Description: See metadata (FGDC section) for complete information about this layer - https://landscape.blm.gov/COP_2010_metadata/COP_HUC_Boundary_poly.xml This layer describes the boundary for the Colorado Plateau (COP) Ecoregion.
These data are provided by Bureau of Land Management (BLM) "as is" and may contain errors or omissions. The User assumes the entire risk associated with its use of these data and bears all responsibility in determining whether these data are fit for the User's intended use.
These data may not have the accuracy, resolution, completeness, timeliness, or other characteristics appropriate for applications that potential users of the data may contemplate. The User is encouraged to carefully consider the content of the metadata file associated with these data. The BLM should be cited as the data source in any products derived from these data.
Copyright Text: CSS-Dynamac and Conservation Biology Institute (CBI)
Description: See metadata (FGDC section) for complete information about this layer - https://landscape.blm.gov/COP_2010_metadata/COP_BLM_Field_Offices_P2.xml BLM Field Office Boundaries for the Colorado Plateau Ecoregion
These data are provided by Bureau of Land Management (BLM) "as is" and may contain errors or omissions. The User assumes the entire risk associated with its use of these data and bears all responsibility in determining whether these data are fit for the User's intended use.
These data may not have the accuracy, resolution, completeness, timeliness, or other characteristics appropriate for applications that potential users of the data may contemplate. The User is encouraged to carefully consider the content of the metadata file associated with these data. The BLM should be cited as the data source in any products derived from these data.
Color: [168, 112, 0, 255] Background Color: N/A Outline Color: N/A Vertical Alignment: bottom Horizontal Alignment: left Right to Left: false Angle: 0 XOffset: 0 YOffset: 0 Size: 8 Font Family: Arial Font Style: normal Font Weight: normal Font Decoration: none
Description: See metadata (FGDC section) for complete information about this layer - https://landscape.blm.gov/COP_2010_metadata/COP_TES_C_ColoradoPlateauPJWoodland_DIST_30m.xml These data are provided by Bureau of Land Management (BLM) "as is" and may contain errors or omissions. The User assumes the entire risk associated with its use of these data and bears all responsibility in determining whether these data are fit for the User's intended use.
These data may not have the accuracy, resolution, completeness, timeliness, or other characteristics appropriate for applications that potential users of the data may contemplate. The User is encouraged to carefully consider the content of the metadata file associated with these data. The BLM should be cited as the data source in any products derived from these data.
Copyright Text: CSS-Dynamac and Conservation Biology Institute (CBI)
Description: See metadata (FGDC section) for complete information about this layer - https://landscape.blm.gov/COP_2010_metadata/COP_TES_C_ColoradoPlateauPJWoodland_LandfireEVT_DIST_30m.xml These data are provided by Bureau of Land Management (BLM) "as is" and may contain errors or omissions. The User assumes the entire risk associated with its use of these data and bears all responsibility in determining whether these data are fit for the User's intended use.
These data may not have the accuracy, resolution, completeness, timeliness, or other characteristics appropriate for applications that potential users of the data may contemplate. The User is encouraged to carefully consider the content of the metadata file associated with these data. The BLM should be cited as the data source in any products derived from these data.
Copyright Text: CSS-Dynamac and Conservation Biology Institute (CBI)
Description: See metadata (FGDC section) for complete information about this layer - https://landscape.blm.gov/COP_2010_metadata/COP_TES_C_ColoradoPlateauPJWoodland_NatureServe_DIST_30m.xml These data are provided by Bureau of Land Management (BLM) "as is" and may contain errors or omissions. The User assumes the entire risk associated with its use of these data and bears all responsibility in determining whether these data are fit for the User's intended use.
These data may not have the accuracy, resolution, completeness, timeliness, or other characteristics appropriate for applications that potential users of the data may contemplate. The User is encouraged to carefully consider the content of the metadata file associated with these data. The BLM should be cited as the data source in any products derived from these data.
Copyright Text: CSS-Dynamac and Conservation Biology Institute (CBI)
Description: See metadata (FGDC section) for complete information about this layer - https://landscape.blm.gov/COP_2010_metadata/COP_TES_H_ColoradoPlateauPJWoodland_LandfireBpS_DIST_30m.xml These data are provided by Bureau of Land Management (BLM) "as is" and may contain errors or omissions. The User assumes the entire risk associated with its use of these data and bears all responsibility in determining whether these data are fit for the User's intended use.
These data may not have the accuracy, resolution, completeness, timeliness, or other characteristics appropriate for applications that potential users of the data may contemplate. The User is encouraged to carefully consider the content of the metadata file associated with these data. The BLM should be cited as the data source in any products derived from these data.
Copyright Text: CSS-Dynamac and Conservation Biology Institute (CBI)
Description: See metadata (FGDC section) for complete information about this layer - https://landscape.blm.gov/COP_2010_metadata/COP_TES_ColoradoPlateauPJWoodland_LandfireEVT_status_pfc_4km_30m.xml This dataset shows the current distribution of Colorado Plateau Pinyon-Juniper Woodland (LANDFIRE EVT v1.1) within the context of current and near-term terrestrial intactness and long-term potential for energy development and potential for climate change (4KM reporting units).
These data are provided by Bureau of Land Management (BLM) "as is" and may contain errors or omissions. The User assumes the entire risk associated with its use of these data and bears all responsibility in determining whether these data are fit for the User's intended use.
These data may not have the accuracy, resolution, completeness, timeliness, or other characteristics appropriate for applications that potential users of the data may contemplate. The User is encouraged to carefully consider the content of the metadata file associated with these data. The BLM should be cited as the data source in any products derived from these data.
Copyright Text: CSS-Dynamac and Conservation Biology Institute (CBI)
Description: See metadata (FGDC section) for complete information about this layer - https://landscape.blm.gov/COP_2010_metadata/COP_TES_ColoradoPlateauPJWoodland_LandfireEVT_status_n_pfc_4km_30m.xml This dataset shows the current distribution of Colorado Plateau Pinyon-Juniper Woodland (LANDFIRE EVT v1.1) within the context of current and near-term terrestrial intactness and long-term potential for energy development and potential for climate change (4KM reporting units).
These data are provided by Bureau of Land Management (BLM) "as is" and may contain errors or omissions. The User assumes the entire risk associated with its use of these data and bears all responsibility in determining whether these data are fit for the User's intended use.
These data may not have the accuracy, resolution, completeness, timeliness, or other characteristics appropriate for applications that potential users of the data may contemplate. The User is encouraged to carefully consider the content of the metadata file associated with these data. The BLM should be cited as the data source in any products derived from these data.
Copyright Text: CSS-Dynamac and Conservation Biology Institute (CBI)
Name: Long-Term Potential For Energy Development (4KM)
Display Field: ID
Type: Feature Layer
Geometry Type: esriGeometryPolygon
Description: See metadata (FGDC section) for complete information about this layer - https://landscape.blm.gov/COP_2010_metadata/COP_DV_L_PFC_4KM_poly.xml This dataset shows potential for long-term energy development, which was evaluated using a fuzzy logic model. This model integrates factors describing potential for petroleum development, solar development, and wind development.
Petroleum development was derived from the combination of BLM Oil / Gas leases (from CO, UT, and NM; none were available for AZ for this area), BLM Oil Shale / Tar Sands PEIS Alternative B areas, Department of Energy Oil / Gas fields, and areas of high potential for oil / gas development developed by Copeland et al. (2009).
Wind development was extracted from NREL estimates where potential wind class was 3 and above, combined with BLM wind priority areas.
Solar development was extracted from NREL estimates where slopes were less than 1%.
Highly protected areas (e.g., wilderness) were erased from each of the above factors.
These factors were combined using a fuzzy model to show the maximum across the three factors (fuzzy OR operation).
Caution is warranted when interpreting this dataset. Factors describing future potential for development are widely variable, and do not necessarily indicate those areas that have the highest potential for development - only those areas where the potential for development according to one of the factors in this model occupies proportionally more of a given reporting unit. Areas may be divided into higher likelihood of development by investigating additional factors such as local resource management plans (e.g., on BLM land), current and near-term leasing activity, and other planning processes. These sources of information were generally not available at the ecoregion scale.
Copyright Text: CSS-Dynamac and Conservation Biology Institute (CBI)
Name: Long-Term Potential For Climate Change (4KM)
Display Field: CL_L_PFC
Type: Feature Layer
Geometry Type: esriGeometryPolygon
Description: See metadata (FGDC section) for complete information about this layer - https://landscape.blm.gov/COP_2010_metadata/COP_CL_L_PFC_4KM_poly.xml This dataset provides an estimate of areas of higher and lower potential for climate change impacts. It is the result of a fuzzy model that integrates changes in precipitation, runoff, potential natural vegetation, and summer and winter temperature.
