Open Access Peer-reviewed

Modelling Relationship between NDVI and Climatic Variables Using Geographically Weighted Regression

U. Usman1, S. A. Yelwa2,, S.U. Gulumbe1, A. Danbaba1

1Department of Mathematics, Usmanu Danfodiyo University, Sokoto, Nigeria

2Department of Environmental Sciences, Federal University Dutse, Jigawa State, Nigeria

Journal of Mathematical Sciences and Applications. 2013, 1(2), 24-28. DOI: 10.12691/jmsa-1-2-2
Published online: August 25, 2017


Relationship between vegetation and its spatial predictors appears to vary as a function of geographical region and a number of the underlying environmental factors such as the type of vegetation, soil and land use. However, NDVI-climate relationship also varies within one landcover type because there are many cases that show a non-stability of this relationship in space within the same land cover or vegetation type. The purpose of this study is to investigate the applicability of Geographically Weighted Regression (GWR) with the objective of finding the spatial relationship between Normalized Difference Vegetation Index (NDVI) derived from NOAA/AVHRR and Aqua/ Moderate Resolution Imaging Spectroradiometer (AQUA/MODIS) as well as climatic variables (Rainfall, Temperature) data obtained from some weather stations across Northern Nigeria from 1980 - 2010.The results of this study show that there is significant relationship between the NDVI and climate variables (Rainfall, Tmax and Tmin). The study proved the superiority of the local approach provided by GWR over the global Ordinary Least Square (OLS) approach in analysing the relationship between patterns of NDVI and precipitation. This superiority however, was mainly due to spatial variation of the relationship over the local study area because global regression techniques like OLS tend to ignore local information and, therefore, indicate incorrectly that a large part of the variance in NDVI was unexplained. The non-stationary modelling based on the GWR approach therefore, has the potential for a more reliable prediction because the model is more aligned to local circumstances, although more time-series data is needed to allow a more reliable local fitting.


geographical weighted regression, Ordinary Least Square, Normalized Difference Vegetation Index, rainfall, temperature
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