This data set includes geographic coordinates for both pre- and post-adjustment locations used to georectify orthoimagery and digital surface models derived from aerial imagery collected using unoccupied aerial systems (UAS) in October 2016. Georeferencing links are provided for each data type (color or multi-band imagery) and portion of the atoll sampled during different UAS missions. Orthoimagery and DSM derived from a given data set and area were georeferenced using the same set of points.

Georeferenced base cadastral spatial data set (GRPK). Since 1 January 2018, the Georeference-based cadastral dataset is called the former GDR10LT spatial data set. GRPK is a state cadastre where stable natural and anthropogenic objects of the earth’s surface are recorded. This spatial data set consists of spatial objects related to bodies of water, land cover, transport network, engineering communications, geodetic points, altitudes, place names, etc. In addition, each object has unique identifiers and life cycle information.

Spatial features stewarded within the FSVeg Spatial application are organized in a hierarchy. Vegetation polygons and sample points represent the base level spatial features. Sample points fall within their parent vegetation polygon. Vegetation polygons are organized into locations. Locations fall within a ranger district and ranger districts fall within a proclaimed forest. There may be one or more proclaimed forests within the administrative forest. Finally, a region oversees the administrative forests within their geographic area. Vegetation points represent stand exam sample locations within a vegetation polygon. When field crews collect stand exam data within a polygon, the methodology is to establish a set of points commonly referred to as "plots" throughout the polygon. The point is the exact location within the polygon where data is collected. From each point, data may be collected on the land that falls within a fixed or variable radius or along a transect line that runs a fixed distance from plot/point center. Data from these points is later expanded to describe vegetation conditions on the polygon. The points represented in this feature class may or may not be tied to data in the FSVeg database. Sometimes points are digitized and thus appear in this feature class before a crew visits the site, and before data is actually collected. Since this is a working database, there are points that represent incorrect locations, and Forest Service staff have not had an urgent need or the time to move or delete points. These incorrect points will appear in the dataset but they will not and cannot be linked to an FSVeg plot record until the coordinates are corrected. Additionally, there are plot records in FSVeg that have may have no corresponding point feature in this feature class. Typically, older stand exam data was collected at a time when there was no easy way to geo reference the point locations. The user of this data should know that they are viewing a dataset that is used day-to-day, and is changing day-to-day.

Download service - digital elevation model of territory of Lithuania Republic. The size of the grid cell is 5x5 meters. It is a part of the spatial data set of the reference base of territory of Lithuania Republic at the scale 1:50 000 (abbreviated name - GDR50LT) which is the State spatial data set, that stores spatial data of natural and anthropocentric features of terrine which are located in the territory of the Republic of Lithuania. This spatial data set consists of the features related to water bodies, land cover, transport network, engineer communications, geodetic points, relief, geographic names, boundaries of administrative units and protected areas, etc.

Overview

Current maps of compositional variation across south polar ice exposures on Mars do not resolve the meter-scales at which erosional processes are most active, ultimately limiting our understanding of how the deposits form and evolve and how they can be used to interpret long-term climate records. In this study, we use k-means clustering and random forest classification to identify and map a set of universal spectral endmembers across 167 high-resolution observations acquired during southern summer by the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM). The 21 endmembers show distinct combinations and strengths of key infrared absorption features reflecting diverse mixtures of CO₂ ice, H₂O ice, and dust. The resulting compositional framework can be used to characterize the nature of both seasonal CO₂ frost and the residual ices it overlies across a variety of terrains.

Contents

The repository contains three .zip files, which can be expanded to access the files described below:

• classified_maps.zip
  • lookup_files
    • SP_CRISM_RF_ColorMap.clr : An ESRI-formatted color map file that can be used to apply the endmember color scheme to random forest-classified maps in ArcGIS (see Symbology settings).
    • SP_CRISM_RF_ColorMap.txt : A text file that can be loaded into Python as a numpy array and used to generate a matplotlib color ramp. Row indices correspond to endmember numbers (see below) and columns are red, green, and blue values scaled from 0 to 1.
    • SP_CRISM_RF_Endmember_Lookup.csv : A lookup table that can be used to cross-reference between endmember numbers (as stored in GeoTiffs or the indices of the colormaps above) and corresponding endmember names (C1, Dc2, etc.).
  • morphologic_reference
    • Contains GeoTiffs of the R1330 spectral parameter (reflectance at 1330 nm) for each processed CRISM observation. Filenames indicate the observation ID, Mars Year, and solar longitude (Ls) of acquisition ("Ls314-07" = Ls 314.07º). These images can be used as a reference for the surface morphology and albedo of each scene. Tie points used to georeference these images to other datasets can be applied to the random forest classification maps to properly align endmember mapping results.
  • random_forest_classification
    • Contains GeoTiffs of the random forest classification results for each processed CRISM observation. Filenames indicate the observation ID, Mars Year, and solar longitude (Ls) of acquisition ("Ls314-07" = Ls 314.07º). These images are not rendered to display colors consistent with the publication figures, but instead store the endmember classification for each pixel as a number from 0 to 21; use the contents of lookup_files to find the corresponding endmember name or render the image with the color scheme from the publication. To view rendered summary plots of each observation, see the contents of observation_info. To georeference these images to other datasets, use the corresponding morphologic reference (see above) to set tie points.
• observation_info.zip
  • footprint_shapefile
    • Contains the components of an ESRI shapefile that outlines the surface footprint/coverage of each processed CRISM observation. The attributes associated with each observation are the same as those in observation_lookup below. Note that the polygons extend slightly beyond the area shown in maps in random_forest_classification due to the inclusion of border pixels.
  • observation_lookup
    • SP_CRISM_Classified_Obs_Info.csv : Information on each processed CRISM observation; this is the same file as Table S1 in the publication. Includes the MY and Ls of acquisition and (where applicable) the figure panel where the observation appears in the publication. The location of each observation is indicated with Center Latitude/Longitude and the assigned Spatial Domain (see Figure 1 in the publication). The Observation ID can be used to locate the source Targeted Reduced Data Record (TRDRs, Version 3) on the Geosciences Node of the Planetary Data System. The spatial extent of each observation is provided in the footprint_shapefile described above.
  • summary_plots
    • Contains summary plots of the endmember map generated for each processed CRISM observation. Each plot notes the observation ID, Mars Year, and Ls and displays the morphologic reference map, random forest classification map, and a breakdown of the endmembers that are present. To access the maps rendered here, see the contents of classified_maps.zip.
    • Also contains the full-resolution version of Figure S3 from the publication (All_Obs_Unprojected.png), which can be used to lookup observations of interest via small labels above each map.
• spectral_library.zip
  • Contains spectral libraries with the median spectrum of each endmember as presented in Figure 3 in the publication. A basic text file listing the wavelength (WVL) and normalized reflectance values for each endmember is included (SP_CRISM_EndmemberMedians.txt) as well as an ENVI-formatted spectral library (SP_CRISM_EndmemberMedians_ENVI.sli). Note that a subset of the 438 wavelengths sampled in the source CRISM data were removed around the longest and shortest wavelengths and the filter boundary to avoid error-prone bands, leaving these spectral libraries with 404 bands.

Collection of georeference spatial data of the territory of the Republic of Lithuania m 1:100 000 according to the requirements of the international project EuroBoundaryMap - EBM_100LT. This database consists of a vector of administrative boundary lines, a vector of administrative units and a vector of points. Lines collect some attribute information about the boundary, its hierarchy, and other information. Areas and points collect attribute information about the hierarchy and codes of administrative units.

This dataset comprises approximately 18600 nadir images taken from a UAS (Unmanned Aircraft Systems) and saved as .jpg files. The dataset broadly covers the area of proximal Skeiðarársandur area (~63.9 N, 17.3 W), and Skeiðarársandur coastline (63.7 N, 17.5 W) at the mouth of Gígjukvísl on 18th April 2022 in Southern Iceland. The data set broadly stretches for an area 11 km east, and 8 km north in the proximal Skeiðarársandur area. The coverage is variable as the imagery is centred on the proglacial lakes and associated drainage rivers. Data was collected over two field campaigns after the December 2021 Glacial Lake Outburst Flood, with collections occurring in April and June 2022. Flights were conducted at 120 m elevation with >60% overlap between images. Ground control points collected in the GNSS (Global Navigation Satellite System ) dataset were used to georeference the imagery. The images were collected to quantify the impacts of the flood and to try and identify strand lines and high water marks. Newcastle University was responsible for collection of the data.