A powerpoint version of the logic model is available at Vector\Change_Agents\Climate\Clm_Long\Documentation\COP_CL_L_logic_model.pptx
Normalized summer and winter temperature differences (change in temperature between 1968-1999 and 2045-2060 divided by standard deviation of PRISM temperature for 1968-1999) were converted to fuzzy values and the maximum value extracted. This was averaged with fuzzy values for change in runoff and normalized change in annual precipitation. This value was then combined with areas of potential natural vegetation change and the maximum value extracted to provide the final estimate of potential for climate change impacts.
Caution should be exercised in interpreting this dataset. It provides one possible estimate of climate change impacts based on integration of statistically resampled regional climate projections based on boundary conditions from a single global climate model (ECHAM5) compared to current conditions (PRISM). It was not feasible in the scope of this REA to perform this analysis for other available climate projections; however, comparison of results across projections may provide additional insights as to the variability in areas of potential climate impacts. Please note that this dataset does not account for uncertainty of climate projections; this uncertainty is a combination of assumptions inherent in the model construction as well as spatial variability of climate observations over heterogenous landscapes (e.g., sparse weather stations recording past/current climate conditions, unevenly distributed across highly variable terrain).
Also note that the impacts of climate change are likely to be highly specific to particular species and ecosystems. The factors integrated into this dataset are intended to provide an overall estimate across species and ecosystems. Additional analyses (outside the scope of this REA) would be required to address species-specific impacts due to climate.
Copyright Text: CSS-Dynamac and Conservation Biology Institute (CBI)
Description: See metadata (FGDC section) for complete information about this layer - https://landscape.blm.gov/COP_2010_metadata/COP_TES_ColoradoPlateauPJWoodland_NatureServe_status_pfc_4km_30m.xml This dataset shows the current distribution of Colorado Plateau Pinyon-Juniper Woodland (NatureServe Landcover) within the context of current and near-term terrestrial intactness and long-term potential for energy development and potential for climate change (4KM reporting units).
These data are provided by Bureau of Land Management (BLM) "as is" and may contain errors or omissions. The User assumes the entire risk associated with its use of these data and bears all responsibility in determining whether these data are fit for the User's intended use.
These data may not have the accuracy, resolution, completeness, timeliness, or other characteristics appropriate for applications that potential users of the data may contemplate. The User is encouraged to carefully consider the content of the metadata file associated with these data. The BLM should be cited as the data source in any products derived from these data.
Copyright Text: CSS-Dynamac and Conservation Biology Institute (CBI)
Description: See metadata (FGDC section) for complete information about this layer - https://landscape.blm.gov/COP_2010_metadata/COP_TES_ColoradoPlateauPJWoodland_NatureServe_status_pfc_4km_30m.xml This dataset shows the current distribution of Colorado Plateau Pinyon-Juniper Woodland (NatureServe Landcover) within the context of current and near-term terrestrial intactness and long-term potential for energy development and potential for climate change (4KM reporting units).
These data are provided by Bureau of Land Management (BLM) "as is" and may contain errors or omissions. The User assumes the entire risk associated with its use of these data and bears all responsibility in determining whether these data are fit for the User's intended use.
These data may not have the accuracy, resolution, completeness, timeliness, or other characteristics appropriate for applications that potential users of the data may contemplate. The User is encouraged to carefully consider the content of the metadata file associated with these data. The BLM should be cited as the data source in any products derived from these data.
Copyright Text: CSS-Dynamac and Conservation Biology Institute (CBI)
Name: Long-Term Potential For Energy Development (4KM)
Display Field: ID
Type: Feature Layer
Geometry Type: esriGeometryPolygon
Description: See metadata (FGDC section) for complete information about this layer - https://landscape.blm.gov/COP_2010_metadata/COP_DV_L_PFC_4KM_poly.xml This dataset shows potential for long-term energy development, which was evaluated using a fuzzy logic model. This model integrates factors describing potential for petroleum development, solar development, and wind development.
Petroleum development was derived from the combination of BLM Oil / Gas leases (from CO, UT, and NM; none were available for AZ for this area), BLM Oil Shale / Tar Sands PEIS Alternative B areas, Department of Energy Oil / Gas fields, and areas of high potential for oil / gas development developed by Copeland et al. (2009).
Wind development was extracted from NREL estimates where potential wind class was 3 and above, combined with BLM wind priority areas.
Solar development was extracted from NREL estimates where slopes were less than 1%.
Highly protected areas (e.g., wilderness) were erased from each of the above factors.
These factors were combined using a fuzzy model to show the maximum across the three factors (fuzzy OR operation).
Caution is warranted when interpreting this dataset. Factors describing future potential for development are widely variable, and do not necessarily indicate those areas that have the highest potential for development - only those areas where the potential for development according to one of the factors in this model occupies proportionally more of a given reporting unit. Areas may be divided into higher likelihood of development by investigating additional factors such as local resource management plans (e.g., on BLM land), current and near-term leasing activity, and other planning processes. These sources of information were generally not available at the ecoregion scale.
Copyright Text: CSS-Dynamac and Conservation Biology Institute (CBI)
Name: Long-Term Potential For Climate Change (4KM)
Display Field: CL_L_PFC
Type: Feature Layer
Geometry Type: esriGeometryPolygon
Description: See metadata (FGDC section) for complete information about this layer - https://landscape.blm.gov/COP_2010_metadata/COP_CL_L_PFC_4KM_poly.xml This dataset provides an estimate of areas of higher and lower potential for climate change impacts. It is the result of a fuzzy model that integrates changes in precipitation, runoff, potential natural vegetation, and summer and winter temperature.
A powerpoint version of the logic model is available at Vector\Change_Agents\Climate\Clm_Long\Documentation\COP_CL_L_logic_model.pptx
Normalized summer and winter temperature differences (change in temperature between 1968-1999 and 2045-2060 divided by standard deviation of PRISM temperature for 1968-1999) were converted to fuzzy values and the maximum value extracted. This was averaged with fuzzy values for change in runoff and normalized change in annual precipitation. This value was then combined with areas of potential natural vegetation change and the maximum value extracted to provide the final estimate of potential for climate change impacts.
Caution should be exercised in interpreting this dataset. It provides one possible estimate of climate change impacts based on integration of statistically resampled regional climate projections based on boundary conditions from a single global climate model (ECHAM5) compared to current conditions (PRISM). It was not feasible in the scope of this REA to perform this analysis for other available climate projections; however, comparison of results across projections may provide additional insights as to the variability in areas of potential climate impacts. Please note that this dataset does not account for uncertainty of climate projections; this uncertainty is a combination of assumptions inherent in the model construction as well as spatial variability of climate observations over heterogenous landscapes (e.g., sparse weather stations recording past/current climate conditions, unevenly distributed across highly variable terrain).
Also note that the impacts of climate change are likely to be highly specific to particular species and ecosystems. The factors integrated into this dataset are intended to provide an overall estimate across species and ecosystems. Additional analyses (outside the scope of this REA) would be required to address species-specific impacts due to climate.
Copyright Text: CSS-Dynamac and Conservation Biology Institute (CBI)
Description: See metadata (FGDC section) for complete information about this layer - https://landscape.blm.gov/COP_2010_metadata/COP_TES_H_ColoradoPlateauPJWoodland_LandfireBpS_CA_30m.xml 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.
These data are provided by Bureau of Land Management (BLM) "as is" and may contain errors or omissions. The User assumes the entire risk associated with its use of these data and bears all responsibility in determining whether these data are fit for the User's intended use.
These data may not have the accuracy, resolution, completeness, timeliness, or other characteristics appropriate for applications that potential users of the data may contemplate. The User is encouraged to carefully consider the content of the metadata file associated with these data. The BLM should be cited as the data source in any products derived from these data.
Copyright Text: CSS-Dynamac and Conservation Biology Institute (CBI)
Description: See metadata (FGDC section) for complete information about this layer - https://landscape.blm.gov/COP_2010_metadata/COP_TES_H_ColoradoPlateauPJWoodland_LandfireBpS_disturbance_30m.xml These data are provided by Bureau of Land Management (BLM) "as is" and may contain errors or omissions. The User assumes the entire risk associated with its use of these data and bears all responsibility in determining whether these data are fit for the User's intended use.
These data may not have the accuracy, resolution, completeness, timeliness, or other characteristics appropriate for applications that potential users of the data may contemplate. The User is encouraged to carefully consider the content of the metadata file associated with these data. The BLM should be cited as the data source in any products derived from these data.
Copyright Text: CSS-Dynamac and Conservation Biology Institute (CBI)
Description: See metadata (FGDC section) for complete information about this layer - https://landscape.blm.gov/COP_2010_metadata/COP_Edulis_SWreGap_MortExt_poly.xml This dataset is an overlay of the Southwest reGap model of pinyon-juniper woodland distribution and the aerial photos of pinyon-pine mortality from 2000 to 2007.
