Investigations of coastal change and coastal resources often require continuous elevation profiles from the seafloor to coastal terrestrial landscapes. Differences in elevation data collection in the terrestrial and marine environments result in separate elevation products that may not share a vertical datum. This data release contains the assimilation of multiple elevation products into a continuous digital elevation model at a resolution of 3-arcseconds (approximately 90 meters) from the terrestrial landscape to the seafloor for the contiguous U.S., focused on the coastal interface. All datasets were converted to a consistent horizontal datum, the North American Datum of 1983, but the native vertical datum for each dataset was not adjusted. Artifacts in the source elevation products were replaced with other available elevation products when possible, corrected using various spatial tools, or otherwise marked for future correction. This data release contains the assimilation of multiple elevation products into a continuous digital elevation model at a resolution of 3-arcseconds (approximately 90 meters) from the terrestrial landscape to the seafloor for the contiguous U.S. that were constructed using this shapefile.

Urban areas have a manifold and far-reaching impact on our environment, and the three-dimensional structure is a key aspect for characterizing the urban environment.

This dataset features a map of building height predictions for entire Germany on a 10m grid based on Sentinel-1A/B and Sentinel-2A/B time series. We utilized machine learning regression to extrapolate building height reference information to the entire country. The reference data were obtained from several freely and openly available 3D Building Models originating from official data sources (building footprint: cadaster, building height: airborne laser scanning), and represent the average building height within a radius of 50m relative to each pixel. Building height was only estimated for built-up areas (European Settlement Mask), and building height predictions <2m were set to 0m.

Temporal extent The acquisition dates of the different data sources vary to some degree: - Independent variables: Sentinel-2 data are from 2018; Sentinel-1 data are from 2017. - Dependent variables: the 3D building models are from 2012-2020 depending on data provider. - Settlement mask: the ESM is based on a mosaic of imagery from 2014-2016. Considering that net change of building stock is positive in Germany, the building height map is representative for ca. 2015.

Data format The data come in tiles of 30x30km (see shapefile). The projection is EPSG:3035. The images are compressed GeoTiff files (.tif). Metadata are located within the Tiff, partly in the FORCE domain. There is a mosaic in GDAL Virtual format (.vrt), which can readily be opened in most Geographic Information Systems. Building height values are in meters, scaled by 10, i.e. a pixel value of 69 = 6.9m.

Further information For further information, please see the publication or contact David Frantz (david.frantz@geo.hu-berlin.de). A web-visualization of this dataset is available here.

Publication Frantz, D., Schug, F., Okujeni, A., Navacchi, C., Wagner, W., van der Linden, S., & Hostert, P. (2021). National-scale mapping of building height using Sentinel-1 and Sentinel-2 time series. Remote Sensing of Environment, 252, 112128. DOI: https://doi.org/10.1016/j.rse.2020.112128

Acknowledgements The dataset was generated by FORCE v. 3.1 (paper, code), which is freely available software under the terms of the GNU General Public License v. >= 3. Sentinel imagery were obtained from the European Space Agency and the European Commission. The European Settlement Mask was obtained from the European Commission. 3D building models were obtained from Berlin Partner für Wirtschaft und Technologie GmbH, Freie und Hansestadt Hamburg / Landesbetrieb Geoinformation und Vermessung, Landeshauptstadt Potsdam, Bezirksregierung Köln / Geobasis NRW, and Kompetenzzentrum Geodateninfrastruktur Thüringen. This dataset was partly produced on EODC - we thank Clement Atzberger for supporting the generation of this dataset by sharing disc space on EODC.

Funding This dataset was produced with funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950).

The data archive contains the aerial photographs and channel delineations used in our analysis. The images have been geo-referenced to the 1995 digital orthophoto quarter quadrangles as described by Miller and Friedman (2009). The separate images for each year can be viewed as a composite along with that year’s channel delineation using a geographic information system (GIS). The 2003 IKONOS satellite imagery is proprietary and, therefore, cannot be served here. The pre1939 shapefile serves as a reference for the original location of the flood plain that formed before the earliest photos were taken in 1939, and is not associated with any aerial images. The channel delineations for all photo years (including 2003) and the delineation of the outer flood-plain boundary are stored as shapefiles. These shapefiles can be manipulated using GIS applications to reproduce the spatial analyses reported in Miller and Friedman (2009). This metadata record is associated with the project landing page that describes the entire data package. There are nine child items on the main landing page; one represents the pre1939 reference shapefile and the other eight child items are each associated with a different repeat photography year. Each of these eight child items provide all images taken in that year (with the exception of 2003) and a SHP file that delineates channel location. Each year's photography consists of 4-8 scanned and referenced aerial photographs or digital satellite imagery in a geoTIFF format. SHP files from any year can be overlaid on top of the images to visualize change in channel location. TIFF images and associated SHP files are included as attachments or external sources and can be downloaded directly from the ScienceBase page. Reference: Miller, J.R., and J.M. Friedman. 2009. Influence of flow variability on flood-plain formation and destruction, Little Missouri River, North Dakota. Geological Society of America Bulletin 121:752-759.

This dataset is part of Deliverable 4.2, 4.3 and 5.3 and was produced by the WP4 team of the Landmark H2020 project. It contains the following shapefiles: PO5_Current_SFs_PrimaryProductivity.tiff PO5_Current_SFs_ClimateRegulation.tiff PO5_Current_SFs_WaterRegulation_Drought.tiff PO5_Current_SFs_WaterRegulation_WaterLoggging.tiff PO5_Current_SFs_WaterPurification.tiff PO5_Current_SFs_NutrientCycling.tiff PO5_Current_SFs_Biodiversity.tiff PO5_Current_SFs_EnvZone.shp PO5_Current_SFs_NUTS1.shp PO5_Maximization_ClimateRegulation.shp PO5_Maximization_Drought.shp PO5_Maximization_NCycling.shp PO5_Maximization_PrimaryProductivity.shp PO5_Maximization_Waterlogging.shp PO5_Maximization_Waterpurification.shp PO5_Maximization_Waterpurification.shp The tiff-files give the spatial variation in soil function performance for 6 soil functions in in agricultural soils across the EU. The soil functions were mapped by applying a number of crop specific Bayesian networks on a combination of spatial maps which describe soil properties, climate, land use and land management on agricultural soils throughout the European Union. PO5_Current_SFs_EnvZone.shp and PO5_Current_SFs_NUTS1.shp give the z-scores for both grasslands and cropland in 12 environmental zones for the six soil functions. The z-scores give the signed fractional number of standard deviations by which SF means for an environmental zone are above or below the mean value and allow us indicate which areas have a higher or lower soil function performance compared to the mean value. These values were extracted from the tiff-files provided in this dataset. The PO5_Maximization shapefiles give an estimation of the change in soil function performance across the EU when one soil function is maximized through changes in management. This spatial variation is represented in change in z-scores compared to the current SF supply. To develop the scenario, for each of the locations, the soil function was maximized in the underlying Bayesian networks, by allowing it to change different types of management (irrigation, fertilizer, etc.) for each location taking soil, climate and crop type into account. These changes also impact the performance of the other soil functions. For each of the soil functions a separate spatial map was created. Which was then used to calculate z-scores for each of the environmental zones. Z-scores from the current SF maps and scenario maps were then compared to each other to calculate the change in z-scores. This change in z-scores is given in the shapefiles and describes the relative change in soil function performance. Positive values indicate an improvement in soil functioning compared to the current situation, negative values a decrease. More information regarding calculation and interpretation of both this dataset and the soil function maps used to calculate the z-scores can be found in: Vrebos D., J. Staes, R. Schulte, L. O’Sullivan, E. Lugato, A. Jones, A. Georgoulas and P. Meire (2018). Soil function supply maps. LANDMARK Report 4.2. Vrebos D., F. Bampa, R. Creamer, A. Jones, E. Lugato, L. O’Sullivan, P. Meire, R.P.O. Schulte, J. Schröder and J. Staes (2018). Scenarios maps: visualizing optimized scenarios where supply of soil functions matches demands. LANDMARK Report 4.3. and Jones A. et al. (2019). An options document to propose future policy tools for functional soil management. LANDMARK 5.3. All available from www.landmark2020.eu.

