Is an auxiliary table for the Geo Prop feature class to illustrate detailed information about the property. It lists all streets and corresponding house number range under each property. There is a Folio field to relate to GeoProp feature class.Updated: Weekly-Sat The data was created using: Projected Coordinate System: WGS_1984_Web_Mercator_Auxiliary_SphereProjection: Mercator_Auxiliary_Sphere

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.

Abstract

The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

Approved boundaries of the bioregions and subregions (version 5) for defining the reporting regions for bioregional assessments of impacts of coal seam gas and coal mining development on water resources.

This is identical to Bioregional_Assessment_areas_v04 except that the attribute tables include areas (both sq. km and ha) based on the GDA 1994 Australia Albers projection. A spreadsheet version of the attribute table is also provided for the benefit of non ArcGIS users.

Purpose

Provides authoritative boundaries for defining bioregions and subregions to be reported on for the Bioregional Assessments and tabulation of Bioregion and subregion areas.

Dataset History

This dataset contains two spatial shapefiles: "ba_bioregion_alb_gda94_v05.shp" and "ba_subregion_alb_gda94_v05.shp".

The shapefiles are copies of the previous versions (Bioregional Assessment Areas v04), with the following changes.

Two fields have been added to each of the shapefiles' attribute tables.

"albers_km2" and "albers_ha" which list the the bioregion/subregion areas in square kilometres and hectares respectively.

The polygonal areas are calculated in ArcGIS and are based on the GDA_1994_Australia_Albers projection. Parameters as follows:

Projected Coordinate System:\tGDA_1994_Australia_Albers

Projection:\tAlbers

False_Easting:\t0.00000000

False_Northing:\t0.00000000

Central_Meridian:\t132.00000000

Standard_Parallel_1:\t-18.00000000

Standard_Parallel_2:\t-36.00000000

Latitude_Of_Origin:\t0.00000000

Linear Unit: \tMeter

Geographic Coordinate System:\tGCS_GDA_1994

Datum: \tD_GDA_1994

Prime Meridian: \tGreenwich

Angular Unit: \tDegree

It should be noted that area calculations using a different projection (eg UTM or MGA) may yield slightly different areas to those published in this dataset.

Version 5 of this dataset also includes Excel spreadsheet versions of each shapefiles' attrubute table, to enable non ArcGIS users to access the bioregion/subregion area information.

Dataset Citation

Bioregional Assessment Programme (2014) Bioregional Assessment areas v05. Bioregional Assessment Derived Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/25f89049-839d-4736-bd81-97d8e8a40f8e.

Dataset Ancestors

• Derived From Gippsland Project boundary

• Derived From Bioregional Assessment areas v04

• Derived From Natural Resource Management (NRM) Regions 2010

• Derived From Bioregional Assessment areas v03

• Derived From GEODATA TOPO 250K Series 3

• Derived From Bioregional Assessment areas v01

• Derived From Bioregional Assessment areas v02

• Derived From NSW Catchment Management Authority Boundaries 20130917

• Derived From Victoria - Seamless Geology 2014

• Derived From Geological Provinces - Full Extent

• Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb)

