Web mapping application containing parcel, address, and zoning information for Sioux Falls, South Dakota.The City of Sioux Falls Parcel Finder provides access to interactive parcel and address information such as parcel id, owner name, legal description, land use, easements, building photos, zoning, preliminary information, and more. In addition, Parcel Finder has the following features:Search by address, intersection, county parcel id, city parcel id, and owner name.Ability to select features.Selected features can be exported to a csv, or other file types.Layers in the layer list can be turned on and off, and reordered.The layer list, by default, contains the address layer that can be turned on to label the house/building number.Add data from the City of Sioux Falls data repository.Add data featuring Demographic and Lifestyle topics.Measuring tools are back!Drawing tools, allowing you to customize your map, suitable for printing.Expanding printing options.

This data set depicts federal lands having restrictions on access or activities -- that is, lands mangaed by the National Park Service, Defense Department, or Energy Department -- in western North America. The data set was created by reformatting and merging state- and province-based ownership data layers originally acquired from diverse sources (including state GAP programs, USBLM state offices and other sources). For each original dataset 3 additional fields, "Pub_Pvt", "CA_OWN", and "SOURCE" were added and populated based on the specific ownership information contained in the source data. The original coverages were then merged based on the "CA_OWN" field. Finally, NPS, DOD, and DOE lands were selected out of the ownership layer. All work was completed in AcMap 8.3. This product and all source data are available online from SAGEMAP: http://sagemap.wr.usgs.gov.

The USGS Protected Areas Database of the United States (PAD-US) is the nation's inventory of protected areas, including public open space and voluntarily provided, private protected areas, identified as an A-16 National Geospatial Data Asset in the Cadastral Theme (http://www.fgdc.gov/ngda-reports/NGDA_Datasets.html). PAD-US is an ongoing project with several published versions of a spatial database of areas dedicated to the preservation of biological diversity, and other natural, recreational or cultural uses, managed for these purposes through legal or other effective means. The geodatabase maps and describes public open space and other protected areas. Most areas are public lands owned in fee; however, long-term easements, leases, and agreements or administrative designations documented in agency management plans may be included. The PAD-US database strives to be a complete “best available” inventory of protected areas (lands and waters) including data provided by managing agencies and organizations. The dataset is built in collaboration with several partners and data providers (http://gapanalysis.usgs.gov/padus/stewards/). See Supplemental Information Section of this metadata record for more information on partnerships and links to major partner organizations. As this dataset is a compilation of many data sets; data completeness, accuracy, and scale may vary. Federal and state data are generally complete, while local government and private protected area coverage is about 50% complete, and depends on data management capacity in the state. For completeness estimates by state: http://www.protectedlands.net/partners. As the federal and state data are reasonably complete; focus is shifting to completing the inventory of local gov and voluntarily provided, private protected areas. The PAD-US geodatabase contains over twenty-five attributes and four feature classes to support data management, queries, web mapping services and analyses: Marine Protected Areas (MPA), Fee, Easements and Combined. The data contained in the MPA Feature class are provided directly by the National Oceanic and Atmospheric Administration (NOAA) Marine Protected Areas Center (MPA, http://marineprotectedareas.noaa.gov ) tracking the National Marine Protected Areas System. The Easements feature class contains data provided directly from the National Conservation Easement Database (NCED, http://conservationeasement.us ) The MPA and Easement feature classes contain some attributes unique to the sole source databases tracking them (e.g. Easement Holder Name from NCED, Protection Level from NOAA MPA Inventory). The "Combined" feature class integrates all fee, easement and MPA features as the best available national inventory of protected areas in the standard PAD-US framework. In addition to geographic boundaries, PAD-US describes the protection mechanism category (e.g. fee, easement, designation, other), owner and managing agency, designation type, unit name, area, public access and state name in a suite of standardized fields. An informative set of references (i.e. Aggregator Source, GIS Source, GIS Source Date) and "local" or source data fields provide a transparent link between standardized PAD-US fields and information from authoritative data sources. The areas in PAD-US are also assigned conservation measures that assess management intent to permanently protect biological diversity: the nationally relevant "GAP Status Code" and global "IUCN Category" standard. A wealth of attributes facilitates a wide variety of data analyses and creates a context for data to be used at local, regional, state, national and international scales. More information about specific updates and changes to this PAD-US version can be found in the Data Quality Information section of this metadata record as well as on the PAD-US website, http://gapanalysis.usgs.gov/padus/data/history/.) Due to the completeness and complexity of these data, it is highly recommended to review the Supplemental Information Section of the metadata record as well as the Data Use Constraints, to better understand data partnerships as well as see tips and ideas of appropriate uses of the data and how to parse out the data that you are looking for. For more information regarding the PAD-US dataset please visit, http://gapanalysis.usgs.gov/padus/. To find more data resources as well as view example analysis performed using PAD-US data visit, http://gapanalysis.usgs.gov/padus/resources/. The PAD-US dataset and data standard are compiled and maintained by the USGS Gap Analysis Program, http://gapanalysis.usgs.gov/ . For more information about data standards and how the data are aggregated please review the “Standards and Methods Manual for PAD-US,” http://gapanalysis.usgs.gov/padus/data/standards/ .