USGS is assessing the feasibility of map projections and grid systems for lunar surface operations. We propose developing a new Lunar Transverse Mercator (LTM), the Lunar Polar Stereographic (LPS), and the Lunar Grid Reference Systems (LGRS). We have also designed additional grids designed to NASA requirements for astronaut navigation, referred to as LGRS in Artemis Condensed Coordinates (ACC), but this is not released here. LTM, LPS, and LGRS are similar in design and use to the Universal Transverse Mercator (UTM), Universal Polar Stereographic (LPS), and Military Grid Reference System (MGRS), but adhere to NASA requirements. LGRS ACC format is similar in design and structure to historic Army Mapping Service Apollo orthotopophoto charts for navigation. The Lunar Transverse Mercator (LTM) projection system is a globalized set of lunar map projections that divides the Moon into zones to provide a uniform coordinate system for accurate spatial representation. It uses a transverse Mercator projection, which maps the Moon into 45 transverse Mercator strips, each 8°, longitude, wide. These transverse Mercator strips are subdivided at the lunar equator for a total of 90 zones. Forty-five in the northern hemisphere and forty-five in the south. LTM specifies a topocentric, rectangular, coordinate system (easting and northing coordinates) for spatial referencing. This projection is commonly used in GIS and surveying for its ability to represent large areas with high positional accuracy while maintaining consistent scale. The Lunar Polar Stereographic (LPS) projection system contains projection specifications for the Moon’s polar regions. It uses a polar stereographic projection, which maps the polar regions onto an azimuthal plane. The LPS system contains 2 zones, each zone is located at the northern and southern poles and is referred to as the LPS northern or LPS southern zone. LPS, like is equatorial counterpart LTM, specifies a topocentric, rectangular, coordinate system (easting and northing coordinates) for spatial referencing. This projection is commonly used in GIS and surveying for its ability to represent large polar areas with high positional accuracy, while maintaining consistent scale across the map region. LGRS is a globalized grid system for lunar navigation supported by the LTM and LPS projections. LGRS provides an alphanumeric grid coordinate structure for both the LTM and LPS systems. This labeling structure is utilized in a similar manner to MGRS. LGRS defines a global area grid based on latitude and longitude and a 25×25 km grid based on LTM and LPS coordinate values. Two implementations of LGRS are used as polar areas require a LPS projection and equatorial areas a transverse Mercator. We describe the difference in the techniques and methods report associated with this data release. Request McClernan et. al. (in-press) for more information. ACC is a method of simplifying LGRS coordinates and is similar in use to the Army Mapping Service Apollo orthotopophoto charts for navigation. These data will be released at a later date. Two versions of the shape files are provided in this data release, PCRS and Display only. See LTM_LPS_LGRS_Shapefiles.zip file. PCRS are limited to a single zone and are projected in either LTM or LPS with topocentric coordinates formatted in Eastings and Northings. Display only shapefiles are formatted in lunar planetocentric latitude and longitude, a Mercator or Equirectangular projection is best for these grids. A description of each grid is provided below: Equatorial (Display Only) Grids: Lunar Transverse Mercator (LTM) Grids: LTM zone borders for each LTM zone Merged LTM zone borders Lunar Polar Stereographic (LPS) Grids: North LPS zone border South LPS zone border Lunar Grid Reference System (LGRS) Grids: Global Areas for North and South LPS zones Merged Global Areas (8°×8° and 8°×10° extended area) for all LTM zones Merged 25km grid for all LTM zones PCRS Shapefiles:` Lunar Transverse Mercator (LTM) Grids: LTM zone borders for each LTM zone Lunar Polar Stereographic (LPS) Grids: North LPS zone border South LPS zone border Lunar Grid Reference System (LGRS) Grids: Global Areas for North and South LPS zones 25km Gird for North and South LPS zones Global Areas (8°×8° and 8°×10° extended area) for each LTM zone 25km grid for each LTM zone The rasters in this data release detail the linear distortions associated with the LTM and LPS system projections. For these products, we utilize the same definitions of distortion as the U.S. State Plane Coordinate System. Scale Factor, k - The scale factor is a ratio that communicates the difference in distances when measured on a map and the distance reported on the reference surface. Symbolically this is the ratio between the maps grid distance and distance on the lunar reference sphere. This value can be precisely calculated and is provided in their defining publication. See Snyder (1987) for derivation of the LPS scale factor. This scale factor is unitless and typically increases from the central scale factor k_0, a projection-defining parameter. For each LPS projection. Request McClernan et. al., (in-press) for more information. Scale Error, (k-1) - Scale-Error, is simply the scale factor differenced from 1. Is a unitless positive or negative value from 0 that is used to express the scale factor’s impact on position values on a map. Distance on the reference surface are expended when (k-1) is positive and contracted when (k-1) is negative. Height Factor, h_F - The Height Factor is used to correct for the difference in distance caused between the lunar surface curvature expressed at different elevations. It is expressed as a ratio between the radius of the lunar reference sphere and elevations measured from the center of the reference sphere. For this work, we utilized a radial distance of 1,737,400 m as recommended by the IAU working group of Rotational Elements (Archinal et. al., 2008). For this calculation, height factor values were derived from a LOLA DEM 118 m v1, Digital Elevation Model (LOLA Science Team, 2021). Combined Factor, C_F – The combined factor is utilized to “Scale-To-Ground” and is used to adjust the distance expressed on the map surface and convert to the position on the actual ground surface. This value is the product of the map scale factor and the height factor, ensuring the positioning measurements can be correctly placed on a map and on the ground. The combined factor is similar to linear distortion in that it is evaluated at the ground, but, as discussed in the next section, differs numerically. Often C_F is scrutinized for map projection optimization. Linear distortion, δ - In keeping with the design definitions of SPCS2022 (Dennis 2023), we refer to scale error when discussing the lunar reference sphere and linear distortion, δ, when discussing the topographic surface. Linear distortion is calculated using C_F simply by subtracting 1. Distances are expended on the topographic surface when δ is positive and compressed when δ is negative. The relevant files associated with the expressed LTM distortion are as follows. The scale factor for the 90 LTM projections: LUNAR_LTM_GLOBAL_PLOT_HEMISPHERES_distortion_K_grid_scale_factor.tif Height Factor for the LTM portion of the Moon: LUNAR_LTM_GLOBAL_PLOT_HEMISPHERES_distortion_EF_elevation_factor.tif Combined Factor in LTM portion of the Moon LUNAR_LTM_GLOBAL_PLOT_HEMISPHERES_distortion_CF_combined_factor.tif The relevant files associated with the expressed LPS distortion are as follows. Lunar North Pole The scale factor for the northern LPS zone: LUNAR_LGRS_NP_PLOT_LPS_K_grid_scale_factor.tif Height Factor for the north pole of the Moon: LUNAR_LGRS_NP_PLOT_LPS_EF_elevation_factor.tif Combined Factor for northern LPS zone: LUNAR_LGRS_NP_PLOT_LPS_CF_combined_factor.tif Lunar South Pole Scale factor for the northern LPS zone: LUNAR_LGRS_SP_PLOT_LPS_K_grid_scale_factor.tif Height Factor for the south pole of the Moon: LUNAR_LGRS_SP_PLOT_LPS_EF_elevation_factor.tif Combined Factor for northern LPS zone: LUNAR_LGRS_SP_PLOT_LPS_CF_combined_factor.tif For GIS utilization of grid shapefiles projected in Lunar Latitude and Longitude, referred to as “Display Only”, please utilize a registered lunar geographic coordinate system (GCS) such as IAU_2015:30100 or ESRI:104903. LTM, LPS, and LGRS PCRS shapefiles utilize either a custom transverse Mercator or polar Stereographic projection. For PCRS grids the LTM and LPS projections are recommended for all LTM, LPS, and LGRS grid sizes. See McClernan et. al. (in-press) for such projections. Raster data was calculated using planetocentric latitude and longitude. A LTM and LPS projection or a registered lunar GCS may be utilized to display this data. Note: All data, shapefiles and rasters, require a specific projection and datum. The projection is recommended as LTM and LPS or, when needed, IAU_2015:30100 or ESRI:104903. The datum utilized must be the Jet Propulsion Laboratory (JPL) Development Ephemeris (DE) 421 in the Mean Earth (ME) Principal Axis Orientation as recommended by the International Astronomy Union (IAU) (Archinal et. al., 2008).

