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Insect outbreaks are major disturbances that affect a land area similar to that of forest fires across North America. The recent mountain pine bark beetle (Dendroctonus ponderosae) outbreak and its associated blue stain fungi (Grosmannia clavigera) are impacting water partitioning processes of forests in the Rocky Mountain region as the spatially heterogeneous disturbance spreads across the landscape.
Since Parresol’s (Can. J. For. Res. 31:865-878, 2001) seminal article on the topic, it has become standard to develop nonlinear tree biomass equations to ensure compatibility among total and component predictions and to fit these equations using multistep generalized least-squares methods.
The Interior West Forest Inventory and Analysis Unit (IWFIA) will soon transition from a regional system to a national FIA system for compiling estimates of forest growth, removals, and mortality. The national system requires regional diameter-growth models to estimate diameters on trees in situations where the initial or terminal diameter is not known at the beginning or end of a measurement interval.
The Forest Inventory and Analysis Database (FIADB) includes a large number of measured and computed variables. The definitions of measured variables are usually well-documented in FIA field and database manuals. Some computed variables, such as live basal area of the condition, are equally straightforward.
Mapping vegetation and landscape change at fine spatial scales is needed to inform natural resource and conservation planning, but such maps are expensive and time-consuming to produce. For Landsat-based methodologies, mapping efforts are hampered by the daunting task of manipulating multivariate data for millions to billions of pixels.
For most simulation modeling efforts, the goal of model developers is to produce simulations that are the best representations of realism as possible. Achieving this goal commonly requires a considerable amount of data to set the initial parameters, followed by validation and model improvement – both of which require even more data.
Long-term trends in forest attributes are typically assessed using strategic inventories such as the US Department of Agriculture (USDA) Forest Service’s Forest Inventory and Analysis (FIA) program. The implicit assumption of any trend analysis is that data are comparable over time.
Raster modeling is an integral component of spatial analysis. However, conventional raster modeling techniques can require a substantial amount of processing time and storage space, often limiting the types of analyses that can be performed. To address this issue, we have developed Function Modeling. Function Modeling is a new modeling framework that streamlines the raster modeling process by utilizing delayed reading methods.
Habitat suitability models can provide guidelines for species conservation by predicting where species of interest are likely to occur. Presence-only models are widely used but typically provide only relative indices of habitat suitability (HSIs), necessitating rigorous evaluation often using independently collected presence-absence data.
FIESTA (Forest Inventory ESTimation for Analysis) is a user-friendly R package that was originally developed to support the production of estimates consistent with current tools available for the Forest Inventory and Analysis (FIA) National Program, such as FIDO (Forest Inventory Data Online) and EVALIDator.