20220930 raster digital data The data layers included in this North Coast Regional Resource Kit were assembled/developed by a partnership that includes the Pacific Southwest Research Station of the U.S. Forest Service, the Fire and Resource Assessment Program (FRAP) of CALFIRE, the Climate and Wildfire Institute, and faculty from the University of California Berkeley and Irvine. This science team is working together at the behest of the California Wildfire and Forest Resilience Task Force. As we continue to develop geospatial data for landscape assessment and planning throughout the state, this partnership has now taken the lead in the creation of the Regional Resource Kits for the four regions of California. The RRK has adopted the Framework for Resilience to provide a structure for assessing landscape conditions, setting objectives, designing projects, and measuring progress towards social-ecological resilience. There are ten pillars that represent the desired outcomes of landscape resilience. Each of the pillars provides a series of elements and under the elements, metrics (the data layers) for assessing landscape conditions and verifying that actions meet resilience objectives. The metrics are organized by the ten pillars of resilience in the Framework for Resilience. The Metrics describe the characteristics of the elements (key characteristics) of each pillar in quantitative or, in a few cases, qualitative terms. Metrics are used to assess, plan for, measure, and monitor progress toward desired outcomes and greater resilience. Metrics are selected to be informative, meaningful, and actionable to meet the needs of management. The metrics are also divided into three “tiers.” Among all these metrics, some are created and relevant statewide. Other metrics are more suited to conditions within a given region. The “Tiers” for metrics included in each RRK: Tier 1 – metrics that are a single, consistent data layer, developed statewide; they can also be clipped to the boundary of the region so values within that region are the only ones included for calculations or regional statistics. Example: Annual Burn Probability. Tier 2 – metrics relevant to a single region or relevant to multiple regions but data layers differ among regions because of varied data availability (sources) across regions. Example: California gnatcatcher habitat suitability. Tier 3 - metrics are those that would be of interest to some land managers for specific applications but not included as a core metric in an RRK. Example: Distribution of the Quino checkerspot butterfly. Each RRK will contain all Tier 1 and Tier 2 data together to comprise the kit. Tier 3 data will be pointed to for reference and use, as needed. Within each Tier, the data layers are available in two forms: 1) data values native to the metric (raw), and 2) translated data values. The raw data values are in the units of the metric, so for example the species richness map will show an estimated number of terrestrial vertebrate species per acre that can range from 0 to any number for each 30-m pixel, and the departure from historical fire return interval (FRID) map will have values that range from -100% to +100% departure. The translated data values represent each metric using a common unit of measure with the same range of values from -1 to +1 that represent values that are generally considered favorable (+1) and unfavorable (-1). In the case of species richness, higher species counts are considered more favorable and lower species counts are considered less favorable. In the case of FRID, values within the historical fire return interval are considered favorable, and high departure from the historical fire return interval is considered less favorable. In both cases, more and less favorable conditions for each metric are represented by values that range from +1 to -1 (respectively) so that multiple metrics can be evaluated together, including summarizing overall conditions at element and pillar levels to characterize socio-ecological resilience. Some data layers within this kit contain null values. We point this out here so users of the data will be aware and take whatever measures appropriate as they use and analyze the data. For some raster datasets in the RRK, areas have been masked (blanked) out and have a cell value of NoData (also referred to as null, NaN or missing). We, as producers and users of the data, cannot ignore NoData or fill them with zeros, since zero is often a valid value for some datasets. Removing NoData cells is not an option, a raster is a continuous grid. For users of the data performing further analyses and combining or "stacking" rasters, these NoData cells will mask out all values in that location in the output. To avoid this issue, the user must create values for the cells before combining them (i.e. 999 or any numeric value that is not real and clearly out of the range of the other values). Reasons for masking (blanking) out cells in RRK data: · Cells are lakes or reservoirs · Cells are urban or agriculture · Cells contain no information relevant to the dataset (i.e. streams, habitat) · Area (cells) subject to fire or other disturbance but the post disturbance condition or value is unknown RRK project: Reducing the risk of large, high intensity fire (and other mega-disturbances) through forest treatments has become a management imperative in California. A Strategy for Shared Stewardship (2018) (https://www.fs.usda.gov:443/sites/default/files/toward-shared-stewardship.pdf) and the USFS Wildfire Crisis Implementation Plan (2022) (https://www.fs.usda.gov:443/sites/default/files/Wildfire-Crisis-Implementation-Plan.pdf) reinforce specific goals for pace and scale of strategic forest treatments over the next decade. Concurrently, the State of California has issued a new Wildfire and Forest Resilience Action Plan (2022) (https://wildfiretaskforce.org:443/wp-content/uploads/2022/04/californiawildfireandforestresilienceactionplan.pdf), designed