This is a collection of all GPS- and computer-generated geospatial data specific to the Alpine Treeline Warming Experiment (ATWE), located on Niwot Ridge, Colorado, USA. The experiment ran between 2008 and 2016, and consisted of three sites spread across an elevation gradient. Geospatial data for all three experimental sites and cone/seed collection locations are included in this package. ––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Geospatial files include cone collection, experimental site, seed trap, and other GPS location/terrain data. File types include ESRI shapefiles, ESRI grid files or Arc/Info binary grids, TIFFs (.tif), and keyhole markup language (.kml) files. Trimble-imported data include plain text files (.txt), Trimble COR (CorelDRAW) files, and Trimble SSF (Standard Storage Format) files. Microsoft Excel (.xlsx) and comma-separated values (.csv) files corresponding to the attribute tables of many files within this package are also included. A complete list of files can be found in this document in the “Data File Organization” section in the included Data User's Guide. Maps are also included in this data package for reference and use. These maps are separated into two categories, 2021 maps and legacy maps, which were made in 2010. Each 2021 map has one copy in portable network graphics (.png) format, and the other in .pdf format. All legacy maps are in .pdf format. .png image files can be opened with any compatible programs, such as Preview (Mac OS) and Photos (Windows). All GIS files were imported into geopackages (.gpkg) using QGIS, and double-checked for compatibility and data/attribute integrity using ESRI ArcGIS Pro. Note that files packaged within geopackages will open in ArcGIS Pro with “main.” preceding each file name, and an extra column named “geom” defining geometry type in the attribute table. The contents of each geospatial file remain intact, unless otherwise stated in “niwot_geospatial_data_list_07012021.pdf/.xlsx”. This list of files can be found as an .xlsx and a .pdf in this archive. As an open-source file format, files within gpkgs (TIFF, shapefiles, ESRI grid or “Arc/Info Binary”) can be read using both QGIS and ArcGIS Pro, and any other geospatial softwares. Text and .csv files can be read using TextEdit/Notepad/any simple text-editing software; .csv’s can also be opened using Microsoft Excel and R. .kml files can be opened using Google Maps or Google Earth, and Trimble files are most compatible with Trimble’s GPS Pathfinder Office software. .xlsx files can be opened using Microsoft Excel. PDFs can be opened using Adobe Acrobat Reader, and any other compatible programs. A selection of original shapefiles within this archive were generated using ArcMap with associated FGDC-standardized metadata (xml file format). We are including these original files because they contain metadata only accessible using ESRI programs at this time, and so that the relationship between shapefiles and xml files is maintained. Individual xml files can be opened (without a GIS-specific program) using TextEdit or Notepad. Since ESRI’s compatibility with FGDC metadata has changed since the generation of these files, many shapefiles will require upgrading to be compatible with ESRI’s latest versions of geospatial software. These details are also noted in the “niwot_geospatial_data_list_07012021” file.

Support dataset for the workshop: "Introduction to Geospatial Raster and Vector Data with Python", from the Carpentries Incubator. The focus will be the wildfires that affected Rhodes in July 2023.

Natural Earth is a public domain map dataset available at 1:10m, 1:50m, and 1:110 million scales. Featuring tightly integrated vector and raster data, with Natural Earth you can make a variety of visually pleasing, well-crafted maps with cartography or GIS software.

Large scale data, 1:10m

The most detailed. Suitable for making zoomed-in maps of countries and regions. Show the world on a large wall poster.

Medium scale data, 1:50m

Suitable for making zoomed-out maps of countries and regions. Show the world on a tabloid size page.

Small scale data, 1:110m

Suitable for schematic maps of the world on a postcard or as a small locator globe.

