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As data sets of multiple types and scales proliferate, it will be increasingly important to be able to flexibly combine them in ways that retain relevant information. A case in point is Amazonia, a large, data-poor region where most whole-basin data sets are limited to understanding land cover interpreted through a variety of remote sensing techniques and sensors. A growing body of work, however, indicates that the future state of much of Amazonia depends on the land use to which converted areas are put, but land use in the tropics is difficult to assess from remotely sensed data alone. An earlier paper developed new snapshots of agricultural land use in this region using a statistical fusion of satellite data and agricultural census data, an underutilized ancillary data source available across Amazonia. The creation of these land-use maps, which have the spatial detail of a satellite image and the attribute information of an agricultural census, required the development of a new statistical technique for merging data sets at different scales and of fundamentally different data types. Here we describe and assess this nonlinear technique, which reinterprets existing land cover classifications by determining what categories are most highly related to the polygon land-use data across the study area. Although developed for this region, the technique appears to hold broad promise for the systematic fusion of multiple data sets that are closely related but of different origins.
