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. This paper explores the use of fuzzy difference methods in orderto understand the differences between forest classes. The context for thiswork is provided by REDD + , which seeks to reduce the net emissions ofgreenhouse gases by rewarding the conservation of forests in developingcountries. REDD + requires that local inventories of forest are undertaken andpayments are made on the basis of the amount of forest (and associated carbonstorage). At the most basic level this involves classifying land into forestand non-forest. However, the critical issues affecting the uptake, buy-in andultimately the success of REDD + are the lack of universally agreed definitionof forest to support REDD + mapping activities, and where such a definition isimposed, the marginalization of local community voices and local landscapeconceptualizations. This tension is at the heart of REDD + . This paperaddresses these issues by linking methods to quantify changes in fuzzy landcover to the concept of data primitives, which have been previouslyproposed as a suitable approach to move between land cover classes withdifferent semantics. These are applied to case study that quantifies thedifference in areas for two definitions of forest derived from the GLC and FAOdefinitions of forest. The results show how data primitives allow divergentconcepts of forest to be represented and mapped from the same data and howthe fuzzy sets approach can be used to quantify the differencesand non-intersections of different concepts of forest. Together these methodsprovide for transparent translations between alternative conceptualizationsof forest, allowing for plural notions of forest to be mapped and quantified.In particular, they allow for moving from an object-based notion of forest(and land cover in general) to a field-based one, entirely avoiding the needfor forest boundaries.

Fuzzy difference and data primitives: a transparent approach for supporting different definitions of forest in the context of REDD+