This intersection points feature class represents current intersections in the City of Los Angeles. Few intersection points, named pseudo nodes, are used to split the street centerline at a point that is not a true intersection at the ground level. The Mapping and Land Records Division of the Bureau of Engineering, Department of Public Works provides the most current geographic information of the public right of way. The right of way information is available on NavigateLA, a website hosted by the Bureau of Engineering, Department of Public Works.Intersection layer was created in geographical information systems (GIS) software to display intersection points. Intersection points are placed where street line features join or cross each other and where freeway off- and on-ramp line features join street line features. The intersection points layer is a feature class in the LACityCenterlineData.gdb Geodatabase dataset. The layer consists of spatial data as a point feature class and attribute data for the features. The intersection points relates to the intersection attribute table, which contains data describing the limits of the street segment, by the CL_NODE_ID field. The layer shows the location of the intersection points on map products and web mapping applications, and the Department of Transportation, LADOT, uses the intersection points in their GIS system. The intersection attributes are used in the Intersection search function on BOE's web mapping application NavigateLA. The intersection spatial data and related attribute data are maintained in the Intersection layer using Street Centerline Editing application. The City of Los Angeles Municipal code states, all public right-of-ways (roads, alleys, etc) are streets, thus all of them have intersections. List of Fields:Y: This field captures the georeferenced location along the vertical plane of the point in the data layer that is projected in Stateplane Coordinate System NAD83. For example, Y = in the record of a point, while the X = .CL_NODE_ID: This field value is entered as new point features are added to the edit layer, during Street Centerline application editing process. The values are assigned automatically and consecutively by the ArcGIS software first to the street centerline spatial data layer, then the intersections point spatial data layer, and then the intersections point attribute data during the creation of new intersection points. Each intersection identification number is a unique value. The value relates to the street centerline layer attributes, to the INT_ID_FROM and INT_ID_TO fields. One or more street centerline features intersect the intersection point feature. For example, if a street centerline segment ends at a cul-de-sac, then the point feature intersects only one street centerline segment.X: This field captures the georeferenced location along the horizontal plane of the point in the data layer that is projected in Stateplane Coordinate System NAD83. For example, X = in the record of a point, while the Y = .ASSETID: User-defined feature autonumber.USER_ID: The name of the user carrying out the edits.SHAPE: Feature geometry.LST_MODF_DT: Last modification date of the polygon feature.LAT: This field captures the Latitude in deciaml degrees units of the point in the data layer that is projected in Geographic Coordinate System GCS_North_American_1983.OBJECTID: Internal feature number.CRTN_DT: Creation date of the polygon feature.TYPE: This field captures a value for intersection point features that are psuedo nodes or outside of the City. A pseudo node, or point, does not signify a true intersection of two or more different street centerline features. The point is there to split the line feature into two segments. A pseudo node may be needed if for example, the Bureau of Street Services (BSS) has assigned different SECT_ID values for those segments. Values: • S - Feature is a pseudo node and not a true intersection. • null - Feature is an intersection point. • O - Intersection point is outside of the City of LA boundary.LON: This field captures the Longitude in deciaml degrees units of the point in the data layer that is projected in Geographic Coordinate System GCS_North_American_1983.

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.

The product High Resolution Digital Elevation Model (HRDEM) is part of the CanElevation Series created in support to the National Elevation Strategy implemented by NRCan. 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 generated. Most of these datasets have optical imagery as their source data. They are generated at a 5 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.

The HRDEM product is referenced to the Canadian Geodetic Vertical Datum of 2013 (CGVD2013), which is now the reference standard for heights across Canada.

Complete coverage of the Canadian landmass is gradually implemented. HRDEM datasets are processed and made available as the data is acquired.

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.

Collaboration is a key factor to the success of the National Elevation Strategy. Refer to the “Supporting Document” section to access the list of the different partners including links to their respective data.

A point feature class of Community Based Organizations (CBO)'s funded through Miami-Dade County general obligation funds during 2017. This point feature class contains a list of Community Based Organizations (CBOs) funded through Miami-Dade County General Obligation Funds during the fiscal year. CBOs are based on a set of social service priorities. The CBOs are non-profit organizations or government entities contracted by the County to serve serving the community of Miami-Dade. In 2008 when the Board of County Commissioners created the Office of Grants Coordination, responsibility for CBO Funding allocations was returned to the County, under the oversight of the CBO Advisory Board.Updated: Not Planned The data was created using: Projected Coordinate System: WGS_1984_Web_Mercator_Auxiliary_SphereProjection: Mercator_Auxiliary_Sphere

