2025 Spring Aerials Lorain County Ohio.ECW FormatProjected Coordinate System NAD 1983 StatePlane Ohio North FIPS 3401 (US Feet)Projection Lambert Conformal ConicWKID 3734Previous WKID 102722Authority EPSGLinear Unit US Survey Feet (0.3048006096012192)False Easting 1968500.0False Northing 0.0Central Meridian -82.5Standard Parallel 1 40.43333333333333Standard Parallel 2 41.7Latitude Of Origin 39.66666666666666

Continuous Wavelet Transform was applied to bed elevation profiles (BEP) and used in the study in order to recognise the spatial distribution of bedforms and discriminate between their hierarchical scales. In particular, the spatial distribution of the hierarchical scales is highlighted by averaging wavelet power spectra over different bands, and displayed as the wavelet variance of the BEP (see map). Four dune classes were defined, following Ashley (1990): small dunes (1-5 m), medium dunes (5-10 m), large dunes (10-100 m), and very large dunes (>100 m).

Geographic analysis of impervious cover and demographic attributes by 2010 Census block group. Demographic attributes and Census block groups were downloaded from the EJScreen web page (www.epa.gov/ejscreen). EJScreen technical documentation is available at website; click on the technical information link on the landing page; the link of the documentation is in the center of the web page. NLCD2019 data for impervious cover were downloaded from www.mrlc.gov. The impervious cover for 2001 and 2019 in the NLCD2019 database were used in the analysis. The Census block groups downloaded from EJScreen were projected into Albers Conic Equal Area to match the NLCD2019 projection (Albers Conic Equal Area: central meridian=96.0 W; origin latitude=23.0 N; false easting=0.0; false northing=0.0; standard parallel 1=29.5 N; standard parallel 2=49.5 N; Geographic coordinate system=WGS84; WKID=4326) .

City Of Sacramento's Survey Division has developed a high accuracy GPS control point grid. This file currently contains data points for the entire City of Sacramento. The latitude and longitude values have an accuracy level of +/- .05 feet. Elevation data has accuracy of +/- .24 feet.

Field: GPSNUMBER Alias: Survey reference number Field Description: Reference to latitude/longitude minute

Field: NORTHINGFT Alias: False Northing, California State Plane, Zone II, Feet

Field: EASTINGFT Alias: False Easting, California State Plane, Zone II, Feet

Field: ELVORTHOFT Alias: Elevation Ortho (ft)- a preliminary ground elevation to which the orthometric leveling correction has been applied

Field: DFNGVD29FT Alias: Differential NGVD 29- elevation obtained by spirit leveling based on the national geodetic vertical datum of 1929

Field: STREET Alias: Street location of control point

Field: XSTREET Alias: Cross street or reference information

Field: MONTYPE Alias: Control point or monument type

Field: LAT_DMS Alias: Latitude values in Degrees, Minutes, Seconds

Field: LONG_DMS Alias: Latitude values in Degrees, Minutes, Seconds

Field: ELLIPSHT Alias: Ellipsoid Height- the distance, measured along the mormal, from the surface of the ellipsoid to a point

Field: CNVERGENCE Alias: The angle difference at a given location between grid north and astronomic north

Field: GRDSCLFCTR Alias: Grid Scale Factor- a multiplier for reducing a sea level lengths to grid lengths

Field: COMBNDFCTR Alias: Combined Factor- multiplier obtained from the product of the sea level and grid scale factor and applied to ground distance to obtain grid distance

Field: GEOIDHT Alias: Distance of the geoid above (positive) or below (negative) the mathematical reference spheroid

Field: ARCHIVELOC Alias: Use To be Determined Field Description: Associated with crossed out GPS No.-Point ID

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

The Web-Enabled Landsat Data (WELD) 5-year Land Cover Land Use Change (LCLUC) is a composite of 30 m land use land change product for the contiguous United States (CONUS) generated from 5 years of consecutive growing season WELD inputs from April 15, 2006, to November 17, 2010. WELD LCLUC is offered in HDF format. This product includes the following bands: tree cover, bare ground, water surface, snow and ice, and number of good acquisitions which are composited over the 5-year period.

