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. This dataset contains the following: HUN_GW_modelling_DEM.txt - an xyz textfile of 90m resolution elevations for the Hunter GW modelling domain. HUN_GW_Model_topography.gdb - an ESRI file …Show full descriptionAbstract 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. This dataset contains the following: HUN_GW_modelling_DEM.txt - an xyz textfile of 90m resolution elevations for the Hunter GW modelling domain. HUN_GW_Model_topography.gdb - an ESRI file geodatabase containing interim rasters used to create the DEM which was exported to the above xyz textfile. The rasters contained are: 1 HUN_GW_Model_Bathy90 - extract from the source bathymetry layer resampled and reprojected to MGA z56 90m 2 HUN_GW_Model_SRTM90 - extract from the source 3sec SRTM layer resampled and reprojected to MGA z56 90m 3 HUN_Offshore_Bathy90 - (1) above clipped to areas where (2) has cellvalue of 0 (ie SRTM offshore area) 4 HUN_SRTM_Bathy90m_mosaic - (2) with offshore area replaced with (3) 5 HUN_GW_SRTM_Bathy90m_mosaic - (4) clipped to GW model domain Purpose Input for groundwater modelling for Hunter subregion. Dataset History Rectangular extract (encompassing hunter rectagular extent plus some additional offshore area) of source bathymery and SRTM elevation rasters were resampled and reprojected to congruent 90m cell resolution MGA zone 54 rasters. The cell values of the offshore area of the SRTM (ie where cellvalue = 0 or nodata) were replaced with those of the 90m bathymety data to create a seamless mosiac of SRTM elevations on land and bathymetry elevations offshore. This was then clipped to the GW modelling domain and the raster exported as an xyz acii file. Dataset Citation Bioregional Assessment Programme (2014) HUN GW modelling DEM v01. Bioregional Assessment Derived Dataset. Viewed 22 June 2018, http://data.bioregionalassessments.gov.au/dataset/f0927e8a-cc41-419b-ae68-660d5d5fa47b. Dataset Ancestors Derived From Geoscience Australia, 3 second SRTM Digital Elevation Model (DEM) v01 Derived From Australian Coal Basins Derived From Hunter Groundwater Model extent Derived From Bathymetry GA 2009 9sec v4

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description: This data set contains imagery from the National Agriculture Imagery Program (NAIP). The NAIP acquires digital ortho imagery during the agricultural growing seasons in the continental U.S.. A primary goal of the NAIP program is to enable availability of ortho imagery within one year of acquisition. The NAIP provides two main products: 1 meter ground sample distance (GSD) ortho imagery rectified to a horizontal accuracy within +/- 5 meters of reference digital ortho quarter quads (DOQQ's) from the National Digital Ortho Program (NDOP) or from the National Agriculture Imagery Program (NAIP); 1 meter GSD ortho imagery rectified within +/- 6 meters to true ground. The tiling format of NAIP imagery is based on a 3.75' x 3.75' quarter quadrangle with a 300 meter buffer on all four sides. The NAIP imagery is formatted to the UTM coordinate system using the North American Datum of 1983 (NAD83). The NAIP imagery may contain as much as 10% cloud cover per tile. The quarter quads have been reprojected from UTM 11 or UTM 12 to IDTM83 projection (ESPG 102605), mosaicked into 100k tiles, and compressed using ERDAS Imagine for use in ArcGIS.; abstract: This data set contains imagery from the National Agriculture Imagery Program (NAIP). The NAIP acquires digital ortho imagery during the agricultural growing seasons in the continental U.S.. A primary goal of the NAIP program is to enable availability of ortho imagery within one year of acquisition. The NAIP provides two main products: 1 meter ground sample distance (GSD) ortho imagery rectified to a horizontal accuracy within +/- 5 meters of reference digital ortho quarter quads (DOQQ's) from the National Digital Ortho Program (NDOP) or from the National Agriculture Imagery Program (NAIP); 1 meter GSD ortho imagery rectified within +/- 6 meters to true ground. The tiling format of NAIP imagery is based on a 3.75' x 3.75' quarter quadrangle with a 300 meter buffer on all four sides. The NAIP imagery is formatted to the UTM coordinate system using the North American Datum of 1983 (NAD83). The NAIP imagery may contain as much as 10% cloud cover per tile. The quarter quads have been reprojected from UTM 11 or UTM 12 to IDTM83 projection (ESPG 102605), mosaicked into 100k tiles, and compressed using ERDAS Imagine for use in ArcGIS.

This phase of Project 3DGBR involved manual digitising of geomorphic map boundaries for the key seafloor features identified in the gbr100 grid, particularly for the inter-reefal area on the GBR shelf and in the Coral Sea Conservation Zone (CSCZ). See map for CSCZ boundary at: http://www.environment.gov.au/coasts/mpa/coralsea/index.html.

Methods:

GIS spatial analysis of the gbr100 grid was conducted in order to derive a number of useful background datasets for assisting in the digitising process, such as slope, aspect, hillshading, and dense contour lines.

