessment. The future pattern of forest cover changes for 2034 was forecast using the Multi-Layer Perception (MLP) Markov chain model in IDRISI. Three land cover classes were identified: Built up, Shrubs and Forest land. Built up and Shrubs increased at an annual rate of 0.09% and 0.18% respectively and forest decreased at an annual rate of 0.27% between 1984 and 2017. Large area of forest land has been converted to built-up and shrubs with no significant replacement from 2000 till date. The forest was projected to decrease between 2017 till 2034 at the rate of 0.15% per annum.