This spatial data set provides information pertaining to the known land use and disturbance history for lands within the March 2018 administrative boundary of the North Unit of Badlands National Park, South Dakota. Land use and disturbance history presented here are not a comprehensive record of all potential land uses and disturbances but rather a record of known and documented land uses and disturbances based on the best available information. Additional land use and disturbance information may exist but due to time and budget constraints may not have been discovered during the research and development of this data set. The information in this data set was gathered through a variety of sources including but not limited to communication with National Park Service staff, historical documents, land patent records, online information searches, aerial imagery, historical photographs, and spatial data repositories. Data are presented as polygon features, each with a unique area number, its total area (in acres) and the percent of the park the area covers. Polygons were delineated based on existing GIS layers in park records, or, when these were not available, they were digitized using ESRI Arc Map 10.5.1 in conjunction with USDA Natural Resource Conservation Service NAIP orthoimagery based on written descriptions of locations (e.g., Township and Range Survey System) or maps in information sources. History of each polygon is described for one or more of five land use or disturbance types: cultivation, structures, excavation, grazing, and other disturbance. Each land use or disturbance type has six attribute fields. The first field indicates if there is evidence of the land use or disturbance type in the polygon. "Yes" indicates there is evidence and a

This dataset combines the work of several different projects to create a seamless data set for the contiguous United States. Data from four regional Gap Analysis Projects and the LANDFIRE project were combined to make this dataset. In the northwestern United States (Idaho, Oregon, Montana, Washington and Wyoming) data in this map came from the Northwest Gap Analysis Project. In the southwestern United States (Colorado, Arizona, Nevada, New Mexico, and Utah) data used in this map came from the Southwest Gap Analysis Project. The data for Alabama, Florida, Georgia, Kentucky, North Carolina, South Carolina, Mississippi, Tennessee, and Virginia came from the Southeast Gap Analysis Project and the California data was generated by the updated California Gap land cover project. The Hawaii Gap Analysis project provided the data for Hawaii. In areas of the county (central U.S., Northeast, Alaska) that have not yet been covered by a regional Gap Analysis Project, data from the Landfire project was used. Similarities in the methods used by these projects made possible the combining of the data they derived into one seamless coverage. They all used multi-season satellite imagery (Landsat ETM+) from 1999-2001 in conjunction with digital elevation model (DEM) derived datasets (e.g. elevation, landform) to model natural and semi-natural vegetation. Vegetation classes were drawn from NatureServe's Ecological System Classification (Comer et al. 2003) or classes developed by the Hawaii Gap project. Additionally, all of the projects included land use classes that were employed to describe areas where natural vegetation has been altered. In many areas of the country these classes were derived from the National Land Cover Dataset (NLCD). For the majority of classes and, in most areas of the country, a decision tree classifier was used to discriminate ecological system types. In some areas of the country, more manual techniques were used to discriminate small patch systems and systems not distinguishable through topography. The data contains multiple levels of thematic detail. At the most detailed level natural vegetation is represented by NatureServe's Ecological System classification (or in Hawaii the Hawaii GAP classification). These most detailed classifications have been crosswalked to the five highest levels of the National Vegetation Classification (NVC), Class, Subclass, Formation, Division and Macrogroup. This crosswalk allows users to display and analyze the data at different levels of thematic resolution. Developed areas, or areas dominated by introduced species, timber harvest, or water are represented by other classes, collectively refered to as land use classes; these land use classes occur at each of the thematic levels. Raster data in both ArcGIS Grid and ERDAS Imagine format is available for download at http://gis1.usgs.gov/csas/gap/viewer/land_cover/Map.aspx Six layer files are included in the download packages to assist the user in displaying the data at each of the Thematic levels in ArcGIS. In adition to the raster datasets the data is available in Web Mapping Services (WMS) format for each of the six NVC classification levels (Class, Subclass, Formation, Division, Macrogroup, Ecological System) at the following links. http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Class_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Subclass_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Formation_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Division_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Macrogroup_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_Ecological_Systems_Landuse/MapServer

