The Evolution of Forest Inventory

For nearly two centuries’ foresters have worked within the constraints of data and time to build representative ‘inventories’ that describe the spatial and structural variations of a forest to make actionable decisions. At the core of describing these variations are tree size, number, spatial distribution and species which all vary across any landscape and are essential components of a working forest inventory. The quantification of forest density and structure comprises assessing forest stands across the landscape to provide a baseline set of functional metrics that describe the standing volume of similarly forested areas. Describing forests as ‘stands’, and including multiple parameters expressed on a per acre basis, has provided a long and very successful foundation for communication throughout the forestry profession for more than a century. Stands are defined as forested areas stratified into similar tree sizes by density and species class. These are often the result of judgments made by an experienced field forester with the aid of available maps and aerial imagery. The value of differences in height and/or density tallied by stand, and occurring between stands, is by design and helps to define components of the forest to achieve management objectives. These ‘stand-based’ metrics, when presented on a per acre basis, continue today to constitute the primary means of communicating forest conditions between ownerships and across geographic regions. Traditional forestry has based nearly every actionable decision on stand-based inventory data, often summarized by species in 1-inch diameter classes. This convention has provided sufficient detail to characterize the differences between even-aged and multi-aged stand structures necessary for silvicultural planning. This convention also allows for the inclusion of a parameter (or parameters) that define the degree of uniformity or spatial clumping within each stand. These conventions play significant roles in the design and development of a stand-based forest inventory. Primary factors are 1) the sampling design and intensity applied to the forested landscape, and 2) the total cost and time (annual and multi-year) required. The first factor is conventionally defined as cruising portions of the forest as stands, where there is typically only a single variable radius plot for every three to five acres within each stand. This approach would install a minimum of 6 – 15 uniformly distributed plots to estimate the spatial uniformity (clumpiness) within each sampled stand. The second factor (cost and time) provides the greatest motivation for considering alternative inventory methods. Traditional inventory is commonly between $4 – $11/acre and completed over a span of 5-years where 20% of the property is sampled as stands the first year. These cruised stands are then extrapolated to the remainder of the property in order to provide a complete inventory. It is with these aspects in mind that we suggest some newly available methods for developing a forest inventory. There are two main approaches suggested here, one is founded on satellite derived data providing an inventory at an area-based scale (10m or 20m pixels). The second centers on aerial Lidar systems that may or may-not also include satellite data and provide information at a treebased scale (single-tree or near-census resolution). Satellite-based Inventory TOne satellite-based method uses 10 spectral bands (visible, infrared and short-wave) regressed against observed 1/5th acre ground plots of single species, height classes and density classes. These regressions provide mappings of species, height and stand density at a resolution of 10-meters (40 observations / acre) across the landscape. Stand polygon boundaries are created using three (or more) height classes by three (or more) density classes. Speciesspecific tree lists within each polygon are then generated at a frequency of 40 observations per acre. The variation in stand density among these observations and within each stand is then used to compute the degree of spatial clumpiness. Satellite pixels representing high density ground Correspondence