A range of research interests beyond global environmental change science increasingly calls for advances in land-change models and, specifically, models that have fine-grained locational outputs. The rationale for such modeling about land change has been articulated elsewhere in this book (Ch. 1; Introduction to Part IV) and need not be reiterated here. It is important to note, however, that advances in question are assisted by the advances in the analytical sophistication of geographical information systems, hardware (GPS) that permits geographical coordinates to be established easily in the field, and for land-change studies, increasing temporal and spatial resolution of satellite imagery. Much of the first phase of land-change models that incorporate these systems and data has been empirical-based, time series assessments, such as Markov-chain models (e.g. Turner 1988), that let the record of land change determine future projections, or the spatial level of assessment has been large-grain (e.g. counties, states, regions). The SYPR project seeks a different approach demonstrated here: to test theories of land change in regard to their ability to explain fine-grained land change in the region at different spatial scales of assessment. Two complementary econometric modeling approaches are used here to investigate the factors that affect deforestation at the regional and household scales of analysis. Both approaches use the individual satellite pixels as the data on land-use change, from the classification of TM imagery described in Ch. 6. A regional model spans the entire study area of agricultural ejidos, and links the satellite imagery with publicly available geophysical data and socio-demographic government census data. The second model focuses exclusively on the parcels associated with the household survey data collected specifically for this project, discussed in Part III, especially Ch. 8. This latter approach uses the same geophysical data of the aggregate approach, but uses the much richer socio-demographic data derived from the linkage of individual farm plots and the satellite imagery via the sketch mapping exercise described in Chs. 8 and 9. While both models take a theoretical approach of individual maximization, they differ in a number of ways, the most important of which is the role of time in the decision-making process.
Integration of Satellite Imagery, Geology and Geophysical Data