to strategically accelerate efforts to restore the health and resilience of California forests through a joint State of California - Forest Service framework to improve and enhance forest stewardship in California. The social incentives and the scientific knowledge to pursue meaningful restoration of forested landscapes in California are firmly established. High quality geospatial data are an essential ingredient to address restoration/conservation of the broad suite of core socio-ecological values across landscapes, and to drive analytic tools for planning management investments. To support these initiatives an interagency team of scientists from the Forest Service/Pacific Southwest Research Station, California Natural Resources Agency/CALFIRE, and the University of California at Berkeley and University of California at Irvine collaborated on development of a comprehensive set of mapped data layers needed to accomplish large-scale landscape planning and restoration. Landscape level assessment using high quality data developed from ecological modeling techniques, informative analytical approaches and the resulting credible scientific outputs will be fundamental to inform and support large landscape restoration planning and execution. The data layers included in this kit are meant to assist land managers in assessing their current landscape and plan for treatments to enhance resilience to human and natural disturbances. Thus each layer represents what the interagency team believes are the most relevant and reliable geospatial data available at this time. Each layer has been examined by the team and is supported by published data and/or was developed using standard methods. The methods for developing each layer are documented in the metric dictionary; however, the accuracy of each layer has not been quantified. It is anticipated that all data layers will be updated and refined as methods and source data evolve and improve. RRK Components: The authors and their partners are committed to increasing the “pace and scale” of forest treatments in California. Multiple federal and state initiatives in the last few years detail this commitment. Land managers need support to plan and implement treatments that will address restoration at a landscape scale. An essential component of these initiatives is the spatial data representing landscape conditions combined with new analytical tools for planning management investments. The authors joined forces to develop and/or collect and assemble existing sources of spatial data. This project, referred to as RRK (Regional Resource Kit), combines the expertise and experience of research and management to build this library of data on landscape conditions. These data reflect landscape conditions across ten “Pillars of Resilience” which address the full array of landscape management objectives. • Pillars are the desired long-term, landscape-scale outcomes of restoring resilience. They include ecological values, such as biodiversity, as well as societal benefits to communities, such as water security. • Elements represent the primary processes and functions that altogether make up a pillar, such as focal species, water quality, or economic health. • Metrics describe the characteristics of elements in quantitative or qualitative terms. Users can use metrics to assess, plan for, measure, and monitor progress towards desired outcomes and greater resilience. While pillars and elements are consistent across all of California, the metrics that a group uses may vary from region to region based on ecological and social differences (for example forest types, economy), available data, and user preferences. The ten pillars are Forest and Shrubland Resilience, Water Security, Carbon Sequestration, Air Quality, Fire Dynamics, Fire Adapted Communities, Economic Diversity, Social & Cultural Well-Being, Wetland Integrity, Biodiversity Conservation. The individual “Metrics” are used to assess, plan for, measure, and monitor progress toward desired outcomes and greater resilience. Metrics are selected to be informative, meaningful, and actionable to meet the needs of management. Landscape level assessment using these high-quality data, combined with decision support tools that can help evaluate alternative treatment strategies, are fundamental to inform and support large landscape restoration planning. These data are assembled in one place to provide comprehensive access for land managers. They represent conditions as of 2022 (or otherwise, as noted) and intentions are to refresh these data on an annual basis. Each metric includes the following information to help users of the data (and for use with any decision support tools) to understand the details of the metric. This information is also included in a "metric dictionary" that comes with this set of metrics. • Tier group the metric is in (1, 2, or 3) • The vintage of the data • The definition meant by a given metric • The expected use(s) of the metric • The resolution of the developed data • The data sources used to derive the metric • The intended method of metric derivation • Where reasonable, a desired management target References have been included in the metric dictionary to help the reader understand methods used for deriving metrics and will be updated periodically, as necessary. This information will help people make better use of all the assembled information and how it can best be used with various decision support tools. Complete Unknown -123.046475 -119.297108 38.392294 34.819494 ISO 19115 Topic Categories environment, geoscientific Information, planning Cadastre, land management, landscape restoration None Appropriate use includes regional to statewide assessments of vegetation cover, land cover, or land use change trends, total extent of vegetation cover, land cover, or land use change, and aggregated summaries of vegetation cover, land cover, or land use change. Further use includes applying these data to assess management