Spatial models that classify, map, or project habitat conditions now or into the future require that vegetation attributes considered important elements for habitat be inferred from available spatial data sources. Often these data sources are based on forest inventory mapping which in turn has usually been calibrated using field plots with sampling designs tailored for accurately measuring selected stand attributes that may not correlate well to habitat classification. We evaluated both sample plot data and remotely sensed imagery that may be appropriate for enhancing the evaluation of wildlife habitat characteristics in mapped polygons. Each data set was evaluated against a series of selection criteria intended to identify datasets appropriate for our analyses. We also evaluated whether sample plots were well represented throughout the study area, or throughout BEC variants included in our chosen test case - habitat classification for Northern Spotted Owl (SPOW) recovery planning. We were able to construct aggregate spatial databases of plot and remotely sensed imagery from the datasets surveyed. We selected VRI plots and Permanent Sample Plots for inclusion in the aggregate plot database, although neither dataset contained all attributes needed for habitat assessment in our study species. Weaknesses in the other ecological sample plot datasets included: lack of geographic coordinates, limited ability to infer or 'scale-up' measurements of required attributes at the polygon scale from the plot scale, inappropriate sampling design, and poor ecological representation in plot layout. Two sources of remotely-sensed imagery were also examined (LANDSAT-7 and SPOT 5) in relation to VRI mapped polygon data, and their utility for our purpose is reviewed. Pilot methods for extracting needed attributes from these sources and using them to enhance mapped polygons are reviewed and tested in a subsequent report.
Cortex Consultants Inc.
Cortex Consultants Inc.. 2006. Development of multi-scale habitat classification methods for refining strategic habitat supply modelling: data sources and evaluation. Forest Investment Account (FIA) - Forest Science Program. Forest Investment Account Report. FIA2006MR130