It is about updating to GIS information database, Decision Support Tool (DST) in collaboration with IWMI. With the support of the Fish for Livelihoods field team and IPs (MFF, BRAC Myanmar, PACT Myanmar, and KMSS) staff, collection of Global Positioning System GPS location data for year-1 (2019-20) 1,167 SSA farmer ponds, and year-2 (2020-21) 1,485 SSA farmer ponds were completed with different GPS mobile applications: My GPS Coordinates, GPS Status & Toolbox, GPS Essentials, Smart GPS Coordinates Locator and GPS Coordinates. The Soil and Water Assessment Tool (SWAT) model that integrates climate change analysis with water availability will provide an important tool informing decisions on scaling pond adoption. It can also contribute to a Decision Support Tool to better target pond scaling. GIS Data also contribute to identify the location point of the F4L SSA farmers ponds on the Myanmar Map by fiscal year from 1 to 5.

Geo-referenced datasets.

DNRGPS is an update to the popular DNRGarmin application. DNRGPS and its predecessor were built to transfer data between Garmin handheld GPS receivers and GIS software.

DNRGPS was released as Open Source software with the intention that the GPS user community will become stewards of the application, initiating future modifications and enhancements.

DNRGPS does not require installation. Simply run the application .exe

See the DNRGPS application documentation for more details.

Compatible with: Windows (XP, 7, 8, 10, and 11), ArcGIS shapefiles and file geodatabases, Google Earth, most hand-held Garmin GPSs, and other NMEA output GPSs

Limited Compatibility: Interactions with ArcMap layer files and ArcMap graphics are no longer supported. Instead use shapefile or geodatabase.

Prerequisite: .NET 4 Framework

DNR Data and Software License Agreement

Subscribe to the DNRGPS announcement list to be notified of upgrades or updates.

Dataset contains training material on using open source Geographic Information Systems (GIS) to improve protected area planning and management from workshops that were conducted on February 19-21 and October 6-7, 2020. Specifically, the dataset contains lectures on GIS fundamentals, QGIS 3.x, and global positioning system (GPS), as well as country-specific datasets and a workbook containing exercises for viewing data, editing/creating datasets, and creating map products in QGIS. Supplemental videos that narrate a step-by-step recap and overview of these processes are found in the Related Content section of this dataset.

Funding for this workshop and material was funded by the Biodiversity and Protected Areas Management (BIOPAMA) programme. The BIOPAMA programme is an initiative of the Organisation of African, Caribbean and Pacific (ACP) Group of States financed by the European Union's 11th European Development Fund. BIOPAMA is jointly implemented by the International Union for Conservation of Nature {IUCN) and the Joint Research Centre of the European Commission (EC-JRC). In the Pacific region, BIOPAMA is implemented by IUCN's Oceania Regional Office (IUCN ORO) in partnership with the Secretariat of the Pacific Regional Environment Programme (SPREP). The overall objective of the BIOPAMA programme is to contribute to improving the long-term conservation and sustainable use of biodiversity and natural resources in the Pacific ACP region in protected areas and surrounding communities through better use and monitoring of information and capacity development on management and governance.

City Of Sacramento's Survey Division has developed a high accuracy GPS control point grid. This file currently contains data points for the entire City of Sacramento. The latitude and longitude values have an accuracy level of +/- .05 feet. Elevation data has accuracy of +/- .24 feet.

Field: GPSNUMBER Alias: Survey reference number Field Description: Reference to latitude/longitude minute

Field: NORTHINGFT Alias: False Northing, California State Plane, Zone II, Feet

Field: EASTINGFT Alias: False Easting, California State Plane, Zone II, Feet

Field: ELVORTHOFT Alias: Elevation Ortho (ft)- a preliminary ground elevation to which the orthometric leveling correction has been applied

Field: DFNGVD29FT Alias: Differential NGVD 29- elevation obtained by spirit leveling based on the national geodetic vertical datum of 1929

Field: STREET Alias: Street location of control point

Field: XSTREET Alias: Cross street or reference information

Field: MONTYPE Alias: Control point or monument type

Field: LAT_DMS Alias: Latitude values in Degrees, Minutes, Seconds

Field: LONG_DMS Alias: Latitude values in Degrees, Minutes, Seconds

Field: ELLIPSHT Alias: Ellipsoid Height- the distance, measured along the mormal, from the surface of the ellipsoid to a point

Field: CNVERGENCE Alias: The angle difference at a given location between grid north and astronomic north

Field: GRDSCLFCTR Alias: Grid Scale Factor- a multiplier for reducing a sea level lengths to grid lengths

Field: COMBNDFCTR Alias: Combined Factor- multiplier obtained from the product of the sea level and grid scale factor and applied to ground distance to obtain grid distance

Field: GEOIDHT Alias: Distance of the geoid above (positive) or below (negative) the mathematical reference spheroid

Field: ARCHIVELOC Alias: Use To be Determined Field Description: Associated with crossed out GPS No.-Point ID