The digitising initially focused on the deep-water (>100 m) environment to develop geomorphic maps for the continental slope, Queensland and Townsville Troughs lying within the Great Barrier Reef World Heritage Area (GBRWHA), and for the Queensland Plateau, Coral Sea Basin, Tasman Basin, and Lord Howe Rise area lying within the adjoining Coral Sea Conservation Zone (CSCZ). The project lastly focuses on the shallow-water (<100 m) environment to develop geomorphic maps for the GBR shelf to complement the shallow reef feature maps provided by GBRMPA. These shallow-water geomorphic features will be added to the project as they come available.

Format:

This dataset consists of 21 shapefiles and a GeoTiff raster file containing hillshading. Each of the shapefiles is described below.

Group Layer 1. Boundaries: gbrwha_outer.shp This Great Barrier Reef World Heritage Area (GBRWHA) layer was initially provided by GBRMPA using a GDA94 datum. The shapefile was reprojected to the WGS84 datum, and then the western coastline boundaries deleted to derive a line shapefile showing only the outer boundary of the GBRWHA where it extends away from the mainland.

qld_gbrwha_cscz.shp This line shapefile combines both the GBRWHA and Coral Sea Conservation Zone (CSCZ) areas, with a western boundary limit at the Queensland mainland coastline. This area was used to clip all geomorphic features created in this project.

Group Layer 2. GBRMPA features: gbr_dryreef.shp The GBR shelf dryreefs shapefile was initially provided by GBRMPA for this project using a GD94 datum. The shapefile was reprojected to the WGS84 datum and not modified in any other way. It is provided here only for completeness but and products using this shapefile should also acknowledge GBRMPA (see under licensing).

gbr_features.shp The GBR shelf features were initially provided by GBRMPA for this project using a GDA94 datum. The shapefile was reprojected to the WGS84 datum, and then the Ashmore Reef polygon deleted due to a grossly incorrect position. The shapefile comprises Cay, Island, Mainland, Reef, Rock and Sand features. Users may contact GBRMPA to obtain details for the creation of these features. Any products using this shapefile should also acknowledge GBRMPA (see under licensing).

Group Layer 3. Finer-scale features: coralsea_cay.shp Cay is a sand island elevated above Australian Height Datum (AHD), and located on offshore coral reefs and seamounts. Cays were mapped initially using a shapefile provided by Geoscience Australia for this project, and then their boundaries checked or remapped using Landsat imagery as background source data to help delineate the white sand areas against the surrounding ocean.

coralsea_dryreef.shp Dryreef is rock/coral lying at or near the sea surface that may constitute a hazard to surface navigation. Dryreefs were mapped initially using a shapefile provided by Geoscience Australia for this project, which identified those reef areas lying above approximately Lowest Astronomic Tide (LAT). Landsat imagery was used as background source data to check or remap their boundaries.

coralsea_reef.shp Reef is rock/coral lying at or near the sea surface that may constitute a hazard to surface navigation. For this project, the boundaries of reef areas were mapped to show the outer-most extent of each coral reef that could be observed in Landsat imagery, thus identifying the greatest area of each reef observed in the Coral Sea. This methodology is consistent with the methodology used to map the outer-most extents of reefs on the GBR shelf conducted by GBRMPA.

coralsea_ridge.shp Ridge is a long, narrow elevation with steep sides. In this project, ridges were mapped as widely-scattered and uncommon, finer-scale features identified in the gbr100 grid. These elongate ridges are distinct from the smaller knolls or hills which have a more rounded shape. They are usually found on the plateaus of the Lord Howe Rise.

coralsea_bank.shp Bank is an elevation over which the depth of water is relatively shallow but normally sufficient for safe surface navigation. In this project, banks were mapped as the base or pedestal boundaries of the coral reefs found in the Coral Sea. For example, the coral atolls and reefs on the Queensland Plateau are considered banks and their bases digitised where they emerge from the surrounding flat seafloor.

coralsea_knoll.shp Knoll is a relatively small isolated elevation of a rounded shape. This shapefile also includes Abyssal hill, a low (100 ¿ 500 m) elevation on the deep seafloor. For this project, knolls and abyssal hills were mapped using background datasets that showed relatively steep changes in elevation contours and variations in slope gradients. Knolls are numerous throughout the Coral Sea area and are greatly underestimated.

coralsea_canyon.shp Canyon is a relatively narrow, deep depression with steep sides, the bottom of which generally has a continuous slope, developed characteristically on continental slopes. Canyons were mapped by closely following the narrow sides of canyon axes, digitising from the foot of the canyon where they merge with the surrounding basin floor, and up to the canyon head and into any connecting side gullies. This project identified numerous canyons on any slope gradient >1° and are also greatly underestimated across the area.

coralsea_seamount.shp Seamount is a large isolated elevation >1000 m in relief above the seafloor, characteristically of conical form. This shapefile also includes Guyot, a seamount having a comparatively smooth flat top. Seamounts and guyots were mapped mostly within the Tasmantid Seamount Chain with elevations >1000 m. This project identified several large knolls and hills close to 1000 m in height within this chain that may also be seamounts but currently lack detailed bathymetry data.

