After determining harvest and transportation costs across a landscape, the two can be linked to one another through overlay analysis [21]. Specifically, absolute harvesting costs can be combined with absolute transportation costs based on estimates of woody biomass residues for a given harvest unit and the spatial proximity of that harvest unit to the closest loading area. These combined costs are attributed to the harvest unit and compared in relative fashion across the landscape (cost per acre or weight of material). Furthermore, estimates of available woody biomass residues for the harvesting unit are used to represent potential flow of material from that location. Using these costs and potential flows, questions such as how much woody biomass is available across a landscape, where are the least expensive areas to procure woody biomass, from which locations is it profitable to market woody biomass, and are there timing components related to harvest locations that can reduce logistical costs, can be answered in a relatively quick and easy manner.

When utilizing base data and rules to derive cost and potential woody biomass flows from a landscape, it is important to consider the scale and the level of precision needed to answer these types of questions. Base data and rules that are too coarse may not provide an adequate level of detail to properly estimate woody biomass and flows. On the other hand, too fine a scale may present issues related to finding and developing complete data sets, digital storage space requirements, and total processing time and memory it takes to perform spatial analyses for the landscape of interest. Once defined for harvesting units, these costs and potential flows can be used to plan harvesting schedules across both space and time for a given landscape. Multiple simulations depicting various policies, objectives, and conditions can be compared to evaluate the impacts of decisions made based upon the constraints of those criteria. Moreover, if objectives and constraints can be spatially represented in a relative fashion they, can be optimized across the landscape to minimize logistic costs and maximize woody biomass flows. Such analysis can help reduce biomass supply costs, especially in complex procurement environments.