opportunities for treatments to restore landscape resiliency. The authors make no warranty, expressed or implied, including the warranties of merchantability and fitness for a particular purpose, nor assumes any legal liability or responsibility for the accuracy, reliability, completeness, or utility of these geospatial data, or for the improper or incorrect use of these geospatial data. These geospatial data and related maps or graphics are not legal documents and are not intended to be used as such. The data and maps may not be used to determine title, ownership, legal descriptions or boundaries, legal jurisdiction, or restrictions that may be in place on either public or private land. Natural hazards may or may not be depicted on the data and maps, and land users should exercise due caution. The data are dynamic and may change over time. The user is responsible to verify the limitations of the geospatial data and to use the data accordingly. For all data layers you are free to share, copy, and redistribute the material in any medium or format AND adapt, remix, transform, and build upon the material for any purpose, even commercially under the following terms:Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original. No commercial use – the user is responsible for acknowledging those data layers within this RRK (as determined by the source of the data) that are not permitted for commercial use. No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything this license permits. Climate and Wildfire Institute Peter A. Stine Project Manager, Regional Resource Kit Project pstine@climateandwildfire.org Climate and Wildfire Institute Carol Clark Senior Geospatial Data Analyst cclark@climateandwildfire.org Climate and Wildfire Institute Peter A. Stine Project Manager, Regional Resource Kit Project pstine@climateandwildfire.org Esri ArcGIS 12.9.2.32739 Raster Pixel 3300 2550 20230902 Climate and Wildfire Institute Peter A. Stine Project Manager, Regional Resource Kit Project pstine@climateandwildfire.org FGDC Content Standard for Digital Geospatial Metadata FGDC-STD-001-1998 local time Peter A. Stine Climate and Wildfire Institute Project Manager, Regional Resource Kit Project pstine@climateandwildfire.org 20230921 ArcGIS Metadata 1.0 <DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>Appropriate use includes regional to statewide assessments of vegetation cover, land cover, or land use change trends, total extent of vegetation cover, land cover, or land use change, and aggregated summaries of vegetation cover, land cover, or land use change. Further use includes applying these data to assess management opportunities for treatments to restore landscape resiliency. The authors make no warranty, expressed or implied, including the warranties of merchantability and fitness for a particular purpose, nor assumes any legal liability or responsibility for the accuracy, reliability, completeness, or utility of these geospatial data, or for the improper or incorrect use of these geospatial data. These geospatial data and related maps or graphics are not legal documents and are not intended to be used as such. The data and maps may not be used to determine title, ownership, legal descriptions or boundaries, legal jurisdiction, or restrictions that may be in place on either public or private land. Natural hazards may or may not be depicted on the data and maps, and land users should exercise due caution. The data are dynamic and may change over time. The user is responsible to verify the limitations of the geospatial data and to use the data accordingly. For all data layers you are free to share, copy, and redistribute the material in any medium or format AND adapt, remix, transform, and build upon the material for any purpose, even commercially under the following terms:</SPAN></P><P><SPAN>Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. </SPAN></P><P><SPAN>ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original. </SPAN></P><P><SPAN>No commercial use – the user is responsible for acknowledging those data layers within this RRK (as determined by the source of the data) that are not permitted for commercial use. </SPAN></P><P><SPAN>No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything this license permits.</SPAN></P></DIV></DIV></DIV> 38.392294 -123.046475 34.819494 -119.297108 Esri ArcGIS 12.9.2.32739 Project Manager, Regional Resource Kit Project Climate and Wildfire Institute pstine@climateandwildfire.org Peter A. Stine Senior Geospatial Data Analyst Climate and Wildfire Institute cclark@climateandwildfire.org Carol Clark U.S. Department of Interior, Bureau of Indian Affairs Developed by the U.S. Department of the Interior RRK project: Reducing the risk of large, high intensity fire (and other mega-disturbances) through forest treatments has become a management imperative in California. A Strategy for Shared Stewardship (2018) (https://www.fs.usda.gov:443/sites/default/files/toward-shared-stewardship.pdf) and the USFS Wildfire Crisis Implementation Plan (2022) (https://www.fs.usda.gov:443/sites/default/files/Wildfire-Crisis-Implementation-Plan.pdf) reinforce specific goals for pace and scale of strategic forest treatments over the next decade. Concurrently, the State of California has issued a new Wildfire and Forest Resilience Action Plan (2022) (https://wildfiretaskforce.org:443/wp-content/uploads/2022/04/californiawildfireandforestresilienceactionplan.pdf), designed to strategically accelerate efforts to restore the health and resilience of California forests through a joint State of California - Forest Service framework to improve and enhance forest stewardship in California. The social incentives and the scientific knowledge to pursue meaningful restoration of forested landscapes in California are firmly established. High quality geospatial data are an essential ingredient to address restoration/conservation of the broad suite of core socio-ecological values across landscapes, and