Direct link to download a geodatabase containing elevation contours for the entire state of Colorado at contour intervals of 10, 20, and 40. The most recent contour line was entered on 10/01/2009. Dataset is maintained by the U.S. Geological Survey.

This digital dataset release of the La Junta, Colorado and Kansas quadrangle is composed of previously published elevation contours, structure contours on the limits of the Morrison, Dakota, and Purgatorie Formations, and geologic formational data. The digitizing of this map is to provide a more accessible dataset to be available for public usage. The original dataset was part of an eight-part series of maps in Colorado and Kansas, this map modified in part by reconnaissance by G.R. Scott in 1968. The entirety of this dataset includes both spatial and non-spatial data held in a singular, GeMS compliant geodatabase. This geodatabase includes a geologic map, geologic map feature class holding contact and fault lines, iso value lines, structure contours and other geologic lines, geologic map units, and well data; nonspatial data recorded in standalone tables such as a description of map units, glossary, data source reference, geomaterials dictionary, and their entities and attributes. Data source references include web links to published standards, data dictionaries, and any other referenced data within the published map.

Information on water depth in river channels is important for a number of applications in water resource management but can be difficult to obtain via conventional field methods, particularly over large spatial extents and with the kind of frequency and regularity required to support monitoring programs. Remote sensing methods could provide a viable alternative means of mapping river bathymetry (i.e., water depth). The purpose of this study was to develop and test new, spectrally based techniques for estimating water depth from satellite image data. More specifically, a neural network-based temporal ensembling approach was evaluated in comparison to several other neural network depth retrieval (NNDR) algorithms. These methods are described in a manuscript titled "Neural Network-Based Temporal Ensembling of Water Depth Estimates Derived from SuperDove Images" and the purpose of this data release is to make available the depth maps produced using these techniques. The images used as input were acquired by the SuperDove cubesats comprising the PlanetScope constellation, but the original images cannot be redistributed due to licensing restrictions; the end products derived from these images are provided instead. The large number of cubesats in the PlanetScope constellation allows for frequent temporal coverage and the neural network-based approach takes advantage of this high density time series of information by estimating depth via one of four NNDR methods described in the manuscript: 1. Mean-spec: the images are averaged over time and the resulting mean image is used as input to the NNDR. 2. Mean-depth: a separate NNDR is applied independently to each image in the time series and the resulting time series of depth estimates is averaged to obtain the final depth map. 3. NN-depth: a separate NNDR is applied independently to each image in the time series and the resulting time series of depth estimates is then used as input to a second, ensembling neural network that essentially weights the depth estimates from the individual images so as to optimize the agreement between the image-derived depth estimates and field measurements of water depth used for training; the output from the ensembling neural network serves as the final depth map. 4. Optimal single image: a separate NNDR is applied independently to each image in the time series and only the image that yields the strongest agreement between the image-derived depth estimates and the field measurements of water depth used for training is used as the final depth map. MATLAB (Version 24.1, including the Deep Learning Toolbox) source code for performing this analysis is provided in the function NN_depth_ensembling.m available on the main landing page for the data release of which this is a child item, along with a flow chart illustrating the four different neural network-based depth retrieval methods. To develop and test this new NNDR approach, the method was applied to satellite images from the Colorado River near Lees Ferry, AZ, acquired in March and April of 2021. Field measurements of water depth available through another data release (Legleiter, C.J., Debenedetto, G.P., and Forbes, B.T., 2022, Field measurements of water depth from the Colorado River near Lees Ferry, AZ, March 16-18, 2021: U.S. Geological Survey data release, https://doi.org/10.5066/P9HZL7BZ) were used for training and validation. The depth maps produced via each of the four methods described above are provided as GeoTIFF files, with file name suffixes that indicate the method employed: Colorado_mean-spec.tif, Colorado_mean-depth.tif, Colorado_NN-depth.tif, and Colorado-single-image.tif. In addition, to assess the robustness of the Mean-spec and NN-depth methods to the introduction of a large pulse of sediment by a flood event that occurred partway through the image time series, depth maps from before and after the flood are provided in the files Colorado_Mean-spec_after_flood.tif, Colorado_Mean-spec_before_flood.tif, Colorado_NN-depth_after_flood.tif, and Colorado_NN-depth_before_flood.tif. The spatial resolution of the depth maps is 3 meters and the pixel values within each map are water depth estimates in units of meters.

