The Digital Surficial Geologic-GIS Map of Bent's Old Fort National Historic Site, Colorado is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (beol_surficial_geology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (beol_surficial_geology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (beol_surficial_geology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) a readme file (beol_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (beol_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (beol_surficial_geology_metadata_faq.pdf). Please read the beol_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri.htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: Colorado State University. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (beol_surficial_geology_metadata.txt or beol_surficial_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:24,000 and United States National Map Accuracy Standards features are within (horizontally) 12.2 meters or 40 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

CSU 1950 Aerial Magenta

CSU 1941 Update Yellow

CSU for Intermittent and Ephemeral Streams in the CRVFO. 1:24,000 scale GIS dataset containing hydrographic features for Colorado and Colorado Division of Wildlife (CDOW) aquatic management codes.

This data was downloaded by Tara Atwood, Intern, Geospatial Centroid, on 3/19/2021 from INEGI. This data is part of an effort to provide base-level spatial data for the Todos Santos region via ArcGIS Hub for CSU researchers and others doing work in this area. The original name for this layer is 702825736897_b. The link to the original source is here: http://en.www.inegi.org.mx/app/biblioteca/ficha.html?upc=702825736897

The Forest CoverType dataset

1. Title of Database:

   Forest Covertype data

2. Sources:

   (a) Original owners of database: Remote Sensing and GIS Program Department of Forest Sciences College of Natural Resources Colorado State University Fort Collins, CO 80523 (contact Jock A. Blackard, jblackard 'at' fs.fed.us or Dr. Denis J. Dean, denis.dean 'at' utdallas.edu)

   NOTE: Reuse of this database is unlimited with retention of copyright notice for Jock A. Blackard and Colorado State University.

   (b) Donors of database: Jock A. Blackard (jblackard 'at' fs.fed.us) GIS Coordinator USFS - Forest Inventory & Analysis Rocky Mountain Research Station 507 25th Street Ogden, UT 84401

   Dr. Denis J. Dean (denis.dean 'at' utdallas.edu)
   Professor
   Program in Geography and Geospatial Sciences
   School of Economic, Political and Policy Sciences
   800 West Campbell Rd
   Richardson, TX 75080-3021 
   
   Dr. Charles W. Anderson (anderson 'at' cs.colostate.edu)
   Associate Professor
   Department of Computer Science
   Colorado State University
   Fort Collins, CO 80523 USA
   

   (c) Date donated: August 1998

3. Past Usage:

   Blackard, Jock A. and Denis J. Dean. 2000. "Comparative Accuracies of Artificial Neural Networks and Discriminant Analysis in Predicting Forest Cover Types from Cartographic Variables." Computers and Electronics in Agriculture 24(3):131-151.

   Blackard, Jock A. and Denis J. Dean. 1998. "Comparative Accuracies of Neural Networks and Discriminant Analysis in Predicting Forest Cover Types from Cartographic Variables." Second Southern Forestry GIS Conference. University of Georgia. Athens, GA. Pages 189-199.

   Blackard, Jock A. 1998. "Comparison of Neural Networks and Discriminant Analysis in Predicting Forest Cover Types." Ph.D. dissertation. Department of Forest Sciences.
   Colorado State University. Fort Collins, Colorado.
   165 pages.

   Abstract of dissertation: Natural resource managers responsible for developing ecosystem management strategies require basic descriptive information including inventory data for forested lands to support their decision-making processes. However, managers generally do not have this type of data for inholdings or neighboring lands that are outside their immediate jurisdiction. One method of obtaining this information is through the use of predictive models.
   Two predictive models were examined in this study, a feedforward neural network model and a more traditional statistical model based on discriminant analysis. The overall objectives of this research were to first construct these two predictive models, and second to compare and evaluate their respective classification accuracies when predicting forest cover types in undisturbed forests.
   The study area included four wilderness areas found in the Roosevelt National Forest of northern Colorado. A total of twelve cartographic measures were utilized as independent variables in the predictive models, while seven major forest cover types were used as dependent variables. Several subsets of these variables were examined to determine the best overall predictive model.
   For each subset of cartographic variables examined in this study, relative classification accuracies indicate the neural network approach outperformed the traditional discriminant analysis method in predicting forest cover types.
   The final neural network model had a higher absolute classification accuracy (70.58%) than the final corresponding linear discriminant analysis model(58.38%). In support of these classification results, thirty additional networks with randomly selected initial weights were derived. From these networks, the overall mean absolute classification accuracy for the neural network method was 70.52%, with a 95% confidence interval of 70.26% to 70.80%. Consequently, natural resource managers may utilize an alternative method of predicting forest cover types that is both superior to the traditional statistical methods and adequate to support their decision-making processes for developing ecosystem management strategies.

   -- Classification performance -- first 11,340 records used for training data subset -- next 3,780 records used for validation data subset -- last 565,892 records used for testing data subset -- 70% Neural Network (backpropagation) -- 58% Linear Discriminant Analysis

4. Relevant Information Paragraph:

   Predicting forest cover type from cartographic variables only (no remotely sensed data). The actual forest cover type for a given observation (30 x 30 meter cell) was determined from US Forest Service (USFS) Region 2 Resource Information System (RIS) data. Independent variables were derived from data originally obtained from US Geological Surve...