Group Layer 4. Broader-scale features: gbr_shelf.shp Shelf is a zone adjacent to a continent (or around an island) extending from the low water line to a depth at which there is usually a marked increase of slope towards oceanic depths. The eastern boundary of the Queensland continental shelf was mapped by closely following the change in gradient along the shelf edge. The shelf break in the north was at approximately 80 m and became deeper at about 110 m towards the south. The western boundary was clipped at the Queensland mainland coastline.

coralsea_slope.shp Slope lies seaward from the shelf edge to the upper edge of a continental rise or the point where there is a general reduction in slope. The continental slope was mapped lying adjacent to the shelf and extending into the adjacent deep basins and troughs. The shelf feature was used to erase the western boundary of the slope and the various basins and troughs erased the eastern slope border. The slope has extensive canyons incising its surface.

coralsea_terrace.shp Terrace is a relatively flat horizontal or gently inclined surface, sometimes long and narrow, which is bounded by a steeper ascending slope on one side and by a steeper descending slope on the opposite side. In this project, one broad-scale terrace feature was mapped lying on the slope between the Swains Reefs and Capricorn-Bunker Group of reefs, and near the Capricorn Trough.

coralsea_plateau.shp Plateau is a flat or nearly flat area of considerable extent, dropping off abruptly on one or more sides. Extensive areas of plateaus were mapped across the Coral Sea with the largest being the Queensland Plateau. Lord Howe Rise consists of a series of plateaus separated by broad-scale valleys linking adjacent basins and troughs. Plateau boundaries were mapped around their bases where the gradient first becomes steeper. The exceptions are the Marion and Saumarez Plateaus on the Queensland continental slope, where the boundaries were mapped as the slope gradient becomes flat or nearly flat.

coralsea_valley.shp Valley is a relatively shallow, wide depression, the bottom of which usually has a continuous gradient. This term is generally not used for features that have canyon-like characteristics for a significant portion of their extent. The shapefile includes Hole, a local depression, often steep sided, of the seafloor. Valleys and holes were mapped as long shallow depressions that often separated the numerous plateaus. These features link the basins and troughs that surround these plateaus, and in some cases can be incised with finer-scale canyons.

coralsea_trough.shp Trough is a long depression of the seafloor characteristically flat bottomed and steep sided and normally shallower than a trench. In this project, two trough features were mapped that are essentially long basins. The larger feature is a combined Queensland and Townsville Trough lying between the continental slope and the Queensland Plateau. The smaller feature is the Bligh Trough separating the northern slope and Eastern Plateau. Both trough features feed into the Osprey Embayment and huge Bligh Canyon.

coralsea_rise.shp Rise is a gentle slope rising from the oceanic depths towards the foot of a continental slope. For this project, an elongate rise is mapped between the Queensland Plateau and the adjacent Coral Sea Basin. The Queensland Plateau is remnant continental crust from the Gondwana breakup and so its seaward edge provides a geomorphic extension of the Australian margin, albeit at a much deeper depth than the

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This dataset shows FWC-designated bird sanctuaries occurring throughout Florida. The boundaries of each sanctuary were digitized from a variety of sources including historical documents such as FWC establishment orders and parcel plat maps. To ensure accuracy of the digitization process for each bird sanctuary, reference datasets and documents were compiled and divided into three regions: North, East, and West. All reference data was then re-projected to NAD_1983_2011_StatePlane_Florida_East_FIPS_0901_Ft_US, NAD_1983_2011_StatePlane_Florida_West_FIPS_0902_Ft_US, or NAD_1983_2011_StatePlane_Florida_North_FIPS_0903_Ft_US, respectively. These regions and projections were chosen to match the use of US feet in the legal descriptions of each bird sanctuary and any accompanying plat/parcel surveys. After all reference data was prepared, an individual bird sanctuary dataset for each region was created in the appropriate projection. The legal descriptions establishing the bird sanctuaries varied widely in terms of detail. If specific plat books or plat maps were referenced in the legal description, staff used these documents in combination with the available countywide parcels dataset and World Street Map to digitize the sanctuaries. All three bird sanctuary datasets were then merged into one dataset, and this final statewide dataset was reprojected to NAD_1983_2011_Florida_FGDL_Albers. Please see the sources section for more information regarding the reference datasets and legal descriptions.

description: This coverage contains land-cover information for all of Ohio and portions of Indiana, Michigan, Kentucky, West Virginia, Pennsylvania, and New York. This dataset was derived from the U.S. Geological Survey's National Land Cover Dataset (NLCD). NLCD raster grids were downloaded from the USGS EROS Data Center web server at http://landcover.usgs.gov/natllandcover.html, by state. These grids were then reprojected, mosaiced and clipped against a polygon coverage representing the study area. Grid cell resolution is approximately 30 meters or 1 arc-second.; abstract: This coverage contains land-cover information for all of Ohio and portions of Indiana, Michigan, Kentucky, West Virginia, Pennsylvania, and New York. This dataset was derived from the U.S. Geological Survey's National Land Cover Dataset (NLCD). NLCD raster grids were downloaded from the USGS EROS Data Center web server at http://landcover.usgs.gov/natllandcover.html, by state. These grids were then reprojected, mosaiced and clipped against a polygon coverage representing the study area. Grid cell resolution is approximately 30 meters or 1 arc-second.