no abstract provided

This spatial data set provides information pertaining to the known land use and disturbance history for lands within the March 2018 administrative boundary of Wind Cave National Park, South Dakota. Land use and disturbance history presented here are not a comprehensive record of all potential land uses and disturbances but rather a record of known and documented land uses and disturbances based on the best available information. Additional land use and disturbance information may exist but due to time and budget constraints may not have been discovered during the research and development of this data set. The information in this data set was gathered through a variety of sources including but not limited to communication with National Park Service staff, historical documents, land patent records, online information searches, aerial imagery, historical photographs, and spatial data repositories. Data are presented as polygon features, each with a unique area number, its total area (in acres) and the percent of the park the area covers. Polygons were delineated based on existing GIS layers in park records, or, when these were not available, they were digitized using ESRI Arc Map 10.5.1 in conjunction with USDA Natural Resource Conservation Service NAIP orthoimagery based on written descriptions of locations (e.g., Township and Range Survey System) or maps in information sources. History of each polygon is described for one or more of five land use or disturbance types: cultivation, structures, excavation, grazing, and other disturbance. Each land use or disturbance type has six attribute fields. The first field indicates if there is evidence of the land use or disturbance type in the polygon. "Yes" indicates there is evidence and a

The National Park Service (NPS), in conjunction with the Biological Resources Division (BRD) of the U.S. Geological Survey (USGS), has implemented a program to "develop a uniform hierarchical vegetation methodology" at a national level. The program will also create a geographic information system (GIS) database for the parks under its management. The purpose of the data is to document the state of vegetation within the NPS service area during the 1990's, thereby providing a baseline study for further analysis at the Regional or Service-wide level. The vegetation at Jewel Cave National Monument was mapped using 1:16,000 scale U.S. Forest Service Color Aerial Photography acquired August 22, 1993. The mapping classification used two separate classification systems. All natural vegetation used the National Vegetation Classification System (NVCS) as a base. The vegetation classification was created after extensive on site sampling and numerical analysis. The vegetation map units were derived from the vegetation classification. Other non-natural or cultural mapping units used the Anderson Level II classification system. The mapped area includes a buffer around the Monument boundary.

This mapping effort originates from a long-term vegetation monitoring program that is part of a larger Inventory and Monitoring (I&M) program started by the National Park Service (NPS). I&M goals are, among others, to map the vegetation of all national parks and monuments and provide a baseline inventory of vegetation. The I&M program currently works in close cooperation with the Biological Resources Division (BRD) of the United States Geological Survey (USGS). The USGS/BRD continues overall management and oversight of all ongoing mapping efforts in close cooperation with the NPS.

The purposes of the mapping effort are varied and include the following: Provides support for NPS Resources Management. Promotes vegetation-related research for both NPS and USGS/BRD. Provides support for NPS Planning and Compliance. Adds to the information base for NPS Interpretation. Assists in NPS Operations.

The location of the mapping is Jewel Cave National Monument and about a 2 mile environs around Monument Boundaries - Black Hills, South Dakota.

