RMRS Raster Utility

Spatial modeling is an integral component of most geographic information systems (GISs). However, conventional GIS modeling techniques can require substantial processing time and storage space and have limited statistical and machine learning functionality. To address these limitations, many have parallelized spatial models using multiple coding libraries and have applied those models in a multiprocessor environment. Few, however, have recognized the inefficiencies associated with the underlying spatial modeling framework used to implement such analyses. In this project, we identify a common inefficiency in processing spatial models and demonstrate a novel approach to address it using lazy evaluation techniques. We introduce a new coding library that integrates Accord.NET and ALGLIB numeric libraries and uses lazy evaluation to facilitate a wide range of spatial, statistical, and machine learning procedures within a new GIS modeling framework called Function Modeling. 

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Longleaf Restoration

Accurate information is important for effective management of natural resources. In the field of forestry, field measurements of forest characteristics such as species composition, basal area, and stand density are used to inform and evaluate management activities. Quantifying these metrics accurately across large landscapes in a meaningful way is extremely important to facilitate informed decision-making. Over various studies we explore using readily available field and remotely sensed data to develop spatially explicit estimates of forest characteristics that can be used to inform restoration management. Key groups and agencies that have supported this research include: National Fish and Wildlife Foundation, The Longleaf Alliance, USFS State and Private, Florida National Forests, USDA RESTORE, and The Nature Conservancy.

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Fire Resilient Landscapes

Big data and the information and knowledge we glean from it are fundamentally changing the way in which resource management decisions are being made. The use of remotely sensed data, ever expanding computer technology, and various processing techniques are helping to provide natural resource managers with depictions of various aspects of ecosystems at unprecedented spatial and temporal resolutions. While the technologies used to gather data about natural resources have arguably outpaced our abilities to efficiently manipulate and use those data for decision making, newer tools, algorithms, and approaches are actively being developed to address this limitation and provide new opportunities to embrace the volume, variety, and velocity of big data streams. Using these new tools we are now able to quickly and efficiently address questions related to fuels, fire behavior, and resource management at spatial scales that are useful for fire and natural resource decision making. 

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Biomass & Logistics

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. 

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Wildland Fire Management

There is little known about the self-organization of convective structures and near-fire smoke plume development for the purpose of improving fire management. Our research bridges this gap in knowledge through a combination of robust science data collection on experimental fires, expansion of modeling capabilities, and detailed physics-based simulation modeling. Collectively, the tools and understandings garnered thought this research will be used to substantially advance science-based prescribed fire management. 

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Parallel Geospatial Libraries

Data-driven decision making is key to providing effective and efficient wildfire protection and sustainable use of natural resources. Due to the complexity of natural systems, the decision(s) to alter those resources needs clear justification based on substantial amounts of information that are both accurate and precise at various spatial scales. To build that information and incorporate it into the decision-making process, new analytical processes and frameworks are required that incorporate novel computational, spatial, statistical, and machine learning concepts with field data and expert knowledge in a manner that is easily digestible by natural resource managers and practitioners.

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