to drive analytic tools for planning management investments. To support these initiatives an interagency team of scientists from the Forest Service/Pacific Southwest Research Station, California Natural Resources Agency/CALFIRE, and the University of California at Berkeley and University of California at Irvine collaborated on development of a comprehensive set of mapped data layers needed to accomplish large-scale landscape planning and restoration. Landscape level assessment using high quality data developed from ecological modeling techniques, informative analytical approaches and the resulting credible scientific outputs will be fundamental to inform and support large landscape restoration planning and execution. The data layers included in this kit are meant to assist land managers in assessing their current landscape and plan for treatments to enhance resilience to human and natural disturbances. Thus each layer represents what the interagency team believes are the most relevant and reliable geospatial data available at this time. Each layer has been examined by the team and is supported by published data and/or was developed using standard methods. The methods for developing each layer are documented in the metric dictionary; however, the accuracy of each layer has not been quantified. It is anticipated that all data layers will be updated and refined as methods and source data evolve and improve. RRK Components: The authors and their partners are committed to increasing the “pace and scale” of forest treatments in California. Multiple federal and state initiatives in the last few years detail this commitment. Land managers need support to plan and implement treatments that will address restoration at a landscape scale. An essential component of these initiatives is the spatial data representing landscape conditions combined with new analytical tools for planning management investments. The authors joined forces to develop and/or collect and assemble existing sources of spatial data. This project, referred to as RRK (Regional Resource Kit), combines the expertise and experience of research and management to build this library of data on landscape conditions. These data reflect landscape conditions across ten “Pillars of Resilience” which address the full array of landscape management objectives. • Pillars are the desired long-term, landscape-scale outcomes of restoring resilience. They include ecological values, such as biodiversity, as well as societal benefits to communities, such as water security. • Elements represent the primary processes and functions that altogether make up a pillar, such as focal species, water quality, or economic health. • Metrics describe the characteristics of elements in quantitative or qualitative terms. Users can use metrics to assess, plan for, measure, and monitor progress towards desired outcomes and greater resilience. While pillars and elements are consistent across all of California, the metrics that a group uses may vary from region to region based on ecological and social differences (for example forest types, economy), available data, and user preferences. The ten pillars are Forest and Shrubland Resilience, Water Security, Carbon Sequestration, Air Quality, Fire Dynamics, Fire Adapted Communities, Economic Diversity, Social & Cultural Well-Being, Wetland Integrity, Biodiversity Conservation. The individual “Metrics” are used to assess, plan for, measure, and monitor progress toward desired outcomes and greater resilience. Metrics are selected to be informative, meaningful, and actionable to meet the needs of management. Landscape level assessment using these high-quality data, combined with decision support tools that can help evaluate alternative treatment strategies, are fundamental to inform and support large landscape restoration planning. These data are assembled in one place to provide comprehensive access for land managers. They represent conditions as of 2022 (or otherwise, as noted) and intentions are to refresh these data on an annual basis. Each metric includes the following information to help users of the data (and for use with any decision support tools) to understand the details of the metric. This information is also included in a "metric dictionary" that comes with this set of metrics. • Tier group the metric is in (1, 2, or 3) • The vintage of the data • The definition meant by a given metric • The expected use(s) of the metric • The resolution of the developed data • The data sources used to derive the metric • The intended method of metric derivation • Where reasonable, a desired management target References have been included in the metric dictionary to help the reader understand methods used for deriving metrics and will be updated periodically, as necessary. This information will help people make better use of all the assembled information and how it can best be used with various decision support tools. The data layers included in this Northern California Regional Resource Kit were assembled/developed by a partnership that includes the Pacific Southwest Research Station of the U.S. Forest Service, the Fire and Resource Assessment Program (FRAP) of CALFIRE, the Climate and Wildfire Institute, and faculty from the University of California Berkeley and Irvine. This science team is working together at the behest of the California Wildfire and Forest Resilience Task Force. As we continue to develop geospatial data for landscape assessment and planning throughout the state, this partnership has now taken the lead in the creation of the Regional Resource Kits for the four regions of California. The RRK has adopted the Framework for Resilience to provide a structure for assessing landscape conditions, setting objectives, designing projects, and measuring progress towards social-ecological resilience. There are ten pillars that represent the desired outcomes of landscape resilience. Each of the pillars provides a series of