Mixed methods in land change research: towards integration

Land change science has been strongly influenced by geographers working in geographic information science (Gutman et al. 2004). These geographers typically employ GIS and remote sensing technology to determine the type and magnitude of naturaland human-induced changes on the landscape, focusing on changes in land cover (i.e. the biophysical aspects of land dynamics). As it has matured, however, land change science has moved toward inclusion of themes from geography’s human– environment tradition and other interdisciplinary endeavours to link natural and human systems in order to understand the human effects and implications of land change (Rindfuss et al. 2004; Moran and Ostrom 2005). Land change science has therefore emerged as an integrative science that attempts to bridge understandings of both land-use (i.e. human and social aspects of land dynamics) and land-cover change as two components of a coupled natural and human system. This new approach has emphasised stronger inclusion of the human processes to establish human–environment interaction methods and models instead of approaches that conceive of human–environment interaction in uni-directional terms, i.e. as human impact on environment as represented in land cover. Methodologically, the challenge remains to suitably integrate diverse datasets and approaches. Mixed methods ranging from participant observation, interviews, GIS, remote sensing, statistics and computation are used to generate explanation and predictive and representative models and plausible scenarios in land change science (Dearing et al. 2006; Gimblett et al. 2001, Parker et al. 2003; Robinson et al. 2007). This paper considers the question of the appropriate balance between (1) quantitative and qualitative approaches, (2) simulation and observation, and (3) diversity and integration central to the pursuit of an integrative approach to mixed methods in land change science. The paper discusses why the current use of mixed methods remains complementary rather than integrative by addressing gaps and possible solutions. It employs examples primarily from land use, agent-based modelling and scenario development as they reflect some of the latest developments that link social dynamics of land use with land cover. The examples are by no means exhaustive, and they mainly serve to illustrate the main question of appropriate balance.