Jewel Cave National Monument was responsible for obtaining permission from adjacent land owners for property access for sampling purposes. Most of the private lands were under some form of grazing or farming. Consequently, sampling on these lands was not necessary. The remainder of the lands within the mapping area are U.S. Forest Service Lands so permission was not necessary. To reduce duplicating previous work and to help in our effort we collected existing vegetation reports and maps from the staff at Jewel Cave National Monument. These materials were referenced during the mapping process and the information contained in them was incorporated where it was deemed useful. Because soils also affect the distribution of vegetation, soil maps and soil descriptions were also obtained as reference. These were not converted to a digital file. Digital elevation models (DEM) were obtained to create slope and aspect maps that helped in determining vegetation community distribution. The sampling approach used in this mapping effort was typical of small park sampling, where all polygons within the park boundary are sampled. Two levels of field data gathering were conducted in this park; plots and observations. Plots represented the most intensive sampling of the landscape and used TNC's 'Plot Form'. Observations consisted of brief descriptions and were designed to obtain a quick overview of the landscape without spending a large amount of time at each sample site. Observation points used the 'Observation Form' data sheet. Examples of both 'Plot' and 'Observation' forms are included in the companion report by TNC. Initially, plots were used to describe the vegetation of the park. A total of 28 plots were obtained from July 29 through August 1, 1996. These plots were used by TNC to describe the vegetation associations found within the park. These descriptions are in the companion report by TNC. Map Validation A field trip was conducted in May of 1997 to assess the initial mapping effort and to refine map classes.

Information for this metadata was obtained from the site "http://biology.usgs.gov/npsveg/jeca/metajecaspatial.html" and put into NASA Directory Interchange Format.

Mineral resource occurrence data covering the world, most thoroughly within the U.S. This database contains the records previously provided in the Mineral Resource Data System (MRDS) of USGS and the Mineral Availability System/Mineral Industry Locator System (MAS/MILS) originated in the U.S. Bureau of Mines, which is now part of USGS. The MRDS is a large and complex relational database developed over several decades by hundreds of researchers and reporters. While database records describe mineral resources worldwide, the compilation of information was intended to cover the United States completely, and its coverage of resources in other countries is incomplete. The content of MRDS records was drawn from reports previously published or made available to USGS researchers. Some of those original source materials are no longer available. The information contained in MRDS was intended to reflect the reports used as sources and is current only as of the date of those source reports. Consequently MRDS does not reflect up-to-date changes to the operating status of mines, ownership, land status, production figures and estimates of reserves and resources, or the nature, size, and extent of workings. Information on the geological characteristics of the mineral resource are likely to remain correct, but aspects involving human activity are likely to be out of date.

This data set contains raw unregistered Landsat digital data covering most of Alaska. Data obtained from EROS Data Center in Sioux Falls, South Dakota. Data acquired from 1980 and is ongoing. Some Landsat scenes date back to 1972. The data set currently has 585 records with a growth chart at 5-10 records per year. The amount of storage required varies by medium used or full scene or subscene selection; the file structure is sequential. Spatial referencing of data is by 57 x 59 meter grid cell size-MSS data. Data are available on 9-track, 800 bpi, 1600 bpi, 6250 bpi, unlabeled, unblocked, BCD, fixed record length tape. Subsets and custom formats are available. Limited documentation is available. The data is organized in 7 1/2 ' or 15 ' quads. Data is used for false color composites, land cover analysis, geologic analysis, hydrogeologic analysis, land use planning, basis for update of topographic maps, production of image maps.

This series of three-period land use land cover (LULC) datasets (1975, 2000, and 2013) aids in monitoring change in West Africa’s land resources (exception is Tchad at 4 kilometers). To monitor and map these changes, a 26 general LULC class system was used. The classification system that was developed was primarily inspired by the “Yangambi Classification†(Trochain, 1957). This fairly broad class system for LULC was used because the classes can be readily identified on Landsat satellite imagery. A visual photo-interpretation approach was used to identify and map the LULC classes represented on Landsat images. The Rapid Land Cover Mapper (RLCM) was used to facilitate the photo-interpretation using Esri’s ArcGIS Desktop ArcMap software. Citation: Trochain, J.-L., 1957, Accord interafricain sur la définition des types de végétation de l’Afrique tropicale: Institut d’études centrafricaines.