Physical Clustering of the World's Oceans (based on data extracted from World Ocean Atlas 2013 version 2).

Data.

The physical regions are based on the observations of the World Ocean Atlas 2013 version 2 (WOA13v2*; https://www.nodc.noaa.gov/OC5/woa13/).

We extracted the decadal annual means for nine variables. These variables included: Temperature (°C), Salinity (unitless), Density (kg/m3), Dissolved Oxygen (ml/l), Apparent Oxygen Utilization (ml/l), Silicate (µmol/l), Phosphate (µmol/l), Density (kg/m^3) and Nitrate (µmol/l). The datasets for Temperature, Salinity and Dissolved oxygen were provided at 0.25° resolution. We therefore reprojected the remaining WOA13v2 datasets to the same projection by making each 1° cell in these datasets at 0.25° resolution, while assigning the original value to the four finer resolution cells.

For the seafloor physical regions we included two additional dataset derived from GEBCO bathymetry data (https://www.gebco.net/). The first dataset was the bathymetry across the seafloor, this layer was re-projected to 0.25° resolution, were the cell values were based on the mean values of the finer scale GEBCO layer. We then computed the slope of depth based on the bathymetry raster using the ‘terrain’ function in the ‘raster’ package.

Analysis.

We generated physical clusters for the globe at the surface (0m), 200m, 1000m and the seafloor. For the surface, 200m and 1000m regions, we extracted the single depth layers from the WOA13v2 datasets and generated a matrix which represented the sites by the variables.

For the seafloor, we had to generate interpolated layers at the seafloor based on the WOA13v2 data. We did this by looking at the mean depth of the bathymetry data and undertaking a tri-linear (cubic) interpolation of the WOA13v2 data at that seafloor depth. We subsequently ran a tri-linear interpolation of the WOA13v2 for each variable and generated maps of seafloor environmental conditions. One these maps were generated we extracted each variable into a seafloor site by seafloor physical variable matrix.

All four site by physical variables datasets (0, 200, 1000 and seafloor) were then scaled in an attempt to centre and normalise the data.

For each of these four datasets we then fitted a k-means clustering model from 2 to 40 clusters and looked at the resulting model loglikelihood, AIC and BIC. We then selected the number of clusters at the point were the the log-likelihood converged (i.e. the point were additional centroids only gave a marginal increase in log-likelihood).

The resulting cluster identity was then assigned to each site and used to generate maps of the physical clusters for each dataset. These rasters were then converted to shapefiles.

• Boyer, T.P., J. I. Antonov, O. K. Baranova, C. Coleman, H. E. Garcia, A. Grodsky, D. R. Johnson, R. A. Locarnini, A. V. Mishonov, T.D. O'Brien, C.R. Paver, J.R. Reagan, D. Seidov, I. V. Smolyar, and M. M. Zweng, 2013: World Ocean Database 2013, NOAA Atlas NESDIS 72, S. Levitus, Ed., A. Mishonov, Technical Ed.; Silver Spring, MD, 209 pp., http://doi.org/10.7289/V5NZ85MT

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. This dataset contains the following: HUN_GW_modelling_DEM.txt - an xyz textfile of 90m resolution elevations for the Hunter GW modelling domain. HUN_GW_Model_topography.gdb - an ESRI file geodatabase containing interim rasters used to create the DEM which was exported to the above xyz textfile. The rasters contained are: 1 HUN_GW_Model_Bathy90 - extract from the source bathymetry layer resampled and reprojected to MGA z56 90m 2 HUN_GW_Model_SRTM90 - extract from the source 3sec SRTM layer resampled and reprojected to MGA z56 90m 3 HUN_Offshore_Bathy90 - (1) above clipped to areas where (2) has cellvalue of 0 (ie SRTM offshore area) 4 HUN_SRTM_Bathy90m_mosaic - (2) with offshore area replaced with (3) 5 HUN_GW_SRTM_Bathy90m_mosaic - (4) clipped to GW model domain Purpose Input for groundwater modelling for Hunter subregion. Dataset History