Please note that this dataset is not an official City of Toronto land use dataset. It was created for personal and academic use using City of Toronto Land Use Maps (2019) found on the City of Toronto Official Plan website at https://www.toronto.ca/city-government/planning-development/official-plan-guidelines/official-plan/official-plan-maps-copy, along with the City of Toronto parcel fabric (Property Boundaries) found at https://open.toronto.ca/dataset/property-boundaries/ and Statistics Canada Census Dissemination Blocks level boundary files (2016). The property boundaries used were dated November 11, 2021. Further detail about the City of Toronto's Official Plan, consolidation of the information presented in its online form, and considerations for its interpretation can be found at https://www.toronto.ca/city-government/planning-development/official-plan-guidelines/official-plan/ Data Creation Documentation and Procedures Software Used The spatial vector data were created using ArcGIS Pro 2.9.0 in December 2021. PDF File Conversions Using Adobe Acrobat Pro DC software, the following downloaded PDF map images were converted to TIF format. 9028-cp-official-plan-Map-14_LandUse_AODA.pdf 9042-cp-official-plan-Map-22_LandUse_AODA.pdf 9070-cp-official-plan-Map-20_LandUse_AODA.pdf 908a-cp-official-plan-Map-13_LandUse_AODA.pdf 978e-cp-official-plan-Map-17_LandUse_AODA.pdf 97cc-cp-official-plan-Map-15_LandUse_AODA.pdf 97d4-cp-official-plan-Map-23_LandUse_AODA.pdf 97f2-cp-official-plan-Map-19_LandUse_AODA.pdf 97fe-cp-official-plan-Map-18_LandUse_AODA.pdf 9811-cp-official-plan-Map-16_LandUse_AODA.pdf 982d-cp-official-plan-Map-21_LandUse_AODA.pdf Georeferencing and Reprojecting Data Files The original projection of the PDF maps is unknown but were most likely published using MTM Zone 10 EPSG 2019 as per many of the City of Toronto's many datasets. They could also have possibly been published in UTM Zone 17 EPSG 26917 The TIF images were georeferenced in ArcGIS Pro using this projection with very good results. The images were matched against the City of Toronto's Centreline dataset found here The resulting TIF files and their supporting spatial files include: TOLandUseMap13.tfwx TOLandUseMap13.tif TOLandUseMap13.tif.aux.xml TOLandUseMap13.tif.ovr TOLandUseMap14.tfwx TOLandUseMap14.tif TOLandUseMap14.tif.aux.xml TOLandUseMap14.tif.ovr TOLandUseMap15.tfwx TOLandUseMap15.tif TOLandUseMap15.tif.aux.xml TOLandUseMap15.tif.ovr TOLandUseMap16.tfwx TOLandUseMap16.tif TOLandUseMap16.tif.aux.xml TOLandUseMap16.tif.ovr TOLandUseMap17.tfwx TOLandUseMap17.tif TOLandUseMap17.tif.aux.xml TOLandUseMap17.tif.ovr TOLandUseMap18.tfwx TOLandUseMap18.tif TOLandUseMap18.tif.aux.xml TOLandUseMap18.tif.ovr TOLandUseMap19.tif TOLandUseMap19.tif.aux.xml TOLandUseMap19.tif.ovr TOLandUseMap20.tfwx TOLandUseMap20.tif TOLandUseMap20.tif.aux.xml TOLandUseMap20.tif.ovr TOLandUseMap21.tfwx TOLandUseMap21.tif TOLandUseMap21.tif.aux.xml TOLandUseMap21.tif.ovr TOLandUseMap22.tfwx TOLandUseMap22.tif TOLandUseMap22.tif.aux.xml TOLandUseMap22.tif.ovr TOLandUseMap23.tfwx TOLandUseMap23.tif TOLandUseMap23.tif.aux.xml TOLandUseMap23.tif.ov Ground control points were saved for all georeferenced images. The files are the following: map13.txt map14.txt map15.txt map16.txt map17.txt map18.txt map19.txt map21.txt map22.txt map23.txt The City of Toronto's Property Boundaries shapefile, "property_bnds_gcc_wgs84.zip" were unzipped and also reprojected to EPSG 26917 (UTM Zone 17) into a new shapefile, "Property_Boundaries_UTM.shp" Mosaicing Images Once georeferenced, all images were then mosaiced into one image file, "LandUseMosaic20211220v01", within the project-generated Geodatabase, "Landuse.gdb" and exported TIF, "LandUseMosaic20211220.tif" Reclassifying Images Because the original images were of low quality and the conversion to TIF made the image colours even more inconsistent, a method was required to reclassify the images so that different land use classes could be identified. Using Deep learning Objects, the images were re-classified into useful consistent colours. Deep Learning Objects and Training The resulting mosaic was then prepared for reclassification using the Label Objects for Deep Learning tool in ArcGIS Pro. A training sample, "LandUseTrainingSamples20211220", was created in the geodatabase for all land use types as follows: Neighbourhoods Insitutional Natural Areas Core Employment Areas Mixed Use Areas Apartment Neighbourhoods Parks Roads Utility Corridors Other Open Spaces General Employment Areas Regeneration Areas Lettering (not a land use type, but an image colour (black), used to label streets). By identifying the letters, it then made the reclassification and vectorization results easier to clean up of unnecessary clutter caused by the labels of streets. Reclassification Once the... Visit https://dataone.org/datasets/sha256%3A3e3f055bf6281f979484f847d0ed5eeb96143a369592149328c370fe5776742b for complete metadata about this dataset.

