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Examining the NDVI-rainfall relationship in the semi-arid Sahel using geographically weighted regression

Georganos, Stefanos; Abdi, Abdulhakim M. LU ; Tenenbaum, David E. LU and Kalogirou, Stamatis (2017) In Journal of Arid Environments p.64-74
Abstract
The Sahel of Africa is an eco-sensitive zone with complex relations emerging between vegetation productivity and rainfall. These relationships are spatially non-stationary, non-linear, scale dependant and often fail to be successfully modelled by conventional regression models. In response, we apply a local modelling technique, Geographically Weighted Regression (GWR), which allows for relationships to vary in space. We applied the GWR using climatic data (Normalized Vegetation Difference Index and rainfall) on an annual basis during the growing seasons (June–September) for 2002–2012. The operating scale of the Sahelian NDVI–rainfall relationship was found to stabilize around 160 km. With the selection of an appropriate scale, the spatial... (More)
The Sahel of Africa is an eco-sensitive zone with complex relations emerging between vegetation productivity and rainfall. These relationships are spatially non-stationary, non-linear, scale dependant and often fail to be successfully modelled by conventional regression models. In response, we apply a local modelling technique, Geographically Weighted Regression (GWR), which allows for relationships to vary in space. We applied the GWR using climatic data (Normalized Vegetation Difference Index and rainfall) on an annual basis during the growing seasons (June–September) for 2002–2012. The operating scale of the Sahelian NDVI–rainfall relationship was found to stabilize around 160 km. With the selection of an appropriate scale, the spatial pattern of the NDVI-rainfall relationship was significantly better explained by the GWR than the traditional Ordinary Least Squares (OLS) regression. GWR performed better in terms of predictive power, accuracy and reduced residual autocorrelation. Moreover, GWR formed spatial clusters with local regression coefficients significantly higher or lower than those that the global OLS model resulted in, highlighting local variations. Areas near wetlands and irrigated lands displayed weak correlations while humid areas such as the Sudanian region at southern Sahel produced higher and more significant correlations. Finally, the spatial relationship of rainfall and NDVI displayed temporal variations as there were significant differences in the spatial trends throughout the study period.

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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Geographically weighted regression, Non-stationarity, Scale dependency, Sahel, Earth observation, Drylands, Africa, Semiarid, arid
in
Journal of Arid Environments
pages
64 - 74
publisher
Elsevier
external identifiers
  • scopus:85021799252
  • wos:000409155600008
ISSN
0140-1963
DOI
10.1016/j.jaridenv.2017.06.004
language
English
LU publication?
yes
id
46943d06-7b2d-4c63-bab8-2f4ceb1acd8d
date added to LUP
2017-07-14 11:39:54
date last changed
2018-05-20 04:35:43
@article{46943d06-7b2d-4c63-bab8-2f4ceb1acd8d,
  abstract     = {The Sahel of Africa is an eco-sensitive zone with complex relations emerging between vegetation productivity and rainfall. These relationships are spatially non-stationary, non-linear, scale dependant and often fail to be successfully modelled by conventional regression models. In response, we apply a local modelling technique, Geographically Weighted Regression (GWR), which allows for relationships to vary in space. We applied the GWR using climatic data (Normalized Vegetation Difference Index and rainfall) on an annual basis during the growing seasons (June–September) for 2002–2012. The operating scale of the Sahelian NDVI–rainfall relationship was found to stabilize around 160 km. With the selection of an appropriate scale, the spatial pattern of the NDVI-rainfall relationship was significantly better explained by the GWR than the traditional Ordinary Least Squares (OLS) regression. GWR performed better in terms of predictive power, accuracy and reduced residual autocorrelation. Moreover, GWR formed spatial clusters with local regression coefficients significantly higher or lower than those that the global OLS model resulted in, highlighting local variations. Areas near wetlands and irrigated lands displayed weak correlations while humid areas such as the Sudanian region at southern Sahel produced higher and more significant correlations. Finally, the spatial relationship of rainfall and NDVI displayed temporal variations as there were significant differences in the spatial trends throughout the study period.<br/><br/>},
  author       = {Georganos, Stefanos and Abdi, Abdulhakim M. and Tenenbaum, David E. and Kalogirou, Stamatis},
  issn         = {0140-1963},
  keyword      = {Geographically weighted regression,Non-stationarity,Scale dependency,Sahel,Earth observation,Drylands,Africa,Semiarid,arid},
  language     = {eng},
  pages        = {64--74},
  publisher    = {Elsevier},
  series       = {Journal of Arid Environments},
  title        = {Examining the NDVI-rainfall relationship in the semi-arid Sahel using geographically weighted regression},
  url          = {http://dx.doi.org/10.1016/j.jaridenv.2017.06.004},
  year         = {2017},
}