Soil is the foundation of life on earth. More living things by weight live in the soil than upon it. It determines what crops we can grow, what structures we can build, what forests can take root.This layer contains the chemical soil variable organic carbon density (ocd) which measures carbon mass in proportion to volume of soil (mass divided by volume.)From Agriculture Victoria: Soil carbon provides a source of nutrients through mineralisation, helps to aggregate soil particles (structure) to provide resilience to physical degradation, increases microbial activity, increases water storage and availability to plants, and protects soil from erosion.This layer is a general, medium scale global predictive soil layer suitable for global mapping and decision support. In many places samples of soils do not exist so this map represents a prediction of what is most likely in that location. The predictions are made in six depth ranges by soilgrids.org, funded by ISRIC based in Wageningen, Netherlands.Each 250m pixel contains a value predicted for that area by soilgrids.org from best available data worldwide. Data for organic carbon density are provided at six depth ranges from the surface to 2 meters below the surface. Each variable and depth range may be accessed in the layer's multidimensional properties.Dataset SummaryPhenomenon Mapped: Organic carbon density in kg/m³Cell Size: 250 metersPixel Type: 32 bit float, converted from online data that is 16 Bit Unsigned IntegerCoordinate System: Web Mercator Auxiliary Sphere, projected via nearest neighbor from goode's homolosine land (250m)Extent: World land area except AntarcticaVisible Scale: All scales are visibleNumber of Columns and Rows: 160300, 100498Source: Soilgrids.orgPublication Date: May 2020Data from the soilgrids.org mean predictions for ocd were used to create this layer. You may access organic carbon density values in one of six depth ranges. To select one choose the depth variable in the multidimensional selector in your map client.Mean depth (cm)Actual depth range of data-2.50-5cm depth range-105-15cm depth range-22.515-30cm depth range-4530-60cm depth range-8060-100cm depth range-150100-200cm depth rangeWhat can you do with this Layer?This layer is suitable for both visualization and analysis across the ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application.Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map: In ArcGIS Online, you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "world soils soilgrids" in the search box and browse to the layer. Select the layer then click Add to Map. In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "world soils soilgrids" in the search box, browse to the layer then click OK.In ArcGIS Pro you can use the built-in raster functions or create your own to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro.Online you can filter the layer to show subsets of the data using the filter button and the layer's built-in raster functions.This layer is part of the Living Atlas of the World that provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.More information about soilgrids layersAnswers to many questions may be found at soilgrids.org (ISRIC) frequently asked questions (faq) page about the data.To make this layer, Esri reprojected the expected value of ISRIC soil grids from soilgrids' source projection (goode's land WKID 54052) to web mercator projection, nearest neighbor, to facilitate online mapping. The resolution in web mercator projection is the same as the original projection, 250m. But keep in mind that the original dataset has been reprojected to make this web mercator version.This multidimensional soil collection serves the mean or expected value for each soil variable as calculated by soilgrids.org. For all other distributions of the soil variable, be sure to download the data directly from soilgrids.org. The data are available in VRT format and may be converted to other image formats within ArcGIS Pro.Accessing this layer's companion uncertainty layerBecause data quality varies worldwide, the uncertainty of the predicted value varies worldwide. A companion uncertainty layer exists for this layer which you can use to qualify the values you see in this map for analysis. Choose a variable and depth in the multidimensional settings of your map client to access the companion uncertainty layer.

We have taken the Uso del Suelo y Vegetacion land cover classification products for Mexico (courtesy of Mexico's Instituto Nacional de Estadistica y Geografia, or INEGI) for years 1985, 1993, 2002, 2007, and 2011 (INEGI, 2015); and harmonized their classes with the classes of the Multi-Resolution Land Characteristics Consortium (MRLC) National Land Cover Database (NLCD) (Homer et al., 2015). Details of processing, along with the processing scripts, are archived in GitHub in the NLCD_INEGI project (Bohn, 2019).

This project contains the following g-zipped tar files:

• SERIE_I.tgz - land cover from 1985
• SERIE_II.tgz - land cover from 1993
• SERIE_III.tgz - land cover from 2002
• SERIE_IV.tgz - land cover from 2007
• SERIE_V.tgz - land cover from 2011

On LINUX, the contents of these files can be extracted via "tar":

tar -xvzf SERIE_I.tgz >& log.tar.txt

On Windows, applications such as "7-zip" can extract the contents.

Each of these .tgz files contain a folder with the same name but without the ".tgz". Within each of these folders are the following sub-folders:

• For SERIE_I to SERIE_IV:
  • metatiles/ - original land cover shapefiles, with Mexico divided into "metatiles" along UTM zones, as documented in Processing_of_INEGI_USOSV_dataset.docx
  • geo/ - shapefiles from "metatiles", reprojected into geographic
  • entire/ - shapefiles from "geo" merged into a single file for the entire country
• For SERIE_V:
  • entire/ - original land cover shapefile in Lambert Conical projection, covering all of Mexico
  • geo/ - shapefile from "entire" reprojected into geographic
• SERIE_I to SERIE_V:
  • cve_union/ - shapefiles covering all of Mexico, in geographic projection, with land cover reclassified to NLCD 2011 legend
  • rasters/ - files from "cve_union", rasterized at 0.000350884 degree resolution
  • ascii/ - raster files from "rasters", exported to ascii ESRI grid file format

Output files (in the "ascii" folders) are ESRI ascii raster grid files, in geographic projection, with cellsize = 0.000350884 degrees.