Summary This feature class documents the fire history on CMR from 1964 - present. This is 1 of 2 feature classes, a polygon and a point. This data has a variety of different origins which leads to differing quality of data. Within the polygon feature class, this contains perimeters that were mapped using a GPS, hand digitized, on-screen digitized, and buffered circles to the estimated acreage. These 2 files should be kept together. Within the point feature class, fires with only a location of latitude/longitude, UTM coordinate, TRS and no estimated acreage were mapped using a point location. GPS started being used in 1992 when the technology became available. Records from FMIS (Fire Management Information System) were reviewed and compared to refuge records. Polygon data in FMIS only occurs from 2012 to current and many acreage estimates did not match. This dataset includes ALL fires no matter the size. This feature class documents the fire history on CMR from 1964 - present. This is 1 of 2 feature classes, a polygon and a point. This data has a variety of different origins which leads to differing quality of data. Within the polygon feature class, this contains perimeters that were mapped using a GPS, hand digitized, on-screen digitized, and buffered circles to the estimated acreage. These 2 files should be kept together. Within the point feature class, fires with only a location of latitude/longitude, UTM coordinate, TRS and no estimated acreage were mapped using a point location. GPS started being used in 1992 when the technology became available. Data origins include: Data origins include: 1) GPS Polygon-data (Best), 2) GPS Lat/Long or UTM, 3)TRS QS, 4)TRS Point, 6)Hand digitized from topo map, 7) Circle buffer, 8)Screen digitized, 9) FMIS Lat/Long. Started compiling fire history of CMR in 2007. This has been a 10 year process.FMIS doesn't include fires polygons that are less than 10 acres. This dataset has been sent to FMIS for FMIS records to be updated with correct information. The spreadsheet contains 10-15 records without spatial information and weren't included in either feature class. Fire information from 1964 - 1980 came from records Larry Eichhorn, BLM, provided to CMR staff. Mike Granger, CMR Fire Management Officer, tracked fires on an 11x17 legal pad and all this information was brought into Excel and ArcGIS. Frequently, other information about the fires were missing which made it difficult to back track and fill in missing data. Time was spent verifiying locations that were occasionally recorded incorrectly (DMS vs DD) and converting TRS into Lat/Long and/or UTM. CMR is divided into 2 different UTM zones, zone 12 and zone 13. This occasionally caused errors in projecting. Naming conventions caused confusion. Fires are frequently names by location and there are several "Soda Creek", "Rock Creek", etc fires. Fire numbers were occasionally missing or incorrect. Fires on BLM were included if they were "Assists". Also, fires on satellite refuges and the district were also included. Acreages from GIS were compared to FMIS acres. Please see documentation in ServCat (URL) to see how these were handled.

CDFW BIOS GIS Dataset, Contact: Erin Zulliger, Description: Migration corridor, stopover, and winter range locations for Rocky Mountain elk (Cervus canadensis nelsoni) from the East Shasta Valley herd, Siskiyou County, California, and Klamath County, Oregon. Corridors, stopovers, and winter ranges were developed in Migration Mapper with Brownian Bridge Movement Models using GPS locations from collared elk. Migration corridors represent movement routes used by elk between winter and summer range habitats.

