Abstract. 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.