Crowther_Nature_Files.zip This description pertains to the original download. Details on revised (newer) versions of the datasets are listed below. When more than one version of a file exists in Figshare, the original DOI will take users to the latest version, though each version technically has its own DOI. -- Two global maps (raster files) of tree density. These maps highlight how the number of trees varies across the world. One map was generated using biome-level models of tree density, and applied at the biome scale. The other map was generated using ecoregion-level models of tree density, and applied at the ecoregion scale. For this reason, transitions between biomes or between ecoregions may be unrealistically harsh, but large-scale estimates are robust (see Crowther et al 2015 and Glick et al 2016). At the outset, this study was intended to generate reliable estimates at broad spatial scales, which inherently comes at the cost of fine-scale precision. For this reason, country-scale (or larger) estimates are generally more robust than individual pixel-level estimates. Additionally, due to data limitations, estimates for Mangroves and Tropical coniferous forest (as identified by WWF and TNC) were generated using models constructed from Topical moist broadleaf forest data and Temperate coniferous forest data, respectively. Because we used ecological analogy, the estimates for these two biomes should be considered less reliable than those of other biomes . These two maps initially appeared in Crowther et al (2015), with the biome map being featured more prominently. Explicit publication of the data is associated with Glick et al (2016). As they are produced, updated versions of these datasets, as well as alternative formats, will be made available under Additional Versions (see below).

Methods: We collected over 420,000 ground-sources estimates of tree density from around the world. We then constructed linear regression models using vegetative, climatic, topographic, and anthropogenic variables to produce forest tree density estimates for all locations globally. All modeling was done in R. Mapping was done using R and ArcGIS 10.1.

Viewing Instructions: Load the files into an appropriate geographic information system (GIS). For the original download (ArcGIS geodatabase files), load the files into ArcGIS to view or export the data to other formats. Because these datasets are large and have a unique coordinate system that is not read by many GIS, we suggest loading them into an ArcGIS dataframe whose coordinate system matches that of the data (see File Format). For GeoTiff files (see Additional Versions), load them into any compatible GIS or image management program.

Comments: The original download provides a zipped folder that contains (1) an ArcGIS File Geodatabase (.gdb) containing one raster file for each of the two global models of tree density – one based on biomes and one based on ecoregions; (2) a layer file (.lyr) for each of the global models with the symbology used for each respective model in Crowther et al (2015); and an ArcGIS Map Document (.mxd) that contains the layers and symbology for each map in the paper. The data is delivered in the Goode homolosine interrupted projected coordinate system that was used to compute biome, ecoregion, and global estimates of the number and density of trees presented in Crowther et al (2015). To obtain maps like those presented in the official publication, raster files will need to be reprojected to the Eckert III projected coordinate system. Details on subsequent revisions and alternative file formats are list below under Additional Versions.----------

Additional Versions: Crowther_Nature_Files_Revision_01.zip contains tree density predictions for small islands that are not included in the data available in the original dataset. These predictions were not taken into consideration in production of maps and figures presented in Crowther et al (2015), with the exception of the values presented in Supplemental Table 2. The file structure follows that of the original data and includes both biome- and ecoregion-level models.

Crowther_Nature_Files_Revision_01_WGS84_GeoTiff.zip contains Revision_01 of the biome-level model, but stored in WGS84 and GeoTiff format. This file was produced by reprojecting the original Goode homolosine files to WGS84 using nearest neighbor resampling in ArcMap. All areal computations presented in the manuscript were computed using the Goode homolosine projection. This means that comparable computations made with projected versions of this WGS84 data are likely to differ (substantially at greater latitudes) as a product of the resampling. Included in this .zip file are the primary .tif and its visualization support files.

References:

Crowther, T. W., Glick, H. B., Covey, K. R., Bettigole, C., Maynard, D. S., Thomas, S. M., Smith, J. R., Hintler, G., Duguid, M. C., Amatulli, G., Tuanmu, M. N., Jetz, W., Salas, C., Stam, C., Piotto, D., Tavani, R., Green, S., Bruce, G., Williams, S. J., Wiser, S. K., Huber, M. O., Hengeveld, G. M., Nabuurs, G. J., Tikhonova, E., Borchardt, P., Li, C. F., Powrie, L. W., Fischer, M., Hemp, A., Homeier, J., Cho, P., Vibrans, A. C., Umunay, P. M., Piao, S. L., Rowe, C. W., Ashton, M. S., Crane, P. R., and Bradford, M. A. 2015. Mapping tree density at a global scale. Nature, 525(7568): 201-205. DOI: http://doi.org/10.1038/nature14967Glick, H. B., Bettigole, C. B., Maynard, D. S., Covey, K. R., Smith, J. R., and Crowther, T. W. 2016. Spatially explicit models of global tree density. Scientific Data, 3(160069), doi:10.1038/sdata.2016.69.

This is an ArcGIS Server Image Service of the 4-band 2021 National Agricultural Imagery Program (NAIP) orthorectified digital aerial photos of Montana. Imagery defaults to natural color. To view the imagery as false-color infrared (CIR), select band 4 as the red image, band 1 as the green, and band 2 as the blue. This data set contains imagery from the National Agriculture Imagery Program (NAIP). These data are digital aerial photos, at 60 centimeter resolution, of the state of Montana, taken in 2021. The data are available from the State Library in two different formats. The most accessible format is a downloadable collection of compressed county mosaic (CCM) 4-Band MrSID images. These data are in UTM coordinates. The FTP folder containing these images is https://ftpgeoinfo.msl.mt.gov/Data/Spatial/MSDI/Imagery/2023_NAIP/UTM_County_Mosaics The data are available from the State Library as a collection 10,505 4-band (near infrared, red, green and blue) TIFF images in UTM coordinates. Each image is about 425 megabytes. The tiling format of the TIFF imagery is based on 3.75 x 3.75 minute quarter-quadrangles with a 300 pixel buffer on all four sides. An ESRI shapefile index showing the extent and acquisition dates of the TIF images is available at:Tile Index: https://ftpgeoinfo.msl.mt.gov/Data/Spatial/MSDI/Imagery/2023_NAIP/NAIP2023_TileIndex_shp.zipPhoto Dates: https://ftpgeoinfo.msl.mt.gov/Data/Spatial/MSDI/Imagery/2023_NAIP/NAIP2023_ImageDates_shp.zipTo order TIFF images from the State Library, select the quadrangles you want from the tiff index shapefile and send them to the Library, along with a storage device of sufficient size to hold them and return postage for the device. More information on ordering can be found at the following website https://msl.mt.gov/geoinfo/data/Aerial_Photos/Ordering

