INR collaborates with a variety of partners to develop maps of vegetation and other important landscape characteristics. Our products range from mid-scale maps that extend across multiple states, to more project-tailored data with smaller geographic footprints. These data have been instrumental to land managers and have enabled critical ecological research.
Mid-scale, existing vegetation mapping provides information about current vegetation composition and structure at the resolution and scale needed to inform a range of conservation, management, and planning activities. INR’s vegetation mapping team uses a variant of nearest neighbor imputation to create mid-scale maps that provide rich data depth and wall-to-wall coverage for all land ownerships within target regions. These maps support collaborative landscape management across administrative boundaries by providing information that can be used to inform planning, from estimating timber supplies, carbon stocks, and potential fuels for wildland fires, to understanding the extent and distribution of habitat for plant and animal species, to modeling future landscape conditions under alternative climate and disturbance scenarios.
Key strengths of INR’s mid-scale existing vegetation maps include the following:
Rich data depth. Our imputation maps are linked to many attributes that provide detailed information about vegetation characteristics including species composition, plant functional groups, community type, forest structure, and more.
Flexibility. INR works with mapping partners to create vegetation classifications and attributes tailored to specific management and planning needs. Because of the rich depth of data linked to each map unit, new summary variables can be created or reformulated after the maps are made.
Suitability for landscape-level assessment. Because INR’s imputation maps are assessed for bias at multiple scales and maintain covariance between modeled attributes, they are well-suited to landscape-level assessments and summaries.
Updatable. Maps can be more rapidly updated using modeled information based on recent disturbances as an alternative to recomputing the whole map.
Over the past two decades, INR has collaborated with the USDA Forest Service Southwestern Region (R3), Pacific Northwest Region (R6), and Intermountain Region (R4) to develop existing vegetation maps of Oregon, Washington, Arizona, New Mexico, Nevada, and parts of Idaho and Wyoming. Since their creation, our imputation maps have significantly improved project implementation and planning by supporting management and shared stewardship across all land ownerships.
USDA Forest Service Region 4 Vegetation Mapping (2020-2024). INR is collaborating with R4’s Vegetation Mapping Program (VMP) to develop existing vegetation maps for all lands within R4.
R3 INREV Fire Update (2021-2023). INR used Monitoring Trends in Burn Severity (MTBS) and Rapid Assessment of Vegetation Condition after Wildfire (RAVG) data, in conjunction with Forest Vegetation Simulator (FVS), to model the impacts of wildlands fires on existing vegetation and create updated vegetation maps that reflect post-fire conditions.
R3 Existing Vegetation (INREV) (2016-2018). INR worked with the USDA Forest Service Region 3 to map existing forest and non-forest vegetation over the entire states of Arizona and New Mexico.
Integrated Landscape Assessment Project (2009-2011). INR worked with the USDA Forest
Service and other partners to map current vegetation and Ecological Response Units (ERUs) across Oregon/Washington and Arizona/New Mexico, and create state-and-transition models to model landscape management alternatives across these states.
Henderson EB, Bell DM, Gregory M. 2019. Vegetation mapping to support greater sage‐grouse habitat monitoring and management: multi‐or univariate approach? Ecosphere. 10(8):e02838. https://doi.org/10.1002/ecs2.2838
Henderson, E.B., Ohmann, J.L., Gregory, M.J., Roberts, H.M. and Zald, H., 2014. Species distribution modelling for plant communities: stacked single species or multivariate modelling approaches?. Applied vegetation science, 17(3), pp.516-527. https://doi.org/10.1111/avsc.12085
Ohmann, JL, MJ Gregory, EB Henderson and HM Roberts (2011). Mapping gradients of community composition with nearest-neighbour imputation: extending plot data for landscape analysis. Journal of Vegetation Science 22(4): 660-676. https://doi.org/10.1111/j.1654-1103.2010.01244.x