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Mgr. Martin Dolejš ()

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DOLEJŠ, M. Podtyp: Příspěvek ve sborníku (mimo kategorie RIV); Current methodological frameworks of driving forces research within the scope of landscape ecology. 2015, During the last decade, much effort has been devoted in landscape ecology to establish and reconsider the appropriate methodological approach to study of driving forces that causes land use and land cover change (LULC). Categorization and some of the methodological frameworks were presented (e.g. standard procedure to study driving forces proposed by Bürgi et. al., 2004), but prevalent methodological approach based on sole quantification (e.g. statistical approach) is lacking in explanation of such complex relationships as causation among driving forces and LULC change. The study aims to research currently published papers dealing with evaluation of LULC change drivers with the focus on used methodological framework. The Hersperger et. al. (2010) proposed four conceptual models that links actors, change and driving forces. These were taken as a base for further categorization of papers. Research papers dealing with real LULC from last five years (n=95) were divided according to schema and methodology used in research of LULC and its drivers (independent variables) were extracted. The study shows that majority papers are quantification-oriented (64 %) followed by papers that incorporates some of the qualitative methods (33 %) to study actors of LULC and its drivers. According to frequency of used drivers, the main attention is given to socio-economical drivers that often (26 %) creates studies based solely on one category (e.g. with no attention to political, cultural or technological drivers). Despite their shortcomings discussed in the literature, the solely quantitative methods are still widely used. Above mentioned obstacles of quantitative oriented approaches (e.g. explanation of causes and consequences) could be resolved by multidisciplinary contribution of social science methods (combination of quantitative and qualitative data in general) of process tracing or analysis of narrative..

 
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