The WELD project is funded by the National Aeronautics and Space Administration (NASA) and is a collaboration between the United States Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center and South Dakota State University, Geospatial Sciences Center of Excellence (GSCE). The Land Processes Distributed Active Archive Center (LP DAAC) is responsible for archiving and distributing NASA WELD, which includes the WELD LCLUC product.

Data Set Characteristics: Projection: Albers Equal Area Datum: World Geodetic System 84 (WGS84) Geographic Extent: CONUS Tile size: 5000 x 5000 (rows/columns) Pixel size: 30 m Tile volume: 17.44 GB Tiles: 483 First standard parallel: 29.5° Second standard parallel: 45.5° Longitude of central meridian: -96.0° Latitude of projection origin: 23.0° False Easting: 0.0 False Northing: 0.0

Active high intensity gas seepage is documented for the first time at the seaward edge of the salt occurrence in the southern Lower Congo Basin. Microbial methane release from the seafloor occurs on the crests of two 800 m high ridges formed by fault-propagation folding. Intense uplift is documented since the end of the Miocene by distinct onlapping reflections on the landward flank of these ridges. A paleo-pockmark structure suggests an onset of seepage coincident with this deformation period. High-resolution seismic imaging reveals methane migration along strata from Oligocene/Miocene fan deposits towards the ridge crests where large gas accumulations form beneath a discontinuous Bottom Simulating Reflection (BSR). Detailed mapping revealed that free gas and gas hydrate occurrences below and above the base of the gas hydrate stability zone are closely linked to sedimentary strata in the flanks of topographic ridges. Gas transport through the gas hydrate stability zone originates from the shallowest area of the BSR directly beneath the seafloor seep sites, suggesting pressure controlled venting. These sites represent the most seaward salt-related gas seepage features documented in the area and illustrate the initiation of long-lasting seepage at the front of an area of compressional tectonics at a passive continental margin.

As part of the Long Term Ecological Research (LTER) project in the McMurdo Dry Valleys of Antarctica, a systematic sampling program has been undertaken to monitor glacial meltwater stream attributes of the region. Optical topographic surveys were performed to produce a layout of the area studied. This file provides the northing, easting, and orthometric heights for points in the transects where such work was performed.

This study investigates the distribution and evolution of seafloor seepage in the vicinity of the salt front, i.e., the seaward boundary of salt-induced deformation in the Lower Congo Basin (LCB). Seafloor topography, backscatter data and TV-sled observations indicate active fluid seepage from the seafloor directly at the salt front, whereas suspected seepage sites appear to be inactive at a distance of >10 km landward of the deformation front. High resolution multichannel seismic data give detailed information on the structural development of the area and its influence on the activity of individual seeps during the geologic evolution of the salt front region. The unimpeded migration of gas from fan deposits along sedimentary strata towards the base of the gas hydrate stability zone within topographic ridges associated with relatively young salt-tectonic deformation facilitates seafloor seepage at the salt front. Bright and flat spots within sedimentary successions suggest geological trapping of gas on the flanks of mature salt structures in the eastern part of the study area. Onlap structures associated with fan deposits which were formed after the onset of salt-tectonic deformation represent potential traps for gas, which may hinder gas migration towards seafloor seeps. Faults related to the thrusting of salt bodies seawards also disrupt along-strata gas migration pathways. Additionally, the development of an effective gas hydrate seal after the cessation of active salt-induced uplift and the near-surface location of salt bodies may hamper or prohibit seafloor seepage in areas of advanced salt-tectonic deformation. This process of seaward shifting active seafloor seepage may propagate as seaward migrating deformation affects Congo Fan deposits on the abyssal plain. These observations of the influence of the geologic evolution of the salt front area on seafloor seepage allows for a characterization of the large variety of hydrocarbon seepage activity throughout this compressional tectonic setting.