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

These data consist of rectified aerial photographs, measurements of active channel width, measurements of river and floodplain bathymetry and topography, and ancillary data. These data are specific to the corridor of the Colorado River in Canyonlands National Park between Potash, Utah and the confluence of the Green and Colorado Rivers near Spanish Bottom, Utah. The time period for these data are 1940 to 2018. The shapefile data are measurements of features of the active river channel and floodplains of the Colorado River. The raster data are aerial images and digital elevation models (DEMs) for segments of the Colorado River in Canyonlands National Park, Utah. The aerial images depict the river channel and adjacent floodplains for most of the corridor of the Colorado River in Canyonlands National Park upstream from the confluence with the Green River. The images were acquired from public sources and orthorectified and mosaiced for this study. The DEMs cover the river channel and adjacent floodplain for the Lockhart Creek segment of the Colorado River within Canyonlands National Park and include both bathymetric and topographic data. The bathymetric data were collected by the U.S. Geological Survey Grand Canyon Monitoring and Research Center with funding provided by the National Park Service. The topographic data are airborne lidar data that were collected for the state of Utah by a contractor. The lidar data are available at https://doi.org/10.5069/G9RV0KSQ.

20-meter contour map spanning the Silver Lake Watershed, including Green Lakes Valley, Niwot Ridge LTER, and parts of adjacent Brainard Lake Recreation Area and Indian Peaks Wilderness. Made from a filtered 10-meter lattice, which was made from the Niwot Ridge LTER TIN model (ltertin). This dataset was made to support hierarchical GIS databases at the Niwot Ridge LTER. Additional information concerning the Niwot Ridge LTER hierarchical GIS can be found in Walker et al. (1993).

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

The top of the Upper Cretaceous Dakota Sandstone is present in the subsurface throughout the Uinta and Piceance basins of UT and CO and is easily recognized in the subsurface from geophysical well logs. This digital data release captures in digital form the results of two previously published contoured subsurface maps that were constructed on the top of Dakota Sandstone datum; one of the studies also included a map constructed on the top of the overlying Mancos Shale. A structure contour map of the top of the Dakota Sandstone was constructed as part of a U.S. Geological Survey Petroleum Systems and Geologic Assessment of Oil and Gas in the Uinta-Piceance Province, Utah and Colorado (Roberts, 2003). This surface, constructed using data from oil and gas wells, from digital geologic maps of Utah and Colorado, and from thicknesses of overlying stratigraphic units, depicts the overall configuration of major structural trends of the present-day Uinta and Piceance basins and was used to ...

Under the direction and funding of the National Cooperative Geologic Mapping Program (NCGMP) with guidance and encouragement from the United States Geological Survey (USGS), there has been a decadal strategic plan in place to call for geologic mapping across the nation. This call has been increasing the need for digital data that has not yet been made available. With such a demand, physical data is being re-released as vector-based, GIS operable data, which is viable as a corporate asset to the USGS. This collection of reports is part of the compilation and synthesis efforts hampered by the distributed nature of subsurface investigations at the USGS and a general lack of cataloging and archiving of 3-D geological models and subsurface products. Subsurface mapping activities are decentralized and the results are released on a project-by-project basis. This has led to repeats in data being created, thus wasting both time and energy of the end users. Having a clear understanding of what data is available for GIS use is paramount in the mapping groups. As digital collections of data continue, data releases like this will not be uncommon. This release features structure contour, isopach, and thickness data of stratigraphic units as well as chronostratigraphy. Units included in this release span from North Dakota to as far south as New Mexico and are as follows: San Andres Limestone, Glorieta Sandstone, Leadville Limestone, Cutler Group, Morrison Formation, Colorado Shale, Fox Hills Sandstone, Goose Egg Formation, Minnelusa Formation, Mowry Shale, Pierre Shale, Sundance Formation Unconformity, Wasatch Formation, Permian age units, Trout Creek Sandstone, Castlegate Sandstone, Exshaw or Kinderhook Black Shale, San Juan Volcanics, Lewis Shale, Almond Formation, Baxter Shale, Dakota Sandstone, Cretaceous Onlap, and Tensleep Sandstone.

