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    Author(s): Jacob John Muller
    Date: 2014
    Source: Missoula, MT: University of Montana. Thesis. 57 p.
    Publication Series: Theses
    Station: Rocky Mountain Research Station
    PDF: View PDF  (1.54 MB)


    The ability of forest resource managers to understand and anticipate landscape-scale change in composition and structure relies upon an adequate characterization of the current forest composition and structure of various patches (or stands), along with the capacity of forest landscape models (FLMs) to predict patterns of growth, succession, and disturbance at multiple scales over time. Comprehensive vegetation maps, which classify patch polygons or raster cells into forest cover types, can be developed from available inventory data (e.g., FIA Grid) in combination with remotely sensed data, but a simple categorical forest type, even one incorporating average size, may not provide adequate resolution for tracking individual species and age cohorts over time in an FLM. This project, undertaken in Eastern Montana forest types, sought to develop strategies for utilizing extensive inventory data from the U.S. Forest Inventory and Analysis (FIA) program to initialize patch-level vegetation information for use in the landscape disturbance model SIMPPLLE (Chew et al 2004). The information provided to SIMPPLLE, includes not only a forest cover dominance type that crosswalks with the Northern Region’s VMAP labels, but also incorporates further species and size information to the cohort level. By processing FIA data through the stand-level growth model FVS (Forest Vegetation Simulator), tracking of individual cohorts could be summarized to enhance resolution and realism in the SIMPPLLE model. Further, by simulating patch level dynamics within FVS for up to 300 years for representative stands, and segregating growing stock by cohort, it was possible to enhance the complexity of stand development pathways to be used within SIMPPLLE model. Specifically, I enable the tracking of individual cohorts (species and 5” breast-height diameter size class) to be passed on to the SIMPPLLE model, while still allowing for large-scale modeling of disturbances and between-patch interactions, which are the scales of interest within the SIMPPLLE FLM.

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    Muller, Jacob John. 2014. Developing strategies to initialize landscape-scale vegetation maps from FIA data to enhance resolution of individual species-size cohort representation in the landscape disturbance model SIMPPLLE. Missoula, MT: University of Montana. Thesis. 57 p.


    forest landscape models (FLM), SIMPPLLE, U.S. Forest Service Inventory and Analysis (FIA) data

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