This product is a GeoTIFF image illustrating the Normalized Difference Vegetative Index (NDVI) across the conterminous United States, as well as subsets for the states of North Dakota, South Dakota, Montana, Wyoming, and Idaho. In order to provide a product that is easily interpreted by our users, the Upper Midwest Aerospace Consortium (UMAC) acquires a weekly NDVI product and a Departure from NDVI average product from the EROS Data Center and converts these products into a GeoTIFF image. This GeoTIFF image illustrates for the user the relative greenness of an area and the comparison of a particular week's index to the average of that week historically. The values in the image are no longer linked to real data values, but are rather simply color coded for illustrative purposes. The dataset covers the year 2000 with some gaps.

This dataset is a digital version of the MAPA DE VEGETAO DO BRASIL (IBGE, 1988), which was digitized at the U.S. Geological Survey's (USGS) EROS Data Center, Sioux Falls, South Dakota. Data are classed into three different classifications: a 59 major vegetation classification map, a 13-class generalization classification map, and a 6-class overprint classification map. Generalized classes were determined from the original subheadings on the original published map which was used use to digitize the data set. Overprint classes are used to describe areas which are best described by a combination of two classes, one from the major classification and the other from the overprint classification. Overprint classes were originally displayed with special symbols printed over major classification shades on the original map. For instance, a series of stipple marks or hash marks may have been printed on top of major class shades to represent areas which had characteristics from the major classification as well as the overprint classification. The overprint classifications are also referred to as subclasses in the original data. Additional information about this data can be obtained from The Woods Hole Research Center, Woods Hole, Massachusetts via their URL at "http://terra.whrc.org/science/tropfor/setLBA.htm".

Digital images of these data are also available from the EOS-WEBSTER Image Gallary. Please see the Data Tab at the following URL: "http://eos-earthdata.sr.unh.edu/". These images can be downloaded as JPEGs and used directly in a document or printed.

These data were modified, as described in documentation provided when data are ordered from EOS-WEBSTER, from the original data. Original data were downloaded from the Woods Hole Research Center Website ("http://terra.whrc.org/science/tropfor/setLBA.htm"). Original author of these data is cited as:

This version of the map was made available by and digitized by: Dr. Norman Bliss, Principal Scientist Land Sciences Section Eros Data Center Sioux Falls, South Dakota 57198

no abstract provided

The Web-enabled Landsat Data (WELD) project is collaboration between the United States Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center and academic partner South Dakota State University Geographic Information Science Center of Excellence. It is funded by NASA's Making Earth System Data Records for Use in Research Environments, with significant USGS cost sharing.

description:

This data layer depicts North Dakota Game and Fish Department Pronghorn Antelope Range Map.

The purpose of the data is to provide a comprehensive list and spatial location of North Dakota Pronghorn Antelope Range Map. This dataset is primarily used as a framework data layer for use in GIS and other mapping applications and does not represent a land survey of the range.

Constraints:
Not to be used for navigation, for informational purposes only. See Game and Fish disclaimer for more information.

This data layer depicts North Dakota Game and Fish Department Pronghorn Antelope Range Map.

The purpose of the data is to provide a comprehensive list and spatial location of North Dakota Pronghorn Antelope Range Map. This dataset is primarily used as a framework data layer for use in GIS and other mapping applications and does not represent a land survey of the range.

Constraints:
Not to be used for navigation, for informational purposes only. See Game and Fish disclaimer for more information.

This resource is a repository of the annual subsurface drainage (so-called "Tile Drainage") maps for the Bois de Sioux Watershed (BdSW), Minnesota and the Red River of the North Basin (RRB), separately. The RRB maps cover a 101,500 km2 area in the United States, which overlies portions of North Dakota, South Daokta, and Minnesota. The maps provide annual subsurface drainage system maps for recent four years, 2009, 2011, 2014, and 2017 (In 2017, the subsurface drainage maps including the Sentinel-1 Synthetic Aperture Radar as an additional input are also provided). Please see Cho et al. (2019) in Water Resources Research (WRR) for full details.