This dataset contains open vector data for railways, forests and power lines, as well an open digital elevation model (DEM) for a small area around a sample forest range in Europe (Germany, Upper Bavaria, Kochel Forest Range, some 70 km south of München, at the edge of Bavarian Alps). The purpose of this dataset is to provide a documented sample dataset in order to demonstrate geospatial preprocessing at FOSS4G2019 based on open data and software. This sample has been produced based on several existing open data sources (detailed below), therefore documenting the sources for obtaining some data needed for computations related to forest accessibility and wood harvesting. For example, they can be used with the open methodology and QGIS plugin Seilaplan for optimising the geometric layout cable roads or with additional open software for computing the forest accessibility for wood harvesting. The vector data (railways, forests and power lines) was extracted from OpenStreetMap (data copyrighted OpenStreetMap contributors and available from https://www.openstreetmap.org). The railways and forests were downloaded and extracted on 18.05.2019 using the open sources QGIS (https://www.qgis.org) with the QuickOSM plugin, while the power lines were downloaded a couple of days later on 23.05.2019. Additional notes for vector data: Please note that OpenStreeMap data extracts such as forests, roads and railways (except power lines) can also be downloaded in a GIS friendly format (Shapefile) from http://download.geofabrik.de/ or using the QGIS built-in download function for OpenStreetMap data. The most efficient way to retrieve specific OSM tags (such as power=line) is to use the QuickOSM plugin for QGIS (using the Overpass API - https://wiki.openstreetmap.org/wiki/Overpass_API) or directly using overpass turbo (https://overpass-turbo.eu/). Finally, the digitised perimeter of the sample forest range is also made available for reproducibility purposes, although any perimeter or area can be digitised freely using the QGIS editing toolbar. The DEM was originally adapted and modified also with QGIS (https://www.qgis.org) based on the elevation data available from two different sources, by reprojecting and downsampling datasets to 25m then selecting, for each individual raster cell, the elevation value that was closer to the average. These two different elevation sources are: Copernicus Land Monitoring Service - EU-DEM v.1.1 (TILE ID E40N20, downloaded from https://land.copernicus.eu/imagery-in-situ/eu-dem/eu-dem-v1.1; this original DEM was produced by the Copernicus Land Monitoring Service “with funding by the European Union” based on SRTM and ASTER GDEM) Digitales Geländemodell 50 m Gitterweite (https://opendata.bayern.de/detailansicht/datensatz/digitales-gelaendemodell-50-m-gitterweite/), produced by the Bayerische Vermessungsverwaltung – www.geodaten.bayern.de –and downloaded from http://www.geodaten.bayern.de/opendata/DGM50/dgm50_epsg4258.tif This methodology was chosen as a way of performing a basic quality check, by comparing the EU-DEM v.1.1 derived from globally available DEM data (such as SRTM) with more authoritative data for the randomly selected region, since using authoritative data is preferred (if open and available). For other sample regions, where authoritative open data is not available, such comparisons cannot longer be performed. Additional notes DEM: a very good DEM open data source for Germany is the open data set collected and resampled by Sonny (sonnyy7@gmail.com) and made available on the Austrian Open Data Portal http://data.opendataportal.at/dataset/dtm-germany. In order to simplify end-to-end reproducibility of the paper planned for FOSS4G2019, we use and distribute an adapted (reprojected and resampled to 25 meters) sample of the above mentioned dataset for the selected forest range. This sample dataset is accompanied by software in Python, as a Jupiter Notebook that generates harmonized output rasters with the same extent from the input data. The extent is given by the polygon vector dataset (Perimeter). These output rasters, such as obstacles, aspect, slope, forest cover, can serve as input data for later computations related to forest accessibility and wood harvesting questions. The obstacles output is obtained by transforming line vector datasets (railway lines, high voltage power lines) to raster. Aspect and slope are both derived from the sample digital elevation model.