This child data release provides the information needed to download from the USGS EarthExplorer portal digital orthophotos acquired along the Tanana and Nenana Rivers near Nenana, Alaska, on August 19, 2021. The primary goal of this study was to assess the feasibility of estimating surface flow velocities from optical image sequences acquired from a fixed-wing aircraft flying along the river by tracking water surface features via particle image velocimetry (PIV). Remote sensing of flow velocities could provide a more efficient, cost-effective alternative to conventional field-based methods of measuring channel hydraulics and thus become an important component of non-contact approaches to streamgaging. Moreover, the ability to collect such data from a moving aircraft opens up the possibility of examining longer river reaches via remote sensing. The USGS collaborated with the US Fish and Wildlife Service (USFWS) to acquire digital orthophotography of the Tanana and Nenana Rivers near Nenana, AK, spanning reaches of approximately 4.2 km along the Tanana and 2.7 km along the Nenana. Data were collected at three different flying heights above ground level: 600 m, 902 m, and 1204 m, resulting in 10, 6, and 4 separate flight lines, respectively. The data from each flying height were nominally referred to as "2K," "3K," and "4k," respectively, for the nominal flying height in feet (2,000, 3,000, or 4,000 feet above ground level). Images were captured using two different systems: a Nikon digital single lens reflex (DSLR) camera that provided standard red-green-blue (RGB) color images and a Lucint multi-camera array that also included a near-infrared band. A GPS point and time stamp associated with each image, along with information on the camera characteristics and a DEM of the study area, were then used as input to the Simactive Correlator 3D photogrammetric software package to produce digital orthophotos in the NAD83_2011 UTM zone 6N projected coordinate system. The pixel sizes of these output images varied among flying heights and between the two imaging systems and ranged from 0.04635 m to 0.1 m. The deliverable products available through this data release include all of the individual orthophotos for each flying height and imaging system as TIFF files with associated .tfw world files. The data set delivered by the USFWS was transferred to the USGS Earth Resources Observation and Science (EROS) Center for archiving and distribution via the EarthExplorer web portal at https://earthexplorer.usgs.gov. EROS also produced metadata describing the orthophotos in the files ak21riverorthosmetadata.docx and EROSmetadata.csv. The orthophotos can can be obtained by visiting the EarthExplorer web site at https://earthexplorer.usgs.gov and using the Entity ID field in the EROSmetadata.csv file. On the EarthExplorer home page, go to the second tab of the panel on the left, labeled Data Sets, select Aerial Imagery/High Resolution Orthoimagery, and click on Additional Criteria at the bottom. On the Additional Criteria tab, click the plus symbol next to Entity ID, enter the Entity ID value from the EROSmetadata.csv file for the orthophoto of interest, and click on Results at the bottom. The image should then appear in the results tab with several options represented by icons to show the footprint, overlay a browse image, or show the metadata and browse in a separate window. To download the data, click on the fifth icon from the left, which features a green download arrow pointing toward a disk drive, and click Download on the resulting pop-up to begin downloading a zip file. This zip archive contains a number of files in two subfolders. For example, for one of the photos form the Lucint 2K flight line, we have: 1) 4080000_rgb_17pe87_rgb_17pe87_20210819t182335z_00000123.zip\ AK\2021\202108_tanana_ak_lucint_2k_5bnd_5cm_utm6_multi\index001 contains a list of all the images in the data set as a text file, a Word document with general metadata about the entire dataset, an Excel file with spatial information (bounding box) for each orthophoto in the data set, and a .prj file with information on the projection (NAD83_2011 UTM Zone 6N). 2) 4080000_rgb_17pe87_rgb_17pe87_20210819t182335z_00000123.zip\ AK\2021\202108_tanana_ak_lucint_2k_5bnd_5cm_utm6_multi\vol001 has the actual image as a tif (like rgb_17pe87_rgb_17pe87_20210819t182335z_00000123.tif) and corresponding *.tfw world file (like rgb_17pe87_rgb_17pe87_20210819t182335z_00000123.tfw). Note that vol001 indicates that the images are from the flight line 1, vol002 for flight line 2, and so on. These files can be opened and viewed in GIS or image processing software.

A polygon feature class of open DERM Contaminated sites - see phase code for status of site. Contaminated sites identifies properties where environmental contamination has been documented in the soil or groundwater. Facilities get listed as a contaminated site by a DERM inspector who finds a violation on the property. Facilities that store potential contaminated materials are permitted and/or tracked by DERM. A site is removed from the active contaminated sites layer/list when the sites is found by DERM to be cleaned up.Updated: Monthly The data was created using: Projected Coordinate System: WGS_1984_Web_Mercator_Auxiliary_SphereProjection: Mercator_Auxiliary_Sphere