Map Metadata (Proj=longlat +datum=WGS84) Raster value key: 0 = NoData, masked by non-agricultural areas (e.g. urban, water, forest, or wetland land) and high gradient cultivated crop areas (slope > 2%) based on the USGS National Land Cover Dataset (NLCD) and the USGS National Elevation Dataset 1 = Undrained (UD) 2 = Subsurface Drained (SD)

Preferred citation: Cho, E., Jacobs, J. M., Jia, X., & Kraatz, S. (2019). Identifying Subsurface Drainage using Satellite Big Data and Machine Learning via Google Earth Engine. Water Resources Research, 55. https://doi.org/10.1029/2019WR024892

Corresponding author: Eunsang Cho (ec1072@wildcats.unh.edu)

Idaho’s landscape-scale wetland condition assessment tool— Methods and applications in conservation and restoration planningLandscape-scale wetland threat and impairment assessment has been widely applied, both at the national level (NatureServe 2009) and in various states, including Colorado (Lemly et al. 2011), Delaware and Maryland (Tiner 2002 and 2005; Weller et al. 2007), Minnesota (Sands 2002), Montana (Daumiller 2003, Vance 2009), North Dakota (Mita et al. 2007), Ohio (Fennessy et al. 2007), Pennsylvania (Brooks et al. 2002 and 2004; Hychka et al. 2007; Wardrop et al. 2007), and South Dakota (Troelstrup and Stueven 2007). Most of these landscape-scale analyses use a relatively similar list of spatial layer inputs to calculate metrics for condition analyses. This is a cost-effective, objective way to obtain this information from all wetlands in a broad geographic area. Similar landscape-scale assessment projects in Idaho (Murphy and Schmidt 2010) used spatial analysis to estimate the relative condition of wetlands habitats throughout Idaho. Spatial data sources: Murphy and Schmidt (2010) reviewed literature and availability of spatial data to choose which spatial layers to include in their model of landscape integrity. Spatial layers preferably had statewide coverage for inclusion in the analysis. Nearly all spatial layers were downloaded from the statewide geospatial data clearinghouse, the Interactive Numeric and Spatial Information Data Engine for Idaho (INSIDE Idaho; http://inside.uidaho.edu/index.html). A complete list of layers used in the landscape integrity model is in Table 1. Statewide spatial layers were lacking for some important potential condition indicators, such as mine tailings, beaver presence, herbicide or pesticide use, non-native species abundance, nutrient loading, off-highway vehicle use, recreational and boating impacts, and sediment accumulation. Statewide spatial layers were also lacking for two presumably important potential indicators of wetland/riparian condition, recent timber harvest and livestock grazing. To rectify this, GIS models of potential recent timber harvest and livestock grazing were created using National Land Cover Data, grazing allotment maps, and NW ReGAP land cover maps. Calculation of landscape and disturbance metrics: We used a landscape integrity model approach similar to that used by Lemly et al. (2011), Vance (2009), and Faber-Langendoen et al. (2006). Spatial analysis in GIS was used to calculate human land use, or disturbance, metrics for every 30 m2 pixel across Idaho. A single raster layer that indicated threats and impairments for that pixel was produced. This was accomplished by first calculating the distance from each human land use category, development type, or disturbance for each pixel. This inverse weighted distance model is based on the assumption that ecological condition will be poorer in areas of the landscape with the most cumulative human activities and disturbances. Condition improves as you move toward least developed areas (Faber-Langendoen et al. 2006, Vance 2009, Lemly et al. 2011). Land uses or disturbances within 50 m were considered to have twice