BLM Colorado Roads and Trails on BLM managed lands. This data uses the national BLM Ground Transportation Linear Features (GTLF) standard allowing for the capture of data from the Travel Management Plan (TMP) as well as from observed route usage. Two additional attributes were added for Colorado specific purposes.

Display BLM Colorado Roads and Trails on BLM managed lands

Display BLM Colorado Roads and Trails on BLM managed lands

description: All roads in the Colorado Plateau eco-region. This file was created by combining TIGER/Line county shapefiles within the Colorado Plateau region and clipping them to the "http://app.databasin.org/app/pages/datasetPage.jsp?id=464e720659aa4195ba5d69e2a40ccb51" 5th-level HUC (10-digit) watersheds, Colorado Plateaus ecoregion 5th-level HUC (10-digit) watersheds, Colorado Plateaus ecoregion The TIGER/Line Files are shapefiles and related database files (.dbf) that are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line File is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The All Roads Shapefile includes all features within the MTDB Super Class "Road/Path Features" distinguished where the MAF/TIGER Feature Classification Code (MTFCC) for the feature in MTDB tha begin with "S". This includes all primary, secondary, local neighborhood, and rural roads, city streets, vehicular trails (4wd), ramps, service drives, alleys, parking lot roads, private roads for service vehicles (logging, oil fields, ranches, etc.), bike paths or trails, bridle/horse paths, walkways/pedestrian trails, and stairways.; abstract: All roads in the Colorado Plateau eco-region. This file was created by combining TIGER/Line county shapefiles within the Colorado Plateau region and clipping them to the "http://app.databasin.org/app/pages/datasetPage.jsp?id=464e720659aa4195ba5d69e2a40ccb51" 5th-level HUC (10-digit) watersheds, Colorado Plateaus ecoregion 5th-level HUC (10-digit) watersheds, Colorado Plateaus ecoregion The TIGER/Line Files are shapefiles and related database files (.dbf) that are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line File is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The All Roads Shapefile includes all features within the MTDB Super Class "Road/Path Features" distinguished where the MAF/TIGER Feature Classification Code (MTFCC) for the feature in MTDB tha begin with "S". This includes all primary, secondary, local neighborhood, and rural roads, city streets, vehicular trails (4wd), ramps, service drives, alleys, parking lot roads, private roads for service vehicles (logging, oil fields, ranches, etc.), bike paths or trails, bridle/horse paths, walkways/pedestrian trails, and stairways.

Travel and transportation are an integral part of virtually every activity that occurs on public lands. The BLM uses comprehensive travel management to determine why and how to manage roads, trail systems and associated areas on public land to best meet transportation needs. Travel management looks at all forms of transportation including travel by foot, horseback and mechanized vehicles such as bikes, as well as all motorized vehicles.

BLM Colorado Roads and Trails on BLM managed lands. This data uses the national BLM Ground Transportation Linear Features (GTLF) standard allowing for the capture of data from the Travel Management Plan (TMP) as well as from observed route usage. Additional attributes were added for Colorado specific purposes. Routes for winter only activities are separated in a separate layer. Routes where electric bikes are allowed can be easily identified by turning on the highlighting layer.

Travel and transportation are an integral part of virtually every activity that occurs on public lands. The BLM uses comprehensive travel management to determine why and how to manage roads, trail systems and associated areas on public land to best meet transportation needs. Travel management looks at all forms of transportation including travel by foot, horseback and mechanized vehicles such as bikes, as well as all motorized vehicles.

Display BLM Colorado Roads and Trails on BLM managed lands

This polygon feature class represents the spatial extent and boundaries for existing BLM Colorado State Office Travel Management Areas and Plans (TMAP) Off-Highway Vehicle (OHV) polygons. They are purposed to show the existing OHV designation of the OHV area. An existing OHV polygon can have an Open, Limited, Closed or Undesignated status.