This is the dataset for oil and gas potential as described in Copeland et al. (2009) Mapping Oil and Gas Development Potential in the US Intermountain West and Estimating Impacts to Species, PLoSOne. The dataset should be cited as: Copeland, H., K. Doherty, D. Naugle, A. Pocewicz, J. Kiesecker (2010) Mapping Oil and Gas Development Potential in the US Intermountain West and Estimating Impacts to Species. The projection of this dataset is: US NAD83 Lambert Conformal Conic False easting 0 False northing 0 Central meridian -107.0 Standard parallel 1 33.0 Standard parallel 2 45.0 Latitude of origin 41.0

TIF files: ---> DEM, ---> Lat, Lon, ---> Mask, ---> DayMET precipitation Daymet (annual) precipitation SVR processing example Daymet data set provides annual and summary climate data for minimum and maximum temperature, precipitation, and vapor pressure (Thornton et al. 2014). Daymet data consider the total accumulated precipitation over the annual period of the daily total precipitation. Precipitation is the sum of all forms of precipitation converted to water equivalent (mm/yr) (Thornton et al. 2014). The Daymet data layers are produced on a 1-km x 1-km gridded surface over the conterminous United States in Lambert Conformal Conic projection (units are meters) with the following parameters, a) horizontal datum: WGS 84, b) 1st standard parallel= 25o, 2nd standard parallel= 60o, c) Central meridian= -100o, and Latitude of origin= 42.5o, d) false easting= 0, false northing= 0. X is in the range -1949774 to -725054 m, and Y is in the range -1144097 to 222385 m. So the data is projected to a rectangular grid instead of a geographic grid. Thus, the 3 independent variables will be h, X (Easting) and Y (Northing) instead of h, ö and ë. The 12 Daymet gridded annual precipitation (P) images for the period 2003 to 2014 are used. Thornton, P.E., Thornton, M.M., Mayer, B.W., Wilhelmi, N., Wei, Y., Devarakonda, R., & Cook, R.B. (2014), Daymet: Daily Surface Weather Data on a 1-km Grid for North America, Version 2. ORNL DAAC, Oak Ridge, Tennessee, USA. http://dx.doi.org/10.3334/ORNLDAAC/1277.

Israeli_butterfly_move_dataPrimary data for estimating movement rates. UniqueID combines the site name, track number and flight step number. Species are identified in Supplemental Information, Table S3. Field types are Wheat and Olives. Locations are Wheat, Olive, Nature and (Field) Margin. See Figure 1 for visual of field types vs locations. Projected coordinate system is GRS_1980_Transverse_Mercator with False Easting = 219529.584 and False Northing = 626907.390. Each move consisted of a move length, measured as the distance between turning or stopping points i and i+1, and a turning angle θi , measured as the angle between move i-1 and i. The file contains Time (seconds), Length (m) and Turn (Cosine of turning angle). Note that steps starting in edge regions, defined as 10m outside the Margin towards Nature or Fields, were omitted from the analysis (see text for details). However, if a move step starts in one of the included Locations and ends in edge region, the step and associat...