This dataset is a compendium of GPS Data collected by Randy Carlson and collaborators on the Virginia Coast Reserve (primarily), Plum Island and North Inlet. A master data table was extracted by Charles L. Carlson during 2013 that includes all the individual point locations recovered from individual surveys. In addition to the data table, the data is also shared as a .zip file containing a static web page with links to particular projects and the underlying data. To use the data, unzip it and use your web browser to open the index.html file. Web page contents include: American Oyster Catchers on the Virginia Coast Reserve - 2003 Lynette Winters - Salicornia - MSL elevation project Dynamic Evolution of Barrier Island Morphology and Ecology from 1996-2002 Documented Using High -Resolution GPS-GIS Topographic Mapping Surveys, Virginia Coast Reserve (for GSA, Denver, CO Oct 27-30, 2002 Broadwater Tower Overwash Fan Photos - Feburary 15, 2002 Hog Island Bay DGPS Drifter Study 2001 Ray Dueser/Nancy Moncrief Small Mammal GPS/GIS A Topographical History of North Myrtle Island, 1974 to 2001 Ray Dueser/Nancy Moncrief - Highest Elevations on VCR Barrier Islands Myrtle Island Planimetric area, Surface area & Volumetric Calculations 1996-2001 Myrtle, Ship Shoal GIS/GPS UTM Shape Files and Grids Myrtle, Ship Shoal, ESNWR, Shirley, Steelman's Landing Text Files Complete List of All Small Mammal Trap locations 1995 - 2001 Ship Shoal Island Small Mammal Traps 1997 - 2000 LTER Cross-Site GPS Surveys Hobcaw Barony / Baruch Institue SET/GPS Survey, South Carolina, December 2000 PIE/LTER - Plum Island Sound GPS Network, July 1998 Montandon Marsh at Bucknell University, Lewisburg, Pennsylvania 1997 Bathymetric Survey Procedures, Schematic Diagrams and Instructions The following instructions and procedures are used with reference to the Trimble 4000 SE Global Positioning System receiver, the Trimble NavBeacon XL, the Innerspace Digital Fathometer (Model 448) and the Innerspace DataLog with Guidance Software. GPS-referenced digital bathymetry Schematic Diagram of DGPS/Digital Fathometer connections for bathymetry Instructions for DataLog w/Guidance Software (Innerspace Digital Fathometer) Instructions for Trimble 4000 SE GPS Receiver and Trimble Navbeacon XL Innerspace Digital Fathometer - Model 448 - Field Protocol for Bathymetric Surveys Archived Bathymetric Projects Hog Island Bay DGPS Bathymetric Survey, 1999/2000 Phillip's Creek DGPS Bathymetric Survey 1999/2000 Oyster Harbor Bathymetric Survey (February 2000) Smith Point, Chesapeake Bay, Maryland DGPS Bathymetric Survey, Sept. 2001 Fishermans/Smith/Mockhorn Bay Bathymetric Survey 2000 to 2001 Post-processed Kinematic GPS data: Is It Precise? (1998) Small Mammal GPS/GIS Applications Hog Island Small Mammal Traps on T1, T2, T4, T5 Fowling Point 1996, 1997 Geomorphology Applications Parramore Island, Virginia Parramore Pimple Overwash Fans 1996 Parramore Pimple Overwash Fans 1997 Parramore Island Overwash Fans June 1998 Parramore Island Plugs - August 1998 Parramore Island Overwash Fan 1999 Hog Island, Virginia Broadwater Tower Overwash Fan June 1998 Photos of Broadwater Tower Overwash Fan - March 13, 1999 Broadwater Tower Overwash Fan 1999 Myrtle Island, Virginia A Topographical History of Myrtle Island, 1996 to 2001 Cobb and Fisherman's Islands, Virginia Cobb Island Overwash Fan July 1998 Fisherman's Island - ESNWR and ODU September /1998 Brownsville Farm GPS/GIS Project Long-Term Inundation Project, Christian/Thomas Brinson/Christian/Blum Project Eileen Appolone (ECU) Lisa Ricker's Static GPS Points in Northampton County Eileen Applone (East Carolina University) Static Survey d99124 Brownsville Farm GPS/GIS Project, Christian/Blum/Brinson VCR/LTER Tide Gauges and Water Level Recorders Red Bank Tide Gauge (part of Fowling Pt. survey) Hog Island WLR's 1996 (Brinson) Hog Island Tide Gauge 12/96 High tide surveys at PIE/LTER with Chuck Hopkinson Jim Morris, USC, at Debidue Island, South Carolina Benchmark BRNV in Brownsville, VCR/LTER Miscellaneous Static Sub-Networks Frank Day/Don Young - North Hog 2/99 (Excel File) or a TEXT file Frank Day 120 YR Old Dune Survey (Excel File) or a TEXT file Kindra Loomis GPS Kinematic/Topographic Survey 12/97 Clubhouse Creek at Parramore Island 1997 Phragmites on Southern Hog Island - (dataset only) (9/98) Oyster Harbor 1997 (Hayden & Porter) Southern Hog 1996 (Zieman) VCR/LTER Sediment Elevation Tables - Mockhorn/Wachapreague, August 2001 Aaron Mills Benchmarks - Research Field in Oyster, October 2001 Birds Nests on the Virginia Coast Reserve VCR Birds 1997 (Erwi... Visit https://dataone.org/datasets/https%3A%2F%2Fpasta.lternet.edu%2Fpackage%2Fmetadata%2Feml%2Fknb-lter-vcr%2F156%2F20 for complete metadata about this dataset.

This dataset includes maps produced from the Australian Antarctic Data Centre GIS for use in environmental management of the 'old' Casey station tip site and the abandoned Wilkes station site: a map of the Windmill Islands showing the locations of Casey and Wilkes, contour maps of Casey and Wilkes and a map showing the water flow directions at Casey. The maps were used for locating contaminated areas and identifying the processes involved in contamination spread. Also included in the dataset is the GIS topographic and derived data used to create the maps.

description: NYSNet is a spatial reference network of continuously operating Global Positioning System (GPS) reference stations (CORS) throughout New York State that can be used for differential GPS applications. Depending on equipment and procedures, this network can provide users the ability to achieve centimeter positioning for surveying applications or sub-meter positioning for GIS mapping applications. Position information from this reference network can be utilized by using static data in post processing or by using the real time network (RTN).; abstract: NYSNet is a spatial reference network of continuously operating Global Positioning System (GPS) reference stations (CORS) throughout New York State that can be used for differential GPS applications. Depending on equipment and procedures, this network can provide users the ability to achieve centimeter positioning for surveying applications or sub-meter positioning for GIS mapping applications. Position information from this reference network can be utilized by using static data in post processing or by using the real time network (RTN).