The DLG quadrangles were converted into Arc/INFO coverages and projected into the Massachusetts State Plane Coordinate System. The long list of items (MAJOR1, MINOR1, MAJOR2, MINOR2...) was then concatenated to a more simplified coding system. For each feature MINORn was truncated to three characters and linked to the other minor codes to create MINOR_TOT. For example, a submerged (612) wetland (111) is now coded MINOR_TOT = 612111. The original MAJORn, MINORn pairs are no longer part of the attribute tables.Quadrangles covering Nantucket and Martha's Vineyard were completely digitized from the 1:25,000 USGS quadrangles. Though not as thoroughly coded as the 1:25,000 DLGs, the linework is all at 1:25,000.The scanned quadrangles were automated in-house by scanning USGS mylar separates at 500 dots per inch. The resulting images were vectorized in GRID and then edited in ARCEDIT. Features missing from the blue line separate (i.e. dams or man-made shore) were digitized from the paper quadrangles. Four quads along the Massachusetts-Connecticut border were obtained from the Connecticut DEP and projected to the Massachusetts State Plane Coordinate System. An ongoing project by the MassDEP GIS Program to redelineate surface water supply watersheds using digital terrain models is adding additional streams within the newly delineated watersheds. These streams are from the MassDEP Wetlands datalayer with some additional on screen digitizing from the 2005 Color OrthoPhotos. Streams added from this process are generally coded as intermittent unless field verification proves otherwise. In 2007 the outlines of all reservoirs were replaced with those from the MassDEP Wetlands datalayer so either dataset can be used with the SWP Zones datalayer. In 2008 field verified streams for the Wachusett Reservoir watershed were provided by DCR West Boylston GIS staff and added.All of the digitized quadrangles were checkplotted at 1:25,000. The 1:25,000 DLG quadrangles were randomly checkplotted. Each of the quadrangles was edgematched to its neighboring quads. The scanned hydrography was compared both to the source mylar and to the paper quadrangles to ensure completeness.

A view of the polygon feature class of Environmentally Endangered Land (EEL) sites that have not been acquired or managed by the EEL Program. The feature class only includes records that are Status = A List or Status = B List.Please be aware that the data represented in the shapefile is generalized and is intended as an illustration only. The data is not intended for in-depth analysis and materials contained in the shapefile provided may contain inaccuracies or recent changes may not have been included. The user is warned to utilize the data at the user’s own risk. It is always best to check with the Environmentally Endangered Lands Program if you need detailed information on any parcel represented in the shapefile. If you have any problems, questions, or need any assistance, please contact EEL at eel@miamidade.gov or 305-372-6687.Updated: AnnuallyThe data was created using:Projected Coordinate System: WGS_1984_Web_Mercator_Auxiliary_SphereProjection: Mercator_Auxiliary_Sphere

Source: GII ,Projected Coordinate System: Adindan_UTM_Zone_37N Projection: Transverse_Mercator Linear Unit: Meter Geographic Coordinate System: GCS_Adindan Datum: D_Adindan Prime Meridian: Greenwich Angular Unit: Degree

Properties and parts of properties, purchased, managed and/or tracked for purchase by Miami-Dade Countys Environmentally Endangered Lands (EEL) program.Some areas include only portions of properties. In those cases, only the portions of the properties that have been either purchased and/or managed by the EEL Program or are on the EEL Acquisition List are included. Please note, property boundaries and folios may change. Please be aware that the data represented in the shapefile is generalized and is intended as an illustration only. The data is not intended for in-depth analysis and materials contained in the shapefile are provided AS IS and may contain inaccuracies. The user is warned to utilize the data at the users own risk. It is always best to check with the EEL Program if you need detailed information on any properties represented in the shapefile. If you have any questions or need assistance, email eel@miamidade.govor call 305-372-6687.The County's EEL Program was established to implement the mandate of the voter referendum to acquire, preserve, enhance, restore, conserve and maintain environmentally endangered lands for the benefit of present and future generations. The EEL Program identifies and purchases these lands for preservation, conservation, restoration, and enhancement. The County, in partnership with the South Florida Water Management District, the State of Florida, and other funding partners, has acquired approximately 24,084 acres of land in Miami-Dade County since the inception of the EEL Program through February 28, 2023. In addition, the EEL Program currently manages over 28,000 acres of land in Miami-Dade County.Updated: As AnnuallyThe data was created using: Projected Coordinate System: WGS_1984_Web_Mercator_Auxiliary_SphereProjection: Mercator_Auxiliary_Sphere