Visual resource inventory visual distance zones are landscapes that are subdivided into three distance zones based on relative visibility from travel routes or observation points. The three zones are foreground-middleground (fm), background (bg), and seldom seen (ss). Foreground-middleground zone includes areas seen from highways, rivers or other viewing locations which are less than 3 to 5 miles away. Seen areas beyond the foreground-middleground (fm) zone but usually less than 15 miles away are in the background (bg) zone. Areas not seen as fm, or bg are in the seldom-seen zone. These data represent the polygon features used to define the vdz.

description: This dataset shows historic change agents and disturbance types within the historic distribution of this vegetation community as mapped in the LANDFIRE Biophysical Settings (BpS v1.0) dataset. The BpS provides an estimate of the distribution of this community under pre- Euroamerican settlement reference conditions (including historic fire regimes). While based on biophysical gradients and limited training plot data, with resultant inaccuracies of prediction, the BpS provides the best available estimate of the distribution of this vegetation community. Existing vegetation classifications are inadequate for estimating the distribution of this community for the purposes of this analysis, because they only provide a single, recent snapshot in time of vegetation conditions that are often variable over time (i.e., a recently burned area may be classified as low cover herbaceous in the existing vegetation, but occupying a site that over long periods of time is occupied by shrubland). Change agents are those factors that have converted this vegetation into another state, such as conversion to urban areas. These were extracted from the LANDFIRE EVT, NLCD Impervious Surfaces, BLM GTLF (roads), current predicted invasive vegetation dataset (produced for this REA), and LANDFIRE Succession Class dataset to express the following change agent types: development, agriculture, invasive vegetation, and uncharacteristic native vegetation. Disturbances are those factors that have occurred within this system in recent years, that may have modified the vegetation community composition, structure, and dynamics. These disturbances may be part of the natural disturbance regime and thus beneficial for the ecological dynamics of a site, or they may be uncharacteristic due to increases of fire frequency (in arid shrublands), fuel buildup due to legacy effects of fire suppression, or presence of invasive vegetation. These disturbances were extracted from USGS fire perimeters (2000-2010), LANDFIRE Disturbance datasets (1999-2008), and BLM Pinyon-Juniper treatments. Caution should be exercised in interpreting this dataset. The BpS provides one possible estimate of the historic distribution of this vegetation community, but may contain inherent inaccuracies and biases (and thus over- or under-represent the distribution of this community). The change agents are based on measures of existing vegetation and landscape condition, and may not contain all factors that have affected this vegetation community. In particular, long-term conversion to other vegetation communities was not addressed in this analysis (overlays of BpS and existing vegetation datasets generally do not indicate long-term versus short-term vegetation conversions). Not all disturbances that affect this community may have been detected in the available datasets used to estimate disturbance. Overall, this dataset should be taken to provide one estimate of the net changes that have affected this vegetation community historically and recently.; abstract: This dataset shows historic change agents and disturbance types within the historic distribution of this vegetation community as mapped in the LANDFIRE Biophysical Settings (BpS v1.0) dataset. The BpS provides an estimate of the distribution of this community under pre- Euroamerican settlement reference conditions (including historic fire regimes). While based on biophysical gradients and limited training plot data, with resultant inaccuracies of prediction, the BpS provides the best available estimate of the distribution of this vegetation community. Existing vegetation classifications are inadequate for estimating the distribution of this community for the purposes of this analysis, because they only provide a single, recent snapshot in time of vegetation conditions that are often variable over time (i.e., a recently burned area may be classified as low cover herbaceous in the existing vegetation, but occupying a site that over long periods of time is occupied by shrubland). Change agents are those factors that have converted this vegetation into another state, such as conversion to urban areas. These were extracted from the LANDFIRE EVT, NLCD Impervious Surfaces, BLM GTLF (roads), current predicted invasive vegetation dataset (produced for this REA), and LANDFIRE Succession Class dataset to express the following change agent types: development, agriculture, invasive vegetation, and uncharacteristic native vegetation. Disturbances are those factors that have occurred within this system in recent years, that may have modified the vegetation community composition, structure, and dynamics. These disturbances may be part of the natural disturbance regime and thus beneficial for the ecological dynamics of a site, or they may be uncharacteristic due to increases of fire frequency (in arid shrublands), fuel buildup due to legacy effects of fire suppression, or presence of invasive vegetation. These disturbances were extracted from USGS fire perimeters (2000-2010), LANDFIRE Disturbance datasets (1999-2008), and BLM Pinyon-Juniper treatments. Caution should be exercised in interpreting this dataset. The BpS provides one possible estimate of the historic distribution of this vegetation community, but may contain inherent inaccuracies and biases (and thus over- or under-represent the distribution of this community). The change agents are based on measures of existing vegetation and landscape condition, and may not contain all factors that have affected this vegetation community. In particular, long-term conversion to other vegetation communities was not addressed in this analysis (overlays of BpS and existing vegetation datasets generally do not indicate long-term versus short-term vegetation conversions). Not all disturbances that affect this community may have been detected in the available datasets used to estimate disturbance. Overall, this dataset should be taken to provide one estimate of the net changes that have affected this vegetation community historically and recently.

