Into the Wilderness…Guided By a High Resolution Forest Inventory Solution

A High Resolution Inventory Solution (HRIS) is a forest and land inventory, complete with all of the attributes (land cover, species, site productivity, structure) at a resolution comparable to our human experience when standing in the field. It can take us from where we are standing to where we want to be, one step at a time.

“The Goal of HRIS is to advance forest inventory in a way that extends well beyond what is possible with traditional inventory. “

An HRIS inventory can be used in the field as a set of guideposts from one place to the next. We can quickly determine whether the guideposts are reliable, and if not, update them along the way. The primary advantage of this kind of an inventory over traditional (ground sample or photo interpreted) inventory, is the ability to reliably illuminate these guideposts at a high level of resolution. Traditional inventory encompasses much larger polygons (e.g. 5 ha or more on average); these can be difficult to follow and even unreliable, particularly when forest and land conditions are more variable in nature.

With HRIS, we always have the flexibility to make larger polygons. We can choose the kinds and content of rules needed to extract broader scale information from the attributes represented on much smaller pieces of land. We do this to support the kinds of decisions that must be made, whether that be to identify species’ habitats, to represent different viewscapes, or to locate prospective timber harvest units for a harvest schedule. Each of these applications requires a different way of looking at the same forest. The aim is to summarize data in a form and at a scale that is most relevant to the forest and land management decisions to be made.

The goal of HRIS is to advance forest inventory in a way that extends well beyond what is possible with traditional inventory. The specific goals of HRIS are:

1. To increase the resolution from 5 ha or larger polygon sizes down to the size of approximately 1/10th of a hectare.
2. To provide a level of detail in relation to all of the attributes required to make forest management decisions, including: (a) land cover classification, (b) differences relating to site productivity (i.e. factors related to differences in potential growth rates of trees and other organisms), © tree species composition and/or plant community, and (d) structural attributes (e.g. crown closure, height, basal area, trees per hectare, height and % cover of shrubs, etc.).
3. To include any attributes that may be required to project the inventory into the future. The goal is to reflect expected changes in species composition and structural attributes with time. This is required to support inventory updates and strategic level planning.
4. To maximize the accuracy and the precision of attribute estimates; to reduce or eliminate any bias; and to evaluate and report on these statistics as they relate to production and uses of the inventory.
5. To provide flexibility in interpretation by way of automating the aggregation of units observed at the highest level of resolution, into larger polygons. This can be obtained with user defined rules combined with alternative algorithms for applying those rules, so as to produce resultant aggregated polygon data sets at a desired scale.
6. To provide flexibility in interpretation by way of estimating new attributes associated with observations in the field (e.g. for fire hazard and risk rating); this allows us to redraw the inventory as and when needed to support new interpretations.

HRIS is a new paradigm that relates attributes observed on the ground to those collected using remote sensing tools. Airborne Light Detection and Ranging (LiDAR) has dramatically advanced our ability to fulfill HRIS goals. LiDAR can be used to develop very accurate assessments of elevation (e.g. +/- 20 cm) at any given location, and these in turn can be used to identify individual trees and canopy profiles. LiDAR can be used to produce a three dimensional portrayal of tree and ground plot canopy sizes and shapes. These data provide measures of what we see on the ground, and can be related to actual measurements on the ground. LiDAR enables us to represent large areas of land extending from a single ground plot (e.g. 20m x 20m) to millions of hectares all at a high resolution (e.g. 20m x 20m). Most significantly, LiDAR combined with colour infrared (CIR) can be used to extend interpretations more broadly to represent land cover, tree species composition, site productivity, and forest stand structure.

The High Resolution Inventory Solution is here now, and it opens the way into the future. We will continue to advance HRIS and in so doing, fulfill the goals mentioned above. It is time to join the HRIS revolution, to advance forest management and forest management practices, and to capitalize on the investment in HRIS for greater prosperity and well-being. There is an exciting and rewarding journey ahead.

Ian Moss, PhD RPF