The database provides information on coastal protection structures (containing geosynthetic materials) located on the coast of the South-East Baltic, in the Kaliningrad Oblast (Russia) and the Pomeranian Voivodeship (Poland). The database contains the following sections: the tabular data about coastal protecting structures; the point vector geodata about these structures and used geosynthetic materials on intaractive map; the satellite images and photos. This dataset contains vector geodata only. Spreadsheets (named “ProtectingStructures_tab.xlsx”) contain the coastal protection structures located on the sea coast of the South-East Baltic. Sheet 1 (named “ProtectingStructuresSEB”) shows a list of coastal protection structures in the South-Eastern Baltic that contain geosynthetics and their characteristics. It has 10 columns (Table 1): the number (the conventional two-level number assigned to the structure); the type (type of structure); the location (closest settlement to which the structure is located); the country (country where the structure is located); Building_Reconstruction_year (the year of building or last reconstruction of the structure); Geosyntetic_type (type of geosynthetic material used in the structure); Length_m (length of the structure in m); Width_beach_m (width of the beach in front of the structure, range, m); Lat (latitude, °); Lon (longitude, °). Sheet 2 (named “Legend”) shows the legend described above. Intaractive map data [ProtectingStructures_pnt.kmz] is the point vector layer that contains the information about coastal protection structures located on the sea coast of the South-East Baltic. Projected Coordinate System is WGS 1984, UTM Zone 34N, Projection is Transverse Mercator. The attribute table has the columns: the number (the conventional two-level number assigned to the structure); the type (type of structure); the location (closest settlement to which the structure is located); the country (country where the structure is located); Building_Reconstruction_year (year of building or last reconstruction of the structure); each type of geosynthetics has a separate column (Geotextile, Gabion_coating, Geocontainers, Geocells, Geomat, PVC_sheet_pile), the absence of geosynthetics is designated as “0”, the presence of the geosynthetics is designated as “1”; Length_m (length of the structure in m); Width_beach_m (width of the beach in front of the structure, range, m); Lat (latitude, °); Lon (longitude, °). The satellite images and photos are embedded in the PDF document. They demonstrate the general location (scale 1 : 50 000) of the coastal protection structures in the satellite image (Fig. 1) and their appearance in the photos.

A point feature class that identifies buoy locations according to the National Data Buoy Centers program that are located near Miami-Dade County waters. Lists active stations by station owner, with their recent marine observations (when available). Provided by NOAA.Updated: Annually The data was created using: Projected Coordinate System: WGS_1984_Web_Mercator_Auxiliary_SphereProjection: Mercator_Auxiliary_Sphere

File List UMBC_quadrat_file.txt (MD5: fcd6a15b57b7808c69405d385e51f20b) UMBC_roles_file.txt (MD5: 8229e4fb4197b520b8266dfb57e95318) UMBC_species_list_file.txt (MD5: 8b0eb15e537c7f2390ffcbb29d8ef116) UMBC_2014_census_file.txt (MD5: fbef2b22c7bc0a0289a780fb059cc5e6) UMBC_measurement_codes_file.txt (MD5: 54f293e7e13ba1cda1f55668db8f5dda) UMBC_personnel_file.txt (MD5: 9025ada14ac616baddd4b7626d50c539) Description This data set reports on a census of all trees within two 6.25-ha plots (250 m × 250 m) of temperate deciduous woodlot patch on the campus of the University of Maryland Baltimore County (UMBC), Baltimore, Maryland, USA. Woodlot patches are primarily of the tulip poplar association. From 2011–2012, a 25 m × 25 m quadrat grid was established for each plot by total station surveying equipment. From 2012–2014, the location and DBH (diameter at breast height, 1.37 m) of all woody stems ≥ 1 cm DBH that reached ≥ 1.37 m tall were censused, whether living or dead. All such stems were also marked with numbered metal tags. All living stems were identified at least to genus and to species when possible. All censused stems and associated data (tag number, DBH, location, species, status) were entered into a geographic information system (GIS) map layer based on stem location within a Universal Transverse Mercator (UTM) projected coordinate system where each stem was represented as a unique point feature. The primary objective of these data is to provide a reference for stem location and diameter for the calibration and evaluation of ground-based 3D remote sensing technologies based on computer vision and personal digital camera or cell phone image collections. With regular re-census, the data could also be used for tracking growth, mortality, and recruitment of patchy forest woodlots within a suburban landscape mosaic.

      Key words: deciduous; diameter; Ecosynth; forest inventory; map; patch; species; survey; taxonomy; temperate; urban forest.