elements and under the elements, metrics (the data layers) for assessing landscape conditions and verifying that actions meet resilience objectives. The metrics are organized by the ten pillars of resilience in the Framework for Resilience. The Metrics describe the characteristics of the elements (key characteristics) of each pillar in quantitative or, in a few cases, qualitative terms. Metrics are used to assess, plan for, measure, and monitor progress toward desired outcomes and greater resilience. Metrics are selected to be informative, meaningful, and actionable to meet the needs of management. The metrics are also divided into three “tiers.” Among all these metrics, some are created and relevant statewide. Other metrics are more suited to conditions within a given region. The “Tiers” for metrics included in each RRK: Tier 1 – metrics that are a single, consistent data layer, developed statewide; they can also be clipped to the boundary of the region so values within that region are the only ones included for calculations or regional statistics. Example: Annual Burn Probability. Tier 2 – metrics relevant to a single region or relevant to multiple regions but data layers differ among regions because of varied data availability (sources) across regions. Example: California gnatcatcher habitat suitability. Tier 3 - metrics are those that would be of interest to some land managers for specific applications but not included as a core metric in an RRK. Example: Distribution of the Quino checkerspot butterfly. Each RRK will contain all Tier 1 and Tier 2 data together to comprise the kit. Tier 3 data will be pointed to for reference and use, as needed. Within each Tier, the data layers are available in two forms: 1) data values native to the metric (raw), and 2) translated data values. The raw data values are in the units of the metric, so for example the species richness map will show an estimated number of terrestrial vertebrate species per acre that can range from 0 to any number for each 30-m pixel, and the departure from historical fire return interval (FRID) map will have values that range from -100% to +100% departure. The translated data values represent each metric using a common unit of measure with the same range of values from -1 to +1 that represent values that are generally considered favorable (+1) and unfavorable (-1). In the case of species richness, higher species counts are considered more favorable and lower species counts are considered less favorable. In the case of FRID, values within the historical fire return interval are considered favorable, and high departure from the historical fire return interval is considered less favorable. In both cases, more and less favorable conditions for each metric are represented by values that range from +1 to -1 (respectively) so that multiple metrics can be evaluated together, including summarizing overall conditions at element and pillar levels to characterize socio-ecological resilience. Some data layers within this kit contain null values. We point this out here so users of the data will be aware and take whatever measures appropriate as they use and analyze the data. For some raster datasets in the RRK, areas have been masked (blanked) out and have a cell value of NoData (also referred to as null, NaN or missing). We, as producers and users of the data, cannot ignore NoData or fill them with zeros, since zero is often a valid value for some datasets. Removing NoData cells is not an option, a raster is a continuous grid. For users of the data performing further analyses and combining or "stacking" rasters, these NoData cells will mask out all values in that location in the output. To avoid this issue, the user must create values for the cells before combining them (i.e. 999 or any numeric value that is not real and clearly out of the range of the other values). Reasons for masking (blanking) out cells in RRK data: · Cells are lakes or reservoirs · Cells are urban or agriculture · Cells contain no information relevant to the dataset (i.e. streams, habitat) · Area (cells) subject to fire or other disturbance but the post disturbance condition or value is unknown - Metric Name: American Indian Land Area Representation (LAR) - Tier: 2 - Data Vintage: 09/2023 - Unit Of Measure: Categorical - Metric Definition and Relevance: These data, developed for the Bureau of Indian Affairs, Department of Interior, provide the details for tracts, parcels, and other relevant BIA lands. The data depict locations of Tribally controlled land mapped to the parcel level for all Land Area Codes (LAC) held in trust or restricted-fee by the United States. The Division of Land Titles and Records (LTR) compiled the data to support the Bureau of Indian Affairs Tribal Reservations. The source information for the parcel records are from the Trust Asset and Accounting Management System (TAAMS), which is the system of record. The term “Indian land” means: (A) Any land located within the boundaries of an Indian reservation, pueblo, or rancheria; (B) Any land not located within the boundaries of an Indian reservation, pueblo, or rancheria, the title to which is help: (i) In trust by the United States for the benefit of an Indian tribe or an individual Indian; (ii) By an Indian tribe or an individual Indian, subject to restriction against alienation under laws of the United States Definition: Indian land from 25 USC § 3501(2) | LII / Legal Information Institute. - Creation Method: The purpose of the American Indian (and Alaska Native) Land Area Representation (AIAN-LAR) Geographic Information System (GIS) dataset is to depict the external extent of federal Indian reservations and the external extent of associated land held in “trust” by the United States, “restricted fee” or “mixed ownership” status for federally recognized tribes and individual Indians. This dataset includes other land area types such as Public Domain Allotments, Dependent Indian Communities and Homesteads.

