Forested land cover (resistance surface component) - A landscape connectivity analysis for the coastal marten (Martes caurina humboldtensis)

The resistance surface that formed the basis of our coastal marten connectivity model is comprised of several data layers that represent forested and non-forested land cover, waterbodies, rivers, roads, and serpentine soils.

This dataset contains the forested land cover data used in the resistance surface.  To see actual resistance values assigned to the forested land cover classes in this raster when the resistance surface is compiled, see the associated spreadsheet of resistance surface data sources and resistance values.

This forested land cover dataset was developed using the Old-growth Structure Index (OGSI), which is the primary estimator of habitat quality and cost-weighted distance in the connectivity model.  OGSI is a parameter derived by the Gradient Nearest Neighbor (GNN) model produced by the Landscape Ecology, Modeling, Mapping & Analysis laboratory in Corvallis, OR (LEMMA 2014a). The GNN model provides fine-scale spatially explicit data on forest structure across a vast area of California, Oregon, and Washington, and is one of the very few datasets available that provides such habitat information in a consistent manner across the CA/OR state border. GNN summarizes detailed data from thousands of forest survey points. It then uses a multi-step process to interpolate them to the unsurveyed areas on the landscape based on several explanatory datasets such as Landsat remote sensing imagery, elevation, climate, and geology (more information about this process can be found at https://lemma.forestry.oregonstate.edu/methods/methods, see also Ohmann & Gregory 2002). Like Landsat imagery, GNN has a spatial grain of 30mX30m (900 m2). OGSI is used to characterize the suitability of forest habitat conditions for old-growth obligate species and processes. It is scaled to specific regions and ecotypes, and is derived from a conceptual model that incorporates: (1) the density of large trees (2) and snags, (3) the size class diversity of live trees, and (4) the amount of down woody material (Davis et al. 2015). These seem well aligned with critically important features in forests inhabited by Pacific martens generally and Humboldt martens specifically, and a range of literature describes the use of habitat types that are consistent with the presence of these features.

Non-forested land cover types (those with <10% tree cover) are not classified by the GNN model.  Non-forested land cover is addressed in a separate data layer in our model.

To develop the forested land cover data, we used our modeling extent to extract the OGSI parameter from the broader GNN dataset.  OGSI values ranged from 0-100 for all non-null pixels.  We then used the Reclassify geoprocessing tool to reclassify these data into 10 equal interval bins (i.e. 0.0000 to 10.0000, 10.0001 to 20.0000, 20.0001 to 30.0000, etc.).  These bins were valued at 1-10 accordingly (i.e. 0.0000 to 10.0000 was classified as 1, etc.).  All non-null pixels in the dataset were classified in this manner, including non-forested pixels, for which an OGSI value of 0 was classified as bin 1.

This is an abbreviated and incomplete description of the dataset.  Please refer to the spatial metadata for a more thorough description of the methods used to produce this dataset, and a discussion of any assumptions or caveats that should be taken into consideration.

Tags: coastal-marten, connectivity-modeling, general-biology-at-risk-biota-te-species, general-biology-species-mammals, general-landscapes-landscape-dynamics, general-landscapes-landscape-ecology, general-management-habitat-management-habitat-models, general-management-landscape-management-landscape-connectivity, gnarly-landscape-utilities, humboldt-coastal-marten, humboldt-marten, least-cost-path-analysis, linkage-mapper, martes-caurina-humboldtensis, pacific-marten
Organization: Department of the Interior