Abstract
Climate change is one of the greatest threats facing the global community and has been mainly induced by increasing atmospheric concentrations of greenhouse gases resulting from fossil fuel energy use and change in vegetation cover. This study used modelling techniques to determine how changes in climate could affect vegetation productivity in the northern part of Nigeria. Climatic parameters (Rainfall, Minimum and Maximum Temperatures) as well as coarse Normalised Difference Vegetation Index (NDVI) data for the growing seasons of 1981-2009 were utilised. Because of the relationship between climatic parameters and vegetation, Spatial method of data interpolation was tested. Results from the prediction elevation values ranged from -3e-9 to 2e-9. It was observed from prediction variance map that the values were higher in the upper portion of the study area which comprised Gusau (GS), Jos (JS), Katsina (KT), Minna (MN) and Zaria (ZR) and lower in the middle and lower parts of the study area which comprised mainly Funtua, Kano, Maiduguri and Sokoto. Further studies are encouraged with high resolution imageries and more meteorological data to cover the montane and forest zone of the country to determine the level of climatic impacts particularly on vegetation productivity in general.
Highlights
This study has identified and evaluated methods appropriate to environmental assessment in the northern part of Nigeria
The assessment determined that long term changes in vegetation productivity or otherwise can be assessed by integrating spatial prediction
The growing season Normalised Difference Vegetation Index (NDVI) images exhibited significant upward trends for each land-cover type with an exception of semi-arid section falling in the extreme northern part of the study area
Summary
Because of the sensitivity of global and regional climatic models, there is more increasing attention in research in these areas. Geostatistics for example, is based on the theory of regionalized variables [1] [2] and [3], which allows one to capitalize on the spatial correlation between neighbouring observations to predict attribute values at unsampled locations. [4] has shown that the geostatistical prediction technique (kriging) provides better estimates of rainfall than conventional methods. [5] found that the results depend on the sampling density and that, for high-resolution networks (e.g. 13 rain gauges over a 35 km2), the kriging method does not show significantly greater predictive skill than simpler techniques, such as the inverse square distance method. A multivariate extension of kriging, known as cokriging, has been used for merging rain gauge and radar-rainfall data e.g. [7] and [8]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