Soil is the foundation of life on earth. More living things by weight live in the soil than upon it. It determines what crops we can grow, what structures we can build, what forests can take root.This layer contains the physical soil variable percent silt (silt).Within the subset of soil that is smaller than 2mm in size, also known as the fine earth portion, silt is defined as particles that are equal to or are between 0.002mm and 0.05mm in size. Silty soils are usually more fertile than other types of soil, with a good balance of air circulation and water retention.This layer is a general, medium scale global predictive soil layer suitable for global mapping and decision support. In many places samples of soils do not exist so this map represents a prediction of what is most likely in that location. The predictions are made in six depth ranges by soilgrids.org, funded by ISRIC based in Wageningen, Netherlands.Each 250m pixel contains a value predicted for that area by soilgrids.org from best available data worldwide. Data for silt are provided at six depth ranges from the surface to 2 meters below the surface. Each variable and depth range may be accessed in the layer's multidimensional properties.Dataset SummaryPhenomenon Mapped: Proportion of silt particles (≥ 0.002 mm and ≤ 0.05 mm) in the fine earth fraction in g/100g (%)Cell Size: 250 metersPixel Type: 32 bit float, converted from online data that is 16 Bit Unsigned IntegerCoordinate System: Web Mercator Auxiliary Sphere, projected via nearest neighbor from goode's homolosine land (250m)Extent: World land area except AntarcticaVisible Scale: All scales are visibleNumber of Columns and Rows: 160300, 100498Source: Soilgrids.orgPublication Date: May 2020Data from the soilgrids.org mean predictions for silt were used to create this layer. You may access the percent silt in one of six depth ranges. To select one choose the depth variable in the multidimensional selector in your map client.Mean depth (cm)Actual depth range of data-2.50-5cm depth range-105-15cm depth range-22.515-30cm depth range-4530-60cm depth range-8060-100cm depth range-150100-200cm depth rangeWhat can you do with this Layer?This layer is suitable for both visualization and analysis across the ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application.Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map: In ArcGIS Online, you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "world soils soilgrids" in the search box and browse to the layer. Select the layer then click Add to Map. In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "world soils soilgrids" in the search box, browse to the layer then click OK.In ArcGIS Pro you can use the built-in raster functions or create your own to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro.Online you can filter the layer to show subsets of the data using the filter button and the layer's built-in raster functions.This layer is part of the Living Atlas of the World that provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.More information about soilgrids layersAnswers to many questions may be found at soilgrids.org (ISRIC) frequently asked questions (faq) page about the data.To make this layer, Esri reprojected the expected value of ISRIC soil grids from soilgrids' source projection (goode's land WKID 54052) to web mercator projection, nearest neighbor, to facilitate online mapping. The resolution in web mercator projection is the same as the original projection, 250m. But keep in mind that the original dataset has been reprojected to make this web mercator version.This multidimensional soil collection serves the mean or expected value for each soil variable as calculated by soilgrids.org. For all other distributions of the soil variable, be sure to download the data directly from soilgrids.org. The data are available in VRT format and may be converted to other image formats within ArcGIS Pro.Accessing this layer's companion uncertainty layerBecause data quality varies worldwide, the uncertainty of the predicted value varies worldwide. A companion uncertainty layer exists for this layer which you can use to qualify the values you see in this map for analysis. Choose a variable and depth in the multidimensional settings of your map client to access the companion uncertainty layer.

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Soil is the foundation of life on earth. More living things by weight live in the soil than upon it. It determines what crops we can grow, what structures we can build, what forests can take root.This layer contains the chemical soil variable nitrogen (nitrogen).Nitrogen is an essential nutrient for sustaining life on Earth. Nitrogen is a core component of amino acids, which are the building blocks of proteins, and of nucleic acids, which are the building blocks of genetic material (RNA and DNA).This layer is a general, medium scale global predictive soil layer suitable for global mapping and decision support. In many places samples of soils do not exist so this map represents a prediction of what is most likely in that location. The predictions are made in six depth ranges by soilgrids.org, funded by ISRIC based in Wageningen, Netherlands.Each 250m pixel contains a value predicted for that area by soilgrids.org from best available data worldwide. Data for nitrogen are provided at six depth ranges from the surface to 2 meters below the surface. Each variable and depth range may be accessed in the layer's multidimensional properties.Dataset SummaryPhenomenon Mapped: Total nitrogen (N) in g/kgCell Size: 250 metersPixel Type: 32 bit float, converted from online data that is 16 Bit Unsigned IntegerCoordinate System: Web Mercator Auxiliary Sphere, projected via nearest neighbor from goode's homolosine land (250m)Extent: World land area except AntarcticaVisible Scale: All scales are visibleNumber of Columns and Rows: 160300, 100498Source: Soilgrids.orgPublication Date: May 2020Data from the soilgrids.org mean predictions for nitrogen were used to create this layer. You may access nitrogen values in one of six depth ranges. To select one choose the depth variable in the multidimensional selector in your map client.Mean depth (cm)Actual depth range of data-2.50-5cm depth range-105-15cm depth range-22.515-30cm depth range-4530-60cm depth range-8060-100cm depth range-150100-200cm depth rangeWhat can you do with this Layer?This layer is suitable for both visualization and analysis across the ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application.Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map: In ArcGIS Online, you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "world soils soilgrids" in the search box and browse to the layer. Select the layer then click Add to Map. In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "world soils soilgrids" in the search box, browse to the layer then click OK.In ArcGIS Pro you can use the built-in raster functions or create your own to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro.Online you can filter the layer to show subsets of the data using the filter button and the layer's built-in raster functions.This layer is part of the Living Atlas of the World that provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.More information about soilgrids layersAnswers to many questions may be found at soilgrids.org (ISRIC) frequently asked questions (faq) page about the data.To make this layer, Esri reprojected the expected value of ISRIC soil grids from soilgrids' source projection (goode's land WKID 54052) to web mercator projection, nearest neighbor, to facilitate online mapping. The resolution in web mercator projection is the same as the original projection, 250m. But keep in mind that the original dataset has been reprojected to make this web mercator version.This multidimensional soil collection serves the mean or expected value for each soil variable as calculated by soilgrids.org. For all other distributions of the soil variable, be sure to download the data directly from soilgrids.org. The data are available in VRT format and may be converted to other image formats within ArcGIS Pro.Accessing this layer's companion uncertainty layerBecause data quality varies worldwide, the uncertainty of the predicted value varies worldwide. A companion uncertainty layer exists for this layer which you can use to qualify the values you see in this map for analysis. Choose a variable and depth in the multidimensional settings of your map client to access the companion uncertainty layer.

