Transporting woody biomass represents another important spatial aspect of logistics costs. Typically, these costs are derived as a series of rates relating to factors such as road speed, fuel consumption, machine hours, and payload. When combined with other costs, these rates can be converted to an absolute value based on hauling distance or time (trip) and the total number of trips required to transport the material. Within a GIS, hauling routes that minimize travel distance and time can be estimated for a route from a starting location (source of biomass) to an ending location (facility) using a road network, source and delivery points, and road network routing [20]. The total number of trips required to transport woody biomass from a given location can be estimated from the total amount of woody biomass available at that location, the associated densities of the woody biomass, and the payload of the truck-trailer configuration. Moreover, trip distance or time and number of trips can be tied together based on the spatial relationship between the source of woody material and the road network.

Minimizing travel distance and time between the source of biomass and a delivery site is straightforward within a GIS. However, on a forested landscape there are many potential sources of biomass for which to determine optimal routes to delivery sites. In this situation, it is easier to think of loading points along a transportation system that can be attributed a minimized trip distance and time. From loading points on the road network, polygons can be created that define the areas closest to each individual loading point, in an automated fashion (Thiessen polygons). Each Thiessen polygon can then be attributed with the transportation costs of its point on the road network, which can be efficiently related to estimates of biomass using spatial relationships.

14.10.2 Estimating Harvest Costs Across a Landscape

Similar to determining transportation costs across a landscape, harvesting costs are derived from rates such as fuel consumption and machine hours. Additionally, absolute costs derived from harvesting rates depend on the total amount and density of standing biomass. While the amount and density of biomass is typically quantified for polygons or a raster surface, the boundaries of those polygons or cells of the raster surface may not represent boundaries of areas that will be harvested. A separate spatial table that defines harvest unit boundaries is often needed to account for management objectives and the logistics of harvesting.

In practice, predicting the location of a harvesting unit boundary is difficult prior to its actual creation. However, within a GIS rules can be created that generalize harvesting policy, management objectives, and stochastic events to create potential harvesting units across a landscape. These rules can quickly become complex and can incorporate a wide range of factors, such as topography, proximity to streams, available tree biomass, maximum harvest unit size, proximity of harvest units to recently harvested land, fire mortality and beetle kill. Often, due to the complexity of building rules for harvest unit boundaries and the reliability of the outputs, a surrogate boundary table such as the Thiessen polygons described in Section 14.10.5 is used to represent harvesting units.

Once harvest unit boundaries are defined, rules and thresholds based on factors such as topography and soil condition can be used to determine the appropriate harvesting system. In addition, total woody biomass, densities, and residues can be calculated for harvest boundaries by spatially relating the geometry of each harvest unit to the estimates of biomass stocks. Absolute costs for the harvest unit are then calculated using the cost rates associated with the selected harvesting system and the weight of the residuals calculated from a treatment.