This GIS Dataset is prepared strictly for illustrative and reference purposes only and should not be used, and is not intended for legal, survey, engineering or navigation purposes. No warranty is made by the Bureau of Indian Affairs (BIA) for the use of the data for purposes not intended by the BIA. This GIS Dataset may contain errors. There is no impact on the legal status of the land areas depicted herein and no impact on land ownership. No legal inference can or should be made from the information in this GIS Dataset. The GIS Dataset is to be used solely for illustrative, reference and statistical purposes and may be used for government to government Tribal consultation.

Reservation boundary data is limited in authority to those areas where there has been settled Congressional definition or final judicial interpretation of the boundary. Absent a settled Congressional definition or final judicial interpretation of a reservation boundary, the BIA recommends consultation with the appropriate Tribe and then the BIA to obtain interpretations of the reservation boundary. The land areas and their representations are compilations defined by the official land title records of the Bureau of Indian Affairs (BIA) which include treaties, statutes, Acts of Congress, agreements, executive orders, proclamations, deeds and other land title documents.

The trust, restricted, and mixed ownership land area shown here, are suitable only for general spatial reference and do not represent the federal government’s position on the jurisdictional status of Indian country. Ownership and jurisdictional status is subject to change and must be verified with plat books, patents, and deeds in the appropriate federal and state offices.

Included in this dataset are the exterior extent of off reservation trust, restricted fee tracts and mixed tracts of land including Public Domain allotments, Dependent Indian Communities, Homesteads and government administered lands and those set aside for schools and dormitories. There are also land areas where there is more than one tribe having an interest in or authority over a tract of land but this information is not specified in the AIAN-LAR dataset.

The dataset includes both surface and subsurface tracts of land (tribal and individually held) “off reservation” tracts and not simply off reservation “allotments” as land has in many cases been subsequently acquired in trust. These data are public information and may be used by various organizations, agencies, units of government (i.e., Federal, state, county, and city), and other entities according to the restrictions on appropriate use. It is strongly recommended that these data be acquired directly from the BIA and not indirectly through some other source, which may have altered or integrated the data for another purpose for which they may not have been intended. Integrating land areas into another dataset and attempting to resolve boundary differences between other entities may produce inaccurate results. It is also strongly recommended that careful attention be paid to the content of the metadata file associated with these data. Users are cautioned that digital enlargement of these data to scales greater than those at which they were originally mapped can cause misinterpretation. The BIA AIAN-LAR dataset’s spatial accuracy and attribute information are continuously being updated, improved and is used as the single authoritative land area boundary data for the BIA mission. These data are available through the Bureau of Indian Affairs, Office of Trust Services, Division of Land Titles and Records, Branch of Geospatial Support.

Interpreting the presence/absence of AIANNH features and LAR features requires care. Essentially, the Land Area Representation (LAR) delineates an area of land that is often but not always under Tribal and/or Trust control of/for a Tribe. It is likely that areas within the LAR that are not Reservation or Trust lands are considered lands of significant interest by the Tribe in question, and direct government-to-government consultation with the concerned Tribe is strongly encouraged to understand the Tribe’s interpretation of the LAR and parcel features. The AIANNH feature is in almost all cases essentially a realty data set that indicates where Tribes have some legal interest in actual land parcels, either because they are part of a reservation, are held in trust for the tribe, or the Tribe itself has a controlling ownership interest in fee.

BIA_AIAN_LAR_202309.tif 2022-09-30 raster digital data environment geoscientific Information planning Cadastre land management landscape restoration ISO 19115 Topic Categories environment geoscientific Information planning Cadastre land management landscape restoration 2 3300 2550 1.0 150000000 5000 FGDC