New Geospatial Approaches for Efficiently Mapping Forest Biomass Logistics at High Resolution over Large Areas

John Hogland, Nathaniel Anderson, Woodam Chung
2018 ISPRS International Journal of Geo-Information  
Adequate biomass feedstock supply is an important factor in evaluating the financial feasibility of alternative site locations for bioenergy facilities and for maintaining profitability once a facility is built. We used newly developed spatial analysis and logistics software to model the variables influencing feedstock supply and to estimate and map two components of the supply chain for a bioenergy facility: (1) the total biomass stocks available within an economically efficient transportation
more » ... distance; (2) the cost of logistics to move the required stocks from the forest to the facility. Both biomass stocks and flows have important spatiotemporal dynamics that affect procurement costs and project viability. Though seemingly straightforward, these two components can be difficult to quantify and map accurately in a useful and spatially explicit manner. For an 8 million hectare study area, we used raster-based methods and tools to quantify and visualize these supply metrics at 10 m 2 spatial resolution. The methodology and software leverage a novel raster-based least-cost path modeling algorithm that quantifies off-road and on-road transportation and other logistics costs. The results of the case study highlight the efficiency, flexibility, fine resolution, and spatial complexity of model outputs developed for facility siting and procurement planning. facilities, there has been an emphasis on producing analyses and tools that can be replicated and used by practitioners in industry and government to inform planning and decision-making at strategic, operational, and tactical scales [10] . Some of the main challenges related to estimating feedstock supply and delivered costs stem from the difficulty in acquiring timely, accurate data and from problems with how the data are stored, scaled, and related to one another. Typically, feedstock and delivered costs are estimated and attributed to vector datasets at the spatial scale of forest stands (polygons) or roads segments (lines) (e.g., [7] ). While intuitive, the observational units in this case are typically variable in size, shape, and length, making spatial relationships mathematically complex to define and fixing estimates of forest characteristics to predefined geometric shapes. Given that polygons and lines are often meant to define homogenous areas or condition classes, once defined, they represent a fundamental observational unit that cannot easily be subdivided or otherwise manipulated post hoc. This approach is generally congruent with how forests are managed (i.e., silviculture is applied to forest stands) and does not pose a significant problem related to estimating supply and cost. However, it fundamentally limits inferences to the shapes and scale defined within the data structure. Moreover, topological relationships between polygons quickly become complex, requiring intensive computer processing time to solve simple geometric relationships over large landscapes, such as adjacency, direction, distance from, disjointedness, containment, and flow. Because of these complexities, many additional attributes related to the geometry of polygons and lines must be specified in attribute tables, requiring specialized skills and a significant amount of time to maintain and clean data, especially with regards to topology [2], which defines the spatial relationships between adjacent or neighboring features, most often in a two-dimensional plane within a geographic information system (GIS). As an alternative, raster-based data storage and spatial analysis can alleviate many of these complexities, while also reducing the processing time associated with determining biomass supply and cost [11] . Within this context, spatial geometries are simple (square cells) and are generally much smaller than a stand or road segment, offering the opportunity for higher resolution analysis. Groups of cells can be attributed with the same values to mimic a stand polygon or road segment line, but can also be easily subdivided in later analyses and related to other cells on the basis of simple topological rules. In addition, multiple aspects of supply and cost can be more easily related to one another as separate raster surfaces through spatial overlays and map algebra, without requiring predetermined groupings of attributes (e.g., the stand or the road segment). This provides a great deal of flexibility in the types of analyses that can be performed and in the types of inferences that can be made, while at the same time significantly reducing the processing time and storage space associated with spatially quantifying feedstock supply and delivered costs [12] . Comparison of Vector and Raster Methods Despite the advantageous characteristics of raster-based approaches for the spatial analysis of biomass logistics, the vast majority of such analyses are performed using a vector-based architecture [7, 13] . In large part, this is due to familiarity with vector data architecture and spatial modeling techniques, but also stems from the accessibility of data, tradition in the ways that spatial data are developed and analyzed (e.g., network analysis of vector road networks), and historical digital storage limitations. For example, in a forest inventory and management context, the boundaries of the forest stands are first delineated, typically based on stand attributes and forest characteristics, and then digitized in GIS. This process can be done manually in GIS or automated, using a forest segmentation or stand delineation algorithm for example [14] . As a whole, the entire stand is represented as one record (row) within a table. Attributes related to the stand (such as geometry, composition, basal area, density, and above ground biomass) are stored within columns of each row, thereby saving digital storage space within the context of the features describing the stand as a whole. For relationships among attributes within a given stand, this is an extremely efficient way to store, retrieve, and process information.
doi:10.3390/ijgi7040156 fatcat:2isptjzuurgvtcgovnl554o7dq