Historical FiresLast updated on 06/17/2022OverviewThe national fire history perimeter data layer of conglomerated Agency Authoratative perimeters was developed in support of the WFDSS application and wildfire decision support for the 2021 fire season. The layer encompasses the final fire perimeter datasets of the USDA Forest Service, US Department of Interior Bureau of Land Management, Bureau of Indian Affairs, Fish and Wildlife Service, and National Park Service, the Alaska Interagency Fire Center, CalFire, and WFIGS History. Perimeters are included thru the 2021 fire season. Requirements for fire perimeter inclusion, such as minimum acreage requirements, are set by the contributing agencies. WFIGS, NPS and CALFIRE data now include Prescribed Burns. Data InputSeveral data sources were used in the development of this layer:Alaska fire history USDA FS Regional Fire History Data BLM Fire Planning and Fuels National Park Service - Includes Prescribed Burns Fish and Wildlife ServiceBureau of Indian AffairsCalFire FRAS - Includes Prescribed BurnsWFIGS - BLM & BIA and other S&LData LimitationsFire perimeter data are often collected at the local level, and fire management agencies have differing guidelines for submitting fire perimeter data. Often data are collected by agencies only once annually. If you do not see your fire perimeters in this layer, they were not present in the sources used to create the layer at the time the data were submitted. A companion service for perimeters entered into the WFDSS application is also available, if a perimeter is found in the WFDSS service that is missing in this Agency Authoratative service or a perimeter is missing in both services, please contact the appropriate agency Fire GIS Contact listed in the table below.AttributesThis dataset implements the NWCG Wildland Fire Perimeters (polygon) data standard.https://www.nwcg.gov/sites/default/files/stds/WildlandFirePerimeters_definition.pdfIRWINID - Primary key for linking to the IRWIN Incident dataset. The origin of this GUID is the wildland fire locations point data layer. (This unique identifier may NOT replace the GeometryID core attribute)INCIDENT - The name assigned to an incident; assigned by responsible land management unit. (IRWIN required). Officially recorded name.FIRE_YEAR (Alias) - Calendar year in which the fire started. Example: 2013. Value is of type integer (FIRE_YEAR_INT).AGENCY - Agency assigned for this fire - should be based on jurisdiction at origin.SOURCE - System/agency source of record from which the perimeter came.DATE_CUR - The last edit, update, or other valid date of this GIS Record. Example: mm/dd/yyyy.MAP_METHOD - Controlled vocabulary to define how the geospatial feature was derived. Map method may help define data quality.GPS-Driven; GPS-Flight; GPS-Walked; GPS-Walked/Driven; GPS-Unknown Travel Method; Hand Sketch; Digitized-Image; Digitized-Topo; Digitized-Other; Image Interpretation; Infrared Image; Modeled; Mixed Methods; Remote Sensing Derived; Survey/GCDB/Cadastral; Vector; OtherGIS_ACRES - GIS calculated acres within the fire perimeter. Not adjusted for unburned areas within the fire perimeter. Total should include 1 decimal place. (ArcGIS: Precision=10; Scale=1). Example: 23.9UNQE_FIRE_ - Unique fire identifier is the Year-Unit Identifier-Local Incident Identifier (yyyy-SSXXX-xxxxxx). SS = State Code or International Code, XXX or XXXX = A code assigned to an organizational unit, xxxxx = Alphanumeric with hyphens or periods. The unit identifier portion corresponds to the POINT OF ORIGIN RESPONSIBLE AGENCY UNIT IDENTIFIER (POOResonsibleUnit) from the responsible unit’s corresponding fire report. Example: 2013-CORMP-000001LOCAL_NUM - Local incident identifier (dispatch number). A number or code that uniquely identifies an incident for a particular local fire management organization within a particular calendar year. Field is string to allow for leading zeros when the local incident identifier is less than 6 characters. (IRWIN required). Example: 123456.UNIT_ID - NWCG Unit Identifier of landowner/jurisdictional agency unit at the point of origin of a fire. (NFIRS ID should be used only when no NWCG Unit Identifier exists). Example: CORMPCOMMENTS - Additional information describing the feature. Free Text.FEATURE_CA - Type of wildland fire polygon: Wildfire (represents final fire perimeter or last daily fire perimeter available) or Prescribed Fire or UnknownGEO_ID - Primary key for linking geospatial objects with other database systems. Required for every feature. This field may be renamed for each standard to fit the feature. Globally Unique Identifier (GUID).Cross-Walk from sources (GeoID) and other processing notesAK: GEOID = OBJECT ID of provided file geodatabase (4580 Records thru 2021), other federal sources for AK data removed. CA: GEOID = OBJECT ID of downloaded file geodatabase (12776 Records, federal fires removed, includes RX)FWS: GEOID = OBJECTID of service download combined history 2005-2021 (2052 Records). Handful of WFIGS (11) fires added that were not in FWS record.BIA: GEOID = "FireID" 2017/2018 data (416 records) provided or WFDSS PID (415 records). An additional 917 fires from WFIGS were added, GEOID=GLOBALID in source.NPS: GEOID = EVENT ID (IRWINID or FRM_ID from FOD), 29,943 records includes RX.BLM: GEOID = GUID from BLM FPER and GLOBALID from WFIGS. Date Current = best available modify_date, create_date, fire_cntrl_dt or fire_dscvr_dt to reduce the number of 9999 entries in FireYear. Source FPER (25,389 features), WFIGS (5357 features)USFS: GEOID=GLOBALID in source, 46,574 features. Also fixed Date Current to best available date from perimeterdatetime, revdate, discoverydatetime, dbsourcedate to reduce number of 1899 entries in FireYear.Relevant Websites and ReferencesAlaska Fire Service: https://afs.ak.blm.gov/CALFIRE: https://frap.fire.ca.gov/mapping/gis-dataBIA - data prior to 2017 from WFDSS, 2017-2018 Agency Provided, 2019 and after WFIGSBLM: https://gis.blm.gov/arcgis/rest/services/fire/BLM_Natl_FirePerimeter/MapServerNPS: New data set provided from NPS Fire & Aviation GIS. cross checked against WFIGS for any missing perimeters in 2021.https://nifc.maps.arcgis.com/home/item.html?id=098ebc8e561143389ca3d42be3707caaFWS -https://services.arcgis.com/QVENGdaPbd4LUkLV/arcgis/rest/services/USFWS_Wildfire_History_gdb/FeatureServerUSFS - https://apps.fs.usda.gov/arcx/rest/services/EDW/EDW_FireOccurrenceAndPerimeter_01/MapServerAgency Fire GIS ContactsRD&A Data ManagerVACANTSusan McClendonWFM RD&A GIS Specialist208-258-4244send emailJill KuenziUSFS-NIFC208.387.5283send email Joseph KafkaBIA-NIFC208.387.5572send emailCameron TongierUSFWS-NIFC208.387.5712send emailSkip EdelNPS-NIFC303.969.2947send emailJulie OsterkampBLM-NIFC208.258.0083send email Jennifer L. Jenkins Alaska Fire Service 907.356.5587 send email

