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Hengl, T., Heuvelink, G. and Stein, A. (2004) A Generic Framework for Spatial Prediction of Soil Variables Based on Regression-Kriging. Geoderma, 122, 75-93.
https://doi.org/10.1016/0016-7061(95)00007-B

has been cited by the following article:

  • TITLE: Assessment of Vegetation Productivity in the Northern Part of Nigeria

    AUTHORS: Sadiq Abdullahi Yelwa, Umar Usman

    KEYWORDS: Climate Change, Regression-Kriging (RK) and Normalized Difference Vegetation Index (NDVI), Linear Model, Digital Elevation Model (DEM)

    JOURNAL NAME: American Journal of Climate Change, Vol.6 No.2, June 20, 2017

    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.