This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. No Abstract Available Resources in this dataset:Resource Title: Website Pointer to html file. File Name: Web Page, url: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-sgs&identifier=806 Webpage with information and links to data files for download

CDFW BIOS GIS Dataset, Contact: VegCAMP Vegetation Classification and Mapping Program, Description: CSU Chicos Geographical Information Center (GIC), with assistance from CDFWs Vegetation Classification and Mapping Program (VegCAMP) created a fine-scale vegetation and land use map of portions of the Modoc Plateau in California. The map follows the National Vegetation Mapping Classification standards as well A Manual of California Vegetation and covers the eco-region subsections Devils Garden and Adin Mountains.

This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. No Abstract Available Resources in this dataset:Resource Title: Website Pointer to html file. File Name: Web Page, url: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-sgs&identifier=502 Webpage with information and links to data files for download

This hillshade layer was created by Tara Atwood, Intern, Geospatial Centroid, on 3/30/2021. This data is part of an effort to provide base-level spatial data for the Todos Santos region via ArcGIS Hub for CSU researchers and others doing work in this area. The link to the original source of the DEM used is here: http://en.www.inegi.org.mx/app/biblioteca/ficha.html?upc=702825736897

CDFW BIOS GIS Dataset, Contact: VegCAMP Vegetation Classification and Mapping Program, Description: CSU Chico Geographical Information Center (GIC), with assistance from CDFWs Vegetation Classification and Mapping Program (VegCAMP) created a fine-scale vegetation and land use map of portions of the Modoc Plateau in California. This map covers approximately 1,945,674 acres in eastern Lassen and southern Modoc Counties. It was produced using a base of true-color 2016 one-meter National Agricultural Imagery Program (NAIP) imagery.

This slope layer was created by Tara Atwood, Intern, Geospatial Centroid, on 3/30/2021. This data is part of an effort to provide base-level spatial data for the Todos Santos region via ArcGIS Hub for CSU researchers and others doing work in this area. The link to the original source of the DEM used is here: http://en.www.inegi.org.mx/app/biblioteca/ficha.html?upc=702825736897

This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. No Abstract Available Resources in this dataset:Resource Title: Website Pointer to html file. File Name: Web Page, url: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-sgs&identifier=803 Webpage with information and links to data files for download

This data was downloaded by Tara Atwood, Intern, Geospatial Centroid, on 3/19/2021 from Berkeley Library GeoData . This data is part of an effort to provide base-level spatial data for the Todos Santos region via ArcGIS Hub for CSU researchers and others doing work in this area. The original name for this layer is nyu_2451_36786. The link to the original source is here: https://geodata.lib.berkeley.edu/catalog/nyu-2451-36786

Context

This dataset contains tree observations from four areas of the Roosevelt National Forest in Colorado. All observations are cartographic variables (no remote sensing) from 30 meter x 30 meter sections of forest. There are over half a million measurements total!

Content

This dataset includes information on tree type, shadow coverage, distance to nearby landmarks (roads etcetera), soil type, and local topography.

Acknowledgement

This dataset is part of the UCI Machine Learning Repository, and the original source can be found here. The original database owners are Jock A. Blackard, Dr. Denis J. Dean, and Dr. Charles W. Anderson of the Remote Sensing and GIS Program at Colorado State University.

Inspiration

• Can you build a model that predicts what types of trees grow in an area based on the surrounding characteristics? A past Kaggle competition project on this topic can be found here.
• What kinds of trees are most common in the Roosevelt National Forest?
• Which tree types can grow in more diverse environments? Are there certain tree types that are sensitive to an environmental factor, such as elevation or soil type?

CSU Sanborn 1918

Coordinates for the Pingree Park USGS topographic quadrangle and the 8 surrounding quadrangles was used to create this polygon.

This zipped file geodatabase contains all vector layers available for the Big Thompson Watershed in the CSU Geospatial Centroid data sharing website. The following layers represent available data in the following themes:- Climate- Land Use- Land Cover- Hydrology- Roads- Infrastructure- Oil/Gas- CensusSee metadata for each layer for data source, use, and other metadata information.

Levee stations, usually in feet but in some cases miles, snapped to 2017 Delta levee centerlines (derived from the 2017 Delta LiDAR). Base source for station locations are surveyed field markers on the levees or distance-derived CAD files, in either case as supplied by local maintaining agency's engineers. DWR collected station location data and snapped the stations into the levee centerline file from 2012. After updated levee centerlines were created, the existing points were snapped to the new lines. So there is some small difference between the supplied station locations, previous station locations and these station locations. In some cases, multiple series of stations exist for a district, generally associated with distinct waterways. Also, district levees may be demarked in feet or in miles. The label fields are simply cartographic support, the label data are identical in all cases, but are provided to support fast labeling at more infrequent intervals as needed. Stationing is not as simple as it may seem. In some cases, multiple sets of stationing exist for a district's levees (see Sherman Island for example). What this dataset intends to represent is the current stationing used by District engineers for that District on levee maintenance and improvement projects. As changes are made to the stationing, and the new stationing data become available to the Levee Program, they will be added to this database. Some islands also have separate groups of stations for various parts of the district. This version is current as of 03/24/2020. Source of the original levee stationing is DWR Delta Levees Program, compiled from data provided by internal files, from CSU Chico State, MBK Engineers, KSN Engineers, Siegfried Engineers, Malani & Associates, Green Mountain Engineers, and DCC Engineers. Processing work done by CA DWR, Division of Engineering, Geodetic Branch, Geospatial Data Support Section, specifically by Arina Ushakova (Research Data Analyst I), and initial QC by Joel Dudas (Senior Engineer, Water Resources).