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

Abstract

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 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.

Estimating Forest Characteristics for Longleaf Pine Restoration Using Normalized Remotely Sensed Imagery in Florida USA

“…For example, DFT, LLP, Pine and Other BAH and TPH, and standard error raster surfaces, can be combined using a geographic information system and map algebra to locate, prioritize, and quantify overstocked pine areas where longleaf pine trees are present. The resulting surfaces from those spatial analyses can then be used to plan thinning and prescribed fire regimes to restore those areas to healthy longleaf ecosystem stand densities and compositions [35]. Similarly, these same surfaces could be used to locate, prioritize, and quantify areas where tree species other than pine make up the dominant component of BAH (DFT Other class) but still have a remnant pine and longleaf element (hardwood encroachment).…”

21st Century Planning Techniques for Creating Fire-Resilient Forests in the American West

“…Our case study highlights new and novel computational tools and techniques used to create fine-grained timely information that accurately describes the existing condition of forest lands and integrates seamlessly with wildfire risk, spread, and suppression analyses to quantify prescription costs and anticipated revenues for forest plan implementation. Case study objectives include: (1) to quantify the existing forest condition using remotely sensed imagery, field data, the Rocky Mountain Research Station (RMRS) Raster Utility spatial modeling tools [22], and ensemble of generalized additive models (EGAMs) [23]; (2) to spatially define desired future condition and prescriptions that leverage the Potential Operational Delineations (PODs) framework to both harden fire control boundaries and bring POD interiors to a more fire-resilient condition; and (3) to spatially map a cost revenue assessment (CRA) that quantifies the existing supply chain and delivered costs [24] associated with implementing desired future conditions (Figure 1). Using these spatially explicit datasets, the RMRS Raster Utility spatial modeling tools, batch processing, and function modeling [25], we then further demonstrate how managers can quickly and easily prioritize treatments based on wildfire risk, budgetary constraints, and anticipated revenues.…”

“…However, coupled with finely grained remotely sensed information, such as Sentinel 2 imagery [34] and the National Elevation Dataset (NED) [35], data such as multiparty monitoring plots and TIGER road networks and statistical and machine-learning relationships can be leveraged to produce finely grained surfaces depicting the existing forest condition [23] and the costs to move biomass from the forest to a given sawmill [24]. In this study, we demonstrate how existing forest monitoring plots [32], TIGER line files [33], Sentinel 2 imagery [34], and PODs derived from fire managers and common fire modeling tools [11] were used to quantify various forest metrics and treatment costs which in turn were used to inform, justify, and improve restoration decisions.…”

Using Forest Inventory Data with Landsat 8 Imagery to Map Longleaf Pine Forest Characteristics in Georgia, USA

“…In 2015, the authors, together with government and NGO partners, began a series of studies aimed at improving the resolution, extent, accuracy and utility of spatial analysis and mapping products available for longleaf pine projects. This work leveraged new statistical methods and novel data structures and algorithms that were initially developed for mapping forest characteristics in the western USA [5][6][7][8]. Image texture metrics and principle components derived from National Agriculture Imagery Program (NAIP) aerial photographic imagery [9] were used with a softmax neural network model to produce probabilistic classification surfaces at 1 m resolution across 11.6 million ha in the southeast USA [10].…”