This dataset was uploaded to Data Basin and is available with additional information at: http://app.databasin.org/app/pages/datasetPage.jsp?id=a708ece3e55845709a5cc731c8182038
These data are provided by Bureau of Land Management (BLM) "as is" and may contain errors or omissions. The User assumes the entire risk associated with its use of these data and bears all responsibility in determining whether these data are fit for the User's intended use.
These data may not have the accuracy, resolution, completeness, timeliness, or other characteristics appropriate for applications that potential users of the data may contemplate. The User is encouraged to carefully consider the content of the metadata file associated with these data. The BLM should be cited as the data source in any products derived from these data.
Description: See metadata (FGDC section) for complete information about this layer - https://landscape.blm.gov/COP_2010_metadata/cop_tipfc_4km_30m.xml This dataset shows the combined model results for current and near-term terrestrial intactness, long-term potential energy development, and long-term potential for climate change. Current terrestrial intactness is based on current measures of landscape development, fire regime and vegetation impacts, and fragmentation. Near-term intactness includes estimates of urban growth and expansion of invasive vegetation. Long-term potential for energy development is based on areas of potential for wind, solar, and petroleum development derived from multiple sources. 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.
This dataset is used to analysis current and near-term status and long-term potential for change of conservation elements with raster distribution data.
These data are provided by Bureau of Land Management (BLM) "as is" and may contain errors or omissions. The User assumes the entire risk associated with its use of these data and bears all responsibility in determining whether these data are fit for the User's intended use.
These data may not have the accuracy, resolution, completeness, timeliness, or other characteristics appropriate for applications that potential users of the data may contemplate. The User is encouraged to carefully consider the content of the metadata file associated with these data. The BLM should be cited as the data source in any products derived from these data.
Copyright Text: CSS-Dynamac and Conservation Biology Institute (CBI)
Description: See metadata (FGDC section) for complete information about this layer - https://landscape.blm.gov/COP_2010_metadata/cop_tipfc_4km_30m.xml This dataset shows the combined model results for current and near-term terrestrial intactness, long-term potential energy development, and long-term potential for climate change. Current terrestrial intactness is based on current measures of landscape development, fire regime and vegetation impacts, and fragmentation. Near-term intactness includes estimates of urban growth and expansion of invasive vegetation. Long-term potential for energy development is based on areas of potential for wind, solar, and petroleum development derived from multiple sources. 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.
This dataset is used to analysis current and near-term status and long-term potential for change of conservation elements with raster distribution data.
These data are provided by Bureau of Land Management (BLM) "as is" and may contain errors or omissions. The User assumes the entire risk associated with its use of these data and bears all responsibility in determining whether these data are fit for the User's intended use.
These data may not have the accuracy, resolution, completeness, timeliness, or other characteristics appropriate for applications that potential users of the data may contemplate. The User is encouraged to carefully consider the content of the metadata file associated with these data. The BLM should be cited as the data source in any products derived from these data.
Copyright Text: CSS-Dynamac and Conservation Biology Institute (CBI)
Name: Long-Term Potential For Energy Development (HUC5) (Scale Dependent)
Display Field: HUC_10
Type: Feature Layer
Geometry Type: esriGeometryPolygon
Description: See metadata (FGDC section) for complete information about this layer - https://landscape.blm.gov/COP_2010_metadata/COP_DV_L_PFC_HUC5_poly.xml This dataset shows potential for long-term energy development, which was evaluated using a fuzzy logic model. This model integrates factors describing potential for petroleum development, solar development, and wind development.
Petroleum development was derived from the combination of BLM Oil / Gas leases (from CO, UT, and NM; none were available for AZ for this area), BLM Oil Shale / Tar Sands PEIS Alternative B areas, Department of Energy Oil / Gas fields, and areas of high potential for oil / gas development developed by Copeland et al. (2009).
Wind development was extracted from NREL estimates where potential wind class was 3 and above, combined with BLM wind priority areas.
Solar development was extracted from NREL estimates where slopes were less than 1%.
Highly protected areas (e.g., wilderness) were erased from each of the above factors.
These factors were combined using a fuzzy model to show the maximum across the three factors (fuzzy OR operation).
Caution is warranted when interpreting this dataset. Factors describing future potential for development are widely variable, and do not necessarily indicate those areas that have the highest potential for development - only those areas where the potential for development according to one of the factors in this model occupies proportionally more of a given reporting unit. Areas may be divided into higher likelihood of development by investigating additional factors such as local resource management plans (e.g., on BLM land), current and near-term leasing activity, and other planning processes. These sources of information were generally not available at the ecoregion scale.
Copyright Text: CSS-Dynamac and Conservation Biology Institute (CBI)
Name: Long-Term Potential For Climate Change (HUC5)
Display Field: HUC_10
Type: Feature Layer
Geometry Type: esriGeometryPolygon
Description: See metadata (FGDC section) for complete information about this layer - https://landscape.blm.gov/COP_2010_metadata/COP_CL_L_PFC_HUC5_poly.xml This dataset provides an estimate of areas of higher and lower potential for climate change impacts. It is the result of a fuzzy model that integrates changes in precipitation, runoff, potential natural vegetation, and summer and winter temperature.
A powerpoint version of the logic model is available at Vector\Change_Agents\Climate\Clm_Long\Documentation\COP_CL_L_logic_model.pptx
Normalized summer and winter temperature differences (change in temperature between 1968-1999 and 2045-2060 divided by standard deviation of PRISM temperature for 1968-1999) were converted to fuzzy values and the maximum value extracted. This was averaged with fuzzy values for change in runoff and normalized change in annual precipitation. This value was then combined with areas of potential natural vegetation change and the maximum value extracted to provide the final estimate of potential for climate change impacts.
Caution should be exercised in interpreting this dataset. It provides one possible estimate of climate change impacts based on integration of statistically resampled regional climate projections based on boundary conditions from a single global climate model (ECHAM5) compared to current conditions (PRISM). It was not feasible in the scope of this REA to perform this analysis for other available climate projections; however, comparison of results across projections may provide additional insights as to the variability in areas of potential climate impacts. Please note that this dataset does not account for uncertainty of climate projections; this uncertainty is a combination of assumptions inherent in the model construction as well as spatial variability of climate observations over heterogenous landscapes (e.g., sparse weather stations recording past/current climate conditions, unevenly distributed across highly variable terrain).
Also note that the impacts of climate change are likely to be highly specific to particular species and ecosystems. The factors integrated into this dataset are intended to provide an overall estimate across species and ecosystems. Additional analyses (outside the scope of this REA) would be required to address species-specific impacts due to climate.
Copyright Text: CSS-Dynamac and Conservation Biology Institute (CBI)
Name: LANDFIRE - Existing Vegetation Type (version 1.1.0)
Display Field:
Type: Raster Layer
Geometry Type: null
Description: See metadata (FGDC section) for complete information about this layer - https://landscape.blm.gov/COP_2010_metadata/COP_TES_C_ColoradoPlateauPJWoodland_LandfireEVT_DIST_30m.xml The LANDFIRE existing vegetation layers describe the following elements of existing vegetation for each LANDFIRE mapping zone: existing vegetation type, existing vegetation canopy cover, and existing vegetation height. Vegetation is mapped using predictive landscape models based on extensive field reference data, satellite imagery, biophysical gradient layers, and classification and regression trees.DATA SUMMARY: The existing vegetation type (EVT) data layer represents the current distribution of the terrestrial ecological systems classification developed by NatureServe for the western Hemisphere (http://www.natureserve.org/publications/usEcologicalsystems.jsp). A terrestrial ecological system is defined as a group of plant community types (associations) that tend to co-occur within landscapes with similar ecological processes, substrates, and/or environmental gradients. EVTs are mapped in LANDFIRE using decision tree models, field reference data, Landsat imagery, digital elevation model data, and biophysical gradient data. Go to http://www.landfire.gov/participate_acknowledgements.php for more information regarding contributors of field plot data. Decision tree models are developed separately for each of the three life-forms -tree, shrub, and herbaceous - using C5 software. Life-form specific cross validation error matrices are generated during this process to assess levels of accuracy of the models. Decision tree relationships are then used to generate life-form specific EVT spatial data layers.The final EVT and Environemtanl Site Potential (ESP) layers are compared and rectified through a series of QA/QC measures. Values of one or more of these data layers are adjusted based on a hierarchical decision tree ruleset in order to align the respective life-forms and life-zone of each ESP and EVT category. The EVT layer is used in many subsequent LANDFIRE data layers. Refer to spatial metadata for date ranges of field plot data and satellite imagery for each LANDFIRE map zone.REFRESH 2008 (lf_1.1.0):Refresh 2008 (lf_1.1.0) used Refresh 2001 (lf_1.0.5) data as a launching point to incorporate disturbance and its severity, both managed and natural, which occurred on the landscape after 2001. Specific examples of disturbance are: fire, vegetation management, weather, and insect and disease. The final disturbance data used in Refresh 2008 (lf_1.1.0) is the result of several efforts that include data derived in part from remotely sensed land change methods, Monitoring Trends in Burn Severity (MTBS), and the LANDFIRE Refresh events data call. Vegetation growth was modeled where both disturbance and non-disturbance occurs.