China's land cover data set includes 5 products: 1) glc2000_lucc_1km_China.asc, a Chinese subset of global land cover data based on SPOT4 remote sensing data developed by the GLC2000 project. The data name is GLC2000.GLC2000 China's regional land cover data is directly cropped from global cover data. For data description, please refer to http : //www-gvm.jrc.it/glc2000/defaultGLC2000.htm 2) igbp_lucc_1km_China.asc, a Chinese subset of global land cover data based on AVHRR remote sensing data supported by IGBP-DIS, the data name is IGBPDIS; IGBPDIS data was prepared using the USGS method, using April 1992 to March 1992 The AVHRR data developed global land cover data with a resolution of 1km. The classification system adopts a classification system developed by IGBP, which divides the world into 17 categories. Its development is based on continents. Applying AVHRR for 12 months to maximize synthetic NDVI data, 3) modis_lucc_1km_China_2001.asc, a subset of MODIS land cover data products in China, the data name is MODIS; MODIS China's regional land cover data is directly cropped from global cover data, and its data description please refer to http://edcdaac.usgs.gov/ modis / mod12q1v4.asp. 4. umd_lucc_1km_China.asc, a Chinese subset of global land cover data based on AVHRR data produced by the University of Maryland, the data name is UMd; the five bands of UMd based on AVHRR data and NDVI data are recombined to suggest a data matrix, using Methodology carried out global land cover classification. The goal is to create data that is more accurate than past data. The classification system largely adopts the classification scheme of IGBP. 5) westdc_lucc_1km_China.asc, China ’s 2000: 100,000 land cover data organized and implemented by the Chinese Academy of Sciences, combined with Yazashi conversion (the largest area method), and finally obtained a land use data product of 1km across the country, data name WESTDC. WESTDC China's regional land cover data is based on the results of a 1: 100,000 county-level land resource survey conducted by the Chinese Academy of Sciences. The land use data were merged and converted into a vector (the largest area method). The Chinese Academy of Sciences resource and environment classification system is adopted. 2: Data format: ArcView GIS ASCII 3: Mesh parameters: ncols 4857 nrows 4045 xllcorner -2650000 yllcorner 1876946 cellsize 1000 NODATA_value -9999 4: Projection parameters: Projection ALBERS Units METERS Spheroid Krasovsky Parameters: 25 00 0.000 / * 1st standard parallel 47 00 0.000 / * 2nd standard parallel 105 00 0.000 / * central meridian 0 0 0.000 / * latitude of projection's origin 0.00000 / * false easting (meters) 0.00000 / * false northing (meters)

Survey control points in the Susquehanna Shale Hills CZO.