This geodatabase was built to cover several geothermal targets developed by Flint Geothermal in 2012 during a search for high-temperature systems that could be exploited for electric power development. Several of the thermal springs and wells in the Routt Hot Spring and Steamboat Springs areahave geochemistry and geothermometry values indicative of high-temperature systems.

Datasets include:

1. Results of reconnaissance shallow (2 meter) temperature surveys
2. Air photo lineaments
3. Groundwater geochemistry
4. Georeferenced geologic map of Routt County
5. Various 1:24,000 scale topographic maps

Information on water depth in river channels is important for a number of applications in water resource management but can be difficult to obtain via conventional field methods, particularly over large spatial extents and with the kind of frequency and regularity required to support monitoring programs. Remote sensing methods could provide a viable alternative means of mapping river bathymetry (i.e., water depth). The purpose of this study was to develop and test new, spectrally based techniques for estimating water depth from satellite image data. More specifically, a neural network-based temporal ensembling approach was evaluated in comparison to several other neural network depth retrieval (NNDR) algorithms. These methods are described in a manuscript titled "Neural Network-Based Temporal Ensembling of Water Depth Estimates Derived from SuperDove Images" and the purpose of this data release is to make available the depth maps produced using these techniques. The images used as ...

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

This dataset describes the bedrock elevation beneith the City and County of Denver with contour lines set at 1 foot intervals.

These snow depth raster maps were generated from digital elevation models (DEMs) derived from light detection and ranging (lidar) data collected during multiple field campaigns in the three study areas near Winter Park, Colorado. Small, uncrewed aircraft systems (sUAS) collected lidar datasets to represent snow-covered and snow-free periods. More information regarding the sUAS used and data collection methods can be found in the Supplemental Information and process step sections of each study area individual metadata file.

