Problem Statement
Current invasive species data in FIA are sparse, plot-based, and irregular in time/space, making it hard to produce continuous annual surfaces or track spread dynamics.
Proposed Solution
Add interpolation methods to the gridfia package to generate annual gridded surfaces for invasive species metrics (presence/absence or percent cover).
Support spatiotemporal models (e.g., GAMs, random forest, kriging).
Output standardized rasters aligned with existing gridFIA outputs.
Include uncertainty layers.
Allow covariates (climate, soils, disturbance, land cover, etc.).
Alternatives Considered
Custom pipelines (R/Python): flexible but non-standard and hard to reproduce.
Other FIA tools (e.g., rFIA): strong for estimation, not interpolation.
Additional Context
High demand for annual invasion maps for management + modeling.
Fits naturally into gridFIA’s gridded framework.
Problem Statement
Current invasive species data in FIA are sparse, plot-based, and irregular in time/space, making it hard to produce continuous annual surfaces or track spread dynamics.
Proposed Solution
Add interpolation methods to the gridfia package to generate annual gridded surfaces for invasive species metrics (presence/absence or percent cover).
Support spatiotemporal models (e.g., GAMs, random forest, kriging).
Output standardized rasters aligned with existing gridFIA outputs.
Include uncertainty layers.
Allow covariates (climate, soils, disturbance, land cover, etc.).
Alternatives Considered
Custom pipelines (R/Python): flexible but non-standard and hard to reproduce.
Other FIA tools (e.g., rFIA): strong for estimation, not interpolation.
Additional Context
High demand for annual invasion maps for management + modeling.
Fits naturally into gridFIA’s gridded framework.