USGS is assessing the feasibility of map projections and grid systems for lunar surface operations. We propose developing a new Lunar Transverse Mercator (LTM), the Lunar Polar Stereographic (LPS), and the Lunar Grid Reference Systems (LGRS). We have also designed additional grids designed to NASA requirements for astronaut navigation, referred to as LGRS in Artemis Condensed Coordinates (ACC). This data release includes LGRS grids finer than 25km (1km, 100m, and 10m) in ACC format. LTM, LPS, and LGRS grids are not released here but may be acceded from https://doi.org/10.5066/P13YPWQD. LTM, LPS, and LGRS are similar in design and use to the Universal Transverse Mercator (UTM), Universal Polar Stereographic (LPS), and Military Grid Reference System (MGRS), but adhere to NASA requirements. LGRS ACC format is similar in design and structure to historic Army Mapping Service Apollo orthotopophoto charts for navigation. The Lunar Transverse Mercator (LTM) projection system is a globalized set of lunar map projections that divides the Moon into zones to provide a uniform coordinate system for accurate spatial representation. It uses a Transverse Mercator projection, which maps the Moon into 45 transverse Mercator strips, each 8°, longitude, wide. These Transverse Mercator strips are subdivided at the lunar equator for a total of 90 zones. Forty-five in the northern hemisphere and forty-five in the south. LTM specifies a topocentric, rectangular, coordinate system (easting and northing coordinates) for spatial referencing. This projection is commonly used in GIS and surveying for its ability to represent large areas with high positional accuracy while maintaining consistent scale. The Lunar Polar Stereographic (LPS) projection system contains projection specifications for the Moon’s polar regions. It uses a polar stereographic projection, which maps the polar regions onto an azimuthal plane. The LPS system contains 2 zones, each zone is located at the northern and southern poles and is referred to as the LPS northern or LPS southern zone. LPS, like its equatorial counterpart LTM, specifies a topocentric, rectangular, coordinate system (easting and northing coordinates) for spatial referencing. This projection is commonly used in GIS and surveying for its ability to represent large polar areas with high positional accuracy while maintaining consistent scale across the map region. LGRS is a globalized grid system for lunar navigation supported by the LTM and LPS projections. LGRS provides an alphanumeric grid coordinate structure for both the LTM and LPS systems. This labeling structure is utilized similarly to MGRS. LGRS defines a global area grid based on latitude and longitude and a 25×25 km grid based on LTM and LPS coordinate values. Two implementations of LGRS are used as polar areas require an LPS projection and equatorial areas a Transverse Mercator. We describe the differences in the techniques and methods reported in this data release. Request McClernan et. al. (in-press) for more information. ACC is a method of simplifying LGRS coordinates and is similar in use to the Army Mapping Service Apollo orthotopophoto charts for navigation. These grids are designed to condense a full LGRS coordinate to a relative coordinate of 6 characters in length. LGRS in ACC format is completed by imposing a 1km grid within the LGRS 25km grid, then truncating the grid precision to 10m. To me the character limit, a coordinate is reported as a relative value to the lower-left corner of the 25km LGRS zone without the zone information; However, zone information can be reported. As implemented, and 25km^2 area on the lunar surface will have a set of a unique set of ACC coordinates to report locations The shape files provided in this data release are projected in the LTM or LPS PCRSs and must utilize these projections to be dimensioned correctly. LGRS ACC Grids Files and Resolution: LGRS ACC Grids in LPS portion: Amundsen_Rim 1km Grid Shapefile 100m Grid Shapefile 10m Grid Shapefile Nobile_Rim_2 1km Grid Shapefile 100m Grid Shapefile 10m Grid Shapefile Haworth 1km Grid Shapefile 100m Grid Shapefile 10m Grid Shapefile Faustini_Rim_A 1km Grid Shapefile 100m Grid Shapefile 10m Grid Shapefile de_Gerlache_Rim_2 1km Grid Shapefile 100m Grid Shapefile 10m Grid Shapefile Connecting_Ridge_Extension 1km Grid Shapefile 100m Grid Shapefile 10m Grid Shapefile Connecting_Ridge 1km Grid Shapefile 100m Grid Shapefile 10m Grid Shapefile Nobile_Rim_1 1km Grid Shapefile 100m Grid Shapefile 10m Grid Shapefile Peak_Near_Shackleton 1km Grid Shapefile 100m Grid Shapefile 10m Grid Shapefile de_Gerlache_Rim' 'Leibnitz_Beta_Plateau 1km Grid Shapefile 100m Grid Shapefile 10m Grid Shapefile Malapert_Massif 1km Grid Shapefile 100m Grid Shapefile 10m Grid Shapefile de_Gerlache-Kocher_Massif 1km Grid Shapefile 100m Grid Shapefile 10m Grid Shapefile LGRS ACC Grids in LTM portion: Apollo_11 1km Grid Shapefile 100m Grid Shapefile 10m Grid Shapefile Apollo_12 1km Grid Shapefile 100m Grid Shapefile 10m Grid Shapefile Apollo_14 1km Grid Shapefile 100m Grid Shapefile 10m Grid Shapefile Apollo_15 1km Grid Shapefile 100m Grid Shapefile 10m Grid Shapefile Apollo_16 1km Grid Shapefile 100m Grid Shapefile 10m Grid Shapefile Apollo_17 1km Grid Shapefile 100m Grid Shapefile 10m Grid Shapefile LTM, LPS, and LGRS PCRS shapefiles utilize either a custom transverse Mercator or polar Stereographic projection. For PCRS grids the LTM and LPS projections are recommended for all LTM, LPS, and LGRS grid sizes. See McClernan et. al. (in-press) for such projections. For GIS utilization of grid shapefiles projected in Lunar Latitude and Longitude should utilize a registered lunar geographic coordinate system (GCS) such as IAU_2015:30100 or ESRI:104903. This only applies to grids that cross multiple LTM zones. Note: All data, shapefiles require a specific projection and datum. The projection is recommended as LTM and LPS or, when needed, IAU_2015:30100 or ESRI:104903. The datum utilized must be the Jet Propulsion Laboratory (JPL) Development Ephemeris (DE) 421 in the Mean Earth (ME) Principal Axis Orientation as recommended by the International Astronomy Union (IAU) (Archinal et. al., 2008).

This metadata refers to the EEA marine assessment grid, to which all data and assessment results have been spatially mapped in order to ensure that data can be compared in a uniform way across the European regional seas.