These data are provided by Bureau of Land Management (BLM) "as is" and may contain errors or omissions. The User assumes the entire risk associated with its use of these data and bears all responsibility in determining whether these data are fit for the User's intended use.
These data may not have the accuracy, resolution, completeness, timeliness, or other characteristics appropriate for applications that potential users of the data may contemplate. The User is encouraged to carefully consider the content of the metadata file associated with these data. The BLM should be cited as the data source in any products derived from these data.
Copyright Text: Wildland Fire Science, Earth Resources Observation and Science Center, U.S. Geological Survey
Description: See metadata (FGDC section) for complete information about this layer - https://landscape.blm.gov/COP_2010_metadata/COP_TES_C_ColoradoPlateauPJWoodland_NatureServe_DIST_30m.xml The dataset represents the work of multiple states and Federal agencies as part of the US Gap Analysis and LandFire programs. Multi-season satellite imagery (Landsat ETM+) from 1999-2001 were used in conjunction with digital elevation model (DEM) derived datasets (e.g. elevation, landform) to model natural and semi-natural vegetation. The minimum mapping unit for this dataset is approximately 1 acre. Landcover classes are drawn from NatureServe's Ecological System concept. Five-hundred and fourty-four land cover classes composed of 12 cultural and 532 Natural/Semi-natural types are described. Land cover classes were mapped with a variety of techniques including decision tree classifiers, terrian modeling, inductive modeling, and unsupervised classification. The 67 USGS mapping zones were modeled independently of one another by multiple spatial analysis laboratories. Following completion of the national data set each individual land cover type was evaluated by NatureServe through individual working groups and two regional workshops attended by State, Federal, and Heritage Program ecologist. Where individual systems were identified with likely errors a description was recorded of the issue and a fix where available was described and initiated by NatureServe. All changes are available in supporting documentation and represent the opinion of multiple experts.
These data are provided by Bureau of Land Management (BLM) "as is" and may contain errors or omissions. The User assumes the entire risk associated with its use of these data and bears all responsibility in determining whether these data are fit for the User's intended use.
These data may not have the accuracy, resolution, completeness, timeliness, or other characteristics appropriate for applications that potential users of the data may contemplate. The User is encouraged to carefully consider the content of the metadata file associated with these data. The BLM should be cited as the data source in any products derived from these data.
Description: See metadata (FGDC section) for complete information about this layer - https://landscape.blm.gov/COP_2010_metadata/COP_TES_H_ColoradoPlateauPJWoodland_LandfireBpS_DIST_30m.xml The LANDFIRE vegetation layers describe the following elements of existing and potential vegetation for each LANDFIRE mapping zone: environmental site potentials, biophysical settings, existing vegetation types, canopy cover, and vegetation height. Vegetation is mapped using predictive landscape models based on extensive field reference data, satellite imagery, biophysical gradient layers, and classification and regression trees.DATA SUMMARYThe biophysical settings (BpS) data layer represents the vegetation that may have been dominant on the landscape prior to Euro-American settlement and is based on both the current biophysical environment and an approximation of the historical disturbance regime. It is a refinement of the environmental site potential map; in this refinement, we attempt to incorporate current scientific knowledge regarding the functioning of ecological processes - such as fire - in the centuries preceding non-indigenous human influence. Map units are based on NatureServe's Ecological Systems classification, which is a nationally consistent set of mid-scale ecological units (Comer and others 2003). LANDFIRES's use of these classification units to describe biophysical settings differs from their intended use as units of existing vegetation. As used in LANDFIRE, map unit names represent the natural plant communities that may have been present during the reference period. Each BpS map unit is matched with a model of vegetation succession, and both serve as key inputs to the LANDSUM landscape succession model (Keane and others 2002). The LANDFIRE BpS data layer is similar in concept to potential natural vegetation group layers produced in efforts related to fire regime condition class (Schmidt and others 2002; http://www.frcc.gov ).The first step in producing the LANDFIRE BpS data layer is the creation of an environmental site potential (ESP) layer. To create the ESP data layer, we first assign field plots to one of the ESP map unit classes (similar to BpS units in nomenclature but sometimes different in meaning). Go to http://www.landfire.gov/participate_acknowledgements.php for more information regarding contributors of field plot data. Assignments are based on presence and abundance of the indicator plant species recorded on the plots and on the ecological amplitude and competitive potential of these species. We then intersect plot locations with a series of 30-meter spatially-explicit gradient layers. Most of the gradient layers used in the predictive modeling of ESP are derived using the WX-BGC simulation model (Keane and Holsinger, in preparation; Keane and others 2002). WX-BGC simulations are based largely on spatially extrapolated weather data from DAYMET (Thornton and others 1997; Thornton and Running 1999; http://www.daymet.org) and on soils data in STATSGO (NRCS 1994). Additional indirect gradient layers, such as elevation, slope, and indices of topographic position, are also used. We use data from plot locations to develop predictive classification tree models, using See5 data mining software (Quinlan 1993; Rulequest Research 1997), for each LANDFIRE map zone. These decision trees are applied spatially to predict the ESP for every pixel across the landscape.Next, map units in the ESP layer are evaluated and, in some cases, split to reflect different fire regimes. These splits are made within each LANDFIRE map zone using a combination of plot data, gradient data, input from vegetation dynamics models, and additional See5 classification tree models. We then merge split map units back into the original ESP layer to create a BpS data layer. Finally, pixel values in the BpS layer are, in some cases, modified based on a comparison with the LANDFIRE existing vegetation type (EVT) layer created with the use of 30-meter Landsat ETM satellite imagery. We make such modifications only in non-vegetated areas (such as water, rock, snow, or ice) and where information in the EVT layer clearly enables a better depiction of the biophysical settings concept.The BpS data layer is used in LANDFIRE to depict reference conditions of vegetation across landscapes. The actual time period for this data set is a composite of both the historical context provided by the fire regime and vegetation dynamics models and the more recent field and geospatial data used to create it. The weather data used in DAYMET were compiled from 1980 to 1997. Refer to spatial metadata for date ranges of field plot data and satellite imagery for each LANDFIRE map zone.
These data are provided by Bureau of Land Management (BLM) "as is" and may contain errors or omissions. The User assumes the entire risk associated with its use of these data and bears all responsibility in determining whether these data are fit for the User's intended use.
These data may not have the accuracy, resolution, completeness, timeliness, or other characteristics appropriate for applications that potential users of the data may contemplate. The User is encouraged to carefully consider the content of the metadata file associated with these data. The BLM should be cited as the data source in any products derived from these data.
