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.

卫星数据提供了全球森林资源的变革视角，但观察到了一系列高度受限的森林属性，从而限制了理论研究和应用研究。针对这一问题，我们建议使用“经济林”作为分析单位，并说明如何通过结合开放地球观测数据集来检索决定人类决策的森林的关键特征。为了实施经济林方法，我们借鉴了经典的微观经济学理论（冯·图嫩的土地利用模型）来确定可以在露天地球观测数据中替代的两个与经济相关的变量（市场可及性和森林生产力）。然后，我们对全球森林变化数据集（v1.5）进行后处理，以生成2000-2015年间森林采伐的逐张地图，获取我们用于分层随机样本的两个变量的代理度量，并使用高斯混合模型将全球森林采伐的数据集分类为经济林的类别。我们绘制了得出的类别图，展示了一种可扩展的低成本方法来分析全球森林资源，该方法捕获了决定经济用途的关键特征。我们采用这种方法对海德的全球森林资源三方类型进行实证测试，规避了空间价值函数的理论重要性所带来的数据挑战，并找到了需要扩展类型的证据。我们还根据以前完整森林景观的地图，得出了地球上经济上无法进入的森林的第一近似值。为了证明我们的知识发现方法的价值，我们将23个欧洲国家/地区的FAOSTAT报告的木材供应分配给我们确定的经济林类别，并利用这些数据来分析林业中的Heckscher-Ohlin-Vanek关系。此练习揭示了汇总数据掩盖的生产结构与比较优势之间的合理且重要的联系，从而直接证明了我们方法的价值。最后，我们讨论经济林概念的潜在应用。提供我们方法价值的直接证据。最后，我们讨论经济林概念的潜在应用。提供我们方法价值的直接证据。最后，我们讨论经济林概念的潜在应用。