6 inch resolution raster image of New York City, classified by landcover type. High resolution land cover data set for New York City. This is the 6 inch version of the high-resolution land cover dataset for New York City. Seven land cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads, and (7) other paved surfaces. The minimum mapping unit for the delineation of features was set at 3 square feet. The primary sources used to derive this land cover layer were the 2010 LiDAR and the 2008 4-band orthoimagery. Ancillary data sources included GIS data (city boundary, building footprints, water, parking lots, roads, railroads, railroad structures, ballfields) provided by New York City (all ancillary datasets except railroads); UVM Spatial Analysis Laboratory manually created railroad polygons from manual interpretation of 2008 4-band orthoimagery. The tree canopy class was considered current as of 2010; the remaining land-cover classes were considered current as of 2008. Object-Based Image Analysis (OBIA) techniques were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. More than 35,000 corrections were made to the classification. Overall accuracy was 96%. This dataset was developed as part of the Urban Tree Canopy (UTC) Assessment for New York City. As such, it represents a 'top down' mapping perspective in which tree canopy over hanging other features is assigned to the tree canopy class. At the time of its creation this dataset represents the most detailed and accurate land cover dataset for the area. This project was funded by National Urban and Community Forestry Advisory Council (NUCFAC) and the National Science Fundation (NSF), although it is not specifically endorsed by either agency. The methods used were developed by the University of Vermont Spatial Analysis Laboratory, in collaboration with the New York City Urban Field Station, with funding from the USDA Forest Service.

Data layer and source, raster/vector, value range/categories (number of subcategories in brackets), and specification of the unit of measurement/impact (proxy).

Cadaster data from PDOK used to illustrate the use of geopandas and shapely, geospatial python packages for manipulating vector data. The brpgewaspercelen_definitief_2020.gpkg file has been subsetted in order to make the download manageable for workshops. Other datasets are copies of those available from PDOK.

Collection of Sentinel-2 satellite scenes employed in the workshop "Introduction to Geospatial Raster and Vector Data with Python". Metadata is provided following the SpatioTemporal Asset Catalog (STAC) specification.

A 6-in resolution 8-class land cover dataset derived from the 2017 Light Detection and Ranging (LiDAR) data capture. This dataset was developed as part of an updated urban tree canopy assessment and therefore represents a ''top-down" mapping perspective in which tree canopy overhanging features is assigned to the tree canopy class. The eight land cover classes mapped were: (1) Tree Canopy, (2) Grass\Shrubs, (3) Bare Soil, (4) Water, (5) Buildings, (6) Roads, (7) Other Impervious, and (8) Railroads. The primary sources used to derive this land cover layer were 2017 LiDAR (1-ft post spacing) and 2016 4-band orthoimagery (0.5-ft resolution). Object based image analysis was used to automate land-cover features using LiDAR point clouds and derivatives, orthoimagery, and vector GIS datasets -- City Boundary (2017, NYC DoITT) Buildings (2017, NYC DoITT) Hydrography (2014, NYC DoITT) LiDAR Hydro Breaklines (2017, NYC DoITT) Transportation Structures (2014, NYC DoITT) Roadbed (2014, NYC DoITT) Road Centerlines (2014, NYC DoITT) Railroads (2014, NYC DoITT) Green Roofs (date unknown, NYC Parks) Parking Lots (2014, NYC DoITT) Parks (2016, NYC Parks) Sidewalks (2014, NYC DoITT) Synthetic Turf (2018, NYC Parks) Wetlands (2014, NYC Parks) Shoreline (2014, NYC DoITT) Plazas (2014, NYC DoITT) Utility Poles (2014, ConEdison via NYCEM) Athletic Facilities (2017, NYC Parks)

For the purposes of classification, only vegetation > 8 ft were classed as Tree Canopy. Vegetation below 8 ft was classed as Grass/Shrub.

To learn more about this dataset, visit the interactive "Understanding the 2017 New York City LiDAR Capture" Story Map -- https://maps.nyc.gov/lidar/2017/ Please see the following link for additional documentation on this dataset -- https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_LandCover.md

This dataset is the product of a geospatial interpolation using groundwater-level data obtained from a U.S. Geological Survey (USGS) synoptic survey of 129 groundwater wells in Fauquier County, VA from October 29 through November 2, 2018 and selected points from the National Hydrography Dataset (NHD). Methodology is detailed in USGS SIR 2022-5014 "Groundwater-level contour map of Fauquier County, VA, October - November 2018." Files include a continuous raster surface of groundwater-level altitudes at a horizontal resolution of 30 meters and vector lines of discrete groundwater-level altitude contours.

Values in Megabytes.