Description: See metadata (FGDC section) for complete information about this layer - https://landscape.blm.gov/COP_2010_metadata/COP_LANDFIRE_Succession_Classes_v1_0.xml Broad-scale alterations of historical fire regimes and vegetation dynamics have occurred in many landscapes in the U.S. through the combined influence of land management practices, fire exclusion, ungulate herbivory, insect and disease outbreaks, climate change, and invasion of non-native plant species. The LANDFIRE Project produces maps of simulated historical fire regimes and vegetation conditions using the LANDSUM landscape succession and disturbance dynamics model. The LANDFIRE Project also produces maps of current vegetation and measurements of current vegetation departure from simulated historical reference conditions. These maps support fire and landscape management planning outlined in the goals of the National Fire Plan, Federal Wildland Fire Management Policy, and the Healthy Forests Restoration Act.Data Summary:Succession Classes categorize current vegetation composition and structure into up to five successional states defined for each LANDFIRE Biophysical Settings (BpS) Model. An additional category defines uncharacteristic vegetation components that are not found within the compositional or structural variability of successional states defined for each BpS model, such as exotic species. These succession classes are similar in concept to those defined in the Interagency Fire Regime Condition Class Guidebook (www.frcc.gov). The presumed historical reference conditions for the succession classes in each BpS model are simulated using the vegetation and disturbance dynamics model LANDSUM (Keane et al. 2002, Keane et al. 2003, Keane et al. 2005, Pratt et al. 2005). The current successional classes and their simulated historical reference conditions are compared to assess departure of vegetation characteristics; this departure can be quantified using methods such as Fire Regime Condition Class (FRCC). Five successional classes, "A" (1) - "E" (5) define successional states represented within a given BpS model. 'UN' (6) represents uncharacteristic native vegetation for the BpS model on which these vegetation conditions are found. These are taken to represent vegetation cover, height, or composition that would not have been expected to occur on the BpS during the reference condition period. 'UE' (7) represents uncharacteristic exotic vegetation for the BpS model on which these vegetation conditions are found. Additional data layer values were included to represent Water (111), Snow / Ice (112), Barren (131), and Sparsely Vegetated (132). Urban (120) and Agriculture (180) are provided to mask out such areas from analysis of vegetation departure. To use this layer for assessing vegetation departure from simulated historical reference conditions, it is necessary to combine this layer with LANDFIRE BpS and LANDFIRE Map Zone data layers. The subsequent combination of Map Zone, Bps, and Succession Class can then be found within LANDFIRE Simulated Historical Reference Condition tables. Caution is warranted in assessing vegetation departure across Map Zone boundaries, as the classification schemes used to produce BpS and Succession Classes may vary slightly between adjacent Map Zones. Furthermore, reference conditions are simulated independently for each Map Zone, resulting in potentially unique measurements of reference conditions for a given BpS between adjacent Map Zones. Holsinger, L., R.E. Keane, B. Steele, M.C. Reeves, and S.D. Pratt. 2005. Assessing departure of current vegetation conditions from historical simulations of vegetation across large landscapes. Chapter 11 in: The LANDFIRE Prototype Project: nationally consistent and locally relevant geospatial data and tools for wildland fire management. M.G. Rollins, Technical Editor. USDA Forest Service, Rocky Mountain Research Station, Missoula Fire Sciences Laboratory. RMRS-GTR-[In prep.] Keane, R.E., R. Parsons, and P. Hessburg. 2002. Estimating historical range and variation of landscape patch dynamics: limitations of the simulation approach. Ecological Modeling 151: 29-49. Keane, R.E., G.J. Cary, and R. Parsons. 2003. Using simulation to map fire regimes: an evaluation of approaches, strategies, and limitations. International Journal of Wildland Fire 12: 309-322. Keane, R.E., L. Holsinger, and S. Pratt. 2006. Simulating historical landscape dynamics using the landscape fire succession model LANDSUM version 4.0. USDA Forest Service, Rocky Mountain Research Station, Missoula Fire Sciences Laboratory. RMRS-GTR-171CD. Pratt, S.D., L. Holsinger, and R.E. Keane. 2005. Modeling historical reference conditions for vegetation and fire regimes using simulation modeling. Chapter 10 in: The LANDFIRE Prototype Project: nationally consistent and locally relevant geospatial data and tools for wildland fire management. M.G. Rollins, Technical Editor. USDA Forest Service, Rocky Mountain Research Station, Missoula Fire Sciences Laboratory. RMRS-GTR-[In prep.]
These data are provided by Bureau of Land Management (BLM) "as is" and may contain errors or omissions. The User assumes the entire risk associated with its use of these data and bears all responsibility in determining whether these data are fit for the User's intended use.
These data may not have the accuracy, resolution, completeness, timeliness, or other characteristics appropriate for applications that potential users of the data may contemplate. The User is encouraged to carefully consider the content of the metadata file associated with these data. The BLM should be cited as the data source in any products derived from these data.
Description: See metadata (FGDC section) for complete information about this layer - https://landscape.blm.gov/COP_2010_metadata/COP_NLCD_Impervious_2006.xml The National Land Cover Database products are created through a cooperative project conducted by the Multi-Resolution Land Characteristics (MRLC) Consortium. The MRLC Consortium is a partnership of federal agencies (www.mrlc.gov), consisting of the U.S. Geological Survey (USGS), the National Oceanic and Atmospheric Administration (NOAA), the U.S. Environmental Protection Agency (EPA), the U.S. Department of Agriculture (USDA), the U.S. Forest Service (USFS), the National Park Service (NPS), the U.S. Fish and Wildlife Service (FWS), the Bureau of Land Management (BLM) and the USDA Natural Resources Conservation Service (NRCS). Previously, NLCD consisted of three major data releases based on a 10-year cycle. These include a circa 1992 conterminous U.S. land cover dataset with one thematic layer (NLCD 1992), a circa 2001 50-state/Puerto Rico updated U.S. land cover database (NLCD 2001) with three layers including thematic land cover, percent imperviousness, and percent tree canopy, and a 1992/2001 Land Cover Change Retrofit Product. With these national data layers, there is often a 5-year time lag between the image capture date and product release. In some areas, the land cover can undergo significant change during production time, resulting in products that may be perpetually out of date. To address these issues, this circa 2006 NLCD land cover product (NLCD 2006) was conceived to meet user community needs for more frequent land cover monitoring (moving to a 5-year cycle) and to reduce the production time between image capture and product release. NLCD 2006 is designed to provide the user both updated land cover data and additional information that can be used to identify the pattern, nature, and magnitude of changes occurring between 2001 and 2006 for the conterminous United States at medium spatial resolution.
For NLCD 2006, there are 3 primary data products: 1) NLCD 2006 Land Cover map; 2) NLCD 2001/2006 Change Pixels labeled with the 2006 land cover class; and 3) NLCD 2006 Percent Developed Imperviousness. Four additional data products were developed to provide supporting documentation and to provide information for land cover change analysis tasks: 4) NLCD 2001/2006 Percent Developed Imperviousness Change; 5) NLCD 2001/2006 Maximum Potential Change derived from the raw spectral change analysis; 6) NLCD 2001/2006 From-To Change pixels; and 7) NLCD 2006 Path/Row Index vector file showing the footprint of Landsat scene pairs used to derive 2001/2006 spectral change with change pair acquisition dates and scene identification numbers included in the attribute table.
In addition to the 2006 data products listed in the paragraph above, two of the original release NLCD 2001 data products have been revised and reissued. Generation of NLCD 2006 data products helped to identify some update issues in the NLCD 2001 land cover and percent developed imperviousness data products. These issues were evaluated and corrected, necessitating a reissue of NLCD 2001 data products (NLCD 2001 Version 2.0) as part of the NLCD 2006 release. A majority of NLCD 2001 updates occur in coastal mapping zones where NLCD 2001 was published prior to the National Oceanic and Atmospheric Administration (NOAA) Coastal Change Analysis Program (C-CAP) 2001 land cover products. NOAA C-CAP 2001 land cover has now been seamlessly integrated with NLCD 2001 land cover for all coastal zones. NLCD 2001 percent developed imperviousness was also updated as part of this process.
Land cover maps, derivatives and all associated documents are considered "provisional" until a formal accuracy assessment can be conducted. The NLCD 2006 is created on a path/row basis and mosaicked to create a seamless national product. Questions about the NLCD 2006 land cover product can be directed to the NLCD 2006 land cover mapping team at the USGS/EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov.
These data are provided by Bureau of Land Management (BLM) "as is" and may contain errors or omissions. The User assumes the entire risk associated with its use of these data and bears all responsibility in determining whether these data are fit for the User's intended use.
These data may not have the accuracy, resolution, completeness, timeliness, or other characteristics appropriate for applications that potential users of the data may contemplate. The User is encouraged to carefully consider the content of the metadata file associated with these data. The BLM should be cited as the data source in any products derived from these data.
Description: See metadata (FGDC section) for complete information about this layer - https://landscape.blm.gov/COP_2010_metadata/COP_LANDFIRE_Disturbance_1999.xml LANDFIRE disturbance data are developed to provide temporal and spatial information related to landscape change for determining vegetation transitions over time and for making subsequent updates to LANDFIRE vegetation, fuel and other data. Disturbance data include attributes associated with disturbance year, type, and severity. These data are developed through use of Landsat satellite imagery, local agency derived disturbance polygons, and other ancillary data. DATA SUMMARY: The disturbance data are developed through a multistep process. Inputs to this process include; Landsat imagery and derived NBR (normalized burn ratio) data; polygon data developed by local agencies for the LANDFIRE Refresh effort; fire data obtained from MTBS (Monitoring Trends in Burn Severity), BARC (Burned Area Reflectance Classification,), and RAVG (Rapid Assessment of Vegetation Condition after Wildfire) fire mapping efforts; and PAD (Protected Area Database) data.Refresh Event polygon data are provided to LANDFIRE by various local, regional, and national agencies and organizations. Disturbance type and year information is included as attributes for each polygon and transferred to the disturbance grids. Severity is determined by using dNBR (difference Normalized Burn Ratio) data classified into high, medium, and low severity levels based on a statistical comparison with the MTBS, BARC, and RAVG fire severity. Vegetation Tracker (Huang, et. al. 2008) algorithms are used to identify disturbances outside of Refresh Events. VCT data are developed for each year identifying disturbed areas as well as severity. Since disturbance type (i.e. causality) is not determined in the VCT process, a spatial analysis is done comparing the VCT output to buffered (1kilometer) Refresh Events and PAD GAP Status information (land use characteristics). While not providing a precise type of disturbance, this analysis provides information useful for narrowing down the types of disturbance that could or could not typically occur.Each zone has ten disturbance grids, one for each year 1999 to 2008. Each grid is attributed with year, disturbance type (if known, otherwise a description of possible types), severity, and the data sources used to create the data.
These data are provided by Bureau of Land Management (BLM) "as is" and may contain errors or omissions. The User assumes the entire risk associated with its use of these data and bears all responsibility in determining whether these data are fit for the User's intended use.