The project lead for the collection of this data was Carrington Hilson. Elk (4 adult females) were captured and equipped with GPS collars (Lotek Iridium) transmitting data from 2017-2021. The Rowdy herd does not migrate between traditional summer and winter seasonal ranges. Therefore, annual home ranges were modeled using year-round data to demarcate high use areas in lieu of modeling the specific winter ranges commonly seen in other ungulate analyses in California. GPS locations were fixed between 1-6 hour intervals in the dataset. To improve the quality of the data set as per Bjørneraas et al. (2010), the GPS data were filtered prior to analysis to remove locations which were: i) further from either the previous point or subsequent point than an individual pronghorn is able to travel in the elapsed time, ii) forming spikes in the movement trajectory based on outgoing and incoming speeds and turning angles sharper than a predefined threshold , or iii) fixed in 2D space and visually assessed as a bad fix by the analyst. The methodology used for this migration analysis allowed for the mapping of the herd''s home range. Brownian bridge movement models (BBMMs; Sawyer et al. 2009) were constructed with GPS collar data from 4 elk, including 7 annual home range sequences, location, date, time, and average location error as inputs in Migration Mapper. BBMMs were produced at a spatial resolution of 50 m using a sequential fix interval of less than 27 hours. Large water bodies were clipped from the final output. Home range is visualized as the 50thpercentile contour (high use) and the 99thpercentile contour of the year-round utilization distribution. Home range designations for this herd may expand with a larger sample.

The project lead for the collection of this data was Carrington Hilson. Elk (2 adult females) were captured and equipped with GPS collars (Lotek Iridium) transmitting data from 2019-2021. The Sinyone herd does not migrate between traditional summer and winter seasonal ranges. Therefore, annual home ranges were modeled using year-round data to demarcate high use areas in lieu of modeling the specific winter ranges commonly seen in other ungulate analyses in California. GPS locations were fixed between 1-6 hour intervals in the dataset. To improve the quality of the data set as per Bjørneraas et al. (2010), the GPS data were filtered prior to analysis to remove locations which were: i) further from either the previous point or subsequent point than an individual pronghorn is able to travel in the elapsed time, ii) forming spikes in the movement trajectory based on outgoing and incoming speeds and turning angles sharper than a predefined threshold , or iii) fixed in 2D space and visually assessed as a bad fix by the analyst. The methodology used for this migration analysis allowed for the mapping of the herd''s home range. Brownian bridge movement models (BBMMs; Sawyer et al. 2009) were constructed with GPS collar data from 2 elk, including 3 annual home range sequences, location, date, time, and average location error as inputs in Migration Mapper. BBMMs were produced at a spatial resolution of 50 m using a sequential fix interval of less than 27 hours. Large water bodies were clipped from the final output. Home range is visualized as the 50thpercentile contour (high use) and the 99thpercentile contour of the year-round utilization distribution. Home range designations for this herd may expand with a larger sample.

