Environmental monitoring and assessment frequently require remote sensing techniques to be\ud deployed. The production of higher level spatial data sets from remote sensing has often been\ud driven by short-term funding constraints and specific information requirements by the funding\ud agencies. As a result, a wide variety of historic data sets exist that were generated using different\ud atmospheric correction methods, classification algorithms, class labelling systems, training\ud sites, map projections, input data and spatial resolutions. Because technology, science and\ud policy objectives are continuously changing, repeated natural resource inventories rarely\ud employ the same methods as in previous surveys and often use class definitions that are\ud inconsistent with earlier data sets (Comber, Fisher and Wadsworth 2003). Since it is generally\ud not economically feasible to recreate these historic land cover/land use data sets, often\ud inconsistent data sets have to be compared.\ud An environmental assessment of land cover and land use change in Central Siberia is\ud presented. It utilises several different digital land cover maps generated from satellite data\ud acquired in different years. The specific characteristics of different land cover maps create\ud difficulties in interpreting change maps as either land cover/land use change or a pure data\ud inconsistency. Many studies do not explicitly deal with these inconsistencies. It is argued that a\ud rigorous treatment of multi-temporal data sets must include an explicit map of consistency\ud between the multi-temporal land cover maps. A method utilising aspects of quantified\ud conceptual overlaps (Ahlqvist 2004) and semantic-statistical approaches (Comber, Fisher and\ud Wadsworth 2004a,b) is presented. The method is applied to reconcile three independent land\ud cover maps of Siberia, which differ in the number and types of classes, spatial resolution,\ud acquisition date, sensor used and purpose.\ud Amap of inconsistency scores is presented that identifies areas ofmost likely land cover change\ud based on the maximum inconsistency between the maps. The method of quantified conceptual\ud overlaps was used to identify regions where further investigations on the causes of the observed\ud inconsistencies seem warranted. The method highlights the value of assessing change between\ud inconsistent spatial data sets, provided that the inconsistency is adequately considered
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