highways_arc: This file includes all state owned or maintained highways, connections, frontage roads, temporary traveled routes and located lines in the states of Oregon and Washington.

GTRN_SEGMENT_PT: Contains points representating of the end points of Facility Asset Management System (FAMS) segments. Publication transportation dataset showing both BLM inventoried and non-inventoried roads in Oregon & Washington. This data does not include highways. For highway data see the citation in the Cross Reference Section.

GTRN_PUB_ROADS_ARC: Publication transportation dataset showing both BLM inventoried and non-inventoried roads in Oregon & Washington. This data does not include highways. For highway data see the citation in the Cross Reference Section.

GTRN_PUB_ROADS_ARC: Publication transportation dataset showing both BLM inventoried and non-inventoried roads in Oregon & Washington. This data does not include highways. For highway data see the citation in the Cross Reference Section.

gtrn_pub_trails_arc: Publication dataset showing both BLM inventoried and non-inventoried trails in Oregon & Washington.

Back_Country_Byways_arc: The BCB theme is graphical display of the centerline of Back Country Byways in Oregon. This layer is displayed as a single line. The source linework was extracted from the BLM's Ground Transportation Roads layer (GTRN).

Back_Country_Byways_arc: The BCB theme is graphical display of the centerline of Back Country Byways in Oregon. This layer is displayed as a single line. The source linework was extracted from the BLM's Ground Transportation Roads layer (GTRN).

This feature class is the BLM Natl GTLF Limited Public Motorized Roads subset of the feature dataset containing the BLM Ground Transportation Linear Features. A linear feature for ground transportation includes roads, primitive roads, primitive routes, trails, temporary routes, and linear disturbances. The Ground Transportation Linear Feature (GTLF) data standard provides a national geospatial data standard of the ground transportation linear features in BLM’s Enterprise GIS (E-GIS). A national BLM GTLF data standard is essential for collecting the landscape-scale data necessary to identify management opportunities and challenges that may not be evident when managing smaller land areas. GTLF data not only serve the crucial function of improving BLM transportation planning, but is also invaluable to numerous other BLM programs affected by transportation (e.g. water and air quality, wildlife habitat fragmentation, engineering, realty, cultural resources). This dataset is a subset of the official national dataset, containing features and attributes intended for public release and has been optimized for online map service performance. The Schema Workbook represents the official national dataset from which this dataset was derived.

This feature class is the BLM Natl GTLF Public Motorized Roads subset of the feature dataset containing the BLM Ground Transportation Linear Features. A linear feature for ground transportation includes roads, primitive roads, primitive routes, trails, temporary routes, and linear disturbances. The Ground Transportation Linear Feature (GTLF) data standard provides a national geospatial data standard of the ground transportation linear features in BLM’s Enterprise GIS (E-GIS). A national BLM GTLF data standard is essential for collecting the landscape-scale data necessary to identify management opportunities and challenges that may not be evident when managing smaller land areas. GTLF data not only serve the crucial function of improving BLM transportation planning, but is also invaluable to numerous other BLM programs affected by transportation (e.g. water and air quality, wildlife habitat fragmentation, engineering, realty, cultural resources). This dataset is a subset of the official national dataset, containing features and attributes intended for public release and has been optimized for online map service performance. The Schema Workbook represents the official national dataset from which this dataset was derived.

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