Broadscale Vegetation Change Assessment across Nigeria from Coarse Spatial and High Temporal Resolution AVHRR Data (English shop)

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Nigeria’s vast land area is mostly covered with natural vegetation. The vegetation cover is wide-ranging and reflects past and present climatic variations. Nigeria’s vegetation cover is rapidly changing due to natural and anthropogenic influences. Nigeria’s population growth rate is one of the highest in the world, where a large proportion of this population are peasant farmers exploiting the rural environment. Previous studies have shown that vegetation cover in some parts of Nigeria is fast declining. The report on the implementation of Agenda 21 (UNCED, 1997) of the United Nations Commission Conference on Environment and Development (UNCED, 1992), which is a long-term global action plan for sustainable development, reported that there is still a persistent decline in the general vegetation cover across Nigeria due to population pressure, overgrazing and continuous exploitation of marginal lands for various purposes. It therefore suggests that periodic assessment and management of this vegetation cover for ecological sustainability would be highly desirable.
With barely $280 GNP per capita and accumulated foreign debts of over US$28 billion, a cost-effective approach to resource management and monitoring could be appropriate. This study employs the use of coarse spatial but high temporal resolution Advanced Very High Resolution Radiometer-Normalised Difference Vegetation Index (AVHRR-NDVI) data from the Pathfinder Land (PAL) dataset to identify and evaluate vegetation cover change. This data is administered by the National Oceanic Atmospheric Administration and National Aeronautics Space Administration (NOAA/NASA) and is, most importantly, freely available. The datasets used were recomposed into monthly Maximum Value (MVC) and annual mean composites in order to assess broadly the changes in vegetation cover across the whole of Nigeria. One hundred and twenty annual gridded point precipitation data for 12 years at 1° resolution covering the whole country were acquired from the Global Precipitation and Climatology Centre (GPCC), and were regressed linearly as a function of 13 other variables in order to create models for estimating spatial rainfall data for the whole country.