Structural Resistance Surface Basemap (WMC_TIRS_EEMS2_270m_Draft_v18_GHK_Raster)

This dataset provides an estimate of landscape intactness, (i.e. condition), based on the extent to which human impacts such as urban development, linear development, natural resource extraction, and agriculture have disrupted the landscape across the study site. Terrestrial intactness values are high in areas where these impacts are low.
 

This dataset was originally created to characterize anthropogenic barriers to small animal movement and emphasizes liner features in terms of impact on the landscape. Additional model refinements include stratifying road impacts by TIGER class, (e.g. different weighting for different types of roads), and taking distance to urban development into account.

This 270 sq. m resolution dataset, updated November 2016, was created using the open-source logic modeling framework Environmental Evaluation Modeling System (EEMS). Spatially-explicit logic modeling hierarchically integrates numerous and diverse datasets into composite layers, quantifying information in a continuous rather than binary fashion. This technique yields accessible decision-support products that stakeholders can use to craft scientifically-rigorous management strategies.
 
Input data used to create this version range in currency from 2011-2015; the majority of data portray the more recent condition of the landscape.
 
This model integrates agriculture development (from FRAP Vegetation, and CDL Cropscape), urban development (from LANDFIRE EVT and NLCD Impervious Surfaces), polluted areas (from NHD treatment ponds and EPA Superfund and Brownfield sites), linear development (OHV routes from owlsheadgps.com, roads from TIGER (broken down by type), utility lines, railroads, and pipelines from various state and BLM sources), point development (communication towers from the FCC), and energy and mining development (from the state’s Office of Mine Reclamation mine dataset, larger mine footprints, state geothermal wells, USGS wind turbines, solar footprints, renewable projects in development, oil refineries and state oil/gas wells). Results are dependent on the quality of available input data for a given area.