the impact of those 50 - 100 m away. For this model, land uses and disturbances > 100 m away were assumed to have zero or negligible impact. Because not all land uses impact wetlands the same way, weights for each land use or disturbance type were then determined using published literature (Hauer et al. 2002, Brown and Vivas 2005, Fennessy et al. 2007, Durkalec et al. 2009). A list of weights applied to each land use or disturbance type is in Table 2. A condition value for each pixel was then calculated. For example, the value for a pixel with a 2-lane highway and railroad within 50 m and a home and urban park between 50 and 100 m would be: Weight x Distance = Impact Factor2-lane highway = 7.81 2 15.62railroad = 7.81 2 + 15.62single family home - low density = 6.91 1 + 6.91recreation / open space - medium intensity = 4.38 1 + 4.38 Total Disturbance Value = 42.53The integrity of each pixel was then ranked relative to all others in Idhao using methods analogous to Stoddard et al. (2005), Fennessy et al. (2007), Mita et al. (2007), and Troelstrup and Stueven (2007). Five condition categories based on the sum of weighted impacts present in each pixel were used: 1 = minimally disturbed (top 1% of wetlands); wetland present in the absence or near absence of human disturbances; zero to few stressors are present; land use is almost completely not human-created; equivalent to reference condition; conservation priority;2 = lightly disturbed (2 - 5%); wetland deviates the least from that in the minimally disturbed class based on existing landscape impacts; few stressors are present; majority of land use is not human-created; these are the best wetlands in areas where human influences are present; ecosystem processes and functions are within natural ranges of variation found in the reference condition, but threats exist; conservation and/or restoration priority; 3 = moderately disturbed (6 - 15%); several stressors are present; land use is roughly split between human-created and non-human land use; ecosystem processes and functions are impaired and somewhat outside the range of variation found in the reference condition, but are still present; ecosystem processes are restorable;4 = severely disturbed (16 - 40%); numerous stressors are present; land use is majority human-created; ecosystem processes and functions are severely altered or disrupted and outside the range of variation found in the reference condition; ecosystem processes are restorable, but may require large investments of energy and money for successful restoration; 5 = completely disturbed (bottom 41 - 100%); many stressors are present; land use is nearly completely human-created; ecosystem processes and functions are disrupted and outside the range of variation in the reference condition; ecosystem processes are very difficult to restore.The resulting layer was then filtered using the map of potential wetland occurrence to show only those pixels potentially supporting wetlands.Results of GIS landscape-scale assessment were verified by comparing results with the condition of wetlands determined by in the field using rapid assessment methods. The landscape assessment matched the rapidly assessed condition estimated in the field 61% of the time (Murphy et al. 2012). Thirty-one percent of the sites were misclassified by one condition class and 8% misclassified by two condition classes. These results were similar to an accuracy assessment of landscape scale assessment performed by Mita et al. (2007) in North Dakota. When sites classified correctly and those only off by one condition class were combined (92% of the samples), results were similar to Vance (2009) in Montana (85%). The model of landscape integrity performed much better than the initial prototype model produced for Idaho by Murphy and Schmidt (2010).

