Thematic land-cover map assimilation and synthesis: the case of locating potential bioenergy feedstock in eastern Ontario,Canada |
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| Authors: | Kirby Calvert Peter Luciani Warren Mabee |
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| Institution: | 1. Department of Geography, Penn State Institutes for Energy and the Environment, Pennsylvania State University, University Park, PA, USA;2. Department of Geography, Faculty of Arts and Science, Queen’s University, Kingston, Ontario, Canada;3. Queen’s Laboratory of Geographical Information and Spatial Analysis, Department of Geography, Queen’s University, Kingston, Ontario, Canada;4. Queen’s Institute for Energy and Environmental Policy, Queen’s University, Kingston, Ontario, Canada;5. Queen’s School of Policy Studies, Queen’s University, Kingston, Ontario, Canada |
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| Abstract: | Insufficient spatial coverage of existing land-cover data is a common limitation to timely and effective spatial analysis. Achieving spatial completeness of land-cover data is the most challenging for large study areas which straddle ecological or administrative boundaries, and where individuals and agencies lack access to, and the means to process, raw data from which to derive spatially complete land-cover maps. In many cases, various sources of secondary data are available, so that land-cover map assimilation and synthesis can resolve this research problem. The following paper develops a reliable and repeatable framework for assimilating and synthesizing pre-classified data sets. Assimilation is achieved through data reformatting and map legend reconciliation in the context of a specific application. Individual maps are assessed for accuracy at various geographic scales and levels of thematic precision, with an emphasis on the ‘area of overlap’, in order to extract information that guides the synthesis process. The quality of the synthesized land-cover data set is evaluated using advanced accuracy assessment methods, including a measure describing the ‘magnitude of disagreement’. This method is applied to derive a seamless thematic map of the land cover of eastern Ontario from two disparate map series. The importance of assessing data quality throughout the process using multiple reference data sets is highlighted, and limitations of the method are discussed. |
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| Keywords: | land-cover map bioenergy data quality accuracy assessment error magnitude |
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