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

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

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

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This dataset represents a unique compiled environmental data set for the circumpolar Arctic ocean region 45N to 90N region. It consists of 170 layers (mostly marine, some terrestrial) in ArcGIS 10 format to be used with a Geographic Information System (GIS) and which are listed below in detail. Most layers are long-term average raster GRIDs for the summer season, often by ocean depth, and represent value-added products easy to use. The sources of the data are manifold such as the World Ocean Atlas 2009 (WOA09), International Bathimetric Chart of the Arctic Ocean (IBCAO), Canadian Earth System Model 2 (CanESM2) data (the newest generation of models available) and data sources such as plankton databases and OBIS. Ocean layers were modeled and predicted into the future and zooplankton species were modeled based on future data: Calanus hyperboreus (AphiaID104467), Metridia longa (AphiaID 104632), M. pacifica (AphiaID 196784) and Thysanoessa raschii (AphiaID 110711). Some layers are derived within ArcGIS. Layers have pixel sizes between 1215.819573 meters and 25257.72929 meters for the best pooled model, and between 224881.2644 and 672240.4095 meters for future climate data. Data was then reprojected into North Pole Stereographic projection in meters (WGS84 as the geographic datum). Also, future layers are included as a selected subset of proposed future climate layers from the Canadian CanESM2 for the next 100 years (scenario runs rcp26 and rcp85). The following layer groups are available: bathymetry (depth, derived slope and aspect); proximity layers (to,glaciers,sea ice, protected areas, wetlands, shelf edge); dissolved oxygen, apparent oxygen, percent oxygen, nitrogen, phosphate, salinity, silicate (all for August and for 9 depth classes); runoff (proximity, annual and August); sea surface temperature; waterbody temperature (12 depth classes); modeled ocean boundary layers (H1, H2, H3 and Wx).This dataset is used for a M.Sc. thesis by the author, and freely available upon request. For questions and details we suggest contacting the authors. Process_Description: Please contact Moritz Schmid for the thesis and detailed explanations. Short version: We model predicted here for the first time ocean layers in the Arctic Ocean based on a unique dataset of physical oceanography. Moreover, we developed presence/random absence models that indicate where the studied zooplankton species are most likely to be present in the Arctic Ocean. Apart from that, we develop the first spatially explicit models known to science that describe the depth in which the studied zooplankton species are most likely to be at, as well as their distribution of life stages. We do not only do this for one present day scenario. We modeled five different scenarios and for future climate data. First, we model predicted ocean layers using the most up to date data from various open access sources, referred here as best-pooled model data. We decided to model this set of stratification layers after discussions and input of expert knowledge by Professor Igor Polyakov from the International Arctic Research Center at the University of Alaska Fairbanks. We predicted those stratification layers because those are the boundaries and layers that the plankton has to cross for diel vertical migration and a change in those would most likely affect the migration. I assigned 4 variables to the stratification layers. H1, H2, H3 and Wx. H1 is the lower boundary of the mixed layer depth. Above this layer a lot of atmospheric disturbance is causing mixing of the water, giving the mixed layer its name. H2, the middle of the halocline is important because in this part of the ocean a strong gradient in salinity and temperature separates water layers. H3, the isotherm is important, because beneath it flows denser and colder Atlantic water. Wx summarizes the overall width of the described water column. Ocean layers were predicted using machine learning algorithms (TreeNet, Salford Systems). Second, ocean layers were included as predictors and used to predict the presence/random absence, most likely depth and life stage layers for the zooplankton species: Calanus hyperboreus, Metridia longa, Metridia pacifica and Thysanoessa raschii, This process was repeated for future predictions based on the CanESM2 data (see in the data section). For zooplankton species the following layers were developed and for the future. C. hyperboreus: Best-pooled model as well as future predictions (rcp26 including ocean layer(also excluding), rcp85 including oocean layers (also excluding) for 2010 and 2100.For parameters: Presence/random absence, most likely depth and life stage layers M. longa: Best-pooled model as well as future predictions (rcp26 including ocean layer(also excluding), rcp85 including oocean layers (also excluding) for 2010 and 2100. For parameters: Presence/rand... Visit https://dataone.org/datasets/f63d0f6c-7d53-46ce-b755-42a368007601 for complete metadata about this dataset.