The marine assessment grid is based on the EEA reference grid system. The EEA reference grid is based on ERTS89 Lambert Azimuthal Equal Area projection with parameters: latitude of origin 52° N, longitude of origin 10° E, false northing 3 210 000.0 m, false easting 4 321 000.0 m. All grid cells are named with a unique identifier containing information on grid cell size and the distance from origin in meters (easting and northing). An important attribute of the EEA reference grid system is that by using an equal area projection all grid cells are having the same area for the same grid size.

In this marine assessment grid, two grid sizes are used: * 100 x 100 km in offshore areas (> 20 km from the coastline) * 20 x 20 km in coastal areas (<= 20 km from the coastline) The grid sizes were choosen after an evaluation of data availability versus the need for sufficient detail in the resulting assessment. The resulting assessment grid is a combination of two grid sizes using the EEA reference grid system.

The overall area of interest used in the grid is based on the marine regions and subregions under the Marine Strategy Framework Directive (MSFD). Additionally, Norwegian (Barent Sea and Norwegian Sea) and Icelandic waters (’Iceland Sea’) have been added (see Surrounding seas of Europe). Note that, within the North East Atlantic region, only the subregions within EEZ boundaries (~200 nm) have been included.

"The Virginia Geographic Information Network (VGIN) acquired the Virginia Base Mapping Program (VBMP) aerial photography through funding provided by the E911 Services Board. The photography was captured in the spring 00 (during the leaf-off season). This is a statewide product. The aerial photography was initially captured at 1- or -foot resolution (contingent on local population density) in true color. In addition, some localities opted up for a 6-inch-resolution product. The data set provided on the DVD is a 1-meter resampled product. It is available in Virginia Lambert Conformal Conic (a customized projection developed by VDOT; see projection information in Appendix A). Note that areas associated with military bases and other points of national interest have been resampled at 5-meter resolution. The imagery is stored in tiles that measure ~3 miles on each side. Additional information on the VBMP aerial photography program and other VBMP data products available in www.vgin.virginia. gov/VBMP/VBMPHandbook_r2.pdf Data Type: Raster Pixel size: 1 Meter Data Format: Mr. Sid Projection Information: Virginia Statewide Lambert Conformal Conic1 Units: Meters Spheroid: GRS 1980 X shift: 0 Y shift: 0 1st Standard Parallel: 37.0 nd Standard Parallel: 39.5 Central Meridian: -79.5 Latitude of Projection Origin: 36.0 False Easting: 0 False Northing: 0 Source: VGINFor more information on this data refer to the supplemental metadata pdf found at: https://secure-archive.gis.vt.edu/gisdata/public/UnitedStates/Virginia/VCE_2002_metadata/METADATA.pdf"

This dataset includes position coordinates and elevation information for wells at the WHOLESCALE San Emidio project location. Well positions in the attached file are characterized by UTM coordinates (Easting, Northing) in meters, and WHOLESCALE coordinates (Easting, Northing) relative in meters from a chosen reference point. The elevation of the top and bottom of open intervals within each well (representing locations of perforated or open-hole sections) are measured in meters (positive) above WGS84 geoid (mean sea level). The WHOLESCALE acronym stands for Water & Hole Observations Leverage Effective Stress Calculations and Lessen Expenses. The goal of the WHOLESCALE project is to simulate the spatial distribution and temporal evolution of stress in the geothermal system at San Emidio in Nevada, United States.