This is an ArcGIS Server Image Service of the 4-band 2021 National Agricultural Imagery Program (NAIP) orthorectified digital aerial photos of Montana. Imagery defaults to natural color. To view the imagery as false-color infrared (CIR), select band 4 as the red image, band 1 as the green, and band 2 as the blue. This data set contains imagery from the National Agriculture Imagery Program (NAIP). These data are digital aerial photos, at 60 centimeter resolution, of the state of Montana, taken in 2021. The data are available from the State Library in two different formats. The most accessible format is a downloadable collection of compressed county mosaic (CCM) 4-Band MrSID images. These data are in UTM coordinates. The FTP folder containing these images is https://ftpgeoinfo.msl.mt.gov/Data/Spatial/MSDI/Imagery/2021_NAIP/UTM_County_Mosaics The data are available from the State Library as a collection 11,776 4-band (near infrared, red, green and blue) TIFF images in UTM coordinates. Each image is about 425 megabytes. The tiling format of the TIFF imagery is based on 3.75 x 3.75 minute quarter-quadrangles with a 300 pixel buffer on all four sides. An ESRI shapefile index showing the extent and acquisition dates of the TIF images is available at https://ftpgeoinfo.msl.mt.gov/Data/Spatial/MSDI/Imagery/2021_NAIP/NAIP2021_Index_Final_UTMZone12.zip To order TIFF images from the State Library, select the quadrangles you want from the tiff index shapefile and send them to the Library, along with a storage device of sufficient size to hold them and return postage for the device. More information on ordering can be found at the following website https://msl.mt.gov/geoinfo/data/Aerial_Photos/Ordering

The High Resolution Digital Elevation Model (HRDEM) product is derived from airborne LiDAR data (mainly in the south) and satellite images in the north. The complete coverage of the Canadian territory is gradually being established. It includes a Digital Terrain Model (DTM), a Digital Surface Model (DSM) and other derived data. For DTM datasets, derived data available are slope, aspect, shaded relief, color relief and color shaded relief maps and for DSM datasets, derived data available are shaded relief, color relief and color shaded relief maps. The productive forest line is used to separate the northern and the southern parts of the country. This line is approximate and may change based on requirements. In the southern part of the country (south of the productive forest line), DTM and DSM datasets are generated from airborne LiDAR data. They are offered at a 1 m or 2 m resolution and projected to the UTM NAD83 (CSRS) coordinate system and the corresponding zones. The datasets at a 1 m resolution cover an area of 10 km x 10 km while datasets at a 2 m resolution cover an area of 20 km by 20 km. In the northern part of the country (north of the productive forest line), due to the low density of vegetation and infrastructure, only DSM datasets are generally generated. Most of these datasets have optical digital images as their source data. They are generated at a 2 m resolution using the Polar Stereographic North coordinate system referenced to WGS84 horizontal datum or UTM NAD83 (CSRS) coordinate system. Each dataset covers an area of 50 km by 50 km. For some locations in the north, DSM and DTM datasets can also be generated from airborne LiDAR data. In this case, these products will be generated with the same specifications as those generated from airborne LiDAR in the southern part of the country. The HRDEM product is referenced to the Canadian Geodetic Vertical Datum of 2013 (CGVD2013), which is now the reference standard for heights across Canada. Source data for HRDEM datasets is acquired through multiple projects with different partners. Since data is being acquired by project, there is no integration or edgematching done between projects. The tiles are aligned within each project. The product High Resolution Digital Elevation Model (HRDEM) is part of the CanElevation Series created in support to the National Elevation Data Strategy implemented by NRCan. Collaboration is a key factor to the success of the National Elevation Data Strategy. Refer to the “Supporting Document” section to access the list of the different partners including links to their respective data.

Impact craters are fundamental in planetary science, providing insights into the geological evolution of planetary surfaces and influencing landing site selection. However, many crater extraction algorithms are either not open-sourced or require complex configurations. To address these issues, we present the Automatic Crater Detection (ACD) Tool, a user-friendly software designed to support scientists, even those without a computational background.Key FeaturesThe ACD Tool utilizes the YOLOv8 deep learning algorithm, enhanced by transfer learning and active learning techniques. This integration enables the detection of craters with a minimum diameter of 10 pixels on terrestrial planets. The tool has undergone rigorous testing on datasets from the Moon, Mars, and Mercury, achieving F1 scores of up to 95%, making it reliable for tasks like surface age estimation and landing site analysis.User Interface and OperationThe interface of the ACD tool is divided into two parts: the left side serves as the input and control panel, while the right side functions as the display area. Users can select among three celestial bodies – Mars, Moon, or Mercury – by clicking on the respective options. The software offers two input modes. In "single image" mode, users directly enter the image paths. In "batch" mode, the program processes all TIFF files within a specified folder. Input images should be GeoTIFF files in Mercator projection to ensure circular shapes for impact craters.The automatic crater detection process begins with a click of the “Start” button.The right-hand viewing interface shows a preview image displaying the crater detection results. In batch mode, the “Previous Image” and “Next Image” buttons allow sequential preview of all images and detection results.Data and OutputInput images should be in GeoTIFF format with Mercator projection centered on the equator to ensure accurate detection.Output results are saved in the specified output path in both shapefile and TXT file formats. The attribute table of the shapefile includes the crater central latitude and longitude (‘Lon’ and ‘Lat’) and the geodesic diameter in meters (‘Dia’). The projection distortion caused by projection has been considered and corrected through computation within the tool. As the craters are extracted in Mercator projection, the corresponding information is provided to better correspond with the images: ‘X_Merc’ and ‘Y_Merc’ for the x and y coordinates in Mercator and ‘D_Merc’ for the distorted diameter measured in the Mercator projection. The confidence score (‘Score’), ranging from 0 to 1, is also provided; higher values indicate greater confidence in the crater detection results. Additionally, a TXT file containing information identical to the shapefile attributes is generated. If an error occurs during the detection process, the names of the erroneous images and the reasons are recorded in an Error.txt file.VersionThe ACD Tool runs on Windows and offers both CPU and GPU versions, allowing users to choose the version that best fits their needs. For faster performance, the GPU version is recommended if a graphics card is available. Otherwise, the CPU version is suitable for systems without a dedicated graphics card.DatasetsOur dataset includes annotated craters on the Moon, Mars, and Mercury. The initial annotations were sourced from publicly available online databases and were further supplemented with manual annotations. For the Moon, LROC WAC images (Speyerer, 2011; Wagner, 2015) and SELENE TC images (Kato et al., 2008) were annotated based on the studies of Robbins (2019) and Wang & Wu (2019), respectively, while LROC NAC images were fully manually annotated. For Mars, crater annotations for the THEMIS-IR mosaic (Edwards et al., 2011) followed the work of Robbins & Hynek (2012), and additional manual annotations were applied to CTX (Malin et al., 2007) and HiRISE (McEwen et al., 2007) images. For Mercury, the MDIS global mosaic (Becker et al., 2009; Hawkins III et al., 2009) underwent complete manual annotation and verification.The dataset includes craters of various sizes and resolutions. On the Moon, the LROC WAC dataset has a resolution of 150 m/pixel, covering 186,302 craters with diameters ranging from 1,000 to 46,692 meters. The LROC NAC dataset, with a resolution of 1.1 m/pixel, includes 34,012 craters with diameters between 7 and 307 meters. The SELENE TC dataset has a resolution of 6.2 m/pixel and includes 54,563 craters with diameters between 120 and 1,949 meters. For Mars, the THEMIS-IR dataset has a resolution of 150 m/pixel and includes 121,517 craters with diameters ranging from 994 to 47,322 meters. The CTX dataset has a resolution of 5 m/pixel and includes 6,678 craters with diameters between 21 and 2,467 meters. The HiRISE dataset, with a resolution of 0.5 to 1 m/pixel, includes 5,978 craters with diameters ranging from 2 to 144 meters. For Mercury, the MDIS dataset has a resolution of 166 m/pixel and includes 17,749 manually annotated craters with diameters ranging from 996 to 26,347 meters.The dataset is organized by body, stored in YOLO annotation format, and located in the respective subfolders within the “Datasets” directory. Each body folder contains “images” and “labels” folders, which store crater images and position annotations, respectively. Within each folder, data is further divided into “train” and “val” subfolders for training and validation.

