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

This paper explores the use of fuzzy difference methods in order to understand the differences between forest classes. The context for this work is provided by REDD+, which seeks to reduce the net emissions of greenhouse gases by rewarding the conservation of forests in developing countries. REDD+ requires that local inventories of forest are undertaken and payments are made on the basis of the amount of forest (and associated carbon storage). At the most basic level this involves classifying land into forest and non-forest. However, the critical issues affecting the uptake, buy-in and ultimately the success of REDD+ are the lack of universally agreed definition of forest to support REDD+ mapping activities, and where such a definition is imposed, the marginalization of local community voices and local landscape conceptualizations. This tension is at the heart of REDD+. This paper addresses these issues by linking methods to quantify changes in fuzzy land cover to the concept of data primitives, which have been previously proposed as a suitable approach to move between land cover classes with different semantics. These are applied to case study that quantifies the difference in areas for two definitions of forest derived from the GLC and FAO definitions of forest. The results show how data primitives allow divergent concepts of forest to be represented and mapped from the same data and how the fuzzy sets approach can be used to quantify the differences and non-intersections of different concepts of forest. Together these methods provide for transparent translations between alternative conceptualizations of 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 need for forest boundaries.