This file contains the polygon SDE Feature Class for Federal Fluid Minerals(Oil and Gas) for the Bureau of Land Management(BLM) Montana/Dakotas. Federal Fluid Minerals as well as Federal Lease status and Indian Minerals/Leases are included. Plat maps are used to find federal mineral ownership and the Bureau of Land Management's LR2000 database is used to find current leasing status. Assistance from the Bureau of Indian Affairs is used to find Indian Mineral/Lease status. BLM Field Office with Oil and Gas responsibilities (Great Falls, Miles City, or North Dakota) provide final review of data.

4/25/2025 - Addition of Mandaree Fire District, Edits to Fargo/Horace Fire Districts based on Fargo boundary changes. Updates to Mandan/Mandan Rural Fire Districts based on Mandan boundary change. 11/22/2024 - Addition of part of former Oberon Fire Dept to Minnewaukan Rural. Addition of part of former Oberon Fire Dept into Maddock Rural Fire Dept. Changes to Bismarck City and Bismarck Rural Fire Dept based on Cooperate Boundary Changes. Addition to Great Carson Fire Dept. Removal of following fire dept that have been dissolved – Edna Rural, Oriska, Emerado and Solan F/P.6/27/2024 - Update to the Valley City and Dickinson Corporate Boundary based on requests from their GIS personal.4/8/2024 - Update to the Valley City Corporate Boundary.12/04/2023 - Updates to Marion Combines, Oberon Dissolution, City of Fargo boundary changes, Bowdon/Goodrich Boundary, Rutland Cayauga/Forman boundary11/08/2023 - Harwood Fire Department Boundary was verified.07/01/2023 - Bismarck Corporate Boundary based on current City of Bismarck GIS boundary10/1/2022 - Addition of Westby Fire Department, Updates to Mandan Fire Protection District and Mandan City Fire based on Mandan Corporate Boundary Change, Update to Bismarck Fire Protection District and Bismarck City Fire due to Bismarck Corporate Boundary Changes (June 2022), Update to Grand Forks City Fire, Ferry Township Fire Protection District and Thompson Fire Protestion District due to changes in Grand Forks Corporate Boundary. Updates to Fargo City Fire and Horace Fire Protection District due to changes in Fargo Corporate Boundary. 2/14/2022- Aligned Minot to corresponding City Boundary11/16/2021- Updated Rural/City Fire Districts to reflect changes in Corporate Boundaries in Fargo, Killdeer and Bismarck.5/4/21 - Created separate Fire District polygons for all city fire departments. Removed city fire department area from rural fire departments. Combined Pembina Fire Departments into one district – 4000. Updates to Sheyenne Rural Territory Lines. Modified Minot and Wahpeton to match corporate boundary changes.2/12/20 - 2851 - Kulm Rural Fire Department and 1561 - Ellendale Fire Protection District was changed to fix a discrepancy provided by the Dickey County Emergency Manager.1/17/20 - 0901 – Casselton Rural Fire Department has become 0900 – Casselton Fire Department. 1811 – Forman – Havana Fire Protection District has become Forman Fire Protection District. 3001 – Lehr Rural Fire Department becomes 3001 - Lehr Fire Department.10/2/18 - A new fire district was created in Dunn and Mercer counties called Twin Buttes Fire Protection District. The new Fire District number is 4911.1/24/18 - The following districts have been updated: Adams Fire Protection District, Ashley Rural Fire Department, Edgeley Rural Fire Department, Edinburg Fire Protection District, Edmore Rural Fire Department, Ellendale Fire Protection District, Fairdale Fire Protection District, Grafton Fire Protection District, Hoople Fire Protection District, Kulm Rural Fire Department, Lankin Fire Protection District, Lehr Rural Fire Department, Michigan Fire Protection District, Park River Fire Protection District, Pisek Fire Protection District, and Wishek Fire Protection District.10/3/17 by bb - From ND Insurance Dept. - the 0000 districts are for districts where there isn't a certified fire department with a fire marshall's office - there are ~10 areas that have this code.1/10/17 - The following districts were edited: Ashley Rural Fire Department, Kulm Rural Fire Department, Leeds Fire Protection District, Spirit Lake Fire Department, Minnewauken Fire Protection District, Bowbells Fire Protection District and Lignite Fire Protection District 1/16/15 - Flaxton Fire Protection District was dissolved and incorporated into the Lignite Fire Protection district and the Bowbells Fire Protection District. Oakes Fire Protection District was updated to match the map sent in by their district.1/28/13 - The following Fire Districts were changed during the 2012 calendar year: Jamestown Fire Protection District, Pingree Fire Protection District, Kenmare Rural Fire Department, Tolley Fire Department, Wales Fire Department, Langdon Fire Protection District, Hampden Fire Protection District Munich Fire Protection District and Nekoma Fire Protection District.1/23/09 Counties: Towner, Benson,Ramsey - Leeds Fire District Counties: Grand Forks - Addition of the Grand Forks Fire District. Possible omission during digital conversion??? Counties: Kidder,Dickey - Dissolved the Merricourt Fire District Counties: Pembina - Matched the map received from Pembina County. 2/25/08 Based data is being maintained in UTM Zone 14N. The data was unprojected via ArcMap for use on the Hub. The information came from Ken Rood and the Insurance Commission. For years the NDDOT would use ChartPak tape and tape the boundaries on our 11 X 17 County Base Maps. Deciding the need for a digital layer, the mapping section started to create the layer during the winter of 2006/2007. The section line layer was used to snap the Fire District Boundaries to. The layer was created in ArcMap and the names and fire district codes were given to us by the Insurance Commission. A geodatabase was created for the layer and topology rules were created to eliminate overlaps and slivers. The link to the maps on the Insurance Commissions site is http://www.nd.gov/ndins/company/details.asp?ID=321