description: This dataset presents the current potential distribution of Colorado River Cutthroat Trout in the context of current and near-term aquatic intactness, and long-term potential for climate change at the watershed (HUC5) scale. Current aquatic intactness is based on current measures of landscape development, water quality, hydrologic impacts, and road impacts. Near-term intactness includes estimates of urban growth. Long-term potential for climate change is based on absolute changes in runoff, precipitation, temperature, and vegetation change estimated using climate projections (RegCM3 regional climate model based on ECHAM5 boundary conditions) and a biogeography model (MAPSS) for the period 2045-2060. The aquatic intactness estimates provide one set of estimates of the current and near-term habitat conditions of this species; long-term potential for climate change indicates areas that may more strongly affect the status of this species . Many additional factors may affect this species beyond those captured in these models; these species-specific attributes could not be integrated using existing data within the scope of this REA. Local analysis are necessary to provide additional estimates of the current distribution of this species as well as incorporating additional factors that strongly influence the status and distribution of this species. Furthermore, the estimates used in this REA were required by BLM to be at the watershed (HUC5); this produces a landscape-scale averaging effect that may make it difficult to determine more locally specific measures of the condition of this species. Future analysis should be performed at a scale that more closely captures the functional neighborhood of this species, such as finer-grained watersheds (HUC6) or a grid of reporting units (e.g., 4KM grid).; abstract: This dataset presents the current potential distribution of Colorado River Cutthroat Trout in the context of current and near-term aquatic intactness, and long-term potential for climate change at the watershed (HUC5) scale. Current aquatic intactness is based on current measures of landscape development, water quality, hydrologic impacts, and road impacts. Near-term intactness includes estimates of urban growth. Long-term potential for climate change is based on absolute changes in runoff, precipitation, temperature, and vegetation change estimated using climate projections (RegCM3 regional climate model based on ECHAM5 boundary conditions) and a biogeography model (MAPSS) for the period 2045-2060. The aquatic intactness estimates provide one set of estimates of the current and near-term habitat conditions of this species; long-term potential for climate change indicates areas that may more strongly affect the status of this species . Many additional factors may affect this species beyond those captured in these models; these species-specific attributes could not be integrated using existing data within the scope of this REA. Local analysis are necessary to provide additional estimates of the current distribution of this species as well as incorporating additional factors that strongly influence the status and distribution of this species. Furthermore, the estimates used in this REA were required by BLM to be at the watershed (HUC5); this produces a landscape-scale averaging effect that may make it difficult to determine more locally specific measures of the condition of this species. Future analysis should be performed at a scale that more closely captures the functional neighborhood of this species, such as finer-grained watersheds (HUC6) or a grid of reporting units (e.g., 4KM grid).

Land Use Plan (LUP) boundaries for the Greater Sage-Grouse (GRSG) National Planning Effort in the Rocky Mountain Region. Modifications were incorporated (see below) based on clarification on the correct boundaries to use from BLM planners. Additionally, definitions for EISs each LUP is included in for the Sage-grouse effort were added. Acreage calculations for each LUP as well as each full EIS were provided. EIS definitions were approved by BLM Planners on 71912.Note that individual RMPs that are subsets of other RMPs are designated as part of xxxx RMP. These polygons are overlapping polygons in the dataset.