These data may not have the accuracy, resolution, completeness, timeliness, or other characteristics appropriate for applications that potential users of the data may contemplate. The User is encouraged to carefully consider the content of the metadata file associated with these data. The BLM should be cited as the data source in any products derived from these data.
Copyright Text: Wildland Fire Science, Earth Resources Observation and Science Center, U.S. Geological Survey
Description: See metadata (FGDC section) for complete information about this layer - https://landscape.blm.gov/COP_2010_metadata/COP_LANDFIRE_Disturbance_2000.xml LANDFIRE disturbance data are developed to provide temporal and spatial information related to landscape change for determining vegetation transitions over time and for making subsequent updates to LANDFIRE vegetation, fuel and other data. Disturbance data include attributes associated with disturbance year, type, and severity. These data are developed through use of Landsat satellite imagery, local agency derived disturbance polygons, and other ancillary data. DATA SUMMARY: The disturbance data are developed through a multistep process. Inputs to this process include; Landsat imagery and derived NBR (normalized burn ratio) data; polygon data developed by local agencies for the LANDFIRE Refresh effort; fire data obtained from MTBS (Monitoring Trends in Burn Severity), BARC (Burned Area Reflectance Classification,), and RAVG (Rapid Assessment of Vegetation Condition after Wildfire) fire mapping efforts; and PAD (Protected Area Database) data.Refresh Event polygon data are provided to LANDFIRE by various local, regional, and national agencies and organizations. Disturbance type and year information is included as attributes for each polygon and transferred to the disturbance grids. Severity is determined by using dNBR (difference Normalized Burn Ratio) data classified into high, medium, and low severity levels based on a statistical comparison with the MTBS, BARC, and RAVG fire severity. Vegetation Tracker (Huang, et. al. 2008) algorithms are used to identify disturbances outside of Refresh Events. VCT data are developed for each year identifying disturbed areas as well as severity. Since disturbance type (i.e. causality) is not determined in the VCT process, a spatial analysis is done comparing the VCT output to buffered (1kilometer) Refresh Events and PAD GAP Status information (land use characteristics). While not providing a precise type of disturbance, this analysis provides information useful for narrowing down the types of disturbance that could or could not typically occur.Each zone has ten disturbance grids, one for each year 1999 to 2008. Each grid is attributed with year, disturbance type (if known, otherwise a description of possible types), severity, and the data sources used to create the data.
These data are provided by Bureau of Land Management (BLM) "as is" and may contain errors or omissions. The User assumes the entire risk associated with its use of these data and bears all responsibility in determining whether these data are fit for the User's intended use.
These data may not have the accuracy, resolution, completeness, timeliness, or other characteristics appropriate for applications that potential users of the data may contemplate. The User is encouraged to carefully consider the content of the metadata file associated with these data. The BLM should be cited as the data source in any products derived from these data.
Copyright Text: Wildland Fire Science, Earth Resources Observation and Science Center, U.S. Geological Survey
Description: See metadata (FGDC section) for complete information about this layer - https://landscape.blm.gov/COP_2010_metadata/COP_LANDFIRE_Disturbance_2001.xml LANDFIRE disturbance data are developed to provide temporal and spatial information related to landscape change for determining vegetation transitions over time and for making subsequent updates to LANDFIRE vegetation, fuel and other data. Disturbance data include attributes associated with disturbance year, type, and severity. These data are developed through use of Landsat satellite imagery, local agency derived disturbance polygons, and other ancillary data. DATA SUMMARY: The disturbance data are developed through a multistep process. Inputs to this process include; Landsat imagery and derived NBR (normalized burn ratio) data; polygon data developed by local agencies for the LANDFIRE Refresh effort; fire data obtained from MTBS (Monitoring Trends in Burn Severity), BARC (Burned Area Reflectance Classification,), and RAVG (Rapid Assessment of Vegetation Condition after Wildfire) fire mapping efforts; and PAD (Protected Area Database) data.Refresh Event polygon data are provided to LANDFIRE by various local, regional, and national agencies and organizations. Disturbance type and year information is included as attributes for each polygon and transferred to the disturbance grids. Severity is determined by using dNBR (difference Normalized Burn Ratio) data classified into high, medium, and low severity levels based on a statistical comparison with the MTBS, BARC, and RAVG fire severity. Vegetation Tracker (Huang, et. al. 2008) algorithms are used to identify disturbances outside of Refresh Events. VCT data are developed for each year identifying disturbed areas as well as severity. Since disturbance type (i.e. causality) is not determined in the VCT process, a spatial analysis is done comparing the VCT output to buffered (1kilometer) Refresh Events and PAD GAP Status information (land use characteristics). While not providing a precise type of disturbance, this analysis provides information useful for narrowing down the types of disturbance that could or could not typically occur.Each zone has ten disturbance grids, one for each year 1999 to 2008. Each grid is attributed with year, disturbance type (if known, otherwise a description of possible types), severity, and the data sources used to create the data.
These data are provided by Bureau of Land Management (BLM) "as is" and may contain errors or omissions. The User assumes the entire risk associated with its use of these data and bears all responsibility in determining whether these data are fit for the User's intended use.
These data may not have the accuracy, resolution, completeness, timeliness, or other characteristics appropriate for applications that potential users of the data may contemplate. The User is encouraged to carefully consider the content of the metadata file associated with these data. The BLM should be cited as the data source in any products derived from these data.
Copyright Text: Wildland Fire Science, Earth Resources Observation and Science Center, U.S. Geological Survey
Description: See metadata (FGDC section) for complete information about this layer - https://landscape.blm.gov/COP_2010_metadata/COP_LANDFIRE_Disturbance_2002.xml LANDFIRE disturbance data are developed to provide temporal and spatial information related to landscape change for determining vegetation transitions over time and for making subsequent updates to LANDFIRE vegetation, fuel and other data. Disturbance data include attributes associated with disturbance year, type, and severity. These data are developed through use of Landsat satellite imagery, local agency derived disturbance polygons, and other ancillary data. DATA SUMMARY: The disturbance data are developed through a multistep process. Inputs to this process include; Landsat imagery and derived NBR (normalized burn ratio) data; polygon data developed by local agencies for the LANDFIRE Refresh effort; fire data obtained from MTBS (Monitoring Trends in Burn Severity), BARC (Burned Area Reflectance Classification,), and RAVG (Rapid Assessment of Vegetation Condition after Wildfire) fire mapping efforts; and PAD (Protected Area Database) data.Refresh Event polygon data are provided to LANDFIRE by various local, regional, and national agencies and organizations. Disturbance type and year information is included as attributes for each polygon and transferred to the disturbance grids. Severity is determined by using dNBR (difference Normalized Burn Ratio) data classified into high, medium, and low severity levels based on a statistical comparison with the MTBS, BARC, and RAVG fire severity. Vegetation Tracker (Huang, et. al. 2008) algorithms are used to identify disturbances outside of Refresh Events. VCT data are developed for each year identifying disturbed areas as well as severity. Since disturbance type (i.e. causality) is not determined in the VCT process, a spatial analysis is done comparing the VCT output to buffered (1kilometer) Refresh Events and PAD GAP Status information (land use characteristics). While not providing a precise type of disturbance, this analysis provides information useful for narrowing down the types of disturbance that could or could not typically occur.Each zone has ten disturbance grids, one for each year 1999 to 2008. Each grid is attributed with year, disturbance type (if known, otherwise a description of possible types), severity, and the data sources used to create the data.
These data are provided by Bureau of Land Management (BLM) "as is" and may contain errors or omissions. The User assumes the entire risk associated with its use of these data and bears all responsibility in determining whether these data are fit for the User's intended use.
These data may not have the accuracy, resolution, completeness, timeliness, or other characteristics appropriate for applications that potential users of the data may contemplate. The User is encouraged to carefully consider the content of the metadata file associated with these data. The BLM should be cited as the data source in any products derived from these data.
