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MRIT: Multi-scale Resource Integration Tool for Landscape Assessment

Understand and apply MRIT functions to integrate and prioritize data layers for fire, biophysical, wildlife, and fisheries assessments. Learn how to derive integrated priorities and assess landscape data effectively.

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MRIT: Multi-scale Resource Integration Tool for Landscape Assessment

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  1. “MRIT” Multi-scale Resource Integration Tool FOR – 438

  2. FOR-438 Tool & Data Flow Overview Landscape Assessment Data re-runs FBAT (Fire Behavior Assessment Tool) FlamMap ACT (Area Change Tool) FRCC Mapping Tool MRIT (Multi-scale Resource Integration Tool)

  3. Objectives • Discuss what it means to integrate • Learn how to derive integrated priorities • Introduce the functions & utility of MRIT • Show examples of MRIT applications • Review MRIT exercise objectives

  4. FIRE Biophysical Vegetation Wildlife Fisheries Communities Integration Integrate –to make whole by bringing all the parts together; to unite.

  5. Integration Purpose –to prevent conflicting objectives; to develop a cohesive strategy

  6. Integration Requirements: Standardized data Common reporting units

  7. MRIT • ArcMap extension • Functions: • Summarizes spatial data • Integrates spatial data • Classifies reporting units by their composition • Application - prioritization

  8. Input Layers • Feature layer – data to be summarized or integrated • Reporting Unit – summary or analysis unit

  9. FRCC Fire Regimes Fire Behavior Ignition probability Vegetation types Erosion probability Roads Road density Streams Stream density Wildlife habitat Fish habitat Wildland-Urban Interface Feature Layer Examples

  10. MRIT Reporting Units Three levels Reporting Unit 1: ….. Watershed Reporting Unit 2: …………….Federal Ownership Reporting Unit 3 ……………………………..…….WUI

  11. Reporting Unit Examples • Political boundaries - states, counties • Admin units – regions, forests, districts • Biophysical units – Sections, Subsections, LTAs • Watersheds • Planning Units - FPUs, FMUs • Stands

  12. Data Resolution – Why summarize data by a reporting unit? • Pixel level • May have accuracy problems • May be too complex & provide little information • Few decisions are made at the pixel level • Reporting Unit level • Changes resolution of the data • May reduce accuracy concerns • Less complex to interpret • May better match the level of decision

  13. MRIT Commands • Composition commands • Composition • Percent composition • Area-weighted average • Integration commands • Integrate • Cluster

  14. Composition Commands • Composition –amount of a feature (pixel count) within a reporting unit (size matters) • Percent Composition – proportion of a reporting unit occupied by a feature • Area-Weighted Average – spatial statistic representing the average “value” of a feature weighted by area; individual values of a feature can also be weighted according to importance

  15. Integration Commands • Integration – classifies reporting units based upon their composition of multiple features (“or” query) • Clustering – classify reporting units based on their similarity in composition of multiple features summarized by acomposition command(“and” query)

  16. CLASSIFICATION METHOD Number of Classes Standardized Values Classification of Reporting UnitsDefault Methodology • Classes = 5 • Based upon relative values (standardized between 0 or 1) • Class thresholds are statistically derived using “natural breaks” of frequency distribution

  17. Example: Classifying LandscapesUsing Composition Commands • Which landscapes have the greatest opportunity to restore FRCC? • Which landscapes are the most “out-of-whack”? • “Landscapes” = subwatersheds • Opportunity = Stand-level Condition Classes 2 & 3

  18. Example: Restoration Opportunities Condition Classes 2 & 3 Stand-level FRCC

  19. Composition Composition CommandsWhich landscapes have the greatest opportunity for restoring FRCC?

  20. Composition CommandsWhich landscapes are most "out-of-whack”? Percent Composition

  21. Composition CommandsWhich landscapes are most “out-of-whack”? Area-Weighted Average Weights: CC2 = 2 CC3 = 3

  22. Composition CommandsWhich landscapes have worst fire behavior potential? Absolute Fire Behavior

  23. Composition CommandsWhich landscapes have worst fire behavior potential? Area-Weighted Average Weights: Low = 1 Mod = 5 High = 10

  24. Composition CommandsWhich landscapes have the greatest proportion of WUI?

  25. Percent Composition Composition CommandsWhich landscapes have the greatest proportion of WUI?

  26. Composition CommandsWhich landscapes have the greatest proportion of Aspen? Aspen Aspen - Conifer

  27. Composition CommandsWhich landscapes have the greatest proportion of Aspen? Percent Composition

  28. Fire Behave Aspen WUI FRCC 2 & 3 IntegrationWhere should I treat??

  29. Integration Considerations: • Just because you can, doesn’t mean you should • Keep it simple; too many features increases complexity • Beware using features that are inversely related • Be sure to match features to appropriate analysis question • Be sure reporting unit matches the decision level Plan Ahead!!!

  30. Reports Spatial Reports Tabular Reports

  31. Access Database Data Storage Attribute Table of Display Layer

  32. ??Help??

  33. MRIT Exercise • Prioritize WUI areas based upon their fire behavior hazard • Prioritize WUI areas based upon their restoration opportunities • Derive integrated priorities of WUI areas based upon fire behavior and restoration opportunity

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