Data CitationPlease cite this dataset as follows:Vasquez, V., Cushman, K., Ramos, P., Williamson, C., Villareal, P., Gomez Correa, L. F., & Muller-Landau, H. (2023). Barro Colorado Island 50-ha plot crown maps: manually segmented and instance segmented. (Version 2). Smithsonian Tropical Research Institute. https://doi.org/10.25573/data.24784053This data is licensed as CC BY 4.0 and is thus freely available for reuse with proper citation. We ask that data users share any resulting publications, preprints, associated analysis code, and derived data products with us by emailing mullerh@si.edu. We are open to contributing our expert knowledge of the study site and datasets to projects that use these data; please direct queries regarding potential collaboration to Vicente Vasquez, vasquezv@si.edu, and Helene Muller-Landau, mullerh@si.edu.Note that this dataset is part of a collection of Panama UAV data on Smithsonian Figshare, which can be viewed at https://smithsonian.figshare.com/projects/Panama_Forest_Landscapes_UAV/115572Additional information about this research can be found at the Muller-Landau lab web site at https://hmullerlandau.com/All required code is freely available at https://github.com/P-polycephalum/ForestLandscapes/blob/main/LandscapeScripts/segmentation.py and it can be cited as:Vicente Vasquez. (2023). P-polycephalum/ForestLandscapes: segmentwise (v0.0.2-beta). Zenodo. https://doi.org/10.5281/zenodo.10380517Data DescriptionThis dataset is part of a larger initiative monitoring forests in Panama using drones (unoccupied aerial vehicles), an initiative led by Dr. Helene Muller-Landau at the Smithsonian Tropical Research Institute. As part of this initiative, we have been collecting repeat imagery of the 50-ha forest dynamics plot on Barro Colorado Island (BCI), Panama, since October 2014 (see Garcia et al. 2021a, b for data products for 2014-2019).Contained within this dataset are two sets of field-derived crown maps, presented in both their raw and improved versions. The 2021 crown mapping campaign was overseen by KC Cushman, accompanied by field technician Pablo Ramos and Paulino Villarreal. Additionally, Cecilia Williamson and KC Cushman reviewed polygon quality and made necessary corrections. Image data occurred on August 1, 2020, utilizing a DJI Phantom 4 Pro at a resolution of 4cm per pixel. A total of 2454 polygons were manually delineated, encompassing insightful metrics like crown completeness and liana load.The 2023 crown mapping campaign, led by Vicente Vasquez and field technicians Pablo Ramos, Paulino Villarreal, involved quality revisions and corrections performed by Luisa Fernanda Gomez Correa and Vicente Vasquez. Image data collection occurred on September 29, 2022, utilizing a DJI Phantom 4 Pro drone at a 4cm per pixel resolution. The 2023 campaign integrated model 230103_randresize_full of the detectree2 model garden (Ball, 2023). Tree crown polygons were generated pre-field visit, with those attaining a field validation score of 7 or higher retained as true tree crowns.The data collection forms are prepared using ArcGIS field maps. The creator of the data forms uses the spatial points from the trees in the ForestGeo 50-ha censuses to facilitate finding the tree tags in the field (Condit et al., 2019). The field technicians confirm that the tree crown is visible from the drone imagery, they proceed to collect variables of interest and delineate the tree crown manually. In the case of the 2023 field campaign, the field technicians were able to skip manual delineation when the polygons generated by 230103_randresize_full were evaluated as true detection.The improved version of the 2023 and 2021 crown map data collection takes as input the raw crown maps and the globally aligned orthomosaics to refine the edges of the crown. We use the model SAM from segment-anything module developed my Meta AI (Krillov, 2023). We adapted the use of their instance segmentation algorithm to take geospatial imagery in the form of tiles. We inputted multiple bounding boxes in the form of CPU torch tensors for each of the files. Furthermore, we perform several tasks to clean the crowns and remove the polygons overlaps to avoid ambiguity. This results in a very well delineated crown map with no overlapping between tree crowns. Despite our diligent efforts in detecting, delineating, and evaluating all visible tree crowns from drone imagery, this dataset exhibits certain limitations. These include missing tags denoted as -9999, erroneous manual delineations or instance segmentation of tree crown polygons, duplicated tags, and undetected tree crowns. These limitations are primarily attributed to human error, logistical constraints, and the challenge of confirming individual tree crown emergence above the canopy. In numerous instances, particularly within densely vegetated areas, delineating polygons and assigning tags to numerous small trees posed significant challenges.MetadataThe dataset comprises four sets of crown maps bundled within .zip files, adhering to the naming convention MacroSite_plot_year_month_day_crownmap_type. As an illustration, a sample file name follows the structure: BCI_50ha_2020_08_01_improved.For a comprehensive understanding of variable nomenclature within each shapefile, exhaustive details are provided in the file named variables_description.csv. Additionally, our dataset incorporates visualization figures corresponding to both raw and refined crown maps.The raw crown maps contain:A GeoTiff-formatted raster image reflecting the image acquisition date during field data collection.The tiles folder housing all tiles utilized for instance segmentation.The most recent version of the raw crown map manually revised and retaining its original naming scheme.A reformatted iteration of the raw crown map, involving column renaming and the reprojection of its coordinate reference system.The improved crown maps contain:"_crownmap_segmented.shp" version: This subproduct has all polygons segmented via the SAM model from the segment-anything process."_crownmap_cleaned.shp" version: This subproduct features one polygon allocated per GlobalID, specifically the one with the highest segment-anything score."_crownmap_avoidance.shp" version: This subproduct is devoid of any overlapping polygons."_crownmap_improved.shp" version: The outcome of the instance crown segmentation workflow, incorporating all original crown map fields.Author contributionsVV wrote the code for standardized workflow for processing, alignment, and segmentation of the tree crowns. MG and MH led the drone imagery collection. HCM conceived the study, wrote the grant proposals to obtain funding, and supervised the research.AcknowledgmentsVicente Vasquez and KC Cushman created the field map forms and coordinated the 2023 and 2021 crown map field campaign. Milton Solano assistance with the ArcGIS platform. Field technicians Pablo Ramos, Paulino Villareal, and Melvin Hernandez delineated and evaluated tree crown polygons. Luisa Gomez-Correa and Cecilia Williamson assisted with quality assurance and quality control after field data collection. Milton Garcia and additional interns in the Muller-Landau lab assisted with drone data collection. Funding and/or in-kind support was provided by the Smithsonian Institution Scholarly Studies grant program (HCM), the Smithsonian Institution Equipment fund (HCM), Smithsonian ForestGEO, the Smithsonian Tropical Research Institute.ReferencesBall, J.G.C., Hickman, S.H.M., Jackson, T.D., Koay, X.J., Hirst, J., Jay, W., Archer, M., Aubry-Kientz, M., Vincent, G. and Coomes, D.A. (2023), Accurate delineation of individual tree crowns in tropical forests from aerial RGB imagery using Mask R-CNN. Remote Sens Ecol Conserv. 9(5):641-655. https://doi.org/10.1002/rse2.332Condit, Richard et al. (2019). Complete data from the Barro Colorado 50-ha plot: 423617 trees, 35 years [Dataset]. Dryad. https://doi.org/10.15146/5xcp-0d46Garcia, M., J. P. Dandois, R. F. Araujo, S. Grubinger, and H. C. Muller-Landau. 2021b. Surface elevation models and associated canopy height change models for the 50-ha plot on Barro Colorado Island, Panama, for 2014-2019. . In Smithsonian Figshare, edited by S. T. R. Institute. https://doi.org/10.25573/data.14417933Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A. C., Lo, W.-Y., Dollár, P., & Girshick, R. (2023). Segment Anything. arXiv preprint arXiv:2304.02643.Scheffler D, Hollstein A, Diedrich H, Segl K, Hostert P. AROSICS: An Automated and Robust Open-Source Image Co-Registration Software for Multi-Sensor Satellite Data. Remote Sensing. 2017; 9(7):676.