Copyright Text: Wildland Fire Science, Earth Resources Observation and Science Center, U.S. Geological Survey
Description: See metadata (FGDC section) for complete information about this layer - https://landscape.blm.gov/COP_2010_metadata/COP_LANDFIRE_Disturbance_2003.xml LANDFIRE disturbance data are developed to provide temporal and spatial information related to landscape change for determining vegetation transitions over time and for making subsequent updates to LANDFIRE vegetation, fuel and other data. Disturbance data include attributes associated with disturbance year, type, and severity. These data are developed through use of Landsat satellite imagery, local agency derived disturbance polygons, and other ancillary data. DATA SUMMARY: The disturbance data are developed through a multistep process. Inputs to this process include; Landsat imagery and derived NBR (normalized burn ratio) data; polygon data developed by local agencies for the LANDFIRE Refresh effort; fire data obtained from MTBS (Monitoring Trends in Burn Severity), BARC (Burned Area Reflectance Classification,), and RAVG (Rapid Assessment of Vegetation Condition after Wildfire) fire mapping efforts; and PAD (Protected Area Database) data.Refresh Event polygon data are provided to LANDFIRE by various local, regional, and national agencies and organizations. Disturbance type and year information is included as attributes for each polygon and transferred to the disturbance grids. Severity is determined by using dNBR (difference Normalized Burn Ratio) data classified into high, medium, and low severity levels based on a statistical comparison with the MTBS, BARC, and RAVG fire severity. Vegetation Tracker (Huang, et. al. 2008) algorithms are used to identify disturbances outside of Refresh Events. VCT data are developed for each year identifying disturbed areas as well as severity. Since disturbance type (i.e. causality) is not determined in the VCT process, a spatial analysis is done comparing the VCT output to buffered (1kilometer) Refresh Events and PAD GAP Status information (land use characteristics). While not providing a precise type of disturbance, this analysis provides information useful for narrowing down the types of disturbance that could or could not typically occur.Each zone has ten disturbance grids, one for each year 1999 to 2008. Each grid is attributed with year, disturbance type (if known, otherwise a description of possible types), severity, and the data sources used to create the data.
These data are provided by Bureau of Land Management (BLM) "as is" and may contain errors or omissions. The User assumes the entire risk associated with its use of these data and bears all responsibility in determining whether these data are fit for the User's intended use.
These data may not have the accuracy, resolution, completeness, timeliness, or other characteristics appropriate for applications that potential users of the data may contemplate. The User is encouraged to carefully consider the content of the metadata file associated with these data. The BLM should be cited as the data source in any products derived from these data.
Copyright Text: Wildland Fire Science, Earth Resources Observation and Science Center, U.S. Geological Survey
Description: See metadata (FGDC section) for complete information about this layer - https://landscape.blm.gov/COP_2010_metadata/COP_LANDFIRE_Disturbance_2004.xml LANDFIRE disturbance data are developed to provide temporal and spatial information related to landscape change for determining vegetation transitions over time and for making subsequent updates to LANDFIRE vegetation, fuel and other data. Disturbance data include attributes associated with disturbance year, type, and severity. These data are developed through use of Landsat satellite imagery, local agency derived disturbance polygons, and other ancillary data. DATA SUMMARY: The disturbance data are developed through a multistep process. Inputs to this process include; Landsat imagery and derived NBR (normalized burn ratio) data; polygon data developed by local agencies for the LANDFIRE Refresh effort; fire data obtained from MTBS (Monitoring Trends in Burn Severity), BARC (Burned Area Reflectance Classification,), and RAVG (Rapid Assessment of Vegetation Condition after Wildfire) fire mapping efforts; and PAD (Protected Area Database) data.Refresh Event polygon data are provided to LANDFIRE by various local, regional, and national agencies and organizations. Disturbance type and year information is included as attributes for each polygon and transferred to the disturbance grids. Severity is determined by using dNBR (difference Normalized Burn Ratio) data classified into high, medium, and low severity levels based on a statistical comparison with the MTBS, BARC, and RAVG fire severity. Vegetation Tracker (Huang, et. al. 2008) algorithms are used to identify disturbances outside of Refresh Events. VCT data are developed for each year identifying disturbed areas as well as severity. Since disturbance type (i.e. causality) is not determined in the VCT process, a spatial analysis is done comparing the VCT output to buffered (1kilometer) Refresh Events and PAD GAP Status information (land use characteristics). While not providing a precise type of disturbance, this analysis provides information useful for narrowing down the types of disturbance that could or could not typically occur.Each zone has ten disturbance grids, one for each year 1999 to 2008. Each grid is attributed with year, disturbance type (if known, otherwise a description of possible types), severity, and the data sources used to create the data.
These data are provided by Bureau of Land Management (BLM) "as is" and may contain errors or omissions. The User assumes the entire risk associated with its use of these data and bears all responsibility in determining whether these data are fit for the User's intended use.
These data may not have the accuracy, resolution, completeness, timeliness, or other characteristics appropriate for applications that potential users of the data may contemplate. The User is encouraged to carefully consider the content of the metadata file associated with these data. The BLM should be cited as the data source in any products derived from these data.
Copyright Text: Wildland Fire Science, Earth Resources Observation and Science Center, U.S. Geological Survey
Description: See metadata (FGDC section) for complete information about this layer - https://landscape.blm.gov/COP_2010_metadata/COP_LANDFIRE_Disturbance_2005.xml LANDFIRE disturbance data are developed to provide temporal and spatial information related to landscape change for determining vegetation transitions over time and for making subsequent updates to LANDFIRE vegetation, fuel and other data. Disturbance data include attributes associated with disturbance year, type, and severity. These data are developed through use of Landsat satellite imagery, local agency derived disturbance polygons, and other ancillary data. DATA SUMMARY: The disturbance data are developed through a multistep process. Inputs to this process include; Landsat imagery and derived NBR (normalized burn ratio) data; polygon data developed by local agencies for the LANDFIRE Refresh effort; fire data obtained from MTBS (Monitoring Trends in Burn Severity), BARC (Burned Area Reflectance Classification,), and RAVG (Rapid Assessment of Vegetation Condition after Wildfire) fire mapping efforts; and PAD (Protected Area Database) data.Refresh Event polygon data are provided to LANDFIRE by various local, regional, and national agencies and organizations. Disturbance type and year information is included as attributes for each polygon and transferred to the disturbance grids. Severity is determined by using dNBR (difference Normalized Burn Ratio) data classified into high, medium, and low severity levels based on a statistical comparison with the MTBS, BARC, and RAVG fire severity. Vegetation Tracker (Huang, et. al. 2008) algorithms are used to identify disturbances outside of Refresh Events. VCT data are developed for each year identifying disturbed areas as well as severity. Since disturbance type (i.e. causality) is not determined in the VCT process, a spatial analysis is done comparing the VCT output to buffered (1kilometer) Refresh Events and PAD GAP Status information (land use characteristics). While not providing a precise type of disturbance, this analysis provides information useful for narrowing down the types of disturbance that could or could not typically occur.Each zone has ten disturbance grids, one for each year 1999 to 2008. Each grid is attributed with year, disturbance type (if known, otherwise a description of possible types), severity, and the data sources used to create the data.
These data are provided by Bureau of Land Management (BLM) "as is" and may contain errors or omissions. The User assumes the entire risk associated with its use of these data and bears all responsibility in determining whether these data are fit for the User's intended use.
These data may not have the accuracy, resolution, completeness, timeliness, or other characteristics appropriate for applications that potential users of the data may contemplate. The User is encouraged to carefully consider the content of the metadata file associated with these data. The BLM should be cited as the data source in any products derived from these data.
Copyright Text: Wildland Fire Science, Earth Resources Observation and Science Center, U.S. Geological Survey
Description: See metadata (FGDC section) for complete information about this layer - https://landscape.blm.gov/COP_2010_metadata/COP_LANDFIRE_Disturbance_2006.xml LANDFIRE disturbance data are developed to provide temporal and spatial information related to landscape change for determining vegetation transitions over time and for making subsequent updates to LANDFIRE vegetation, fuel and other data. Disturbance data include attributes associated with disturbance year, type, and severity. These data are developed through use of Landsat satellite imagery, local agency derived disturbance polygons, and other ancillary data. DATA SUMMARY: The disturbance data are developed through a multistep process. Inputs to this process include; Landsat imagery and derived NBR (normalized burn ratio) data; polygon data developed by local agencies for the LANDFIRE Refresh effort; fire data obtained from MTBS (Monitoring Trends in Burn Severity), BARC (Burned Area Reflectance Classification,), and RAVG (Rapid Assessment of Vegetation Condition after Wildfire) fire mapping efforts; and PAD (Protected Area Database) data.Refresh Event polygon data are provided to LANDFIRE by various local, regional, and national agencies and organizations. Disturbance type and year information is included as attributes for each polygon and transferred to the disturbance grids. Severity is determined by using dNBR (difference Normalized Burn Ratio) data classified into high, medium, and low severity levels based on a statistical comparison with the MTBS, BARC, and RAVG fire severity. Vegetation Tracker (Huang, et. al. 2008) algorithms are used to identify disturbances outside of Refresh Events. VCT data are developed for each year identifying disturbed areas as well as severity. Since disturbance type (i.e. causality) is not determined in the VCT process, a spatial analysis is done comparing the VCT output to buffered (1kilometer) Refresh Events and PAD GAP Status information (land use characteristics). While not providing a precise type of disturbance, this analysis provides information useful for narrowing down the types of disturbance that could or could not typically occur.Each zone has ten disturbance grids, one for each year 1999 to 2008. Each grid is attributed with year, disturbance type (if known, otherwise a description of possible types), severity, and the data sources used to create the data.
These data are provided by Bureau of Land Management (BLM) "as is" and may contain errors or omissions. The User assumes the entire risk associated with its use of these data and bears all responsibility in determining whether these data are fit for the User's intended use.