Rocky Mountain answers to LUP Polygon Selection questions (62012):

From:Schardt, Randall D Sent:Friday, June 15, 2012 6:53 AM To:Prill, Kimberly; Carlson, John C Subject:FW: LUP Polygon selection questions

I have made the changes to the LUP data and responded to what polygons that they should be using for Montana:

Billings and Bighorn Basin(Billings_BighornBasin.pdf ) Billings and Bighorn Basin have overlapping polygons in the small area shown on the map. What should we do with this area? Note that if we use the Billings polygon from the Existing data this overlap will not occur (but we were thinking we should use In-Progress as it should be the most recent)Use the Billings RMP polygon from In-Progress which includes the Billings Bighorn BasinWest HiLine and HiLine(WestHiLine_HiLine.pdf) First, do we want both West Hi-Line and HiLine area calculations? The HiLine polygon will cut into the JVP polygon. Do we need to make a new polygon that is the intersection of HiLine with JVP that represents the part of JVP being revised by HiLine? Use the HiLine RMP polygon from In-Progress.

Lewistown(Lewistown.pdf). We do not have a Lewistown polygon, so can we combine Headwaters and JVP to make a Lewistown poly? Same question about do we still need area calcs for Headwaters and JVP? Use the Lewistown RMP polygon from In-Progress

Hi Frank,For your first question, the Roan Plateau does need to be analyzed. It is a separate planning area from the CRV polygon. So I believe you need to use #8220;Existing#8221; polygons in this case so that the Roan Plateau boundary is included in the analysis.

Christina O#8217;Connell GIS Specialist Colorado River Valley Field Office 2300 River Frontage Road Silt, CO 81652 (970) 876-9011

From:Diekman, Douglas A Sent:Thursday, June 14, 2012 4:10 PM To:Quamen, Frank RCc:Dreyfuss, Erin R; OConnell, Christina M; Cagney, James A; Minnick, Delissa L; Munson, Johanna Subject:FW: Questions from Sage Grouse GIS team at the NOC Importance:High

I called Dave Taylor at our state Office and asked him about accessing national datasets, and Dave directed me to a database that has the state data that is replicated up to the national dataset and advised me that what the NOC is using should be the same as what is in this database. I checked the LUP boundaries and the Land Use Planning Area Boundary In-Progress matches the boundary of the current RMP revision that we are working on. The Land Use Planning Area Boundary Existing matches the boundary of the area of our existing RMP.

From:Dreyfuss, Erin R Sent:Wednesday, June 13, 2012 1:52 PM To:OConnell, Christina M; Diekman, Douglas ACc:Munson, Johanna; Quamen, Frank R Subject:Questions from Sage Grouse GIS team at the NOC Importance:High

Hi Christina and Doug #8211;

See questions below from Frank Quamen at the NOC:

Colorado River Valley There is a significant geometry change between the Existing and In-Progress polygons for the Colorado Plateau. In the Existing polygon, there is a large area of the CRV polygon that is cut out and called Roan Plateau. While we were thinking that we would generally want to use the In-Progress polygons, as they should be newer, if we dont use the Colorado River Valley polygon from the Existing data set, and instead use the polygon from In-Progress, we will have a gap in between the Colorado River Valley area and the White River area. Please advise on what polygons to use. Also, what to do with the Roan Plateau area? Do we just not analyze it?

Grand Junction(GrandJunction.pdf). A polygon for Grand Junction exists in both the Existing and In-Progress GIS layers, and they have different geometries. We think we would want to use the In-Progress polygon, as it should be the most recent, but would like confirmation.

Humans have dramatically altered wildlands in the western United States over the past 100 years by using these lands and the resources they provide. Anthropogenic changes to the landscape, such as urban expansion, construction of roads, power lines, and other networks and land uses necessary to maintain human populations influence the number and kinds of plants and wildlife that remain. We developed the map of the human footprint for the western United States from an analysis of 14 landscape structure and anthropogenic features: human habitation, interstate highways, federal and state highways, secondary roads, railroads, irrigation canals, power lines, linear feature densities, agricultural land, campgrounds, highway rest stops, land fills, oil and gas development, and human induced fires. We used these input layers to develop seven models to estimate the total influence of the human footprint. These models either explored how anthropogenic features influence wildlife populations via changes in habitat (road-induced dispersal of invasive plants, oil and gas developments, human induced fires, and anthropogenic habitat fragmentation) or predators densities (spatial distribution of domestic and synanthropic avian predators). The human footprint map is a composite of these seven models. The final map consists of a 180 meter resolution raster data set with 10 human footprint classes. 180.0 (meter)

The “Biophysical assessment for indemnity selection of federal lands in Colorado: Development” dataset quantifies the estimated surface disturbance from development (urban, agriculture, energy and mineral extraction and transmission, and roads and railroads) at several analysis scales for each of 89 Indemnity Units. The Indemnity Units are federal lands, currently managed by the Bureau of Land Management (BLM), under consideration for transfer of ownership to the State of Colorado in compensation for federal lands not available for transfer at the time of statehood. The companion data release (see "Cross Reference" section) was used to summarize the surface disturbance from development (development footprint) at three analysis scales: the estimated development footprint area (in hectares), as well as the terrestrial development index (TDI) using 1-kilometer and 5-kilometer radius moving windows. The TDI scores represents the percentage of the development footprint within a specifi ...