This dataset is called the Gridded SSURGO (gSSURGO) Database and is derived from the Soil Survey Geographic (SSURGO) Database. SSURGO is generally the most detailed level of soil geographic data developed by the National Cooperative Soil Survey (NCSS) in accordance with NCSS mapping standards. The tabular data represent the soil attributes, and are derived from properties and characteristics stored in the National Soil Information System (NASIS). The gSSURGO data were prepared by merging traditional SSURGO digital vector map and tabular data into a Conterminous US-wide extent, and adding a Conterminous US-wide gridded map layer derived from the vector, plus a new value added look up (valu) table containing "ready to map" attributes. The gridded map layer is offered in an ArcGIS file geodatabase raster format.

The raster and vector map data have a Conterminous US-wide extent. The raster map data have a 30 meter cell size. Each cell (and polygon) is linked to a map unit identifier called the map unit key. A unique map unit key is used to link to raster cells and polygons to attribute tables, including the new value added look up (valu) table that contains additional derived data.

The value added look up (valu) table contains attribute data summarized to the map unit level using best practice generalization methods intended to meet the needs of most users. The generalization methods include map unit component weighted averages and percent of the map unit meeting a given criteria.

The Gridded SSURGO dataset was created for use in national, regional, and state-wide resource planning and analysis of soils data. The raster map layer data can be readily combined with other national, regional, and local raster layers, e.g., National Land Cover Database (NLCD), the National Agricultural Statistics Service (NASS) Crop Data Layer, or the National Elevation Dataset (NED).

The GDAL/OGR libraries are open-source, geo-spatial libraries that work with a wide range of raster and vector data sources. One of many impressive features of the GDAL/OGR libraries is the ViRTual (VRT) format. It is an XML format description of how to transform raster or vector data sources on the fly into a new dataset. The transformations include: mosaicking, re-projection, look-up table (raster), change data type (raster), and SQL SELECT command (vector). VRTs can be used by GDAL/OGR functions and utilities as if they were an original source, even allowing for chaining of functionality, for example: have a VRT mosaic hundreds of VRTs that use look-up tables to transform original GeoTiff files. We used the VRT format for the presentation of hydrologic model results, allowing for thousands of small VRT files representing all components of the monthly water balance to be transformations of a single land cover GeoTiff file.

Presentation at 2018 AWRA Spring Specialty Conference: Geographic Information Systems (GIS) and Water Resources X, Orlando, Florida, April 23-25, http://awra.org/meetings/Orlando2018/

VCGI Training Data: Vector, raster, gpx, and tabular data referred to in VCGI's training manuals

The attached data are some large GIS raster files (GeoTIFFs) made with Natural Earth data. Natural Earth is a free vector and raster map data @ naturalearthdata.com. The data used for creating these large files was the "Cross Blended Hypso with Shaded Relief and Water". Data was concatenated to achieve larger and larger files. Internal pyramids were created, in order that the files can be opened easily in a GIS software such as QGIS or by a (future) GIS data visualisation module integrated in EnviDat. Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com

Wadi Hasa Sample Dataset — GRASS GIS Location
Version 1.0 (2025-09-19)

Overview
--------
This archive contains a complete GRASS GIS *Location* for the Wadi Hasa region (Jordan), including base data and exemplar analyses used in the Geomorphometry chapter. It is intended for teaching and reproducible research in archaeological GIS.

How to use
----------
1) Unzip the archive into your GRASSDATA directory (or a working folder) and add the Location to your GRASS session.
2) Start GRASS and open the included workspace (Workspace.gxw) or choose a Mapset to work in.
3) Set the computational region to the default extent/resolution for reproducibility:
g.region n=3444220 s=3405490 e=796210 w=733450 nsres=30 ewres=30 -p
4) Inspect layers as needed:
g.list type=rast,vector
r.info

Citation & License
------------------
Please cite this dataset as:

Isaac I. Ullah. 2025. *Wadi Hasa Sample Dataset (GRASS GIS Location)*. Zenodo. https://doi.org/10.5281/zenodo.17162040

All contents are released under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. The original Wadi Hasa survey dataset is available at: https://figshare.com/articles/dataset/Wadi_Hasa_Ancient_Pastoralism_Project/1404216 The original Wadi Hasa survey dataset is available at: https://figshare.com/articles/dataset/Wadi_Hasa_Ancient_Pastoralism_Project/1404216

Coordinate Reference System
---------------------------
- Projection: UTM, Zone 36N
- Datum/Ellipsoid: WGS84
- Units: meter
- Coordinate system and units are defined in the GRASS Location (PROJ_INFO/UNITS).

