Identifying economically relevant forest types from global satellite data

Satellite data offer a transformative view of global forest resources but observe a highly restricted set of forest attributes, limiting both theoretical and applied research. In response to this problem, we propose the use of ‘economic forests’ as analytical units and show how key characteristics of forests that determine human decision-making can be retrieved by combining open earth observation datasets. To operationalize the economic forest approach, we draw on classical microeconomic theory (von Thünen's land use model) to identify two economically relevant variables (market accessibility and forest productivity) that can be proxied in open earth observation data. We then post-process the Global Forest Change dataset (v1.5) to produce wall-to-wall maps of forest harvest for the period 2000–2015, obtain proxy measures of our two variables for a stratified random sample, and classify the resulting dataset of global forest harvest into categories of economic forest using Gaussian Mixture Models. We map the resulting categories, demonstrating a scalable and low-cost approach to analyzing global forest resources which captures key features that determine economic use. We employ this approach to empirically test Hyde's tripartite typology of global forest resources, circumventing the data challenge posed by the theoretical importance of spatial value functions, and find evidence that an expanded typology is required. We also produce a first approximation of Earth's economically inaccessible forests, drawing on previous mapping of Intact Forest Landscapes. To demonstrate the value of our approach for knowledge-discovery, we allocate FAOSTAT-reported timber supply for 23 European countries to the categories of economic forests we identify and employ these data to analyze the Heckscher-Ohlin-Vanek relationship in forestry. This exercise reveals plausible and significant relationships between production structure and comparative advantage that are obscured by aggregated data, providing direct evidence of the value of our approach. We conclude by discussing potential applications of the economic forests concept.