These data may not have the accuracy, resolution, completeness, timeliness, or other characteristics appropriate for applications that potential users of the data may contemplate. The User is encouraged to carefully consider the content of the metadata file associated with these data. The BLM should be cited as the data source in any products derived from these data.
Copyright Text: Wildland Fire Science, Earth Resources Observation and Science Center, U.S. Geological Survey
Description: See metadata (FGDC section) for complete information about this layer - https://landscape.blm.gov/COP_2010_metadata/COP_LANDFIRE_Disturbance_2007.xml LANDFIRE disturbance data are developed to provide temporal and spatial information related to landscape change for determining vegetation transitions over time and for making subsequent updates to LANDFIRE vegetation, fuel and other data. Disturbance data include attributes associated with disturbance year, type, and severity. These data are developed through use of Landsat satellite imagery, local agency derived disturbance polygons, and other ancillary data. DATA SUMMARY: The disturbance data are developed through a multistep process. Inputs to this process include; Landsat imagery and derived NBR (normalized burn ratio) data; polygon data developed by local agencies for the LANDFIRE Refresh effort; fire data obtained from MTBS (Monitoring Trends in Burn Severity), BARC (Burned Area Reflectance Classification,), and RAVG (Rapid Assessment of Vegetation Condition after Wildfire) fire mapping efforts; and PAD (Protected Area Database) data.Refresh Event polygon data are provided to LANDFIRE by various local, regional, and national agencies and organizations. Disturbance type and year information is included as attributes for each polygon and transferred to the disturbance grids. Severity is determined by using dNBR (difference Normalized Burn Ratio) data classified into high, medium, and low severity levels based on a statistical comparison with the MTBS, BARC, and RAVG fire severity. Vegetation Tracker (Huang, et. al. 2008) algorithms are used to identify disturbances outside of Refresh Events. VCT data are developed for each year identifying disturbed areas as well as severity. Since disturbance type (i.e. causality) is not determined in the VCT process, a spatial analysis is done comparing the VCT output to buffered (1kilometer) Refresh Events and PAD GAP Status information (land use characteristics). While not providing a precise type of disturbance, this analysis provides information useful for narrowing down the types of disturbance that could or could not typically occur.Each zone has ten disturbance grids, one for each year 1999 to 2008. Each grid is attributed with year, disturbance type (if known, otherwise a description of possible types), severity, and the data sources used to create the data.
These data are provided by Bureau of Land Management (BLM) "as is" and may contain errors or omissions. The User assumes the entire risk associated with its use of these data and bears all responsibility in determining whether these data are fit for the User's intended use.
These data may not have the accuracy, resolution, completeness, timeliness, or other characteristics appropriate for applications that potential users of the data may contemplate. The User is encouraged to carefully consider the content of the metadata file associated with these data. The BLM should be cited as the data source in any products derived from these data.
Copyright Text: Wildland Fire Science, Earth Resources Observation and Science Center, U.S. Geological Survey
Description: See metadata (FGDC section) for complete information about this layer - https://landscape.blm.gov/COP_2010_metadata/COP_LANDFIRE_Disturbance_2008.xml LANDFIRE disturbance data are developed to provide temporal and spatial information related to landscape change for determining vegetation transitions over time and for making subsequent updates to LANDFIRE vegetation, fuel and other data. Disturbance data include attributes associated with disturbance year, type, and severity. These data are developed through use of Landsat satellite imagery, local agency derived disturbance polygons, and other ancillary data. DATA SUMMARY: The disturbance data are developed through a multistep process. Inputs to this process include; Landsat imagery and derived NBR (normalized burn ratio) data; polygon data developed by local agencies for the LANDFIRE Refresh effort; fire data obtained from MTBS (Monitoring Trends in Burn Severity), BARC (Burned Area Reflectance Classification,), and RAVG (Rapid Assessment of Vegetation Condition after Wildfire) fire mapping efforts; and PAD (Protected Area Database) data.Refresh Event polygon data are provided to LANDFIRE by various local, regional, and national agencies and organizations. Disturbance type and year information is included as attributes for each polygon and transferred to the disturbance grids. Severity is determined by using dNBR (difference Normalized Burn Ratio) data classified into high, medium, and low severity levels based on a statistical comparison with the MTBS, BARC, and RAVG fire severity. Vegetation Tracker (Huang, et. al. 2008) algorithms are used to identify disturbances outside of Refresh Events. VCT data are developed for each year identifying disturbed areas as well as severity. Since disturbance type (i.e. causality) is not determined in the VCT process, a spatial analysis is done comparing the VCT output to buffered (1kilometer) Refresh Events and PAD GAP Status information (land use characteristics). While not providing a precise type of disturbance, this analysis provides information useful for narrowing down the types of disturbance that could or could not typically occur.Each zone has ten disturbance grids, one for each year 1999 to 2008. Each grid is attributed with year, disturbance type (if known, otherwise a description of possible types), severity, and the data sources used to create the data.
These data are provided by Bureau of Land Management (BLM) "as is" and may contain errors or omissions. The User assumes the entire risk associated with its use of these data and bears all responsibility in determining whether these data are fit for the User's intended use.
These data may not have the accuracy, resolution, completeness, timeliness, or other characteristics appropriate for applications that potential users of the data may contemplate. The User is encouraged to carefully consider the content of the metadata file associated with these data. The BLM should be cited as the data source in any products derived from these data.
Copyright Text: Wildland Fire Science, Earth Resources Observation and Science Center, U.S. Geological Survey
Description: See metadata (FGDC section) for complete information about this layer - https://landscape.blm.gov/COP_2010_metadata/COP_WildlandFirePerim_Years_2000_2010_poly.xml Wildland fire perimeters for the years 2000 to 2010 for the Colorado Plateau ecoregion, USA. This dataset was merged from individual datasets for each year, using standardized attributes
These data are provided by Bureau of Land Management (BLM) "as is" and may contain errors or omissions. The User assumes the entire risk associated with its use of these data and bears all responsibility in determining whether these data are fit for the User's intended use.
These data may not have the accuracy, resolution, completeness, timeliness, or other characteristics appropriate for applications that potential users of the data may contemplate. The User is encouraged to carefully consider the content of the metadata file associated with these data. The BLM should be cited as the data source in any products derived from these data.
Copyright Text: United States Geological Survey
Conservation Biology Institute
Name: Pinyon-Juniper Treatment Inventory Database of BLM Land
Display Field: OBJECTID
Type: Feature Layer
Geometry Type: esriGeometryPolygon
Description: See metadata (FGDC section) for complete information about this layer - https://landscape.blm.gov/COP_2010_metadata/COP_Pinyon_Juniper_Treatments_poly.xml This dataset contains a set of the polygons for Bureau of Land Managment Field Offices on the Colorado Plateau. This geodatabase consists of a feature class and related data tables creating an inventory of mechanical, herbicide, handthinning, prescribed fire, and other treatments applied to Pinyon-Juniper woodlands on Bureau of Land Management (BLM) and adjacent lands on the Colorado Plateau. Treatment data came from BLM Range Management files. Boundaries were delineated on the most current set of 1 meter digital orthophotograph quads (DOQ) or digital orthophotograph quarter quads (DOQQ) available. Additional reference boundary datasets acquired from BLM were used in digitizing such as roads, fence lines, allotment boundaries, and ownership layers. The data set was hand digitized. Polygon topology was corrected so that no over-lapping occurs.
Metadata obtained from:http://www.mpcer.nau.edu/pj/pjwood/merge_pj_trts_metadata.htm
These data are provided by Bureau of Land Management (BLM) "as is" and may contain errors or omissions. The User assumes the entire risk associated with its use of these data and bears all responsibility in determining whether these data are fit for the User's intended use.
These data may not have the accuracy, resolution, completeness, timeliness, or other characteristics appropriate for applications that potential users of the data may contemplate. The User is encouraged to carefully consider the content of the metadata file associated with these data. The BLM should be cited as the data source in any products derived from these data.
Copyright Text: Merriam-Powell Center for Environmental Research
Description: See metadata (FGDC section) for complete information about this layer - https://landscape.blm.gov/COP_2010_metadata/COP_BLM_GTLF_ln.xml Colorado Plateau Ecoregion Ground Transportation Layer Features from BLM
This dataset was provided by BLM NOC to CBI with no metadata (metadata development underway by NOC data team). Metadata provided here by CBI. These metadata should be replaced in the near future by the official metadata under development by BLM.
These data are provided by Bureau of Land Management (BLM) "as is" and may contain errors or omissions. The User assumes the entire risk associated with its use of these data and bears all responsibility in determining whether these data are fit for the User's intended use.
These data may not have the accuracy, resolution, completeness, timeliness, or other characteristics appropriate for applications that potential users of the data may contemplate. The User is encouraged to carefully consider the content of the metadata file associated with these data. The BLM should be cited as the data source in any products derived from these data.