This data repository contains a set of multi-temporal data products of ecosystem structure derived from four national ALS surveys of the Netherlands (AHN1–AHN4) (folder: 1. Data_products). Four sets of 25 LiDAR-derived vegetation metrics representing ecosystem height, cover, and structural variability are provided at 10 m spatial resolution, providing valuable data sources for a wide range of ecological research and field beyond. All 25 LiDAR metrics were calculated using Laserfarm workflow (https://laserfarm.readthedocs.io/en/latest/) (building on the user-extendable features from the “Laserchicken” software: https://laserchicken.readthedocs.io/en/latest/#features). All metrics are calculated with the normalized point cloud. More details on metric calculation are provided on GitHub (Laserchicken: https://github.com/eEcoLiDAR/laserchicken and Laserfarm: https://github.com/eEcoLiDAR/Laserfarm), as well as on the “Laserchicken” documentation page (https://laserchicken.readthedocs.io/en/latest/). We also provided masks to minimize the influence of water surfaces, buildings and roads, as well as powerlines in the data products (folder: 2. Masks). Since the point density of each AHN dataset has changed significantly which may have influence on generated LiDAR metrics, we also provided the four raster layers of point density (all points) for each AHN dataset (folder: 3. Point_density). Two use cases demonstrated the utility of the presented data products: (use case 1) monitoring forest structural change across time using multi-temporal ALS data and (use case 2) comparison of vegetation structural difference within Natura 2000 sites. The used data are also provided (folder: 4. Use_case). Note that all the raster layers and shapefiles provided in this repository are under the local Dutch coordinate system “RD_new” (EPSG: 28992, NAP:5709).

Three subfolders are included:

1. Data_products

· AHN1.zip

· AHN2.zip

· AHN3.zip

· AHN4.zip

· Maps

It contains four folders with 25 LiDAR metrics at 10 m resolution generated from each AHN dataset. The file names and their corresponding LiDAR metrics can be found in Table 1. An additional folder (Maps) contains the maps (.pdf format) of all 25 metrics for each AHN dataset.

1. Masks

· ahn3_10m_mask_building_road_water.tif

· ahn4_10m_mask_building_road_water.tif

· ahn4_10m_mask_powerline.tif

It contains two mask layers of water surfaces, buildings and roads for both AHN3 and AHN4 data products based on the Dutch cadaster data (TOP10NL) from 2018 (corresponding to AHN3) and 2021 (corresponding to AHN4) (https://www.kadaster.nl/zakelijk/producten/geo-informatie/topnl). In the masks, water surfaces, buildings and roads were merged into one class with pixel value assigned to 1 and the rest has the pixel value of 0. There is also a powerline mask generated from the AHN4 dataset at 10 m resolution, where pixels containing powerlines were assigned a value of 1 and the rest as NoData. We provide those masks to minimize the inaccuracies of the data products caused by human infrastructures and water surfaces.

1. Point_density

· ahn1_10m_point_density.tif

· ahn2_10m_point_density.tif

· ahn3_10m_point_density.tif

· ahn4_10m_point_density.tif

It contains four raster layers (at 10 m resolution) representing the point density of each AHN dataset.

1. Use_case

2. Multi-temporal_AHN

· Data

· Usecase_multi-temporal_AHN.R

It contains the input data for the use case data processing (i.e. Data folder), including the shapefile of the area (i.e. shp folder), and extracted pixel value from six selected LiDAR metrics from AHN1–AHN5 (i.e. Metrics folder), and the selected LiDAR metrics of the area (e.g. Hp95 folder), and the R code for data processing (i.e. Usecase_multi-temporal_AHN.R).

1. Natura2000

· Data

· Natura2000_end2021_HABITATCLASS.csv

· Natura2000_NL_habitat_grouped.csv

· Usecase_Natura2000.R

It contains a folder of the input data used for the use case (i.e. Data folder), including the shapefile (i.e. shp folder) of the Natura 2000 sites in the Netherlands (i.e. Nature2000_NL_RDnew.shp) and the 100 random sample plots from each habitat type (e.g. woodland_points.shp), and the LiDAR metrics from AHN4 used for demonstrating the vegetation structure within each habitat type (i.e. AHN4_metrics folder). The table “Natura2000_end2021_HABITATCLASS.csv” is the original attribute table of Natura 2000 sites, including information related to the description of habitat classes (column “DESCRIPTION”), the code corresponding to the habitat class (column “HABITATCODE”), the code for the specific site (column “SITECODE”), and the percentage of the cover of a specific habitat class in one site (column “PERCENTAGECOVER”). The table “Natura2000_NL_habitat_grouped.csv” contains two subtabs, one (i.e. “Habitatclass”) is the copy of the original attribute table of Natura 2000 sites in the Netherlands, and the other one (i.e. “Habitat_class_summary”) is the grouped habitat type based on the dominant habitat class (i.e. class with the highest percentage cover) in each site. Different colors indicate different habitat types, corresponding to the colors in the first tab (“Habitatclass”) where the dominant habitat class was highlighted for each site.

Code availability

Jupyter Notebooks for processing AHN datasets:

https://github.com/ShiYifang/AHN

Laserfarm workflow repository:

https://github.com/eEcoLiDAR/Laserfarm

Laserchicken software repository:

https://github.com/eEcoLiDAR/laserchicken

Code for downloading AHN dataset: https://github.com/ShiYifang/AHN/tree/main/AHN_downloading

Code for generating masks for AHN datasets: https://github.com/ShiYifang/AHN/tree/main/AHN_masks

Code for demonstration of ecological use cases: https://github.com/ShiYifang/AHN/tree/main/Use_case

The Global Land Surface Water Dataset in 30m Resolution in 2010 (GlobeLand30-WTR2010 for short) was developed based on data mining methodology by integrating and analyzing the 9907 scenes of the USA Landsat TM5, ETM+ data and 2640 scenes of the China environment disaster mitigation satellite (HJ-1) data in 2010（±1）. The total area of the land surface water is 3,675,400 km2, which is 2.73% of the global land surface area. More than 40% of land surface water is located in North America. The global data were organized into 853 tiles, according to the 5° (latitude) x 6° (longitude) within the region from 60°S to 60 N, and 5° (latitude) x 12° (longitude) within the region from 60° N to 80°N (the Antarctic continent is not included). The data tiles are combined into 5 compressed data groups (Asia, Europe, North America, South America, and Africa, and Oceanic Countries), Four different data files are comprised in each of these data groups. They are: (1) land surface water data (raster data with GeoTIFF format); (2) coordinate information data (TIFF WORD format); (3) areas of selected remote sensing data (.shp format); and (4) a metadata file (XML format). In addition, the 853 data file list, including the file names, corresponding geographic coordinates and zoning codes, are listed at the file. The dataset is one of the layers of the Global Land Cover Dataset in 30m Resolution in 2010 (GlobeLand30_2010), which were donated to the United Nations by China in September 2014.