The project lead for the collection of this data was Carrington Hilson. Elk (2 adult females) were captured and equipped with GPS collars (Lotek Iridium) transmitting data from 2017-2021. The Gold Bluff Beach herd does not migrate between traditional summer and winter seasonal ranges. Therefore, annual home ranges were modeled using year-round data to demarcate high use areas in lieu of modeling the specific winter ranges commonly seen in other ungulate analyses in California. GPS locations were fixed between 1-6 hour intervals in the dataset. To improve the quality of the data set as per Bjørneraas et al. (2010), the GPS data were filtered prior to analysis to remove locations which were: i) further from either the previous point or subsequent point than an individual pronghorn is able to travel in the elapsed time, ii) forming spikes in the movement trajectory based on outgoing and incoming speeds and turning angles sharper than a predefined threshold , or iii) fixed in 2D space and visually assessed as a bad fix by the analyst. The methodology used for this migration analysis allowed for the mapping of the herd''s home range. Brownian bridge movement models (BBMMs; Sawyer et al. 2009) were constructed with GPS collar data from 2 elk, including 6 annual home range sequences, location, date, time, and average location error as inputs in Migration Mapper. BBMMs were produced at a spatial resolution of 50 m using a sequential fix interval of less than 27 hours. Large water bodies were clipped from the final output. Home range is visualized as the 50th percentile contour (high use) and the 99th percentile contour of the year-round utilization distribution. Home range designations for this herd may expand with a larger sample.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. To produce the digital map, a combination of 1:12,000-scale color infrared digital ortho-imagery acquired in 2003, 1:12,000-scale true color ortho-rectified imagery acquired in 2005, and all of the GPS referenced ground data were used to interpret the complex patterns of vegetation and land-use. All imagery was acquired from the U.S. Department of Agriculture - Farm Service Agency’s Aerial Photography Field Office and the National Agriculture Imagery Program. In the end, 27 map units (14 vegetated and 13 land-use) were developed and directly cross-walked or matched to corresponding plant associations and land-use classes. All of the interpreted and remotely sensed data were converted to Geographic Information System (GIS) databases using ArcGIS© software. Draft maps were printed, field tested, reviewed, and revised. One hundred and thirty three accuracy assessment (AA) data points were collected in 2006 and used to determine the map’s accuracy. GIS Database 2002-2005: Project Size = 4,600 acres Lyndon B. Johnson National Historical Park = 674 acres Lyndon B. Johnson State Park and Historic Site = 418 acres Base Imagery acquired from the USDA FSA Aerial Photography Field Office acquired through the National Agriculture Imagery Program: 2005 - 1:12,000-scale true color ortho-rectified imagery, compressed county mosaic,2 meter pixel resolution 2003 - 1:12,000-scale color infrared digital ortho-imagery, compressed county mosaic,1 meter pixel resolution 27 Map Classes 14 Vegetated 13 Non-vegetated Minimum Mapping Unit = ½ hectare is the program standard but this was modified at LYJO to ¼ acre. Total Size = 1,080 Polygons Average Polygon Size = 4.3 acres Overall Thematic Accuracy = 92%

The project lead for the collection of this data was Carrington Hilson. Elk (2 adult females) were captured and equipped with GPS collars (Lotek Iridium) transmitting data from 2022-2023. The Little Lake herd does not migrate between traditional summer and winter seasonal ranges. Therefore, annual home ranges were modeled using year-round data to demarcate high use areas in lieu of modeling the specific winter ranges commonly seen in other ungulate analyses in California. GPS locations were fixed between 1-7 hour intervals in the dataset. To improve the quality of the data set as per Bjørneraas et al. (2010), the GPS data were filtered prior to analysis to remove locations which were: i) further from either the previous point or subsequent point than an individual pronghorn is able to travel in the elapsed time, ii) forming spikes in the movement trajectory based on outgoing and incoming speeds and turning angles sharper than a predefined threshold , or iii) fixed in 2D space and visually assessed as a bad fix by the analyst. The methodology used for this migration analysis allowed for the mapping of the herd''s home range. Brownian bridge movement models (BBMMs; Sawyer et al. 2009) were constructed with GPS collar data from 2 elk, including 2 annual home range sequence, location, date, time, and average location error as inputs in Migration Mapper. BBMMs were produced at a spatial resolution of 50 m using a sequential fix interval of less than 27 hours. Home range is visualized as the 50th percentile contour (high use) and the 99th percentile contour of the year-round utilization distribution. Home range designations for this herd may expand with a larger sample.

The project lead for the collection of this data was Carrington Hilson. Elk (9 adult females) were captured and equipped with GPS collars (Lotek Iridium) transmitting data from 2017-2021. The Bald Hills herd does not migrate between traditional summer and winter seasonal ranges. Therefore, annual home ranges were modeled using year-round data to demarcate high use areas in lieu of modeling the specific winter ranges commonly seen in other ungulate analyses in California. GPS locations were fixed between 1-6 hour intervals in the dataset. To improve the quality of the data set as per Bjørneraas et al. (2010), the GPS data were filtered prior to analysis to remove locations which were: i) further from either the previous point or subsequent point than an individual pronghorn is able to travel in the elapsed time, ii) forming spikes in the movement trajectory based on outgoing and incoming speeds and turning angles sharper than a predefined threshold , or iii) fixed in 2D space and visually assessed as a bad fix by the analyst. The methodology used for this migration analysis allowed for the mapping of the herds home range. Brownian bridge movement models (BBMMs; Sawyer et al. 2009) were constructed with GPS collar data from 9 elk, including 18 annual home range sequences, location, date, time, and average location error as inputs in Migration Mapper. BBMMs were produced at a spatial resolution of 50 m using a sequential fix interval of less than 27 hours. Due to a high variance parameter (greater than 8000), one home range sequence was run with a fixed motion variance of 1000. Large water bodies were clipped from the final output. Home range is visualized as the 50th percentile contour (high use) and the 99th percentile contour of the year-round utilization distribution. Home range designations for this herd may expand with a larger sample.

