A review of methods for mapping and prediction of inventory attributes for operational forest management

Forest inventory attributes are an important source of information for a variety of strategic and tactical forest management purposes. However, it is not possible or feasible for field inventories to be conducted contiguously across large areas, especially at a resolution fine enough to be useful for operational management. Therefore, a large number of quantitative modeling and prediction methods have been and are being developed and applied to predict and map forest attributes, with the goal of providing an accurate, spatially continuous, and detailed information base for practitioners of forestry and ecosystem management. This article reviews the most commonly used prediction techniques in the context of a comprehensive modeling framework that includes a discussion of methods, data sources, variable selection, and model validation. The methods discussed include regression, nearest neighbor, artificial neural networks, decision trees, and ensembles such as random forest. No single technique is revealed as universally superior for predicting forest inventory attributes; the ideal approach depends on goals, available training and ancillary data, and the modeler’s interest in tradeoffs between realism and statistical considerations. Useful ancillary data included in the models tend to include climate and topographic variables as well as vegetation indices derived from optical remote sensing systems such as Landsat. However, the use of airborne LiDAR in modeling of forest inventory attributes is increasing rapidly and shows promise for operational forest management applications. Different considerations are encapsulated within a generalized model development framework that provides a structure against which tradeoffs can be evaluated.