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. 

Research Highlights:

• Function modeling improves the efficiency of spatial modeling using big data from remote sensing

• RMRS Raster Utility Website

• Subversion Site

• Tools

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. 

Research Highlights:

• 21st Century Planning Techniques for Creating Fire-Resilient Forests in the American West (Presentation at AFE) 

• Locating Forest Management Units Using Remote Sensing and Geostatistical Tools in North-Central Washington, USA

• Stanislaus National Forest (notebook)

• Blue Mountain Forest Partnership (publication)

Short Course:

• Google Class Code: igys6jc

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. 

Research Highlights:

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

• Spatial and temporal quantification of forest residue volumes and delivered costs

• Estimating FIA plot characteristics using NAIP imagery, function modeling, and the RMRS Raster Utility coding library

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. 

Research Highlights:

• Integrating Datasets of fuels, fires, smoke and plumes (in progress)

• Expansion of Modeling Capabilities (in progress)

• Simulation Modeling at multiple Scales (in progress)

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.

Research Highlights:

• Fast delayed reading, parallel processing python based libraries that incorporate tabular, geospatial, statistical, and machine learning functionality within the DASK processing environment  (Presentation: Project Overview)

• Open-source parallel processing and delayed reading libraries, web-based GIS, and an update on high performance computing pilots (Presentation: HPC, Raster-Tools, web GIS) 

• Automated parallel scheduling across CPU and GPU

• Raster-Tools GitHub: https://github.com/UM-RMRS/raster_tools.git 

• Cross platform design