This is Antarctica’s first comprehensive ecosystem map of ice-free lands. The data comprise a spatially explicit 3-tiered hierarchical ecosystem classification with nine Major Environment Types (tier 1), 33 Habitat Complexes (tier 2) and 269 Bioregional Ecosystem Types (tier 3). These Bioregional Ecosystem Types are aligned with ‘level 4’ of the IUCN Global Ecosystem Typology (Keith et al. 2022).

The spatial data are available in raster format (TIF) at 100 m resolution in the Polar Stereographic Projected Coordinate System (GCS_WGS_1984) for all known ice-free areas south from latitude -57.330551 decimal degrees South (pdf map shows extent of ice-free areas in relation to terrestrial ice and ice shelves). A value attribute table (VAT) provides text fields containing codes and full names for each unit in each level of the classification hierarchy and the spatial extent of tier 3 units in hectares.

Methods of development, source data and uses of the inventory are detailed by Tóth et al. (2025a). Descriptive profiles for tier 1 and 2 units are available in Tóth et al. (2025b).

References
Keith, D.A., Ferrer-Paris, J.R., Nicholson, E., Bishop, M.J., Polidoro, B.A., Ramirez-Llodra, E., Tozer, M.G., Nel, J.L., Nally, R. Mac, Gregr, E.J., Watermeyer, K.E., Essl, F., Faber-Langendoen, D., Franklin, J., Lehmann, C.E.R., Etter, A., Roux, D.J., Stark, J.S., Rowland, J.A., Brummitt, N.A., Fernandez-Arcaya, U.C., Suthers, I.M., Wiser, S.K., Donohue, I., Jackson, L.J., Pennington, R.T., Iliffe, T.M., Gerovasileiou, V., Giller, P., Robson, B.J., Pettorelli, N., Andrade, A., Lindgaard, A., Tahvanainen, T., Terauds, A., Chadwick, M.A., Murray, N.J., Moat, J., Pliscoff, P., Zager, I. & Kingsford, R.T. (2022) A function-based typology for Earth’s ecosystems. Nature 610, 513–518. [doi: 10.1038/s41586-022-05318-4].
Tóth, A.B., Terauds, A., Chown, S.L., Hughes, K.A., Convey, P., Hodgson, D.A., Cowan, D.A., Gibson, J., Leihy, R.I., Murray, N.J., Robinson, S.A., Shaw, J.D., Stark, J.S., Stevens, M.I., van den Hoff, J., Wasley, J. and Keith D.A. (2025a). A dataset of Antarctic ecosystems in ice-free lands: classification, descriptions, and maps. Scientific Data 12, 133. [https://doi.org/10.1038/s41597-025-04424-y]
Tóth, A.B., Terauds, A., Chown, S.L., Hughes, K.A., Convey, P., Hodgson, D.A., Cowan, D.A., Gibson, J., Leihy, R.I., Murray, N.J., Robinson, S.A., Shaw, J.D., Stark, J.S., Stevens, M.I., van den Hoff, J., Wasley, J. & Keith D.A. (2025b). Antarctic Ecosystem Inventory: Descriptive profiles for ice-free lands v1.0. DOI: 110.5281/zenodo.14625890. Australian Antarctic Data Centre.

The pumps and floodgates layer consists of a point geometry representation of features representing pumps and floodgates in Jakarta, Indonesia. Attributes include names, type, and geometries representing the geographical locations of infrastructure as point features. The dataset is a geo-processed version of the data captured by the PetaJakarta.org project, using field-survey and tracing of aerial imagery. The data contains 67 pumps and 28 floodgates respectively. Geo-processing included updating the dataset with verified featured locations acquired by GPS survey in November 2014. Further, geometries were snapped to be coincident with line features representing rivers, canals and streams from the ‘Waterways in Jakarta’ layer. The snapping tolerance distance was 110m or less. Features outside of this threshold in the source data were removed, and it should be noted that this dataset does not represent an exhaustive list of all pumps and floodgates in Jakarta, Indonesia. The data uses the WGS 84 / UTM zone 48S (EPSG:32748) projected coordinate reference system. The dataset is network-ready for building both directed and undirected graphs representing hydrological infrastructure network for Jakarta, Indonesia.

