Geoinformatic Intelligence Methodologies for Drought Spatiotemporal Variability in Greece

Efrosyni Kanellou (), Nicos Spyropoulos () and Nicolas Dalezios ()

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2012, vol. 26, issue 5, 1089-1106

Abstract: One of the most important hazards in terms of cost, frequency of occurrence and impact on humans is drought. Drought indices are estimations of precipitation shortage and water supply deficit. Satellite drought indices are normally radiometric recordings of vegetation condition and dynamics, exploiting the unique spectral signatures of canopy elements, particularly in the red and near-infrared bands. However, the identification of drought based on the Reconnaissance Drought Index (RDI) enables the assessment of hydro-meteorological drought, since it uses hydro-meteorological parameters. RDI is a fairly comprehensive index as it combines the simplicity of use and the successfully assessment and monitoring of the phenomenon. However, the study and understanding of the spatiotemporal variability of drought is not an easy process. In this study the main goal is to use the PCA + clustering method to transform the RDI temporal data (1982–2001) and cluster the different regions of Greece based on that temporal variations. Firstly, Principal Component Analysis (PCA) applied onto 19 annual RDI indices followed by Clustering that was based on certain eigenchannels resulted from the previous PCA analysis. Both methods are linear transformations capable to decorrelate the spatiotemporal information provided in the estimated RDI. The time series presented approach proved to be advantageous in relation to other statistical methods used to describe variability and provide excellent and fast results for stakeholders and environmental organizations. The results are quite satisfactory in classifying the drought-induced climatic regions of Greece. Copyright Springer Science+Business Media B.V. 2012

Keywords: Reconnaissance drought index; Remote sensing; Principal components analysis; Clustering (search for similar items in EconPapers)
Date: 2012
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DOI: 10.1007/s11269-011-9948-1

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