Layered GeoPDF 7.5 Minute Quadrangle Map. Layers of geospatial data include orthoimagery, roads, grids, geographic names, elevation contours, hydrography, and other selected map features.

This digital data release contains geospatial geologic and paleontological data of the 1° x2 °, 1:250,000 Limon quadrangle covering eastern Colorado and western Kansas. The dataset is a digital reproduction of previously published U.S. Geological Survey field mapping which illustrates the spatial configuration of primarily Quaternary surficial units overlying upper Miocene, Oligocene, Paleocene, and Upper Cretaceous bedrock (Sharps, 1980). This quadrangle contains numerous outcrop of the Ogallala Formation, which is a prolific freshwater aquifer throughout the broader great plains. A structure contour map of the top of the Dakota Sandstone are included, which was constructed using selected oil and gas well logs (Sharps, 1980). The Dakota Sandstone is a productive hydrocarbon reservoir within the Limon quadrangle, and the broader Denver-Julesburg Basin. Point data for Mesozoic invertebrate fossil collection localities are depicted on the map, depicted with either Denver or Washington D.C. U.S. Geological Survey catalog numbers (Sharps, 1980). The digital geologic database presented here is an accurate replication of original US. Geological Survey mapping in the Limon quadrangle (Sharps, 1980). Geologic map polygons, fossil points, faunal zones, and structure contours were digitized and attributed as GIS data sets as part of the U.S. Geological Survey’s ongoing studies on a regional and national scale. The geologic map polygons, fossil point features, faunal zone lines, and structure contour lines are distributed as separate feature classes within a geographic information system geodatabase. Contoured elevation values are given in feet, to maintain consistency with the original publication, and in meters. Nonspatial tables define the data sources used, define terms used in the dataset, and describe the geologic units. A tabular data dictionary describes the entity and attribute information for all attributes of the geospatial data and the accompanying nonspatial tables.

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

U.S. Geological Survey 1:24,000 Topographic Maps of Gilpin County, Colorado

Infrastructure, such as roads, airports, water and energy transmission and distribution facilities, sewage treatment plants, and many other facilities, is vital to the sustainability and vitality of any populated area. Rehabilitation of existing and development of new infrastructure requires three natural resources: natural aggregate (stone, sand, and gravel), water, and energy http://rockyweb.cr.usgs.gov/frontrange/overview.htm.

The principal goals of the U.S. Geological Survey (USGS) Front Range Infrastructure Resources Project (FRIRP) were to develop information, define tools, and demonstrate ways to: (1) implement a multidisciplinary evaluation of the distribution and quality of a region's infrastructure resources, (2) identify issues that may affect availability of resources, and (3) work with cooperators to provide decision makers with tools to evaluate alternatives to enhance decision-making. Geographic integration of data (geospatial databases) can provide an interactive tool to facilitate decision-making by stakeholders http://rockyweb.cr.usgs.gov/frontrange/overview.htm.