These datasets pertain to the manuscript entitled 'Streambed hydraulic conductivity estimated by spectral induced polarization imaging can help to improve groundwater modeling', which is currently submitted for revision in Water Resources Research. They comprise raw data from measurements taken in the field at 2 sites in terms of (1) pressure time series during the performed slug tests, (2) impedance from spectral induced polarization, (3) submersion levels of the used electrodes below the top of the water column, and (4) three-dimensional Cartesian coordinates relating the measurements spatially. The coordinates have been projected to a local coordinate system for each site, to comply with a non-disclosure agreement of the measurement locations. The projection of the coordinates still allows to fully reproduce the presented results, if using the methods described in the manuscript. The naming convention throughout the datasets is consistent with site labels used in the manuscript. All data is given as comma-separated values with intuitive file names and self-explanatory headers containing a list of field names. The slug test data includes multiple repetitions of the same measurement, and the impedance measurements contain normal as well as reciprocal readings – as described in the manuscript.

The data is separated into two compressed file archives, named according to the site names given in the manuscript. Each file pertaining to slug tests at a certain location, in the subfolder “slugTestRecordings” has the following naming convention: “_.csv”, where corresponds to the local coordinates given in “coordinates.txt”, and where is an integer describing the largest depth in cm at which a slug test was performed according to the protocol described in the manuscript. Each file pertaining to impedance measurements at a certain profile, in the subfolder “SIPRecordings”, has the following naming convention: “_.dat”, where corresponds to the local coordinates given in “coordinates.txt”, and where is a zero-padded integer describing the measurement frequency in Hz at which the measurement was performed according to the protocol described in the manuscript. Submersion levels of the electrodes below the top of the stream’s water column are given in m in the file “submersionLevels.txt”, corresponding to the local coordinates given in “coordinates.txt”. The local coordinates given in m in “coordinates.txt” have the following convention for the column “locationTag”: “-, where pertains to a name of the electrical array and is a continuous number for the electrode. Slug tests were exclusively perfomed at the location of electrodes and files are, thus, as described above, named accordingly.

A polygone feature class of Short Form Class IV Permit Appliations within Miami-Dade County. The following types of projects may be processed as Short Form Class IV Permit applications:Clearing, farming, filling, dredging, plowing or any other work within wetlands requiring a Class IV Permit and not lying within the Bird Drive Everglades Wetland Basin or the North Trail Wetland Basin where the usage is consistent with existing zoning regulations and where the cumulative area upon which work will be performed does not exceed:(a) One acre of wetlands in areas designated as "Environmental Protection" on the current Miami-Dade County Comprehensive Development Master Plan Map, or(b) Forty acres of wetlands in areas designated as "Open Land" or "Agriculture" on the current Miami-Dade County Comprehensive Development Master Plan Map.Rock mining in the Transitional Northeast Everglades, the East Turnpike Wetland Basin and the C-9 Wetland Basin, when said rock mining has been previously approved as an unusual use by Miami-Dade County. However, a short form application for said rock mining shall be permitted only when the design and development criteria for the proposed rock mining project do not conflict with the prior unusual use approval by Miami-Dade County.The clearing, farming, placement of clean fill, dredging, plowing or any other agricultural site alteration within the North Trail Wetland Basin or the Bird Road Drive Everglades Wetland Basin.Clearing, placement of clean fill or dredging in wetlands associated with a modification of the Central and South Florida Flood Control Project, intended to restore historical patterns of hydrologic flow to Everglades National Park, Florida Bay or Biscayne Bay and performed by the State of Florida or the United States Government. Modifications intended to provide additional drainage of wetland areas shall be subject to the provisions of Section 24-48.2(II)(A).Clearing, placement of clean fill, dredging or other work in wetlands or surface waters associated with the repair, replacement or maintenance of the Central and South Florida Flood Control Project, performed by the State of Florida or the United States Government.Dredging and filling in wetlands for the sole purpose of environmental restoration or environmental enhancement.The construction of monitoring wells or stations in wetlands for the purpose of environmental monitoring or research unless otherwise exempt.Work in wetlands associated with scientific studies conducted by public agencies, research, or academic institutions that does not otherwise qualify for approval under Section 24-48 (1) or 24-48 (2).The minimum dredging or filling in wetlands necessary for the repair or replacement of utility poles and lines.All work requiring a Class II, III, V or VI Permit.All other work not specifically described in the list of projects requiring a standard form permit application or qualifying for approval under Section 24-48 (1) or 24-48 (2).Updated: Monthly The data was created using: Projected Coordinate System: WGS_1984_Web_Mercator_Auxiliary_SphereProjection: Mercator_Auxiliary_Sphere