Default Region (computational extent & resolution)
--------------------------------------------------
- North: 3444220
- South: 3405490
- East: 796210
- West: 733450
- Resolution: 30 (NS), 30 (EW)
- Rows x Cols: 1291 x 2092 (cells: 2700772)

Directory / Mapset Structure
----------------------------
This Location contains the following Mapsets (data subprojects), each with its own raster/vector layers and attribute tables (SQLite):
- Boolean_Predictive_Modeling: 8 raster(s), 4 vector(s)
- ISRIC_soilgrid: 31 raster(s), 0 vector(s)
- Landsat_Imagery: 3 raster(s), 0 vector(s)
- Landscape_Evolution_Modeling: 41 raster(s), 0 vector(s)
- Least_Cost_Analysis: 13 raster(s), 4 vector(s)
- Machine_Learning_Predictive_Modeling: 70 raster(s), 11 vector(s)
- PERMANENT: 4 raster(s), 2 vector(s)
- Sentinel2_Imagery: 4 raster(s), 0 vector(s)
- Site_Buffer_Analysis: 0 raster(s), 2 vector(s)
- Terrain_Analysis: 27 raster(s), 2 vector(s)
- Territory_Modeling: 14 raster(s), 2 vector(s)
- Trace21k_Paleoclimate_Downscale_Example: 4 raster(s), 2 vector(s)
- Visibility_Analysis: 11 raster(s), 5 vector(s)

Data Content (summary)
----------------------
- Total raster maps: 230
- Total vector maps: 34

Raster resolutions present:
- 10 m: 13 raster(s)
- 30 m: 183 raster(s)
- 208.01 m: 2 raster(s)
- 232.42 m: 30 raster(s)
- 1000 m: 2 raster(s)

Major content themes include:
- Base elevation surfaces and terrain derivatives (e.g., DEMs, slope, aspect, curvature, flow accumulation, prominence).
- Hydrology, watershed, and stream-related layers.
- Visibility analyses (viewsheds; cumulative viewshed analyses for Nabataean and Roman towers).
- Movement and cost-surface analyses (isotropic/anisotropic costs, least-cost paths, time-to-travel surfaces).
- Predictive modeling outputs (boolean/inductive/deductive; regression/classification surfaces; training/test rasters).
- Satellite imagery products (Landsat NIR/RED/NDVI; Sentinel‑2 bands and RGB composite).
- Soil and surficial properties (ISRIC SoilGrids 250 m products).
- Paleoclimate downscaling examples (CHELSA TraCE21k MAT/AP).

Vectors include:
- Archaeological point datasets (e.g., WHS_sites, WHNBS_sites, Nabatean_Towers, Roman_Towers).
- Derived training/testing samples and buffer polygons for modeling.
- Stream network and paths from least-cost analyses.

Important notes & caveats
-------------------------
- Mixed resolutions: Analyses span 10 m (e.g., Sentinel‑2 composites, some derived surfaces), 30 m (majority of terrain and modeling rasters), ~232 m (SoilGrids products), and 1 km (CHELSA paleoclimate). Set the computational region appropriately (g.region) before processing or visualization.
- NoData handling: The raw SRTM import (Hasa_30m_SRTM) reports extreme min/max values caused by nodata placeholders. Use the clipped/processed DEMs (e.g., Hasa_30m_clipped_wshed*) and/or set nodata with r.null as needed.
- Masks: MASK rasters are provided for analysis subdomains where relevant.
- Attribute tables: Vector attribute data are stored in per‑Mapset SQLite databases (sqlite/sqlite.db) and connected via layer=1.

Provenance (brief)
------------------
- Primary survey points and site datasets derive from the Wadi Hasa projects (see Figshare record above).
- Base elevation and terrain derivatives are built from SRTM and subsequently processed/clipped for the watershed.
- Soil variables originate from ISRIC SoilGrids (~250 m).
- Paleoclimate examples use CHELSA TraCE21k surfaces (1 km) that are interpolated to higher resolutions for demonstration.
- Satellite imagery layers are derived from Landsat and Sentinel‑2 scenes.

Reproducibility & quick commands
--------------------------------
- Restore default region: g.region n=3444220 s=3405490 e=796210 w=733450 nsres=30 ewres=30 -p
- Set region to a raster: g.region raster=

Change log
----------
- v1.0: Initial public release of the teaching Location on Zenodo (CC BY 4.0).