Data citation: CHEN Jun et al. : Global Land Surface Water Dataset in 30m Resolution (2010) ( GlobeLand30-WTR2010 ) ,Global Change Research Data Publishing & Repository,DOI:10.3974/geodb.2014.02.01.V1, http://www.geodoi.ac.cn/WebEn/doi.aspx?DOI=10.3974/geodb.2014.02.01.V1

Available at: http://www.geodoi.ac.cn/WebEn/doi.aspx?Id=159

LAND_COVER_2011_USGS_IN is a grid (30-meter cell size) showing 2011 Land Cover data in Indiana. This grid is a subset of the National Land Cover Data (NLCD 2011) data set. There are 15 categories of land use shown in this data set when the associated layer file (LAND_COVER_2011_USGS_IN.LYR) is loaded. The following is excerpted from metadata provided by the USGS for the NLCD 2011: "The National Land Cover Database products are created through a cooperative project conducted by the Multi-Resolution Land Characteristics (MRLC) Consortium. The MRLC Consortium is a partnership of Federal agencies (www.mrlc.gov), consisting of the U.S. Geological Survey (USGS), the National Oceanic and Atmospheric Administration (NOAA), the U.S. Environmental Protection Agency (EPA), the U.S. Department of Agriculture - Forest Service (USDA-FS), the National Park Service (NPS), the U.S. Fish and Wildlife Service (USFWS), the Bureau of Land Management (BLM) and the USDA Natural Resources Conservation Service (NRCS). The success of NLCD over nearly two decades is credited to the continuing collaborative spirit of the agencies that make up the MRLC. NLCD 2011 is the most up-to-date iteration of the National Land Cover Database, the definitive Landsat-based, 30-meter resolution land cover database for the Nation. The data in NLCD 2011 are completely integrated with NLCD 2001 (2011 Edition, amended 2014) and NLCD 2006 (2011 Edition, amended 2014). For NLCD 2011, there are 5 primary data products: 1) NLCD 2011 Land Cover 2) NLCD 2006/2011 Land Cover Change Pixels labeled with the 2011 land cover class 3) NLCD 2011 Percent Developed Imperviousness 4) NLCD 2006/2011 Percent Developed Imperviousness Change Pixels 5) NLCD 2011 Tree Canopy Cover provided by an MRLC partner - the USDA Forest Service Remote Sensing Applications Center. In addition, ancillary metadata includes the NLCD 2011 Path/Row Index shapefile showing the footprint of Landsat scenes and change analysis pairs used to derive 2006/2011 spectral change. All Landsat scene acquisition dates are included in the shapefile's attribute table. As part of the NLCD 2011 project, NLCD 2001 and 2006 land cover and impervious data products were revised and reissued (2011 Edition, amended 2014) to provide full compatibility with the new NLCD 2011 products. The 2014 amended version corrects for the over-elimination of small areas of the four developed classes. NLCD Tree Canopy Cover was created using MRLC mapping zones from NLCD 2001 (see Tree Canopy Cover metadata for additional detail). All other NLCD 2011 products were created on a path/row basis and mosaicked to create a seamless national product. Questions about the NLCD 2011 land cover product can be directed to the NLCD 2011 land cover mapping team at the USGS/EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov."

Tiff files: Maps of Above Ground Biomass change (2019-2020) over the study region near Iñapari, Peru, derived from the texture of the NIR band for SPOT-7 (SPOT_DeltaAGB_Map), PlanetScope (PlanetScope_DeltaAGB_Map.tif) and Sentinel-2 (Sentinel2_DeltaAGB_Map.tif) data for a 1-ha resolution.QML file contains the style for the biomass change maps. Shapefile contains location of four selectively logged plots.CSV file contains data on observed changes in these four plots, obtained by TLS and manual inventory.

Abstract:This image database is comprised of digital orthophotography that was flown in June and July of 2005. The orthos have been registered to the terrain at National Map Accuracy standards. The images are stored in tif format. This database calls the path of this imagery and displays it according to the parameters of XMIN, YMIN, XMAX and YMAX. The following specs were used for this ortho flight: Flown: 2005 June and July Flight Height: 9600 feet Focal Length: 12" Pixel Size: 1/2 ft Terrain Model: Pierce County Lidar The construction of the images required the mosaicking of photo images using OrthoVista software which also corrected for color balancing. The image database has been licensed to the County by Mapcon Mapping (or OSI Geomatics). The license agreement only allows certain organizations to directly access the data. Any of the data can be produced in paper copy and distributed to the public. Digital copies of the data are not permitted. Users should note the scale of photography (1:800) and use the data appropriately. Please also note that the orthos have a better horizontal accuracy than some of the current GIS data in County View. The vertical datum for this data is North American Vertical Datum of 1988. If you are using NAD 29 as your vertical datum the elevations are going to be 3.5 feet too high and you will need to lower the elevation by 3.5ftPurpose:The orthophoto imagery serves as a basemap to inform decisions county-wide. The data is not owned by Pierce County and should be acquired from the vendor whose information can be found in the Use Limitation section of this metadata documentation.Supplemental Information:Procedures_Used In 2005 the color photography was captured using a 12" focal length. The photography was flown at an altitude of 9600 feet above mean terrain. Revisions There will be updates to the entire photo area every three years. Reviews_Applied_to_Data The photographic quality of the data is reviewed as deliveries are made. Even with the low flying heights for the photos, some features will be obscured by trees, tree lean, shadow, and building lean. Related_Spatial_and_Tabular_Data_Sets The 2005 Orthophoto Area theme, a polygon-based ArcView shapefile found in CountyView, shows the areas in which there are orthophotos. As additional deliveries of orthophotos arrive, this shapefile will be updated. References_Cited MAPCON MAPPING (OR OSI GEOMATICS), Photos flown between 6/20 - 7/28/2005. Notes The Orthophotos are stored in tiff and Mr Sid formats. The Assessor-Treasurer has requested that the tax parcel data that has been registered to Orthophotography be used to over-lay over the Orthophotos due to horizontal accuracy issues. The 2005 Orthos are now loaded in SDE. The layer is "Orthophotos.DBO.Msc_Cnty_2005" and can be accessed like any other layer in the SDE Orthophotos database. There is an area around Longbranch that is damaged, however, and so will appear as whitespace of different sizes depending on the zoom level. Per the experts at ESRI, the only way to repair this is a reload--which I will start doing immediately. This should take approximately a week based on how long the last one required. In the mean time, except for the small damaged area, the rest should be usable in its present form. It has also been noticed that an area over large parts of Tacoma that do not display when the scale is between 1:855 and 1:1733. There are probably other areas that have similarly damaged pyramids that we haven't found yet. In other words, use at your own risk. Making them available to everyone in the County is not recommended at this point.