This map was digitized using 2010 true-color 1-meter National Agriculture Imagery Program (NAIP) aerial imagery. The minimum mapping unit is 0.25 acre with a 10-foot minimum width. The map classification is the same as used for the Delta Vegetation and Land Use map that was based on 2002 imagery and finalized in 2007. The map [ds292] can be obtained here: https://wildlife.ca.gov/Data/BIOS. The report, which provides the classification detail can be obtained here: https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=18211. Because Liberty Island was mapped as part of the Delta mapping effort, changes over the eight year period between imagery dates can be seen, most obviously land accretion and erosion and invasion of the non-native water primrose, Ludwigia. Mappers used reconnaissance-level data to refine and verify the map. The data were collected at 48 points during two field missions: 1) August 9, 2012 by land covering the levee on the western boundary of the area, and 2) August 28 - 29, 2012 by boat. All vegetation samples were marked with GPS units and can serve as long-term monitoring points. Photos taken at these GPS points can be used for future monitoring. A database of field data and the digital photos are archived at the office of the Vegetation Classification and Mapping Program (VegCAMP) of DFW. This map has not been assessed for accuracy, but over 25% of the polygons were visited during field reconnaissance. Lead staff of the program cross-checked and edited the map attributes and delineations.

Error statistics of the accuracy assessment vs. GPS benchmarks.