Contact
-------
For questions, corrections, or suggestions, please contact Isaac I. Ullah

The map of potential natural vegetation of eastern Africa (V4A) gives the distribution of potential natural vegetation in Ethiopia, Kenya, Tanzania, Uganda, Rwanda, Burundi, Malawi and Zambia.

The map is based on national and local vegetation maps constructed from botanical field surveys - mainly carried out in the two decades after 1950 - in combination with input from national botanical experts. Potential natural vegetation (PNV) is defined as “vegetation that would persist under the current conditions without human interventions”. As such, it can be considered a baseline or null model to assess the vegetation that could be present in a landscape under the current climate and edaphic conditions and used as an input to model vegetation distribution under changing climate.

Vegetation types are defined by their tree species composition, and the documentation of the maps thus includes the potential distribution for more than a thousand tree and shrub species, see the documentation (https://vegetationmap4africa.org/species.html)

The map distinguishes 48 vegetation types, divided in four main vegetation groups: 16 forest types, 15 woodland and wooded grassland types, 5 bushland and thicket types and 12 other types. The map is available in various formats. The online version (https://vegetationmap4africa.org/vegetation_map.html) and for PDF versions of the map, see the documentation (https://vegetationmap4africa.org/documentation.html). Version 2.0 of the potential natural vegetation map and the woody species selection tool was published in 2015 (https://vegetationmap4africa.org/docs/versionhistory/). The original data layers include country-specific vegetation types to maintain the maximum level of information available. This map might be most suitable when carrying out analysis at the national or sub-national level.

When using V4A in your work, cite the publication: Lillesø, J-P.B., van Breugel, P., Kindt, R., Bingham, M., Demissew, S., Dudley, C., Friis, I., Gachathi, F., Kalema, J., Mbago, F., Minani, V., Moshi, H., Mulumba, J., Namaganda, M., Ndangalasi, H., Ruffo, C., Jamnadass, R. & Graudal, L. 2011, Potential Natural Vegetation of Eastern Africa (Ethiopia, Kenya, Malawi, Rwanda, Tanzania, Uganda and Zambia). Volume 1: The Atlas. 61 ed. Forest & Landscape, University of Copenhagen. 155 p. (Forest & Landscape Working Papers; 61 - as well as this repository using the DOI .

The development of V4A was mainly funded by the Rockefeller Foundation and supported by University of Copenhagen

If you want to use the potential natural vegetation map of eastern Africa for your analysis, you can download the spatial data layers in raster format as well as in vector format from this repository

A simplified version of the map can be found on Figshare . That version aggregates country specific vegetation types into regional types. This might be the better option when doing regional-level assessments.

The European Copernicus Coastal Flood Awareness System (ECFAS) project will contribute to the evolution of the Copernicus Emergency Monitoring Service by demonstrating the technical and operational feasibility of a European Coastal Flood Awareness System. Specifically, ECFAS will provide a much-needed solution to bolster coastal resilience to climate risk and reduce population and infrastructure exposure by monitoring and supporting disaster preparedness, two factors that are fundamental to damage prevention and recovery if a storm hits.

The ECFAS Proof-of-Concept development will run from January 2021-December 2022. The ECFAS project is a collaboration between Istituto Universitario di Studi Superiori IUSS di Pavia (Italy, ECFAS Coordinator), Mercator Ocean International (France), Planetek Hellas (Greece), Collecte Localisation Satellites (France), Consorzio Futuro in Ricerca (Italy), Universitat Politecnica de Valencia (Spain), University of the Aegean (Greece), and EurOcean (Portugal), and is funded by the European Commission H2020 Framework Programme within the call LC-SPACE-18-EO-2020 - Copernicus evolution: research activities in support of the evolution of the Copernicus services.

This project has received funding from the European Union’s Horizon 2020 programme

Description of the containing files inside the Dataset.

The dataset was divided at European country level, except the Adriatic area which was extracted as a region and not on a country level due to the small size of the countries. The buffer zone of each data was 10km inland in order to be correlated with the new Copernicus product Coastal Zone LU/LC.