This feature-dataset depicts the boundaries of the Baaj Nwaavjo I’tah Kukveni – Ancestral Footprints of the Grand Canyon National Monument in Arizona. Baaj nwaavjo (BAAHJ – NUH-WAAHV-JOH) means “where Indigenous peoples roam” in the Havasupai language, and i’tah kukveni (EE-TAH – KOOK-VENNY) means “our ancestral footprints” in the Hopi language. The name reflects the significance of the Grand Canyon area, not just to one, but to many Tribal Nations. This designation, which marks the fifth new national monument established by President Biden, honors Tribal Nations and Indigenous peoples by protecting sacred ancestral places and their historically and scientifically important features, while conserving our public lands, protecting wildlife habitat and clean water, and supporting local economies. Baaj Nwaavjo I’tah Kukveni – Ancestral Footprints of the Grand Canyon National Monument conserves nearly 1 million acres of public lands surrounding Grand Canyon National Park. The monument protects thousands of cultural and sacred sites that are precious to Tribal Nations in the Southwest – including the Havasupai Tribe, Hopi Tribe, Hualapai Tribe, Kaibab Band of Paiute Indians, Las Vegas Paiute Tribe, Moapa Band of Paiutes, Paiute Indian Tribe of Utah, Navajo Nation, San Juan Southern Paiute Tribe, Yavapai-Apache Nation, Pueblo of Zuni, and the Colorado River Indian Tribes. These sites include Gray Mountain, called Dziłbeeh by the Navajo, which is a part of Navajo ceremonial songs, stories, and rituals. The President signed the proclamation at Red Butte, a sacred site called Wii'i Gdwiisa by the Havasupai, which towers above the southern portion of the monument. The monument is made up of three distinct areas to the south, northeast, and northwest of Grand Canyon National Park. It is bordered by the Kanab watershed boundary and Kanab Creek drainage in the northwestern area and the Havasupai Indian Reservation and Navajo Nation in the southern area, and stretches from Marble Canyon to the edge of the Kaibab Plateau in the northeastern area. The monument spans 917,618 acres of public lands managed by the Interior Department’s Bureau of Land Management and the Department of Agriculture’s U.S. Forest Service. The national monument only includes federal lands and does not include State and private lands within the boundary or affect the property rights of the State or private land owners. The national monument designation recognizes and respects valid existing rights. The proclamation specifies that maintenance and upgrades to water infrastructure for flood control, utilities, water district facilities, wildlife water catchments, and other similar uses may continue; and that utility lines, pipelines, and roads can continue to be maintained, upgraded, and built consistent with proper care and management of the monument objects. Existing mining claims – predating a 20-year mineral withdraw initiated in 2012 – will remain in place, and the two approved mining operations within the boundaries of the monument would be able to operate. The proclamation respects existing livestock grazing permits on lands within the monument. The Antiquities Act of 1906 grants the President authority to designate national monuments in order to protect “objects of historic or scientific interest.” President Theodore Roosevelt first used the Antiquities Act in 1906 to designate Devils Tower National Monument in Wyoming. Since then, 18 presidents of both parties have used this authority to protect unique natural and historic features in America, including the Statue of Liberty, Colorado’s Canyon of the Ancients, and New Mexico’s Gila Cliff Dwellings. The Baaj Nwaavjo I’tah Kukveni – Ancestral Footprints of the Grand Canyon National Monument is President Biden’s fifth new monument designation, following the creation of the Emmett Till and Mamie Till-Mobley National Monument in Illinois and Mississippi, the Castner Range National Monument in Texas and Avi Kwa Ame National Monument in Nevada, and the Camp Hale-Continental Divide National Monument in Colorado.