LiDAR scanning of the Yukon Coast and Herschel Island took place during the AIRMETH (AIRborne studies of METHane emissions from Arctic wetlands) campaigns (Kohnert et al., 2014) on 10 July 2012 and on 22 July 2013. Point cloud data were acquired with a RIEGL LMSVQ580 laser scanner instrument on board the Alfred Wegener Institute's POLAR-5 science aircraft. The laser scanner was operated with a 60° scan angle at a flight height of around 200 m above ground in 2012 and 500 m in 2013. This resulted in a scan width from 200 (2012) to 500 m (2013) and a mean point-to-point distance of 0.5-1.0 m. During the flight on July 10, 2012 the weather was cloudy with a cloud base around 200 m.a.s.l. . Air temperature ranged between 10 and 12 °C with wind speed ranging from 15 to 19 km/h from easterly direction (70-90°). The last recorded storm was on June 17. During the scanning on July 22, 2013, the weather was nearly cloudless with air temperature 9 °C. Wind speed was 15 km/h from easterly direction (60-80°). The last storm before the acquisition occurred on July 2. Raw laser data were calibrated, combined with the post-processed GPS trajectory, corrected for altitude, and referenced to the EGM (Earth Gravitational Model) 2008 geoid (Pavlis et al., 2008). The final georeferenced point cloud data accuracy was determined to be better than 0.15 ± 0.1 m. The loss of accuracy varied along the flight track because of the vertical accuracy of the post-processed GPS trajectory. The GPS datawere acquired in 50Hz resolutionwith aNovatel OEM4 receiver on board POLAR-5. The GPS trajectory was post-processed using precise ephemerides and the commercial software package Waypoint 8.5 (PPP [precise point positioning] processing). For the interpolation to the final DEM an inverse distance weighting (IDW) algorithm was applied using all cloud points within a 10 m radius of each point. Finally, the DEMs from the different acquisition years were interpolated toraster grids of 1 m horizontal resolution in NAD83 UTM zone 7 coordinate system. To quantify vertical change that is significant at the 99% confidence interval, we used three times RMS error procedure by Jaw (2001). Vertical accuracies for both datasets were estimated to be 0.15 m, which results in the threshold of 0.64 m for significant vertical elevation change. The accuracy of the datasets was additionally tested at locations characterized by the presence of anthropogenic features that presumably remain stable and are not affected by vertical movements because of artificial embankments underneath them. The differences between both DEM datasets were assessed along profiles and were within the previously-stated 0.15 m uncertainty.

IMPERVIOUS_SURFACE_2011_USGS_IN is a grid (30-meter cell size) showing estimated percentages of impervious surfaces in Indiana in 2011.This grid is a subset of the National Land Cover Database (NLCD 2011) suite of data products. The attributes are percentage values of estimated impervious-surface cover within each 30-meter grid cell, or pixel. The following is excerpted from metadata provided by the USGS for the NLCD 2011: "The National Land Cover Database products are created through a cooperative project conducted by the Multi-Resolution Land Characteristics (MRLC) Consortium. The MRLC Consortium is a partnership of Federal agencies (www.mrlc.gov), consisting of the U.S. Geological Survey (USGS), the National Oceanic and Atmospheric Administration (NOAA), the U.S. Environmental Protection Agency (EPA), the U.S. Department of Agriculture - Forest Service (USDA-FS), the National Park Service (NPS), the U.S. Fish and Wildlife Service (USFWS), the Bureau of Land Management (BLM) and the USDA Natural Resources Conservation Service (NRCS). The success of NLCD over nearly two decades is credited to the continuing collaborative spirit of the agencies that make up the MRLC. NLCD 2011 is the most up-to-date iteration of the National Land Cover Database, the definitive Landsat-based, 30-meter resolution land cover database for the Nation. The data in NLCD 2011 are completely integrated with NLCD 2001 (2011 Edition, amended 2014) and NLCD 2006 (2011 Edition, amended 2014). For NLCD 2011, there are 5 primary data products: 1) NLCD 2011 Land Cover 2) NLCD 2006/2011 Land Cover Change Pixels labeled with the 2011 land cover class 3) NLCD 2011 Percent Developed Imperviousness 4) NLCD 2006/2011 Percent Developed Imperviousness Change Pixels 5) NLCD 2011 Tree Canopy Cover provided by an MRLC partner - the USDA Forest Service Remote Sensing Applications Center. In addition, ancillary metadata includes the NLCD 2011 Path/Row Index shapefile showing the footprint of Landsat scenes and change analysis pairs used to derive 2006/2011 spectral change. All Landsat scene acquisition dates are included in the shapefile's attribute table. As part of the NLCD 2011 project, NLCD 2001 and 2006 land cover and impervious data products were revised and reissued (2011 Edition, amended 2014) to provide full compatibility with the new NLCD 2011 products. The 2014 amended version corrects for the over-elimination of small areas of the four developed classes. NLCD Tree Canopy Cover was created using MRLC mapping zones from NLCD 2001 (see Tree Canopy Cover metadata for additional detail). All other NLCD 2011 products were created on a path/row basis and mosaicked to create a seamless national product. Questions about the NLCD 2011 land cover product can be directed to the NLCD 2011 land cover mapping team at the USGS/EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov."

This is an ArcGIS Server Image Service of the 4-band 2021 National Agricultural Imagery Program (NAIP) orthorectified digital aerial photos of Montana. Imagery defaults to natural color. To view the imagery as false-color infrared (CIR), select band 4 as the red image, band 1 as the green, and band 2 as the blue. This data set contains imagery from the National Agriculture Imagery Program (NAIP). These data are digital aerial photos, at 60 centimeter resolution, of most of the state of Montana, taken in 2017. Due to cloud cover, wildfire smoke, and snow cover the imagery acquisition was not completed in 2017 and some areas were acquired in 2018. The data are available from the State Library in two different formats. The most accessible format is a downloadable collection of compressed county mosaic (CCM) natural color MrSID images. These data are in UTM coordinates. The FTP folder containing these images is https://ftpgeoinfo.msl.mt.gov/Data/Spatial/MSDI/Imagery/2017_NAIP/UTM_County_Mosaics. The data are available from the State Library as a collection 11,384 4-band (near infrared, red, green and blue) TIF images in UTM coordinates. Each image is about 400 megabytes. The tiling format of the TIFF imagery is based on 3.75 x 3.75 minute quarter-quadrangles with a 300 pixel buffer on all four sides. An ESRI shapefile index showing the extent and acquisition dates of the TIF images is available at https://ftpgeoinfo.msl.mt.gov/Data/Spatial/MSDI/Imagery/2017_NAIP/NAIP_2017_Index_Montana.zip. To order TIFF images from the State Library, select the quadrangles you want from the tiff index shapefile and send them to the Library, along with a storage device of sufficient size to hold them and return postage for the device.

Die Digitale Stadtgrundkarte im Maßstab 1:1.000 dient als Kartengrundlage zur Erfassung zahlreicher Fachdaten. Sie beinhaltet Objekte zu Gebäuden einschl. deren Ausgestaltung, Siedlungsflächen, Stadttopographie, Vegetation, Verkehr, Gewässer und Relief. Kartenauszüge werden digital in den Dateiformaten PDF, JPG und TIFF(GeoTIFF) angeboten. Datenauszüge gibt es als CAD-Features in den Formaten DXF und DWG. Mit Semantik als GIS-Features sind Datenauszüge in den Formaten Shape File, File Geodatabase oder GeoPackage möglich.