Specifically, the dataset includes the new Coastal LU/LC product which was implemented by the EEA and became available at the end of 2020. Additional information collected in relation to the location and characteristics of transport (road and railway) and utility networks (power plants), population density and time variability. Furthermore, some of the publicly available datasets that were used in CEMS related to the abovementioned assets were gathered such as OpenStreetMap (building footprints, road and railway network infrastructures), GeoNames (populated places but also names of administrative units, rivers and lakes, forests, hills and mountains, parks and recreational areas, etc.), the Global Human Settlement Layer (GHS) and Global Human Settlement Population Grid (GHS-POP) generated by JRC. Below there are tables which present the dataset. Finally, the dataset contains statistics information regarding the population of Europe per sex and age divided in administrative units NUTS level 3.

Copernicus Land Monitoring Service	Resolution	Comment
Coastal LU/LC	1:10.000	A Copernicus hotspot product to monitor landscape dynamics in coastal zones
EU-Hydro - Coastline	1:30.000	EU-Hydro is a dataset for all European countries providing the coastline
Natura 2000	1: 100000	A Copernicus hotspot product to monitor important areas for nature conservation
European Settlement Map	10m	A spatial raster dataset that is mapping human settlements in Europe
Imperviousness Density	10m	The percentage of sealed area
Impervious Built-up	10m	The part of the sealed surfaces where buildings can be found
Grassland 2018	10m	A binary grassland/non-grassland product
Tree Cover Density 2018	10m	Level of tree cover density in a range from 0-100%

Joint Research Center	Resolution	Comment
Global Human Settlement Population Grid GHS-POP)	250m	Residential population estimates for target year 2015
GHS settlement model layer (GHS-SMOD)	1km	The GHS Settlement Model grid delineates and classify settlement typologies via a logic of population size, population and built-up area densities
GHS-BUILT	10m	Built-up grid derived from Sentinel-2 global image composite for reference year 2018
ENACT 2011 Population Grid (ENACT-POP R2020A)	1km	The ENACT is a population density for the European Union that take into account major daily and monthly population variations
JRC Open Power Plants Database (JRC-PPDB-OPEN)	-	Europe’s open power plant database
GHS functional urban areas (GHS-FUA R2019A)	1km	City and its commuting zone (area of influence of the city in terms of labour market flows)
GHS Urban Centre Database (GHS-UCDB R2019A)	1km	Urban Centres defined by specific cut-off values on resident population and built-up surface

Additional Data	Resolution	Comment
Open Street Map (OSM)	-	BF, Transportation Network, Utilities Network, Places of Interest
CEMS	-	Data from Rapid Mapping activations in Europe
GeoNames	-	Populated places, Adm. units, Hydrography, Forests, Hills/Mountains, Parks, etc.
Global Administrative Areas	-	Administrative areas of all countries, at all levels of sub-division
NUTS3 Population Age/Sex Group	-	Eurostat population by age and sex statistics intersected with the NUTS3 Units

Disclaimer:

ECFAS partners provide the data "as is" and "as available" without warranty of any kind. The ECFAS partners shall not be held liable resulting from the use of the information and data provided.

This project has received funding from the Horizon 2020 research and innovation programme under grant agreement No. 101004211

Raster DTM and intermediate results obtained by the glacial valley identification algorithm. The raster was obtained from USGS.

The data were created by transformation of vector cadastral component of SM 5 to raster file. In territories, where vector SM 5 has not been created yet, the cadastral and altimetry components were created by scanning of individual printing masters of planimetry and altimetry from the last issue of the State Map 1:5,000 - derived. The cadastral component does not contain parcel numbers.

This dataset contains the processing unit for Greenland from the Hydrological Derivatives for Modeling and Analysis (HDMA) database. The HDMA database provides comprehensive and consistent global coverage of raster and vector topographically derived layers, including raster layers of digital elevation model (DEM) data, flow direction, flow accumulation, slope, and compound topographic index (CTI); and vector layers of streams and catchment boundaries. The coverage of the data is global (-180º, 180º, -90º, 90º) with the underlying DEM being a hybrid of three datasets: HydroSHEDS (Hydrological data and maps based on SHuttle Elevation Derivatives at multiple Scales), Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010) and the Shuttle Radar Topography Mission (SRTM). For most of the globe south of 60º North, the raster resolution of the data is 3-arc-seconds, corresponding to the resolution of the SRTM. For the areas North of 60º, the resolution is 7.5-arc-seconds (the sma ...