Jurisdictional Unit, 2022-05-21. For use with WFDSS, IFTDSS, IRWIN, and InFORM.This is a feature service which provides Identify and Copy Feature capabilities. If fast-drawing at coarse zoom levels is a requirement, consider using the tile (map) service layer located at https://nifc.maps.arcgis.com/home/item.html?id=3b2c5daad00742cd9f9b676c09d03d13.OverviewThe Jurisdictional Agencies dataset is developed as a national land management geospatial layer, focused on representing wildland fire jurisdictional responsibility, for interagency wildland fire applications, including WFDSS (Wildland Fire Decision Support System), IFTDSS (Interagency Fuels Treatment Decision Support System), IRWIN (Interagency Reporting of Wildland Fire Information), and InFORM (Interagency Fire Occurrence Reporting Modules). It is intended to provide federal wildland fire jurisdictional boundaries on a national scale. The agency and unit names are an indication of the primary manager name and unit name, respectively, recognizing that:There may be multiple owner names.Jurisdiction may be held jointly by agencies at different levels of government (ie State and Local), especially on private lands, Some owner names may be blocked for security reasons.Some jurisdictions may not allow the distribution of owner names. Private ownerships are shown in this layer with JurisdictionalUnitIdentifier=null,JurisdictionalUnitAgency=null, JurisdictionalUnitKind=null, and LandownerKind="Private", LandownerCategory="Private". All land inside the US country boundary is covered by a polygon.Jurisdiction for privately owned land varies widely depending on state, county, or local laws and ordinances, fire workload, and other factors, and is not available in a national dataset in most cases.For publicly held lands the agency name is the surface managing agency, such as Bureau of Land Management, United States Forest Service, etc. The unit name refers to the descriptive name of the polygon (i.e. Northern California District, Boise National Forest, etc.).These data are used to automatically populate fields on the WFDSS Incident Information page.This data layer implements the NWCG Jurisdictional Unit Polygon Geospatial Data Layer Standard.Relevant NWCG Definitions and StandardsUnit2. A generic term that represents an organizational entity that only has meaning when it is contextualized by a descriptor, e.g. jurisdictional.Definition Extension: When referring to an organizational entity, a unit refers to the smallest area or lowest level. Higher levels of an organization (region, agency, department, etc) can be derived from a unit based on organization hierarchy.Unit, JurisdictionalThe governmental entity having overall land and resource management responsibility for a specific geographical area as provided by law.Definition Extension: 1) Ultimately responsible for the fire report to account for statistical fire occurrence; 2) Responsible for setting fire management objectives; 3) Jurisdiction cannot be re-assigned by agreement; 4) The nature and extent of the incident determines jurisdiction (for example, Wildfire vs. All Hazard); 5) Responsible for signing a Delegation of Authority to the Incident Commander.See also: Unit, Protecting; LandownerUnit IdentifierThis data standard specifies the standard format and rules for Unit Identifier, a code used within the wildland fire community to uniquely identify a particular government organizational unit.Landowner Kind & CategoryThis data standard provides a two-tier classification (kind and category) of landownership. Attribute Fields JurisdictionalAgencyKind Describes the type of unit Jurisdiction using the NWCG Landowner Kind data standard. There are two valid values: Federal, and Other. A value may not be populated for all polygons.JurisdictionalAgencyCategoryDescribes the type of unit Jurisdiction using the NWCG Landowner Category data standard. Valid values include: ANCSA, BIA, BLM, BOR, DOD, DOE, NPS, USFS, USFWS, Foreign, Tribal, City, County, OtherLoc (other local, not in the standard), State. A value may not be populated for all polygons.JurisdictionalUnitNameThe name of the Jurisdictional Unit. Where an NWCG Unit ID exists for a polygon, this is the name used in the Name field from the NWCG Unit ID database. Where no NWCG Unit ID exists, this is the “Unit Name” or other specific, descriptive unit name field from the source dataset. A value is populated for all polygons.JurisdictionalUnitIDWhere it could be determined, this is the NWCG Standard Unit Identifier (Unit ID). Where it is unknown, the value is ‘Null’. Null Unit IDs can occur because a unit may not have a Unit ID, or because one could not be reliably determined from the source data. Not every land ownership has an NWCG Unit ID. Unit ID assignment rules are available from the Unit ID standard, linked above.LandownerKindThe landowner category value associated with the polygon. May be inferred from jurisdictional agency, or by lack of a jurisdictional agency. A value is populated for all polygons. There are three valid values: Federal, Private, or Other.LandownerCategoryThe landowner kind value associated with the polygon. May be inferred from jurisdictional agency, or by lack of a jurisdictional agency. A value is populated for all polygons. Valid values include: ANCSA, BIA, BLM, BOR, DOD, DOE, NPS, USFS, USFWS, Foreign, Tribal, City, County, OtherLoc (other local, not in the standard), State, Private.DataSourceThe database from which the polygon originated. Be as specific as possible, identify the geodatabase name and feature class in which the polygon originated.SecondaryDataSourceIf the Data Source is an aggregation from other sources, use this field to specify the source that supplied data to the aggregation. For example, if Data Source is "PAD-US 2.1", then for a USDA Forest Service polygon, the Secondary Data Source would be "USDA FS Automated Lands Program (ALP)". For a BLM polygon in the same dataset, Secondary Source would be "Surface Management Agency (SMA)."SourceUniqueIDIdentifier (GUID or ObjectID) in the data source. Used to trace the polygon back to its authoritative source.MapMethod:Controlled vocabulary to define how the geospatial feature was derived. Map method may help define data quality. MapMethod will be Mixed Method by default for this layer as the data are from mixed sources. Valid Values include: GPS-Driven; GPS-Flight; GPS-Walked; GPS-Walked/Driven; GPS-Unknown Travel Method; Hand Sketch; Digitized-Image; DigitizedTopo; Digitized-Other; Image Interpretation; Infrared Image; Modeled; Mixed Methods; Remote Sensing Derived; Survey/GCDB/Cadastral; Vector; Phone/Tablet; OtherDateCurrentThe last edit, update, of this GIS record. Date should follow the assigned NWCG Date Time data standard, using 24 hour clock, YYYY-MM-DDhh.mm.ssZ, ISO8601 Standard.CommentsAdditional information describing the feature. GeometryIDPrimary key for linking geospatial objects with other database systems. Required for every feature. This field may be renamed for each standard to fit the feature.JurisdictionalUnitID_sansUSNWCG Unit ID with the "US" characters removed from the beginning. Provided for backwards compatibility.JoinMethodAdditional information on how the polygon was matched information in the NWCG Unit ID database.LocalNameLocalName for the polygon provided from PADUS or other source.LegendJurisdictionalAgencyJurisdictional Agency but smaller landholding agencies, or agencies of indeterminate status are grouped for more intuitive use in a map legend or summary table.LegendLandownerAgencyLandowner Agency but smaller landholding agencies, or agencies of indeterminate status are grouped for more intuitive use in a map legend or summary table.DataSourceYearYear that the source data for the polygon were acquired.Data InputThis dataset is based on an aggregation of 4 spatial data sources: Protected Areas Database US (PAD-US 2.1), data from Bureau of Indian Affairs regional offices, the BLM Alaska Fire Service/State of Alaska, and Census Block-Group Geometry. NWCG Unit ID and Agency Kind/Category data are tabular and sourced from UnitIDActive.txt, in the WFMI Unit ID application (https://wfmi.nifc.gov/unit_id/Publish.html). Areas of with unknown Landowner Kind/Category and Jurisdictional Agency Kind/Category are assigned LandownerKind and LandownerCategory values of "Private" by use of the non-water polygons from the Census Block-Group geometry.PAD-US 2.1:This dataset is based in large part on the USGS Protected Areas Database of the United States - PAD-US 2.`. PAD-US is a compilation of authoritative protected areas data between agencies and organizations that ultimately results in a comprehensive and accurate inventory of protected areas for the United States to meet a variety of needs (e.g. conservation, recreation, public health, transportation, energy siting, ecological, or watershed assessments and planning). Extensive documentation on PAD-US processes and data sources is available.How these data were aggregated:Boundaries, and their descriptors, available in spatial databases (i.e. shapefiles or geodatabase feature classes) from land management agencies are the desired and primary data sources in PAD-US. If these authoritative sources are unavailable, or the agency recommends another source, data may be incorporated by other aggregators such as non-governmental organizations. Data sources are tracked for each record in the PAD-US geodatabase (see below).BIA and Tribal Data:BIA and Tribal land management data are not available in PAD-US. As such, data were aggregated from BIA regional offices. These data date from 2012 and were substantially updated in 2022. Indian Trust Land affiliated with Tribes, Reservations, or BIA Agencies: These data are not considered the system of record and are not intended to be used as such. The Bureau of Indian Affairs (BIA), Branch of Wildland Fire Management (BWFM) is not the originator of these data. The