Datasets used in: Young, A.M., Higuera, P.E., Duffy, P.A., and F.S. Hu. Climatic thresholds shape northern high-latitude fire regimes and imply vulnerability to future climate change. In Review at Ecography as of 10/2015. ---------------------------------------------------------------------- ----------------------- Description ---------------------------------- ---------------------------------------------------------------------- These data are gridded maps of Alaska containing the projected 30-yr probabiltiy of fire occurrence for three different time periods in the 21st century: 2010-2039, 2040-2069, 2070-2099. We use downscaled GCM climate data under the RCP6.0 scenario. We provide projections for all 100 models for each of the three different spatial domains: AK, BOREAL, and TUNDRA. Gridded GCM data were processed and provided by the Scenarios Network for Alaska and Arctic Planning (https://www.snap.uaf.edu/). For further details regarding these archived data please refer to YOung et al. (In Review). ---------------------------------------------------------------------- ----------------------- Resolution ----------------------------------- ---------------------------------------------------------------------- Spatial Resolution: 2 km Temporal Resolution: Decadal Time Coverage: 2010-2099 ---------------------------------------------------------------------- ------------------------ File Naming --------------------------------- ---------------------------------------------------------------------- Example: AK_CCSM4_rcp60_pred_map_2010_2039_1.tif 'AK_' - Either models build for all of Alaska (AK), boreal forest only (BOREAL), tundra only (TUNDRA) 'CCSM4' - Specific GCM '_rcp60' - Climate change scenario '_pred_map' - predicted (or projected) 30-yr probabilities of fire occurrence [0-1]. '_2010_2039' - time period for projection '_x' - gbm model. corresponds to 'gbm_x.RData' and historical predictions '.tif' - file extention ---------------------------------------------------------------------- ------------------ Geographic Information ---------------------------- ---------------------------------------------------------------------- Rows: 725 Columns: 687 Spatial Extent (in meters) - - Top: 2390439.786 - Left: -656204.44 - Right: 717795.56 - Bottom: 940439.786 Spatial Reference: Albers Equal Area Datum: North American 1983 False Easting: 0 False Northing: 0 Central Meridian: -154 Standard Parallel 1: 55 Standard Parallel 2: 65 Latitude of Origin: 50

The land use / land cover data set of Heihe River Basin in 2011 is the Remote Sensing Research Office of Institute of cold and drought of Chinese Academy of Sciences. Based on the remote sensing data of landsatm and ETM in 2011, combined with field investigation and verification, a 1:100000 land use / land cover image and vector database of Heihe River Basin is established.
The data set mainly includes 1:100000 land use graph data and attribute data in the middle reaches of Heihe River Basin.
The land cover data of 1:100000 (2011) in Heihe River Basin and the previous land cover are classified into six first-class categories (cultivated land, forest land, grassland, water area, urban and rural residents, industrial and mining land and unused land) and 25 second-class categories by the same hierarchical land cover classification system. The data type is vector polygon and stored in shape format. Land cover classification attributes:
Level 1 type level 2 type attribute code spatial distribution location
Cultivated land: plain dry land 123 is mainly distributed in basin, piedmont, river alluvial, proluvial or lacustrine plain (poor irrigation conditions due to water shortage).
The upland and land 122 is mainly distributed in the hilly area, and generally, the plot is distributed on the gentle slope of the hill, as well as on the top of the ridge and the base.
The dry land 121 is mainly distributed in the mountainous area, the hillside (gentle slope, hillside, steep slope platform, etc.) and the Piedmont belt below 4000 m above sea level.
Woodland: there are woodland (Arbor) 21 mainly distributed in high mountains (below 4000 meters above sea level) or middle mountain slopes, valley slopes, mountain tops, plains, etc.
Shrub land 22 is mainly distributed in the higher mountain area (below 4500m), most of which are hillside, valley and sandy land.
Sparse forest land 23 is mainly distributed in mountainous areas, hills, plains and sandy land, Gobi (Loamy, sandy conglomerate) edge.
Other forest lands 24 are mainly distributed around the oasis ridge, riverside, roadside and rural residential areas.
Grassland: high cover grassland 31 is generally distributed in mountainous area (gentle slope), hilly area (steep slope), river beach, Gobi, sandy land, etc.
The middle cover grassland 32 is mainly distributed in dry areas (low-lying land next door and land between Sandy Hills, etc.).
Low cover grassland 33 mainly grows in dry areas (loess hills and sand edge).
Water area: channel 41 is mainly distributed in plain, inter Sichuan cultivated land and inter mountain valley.
Lake 42 is mainly distributed in low-lying areas.
Reservoir pond 43 is mainly distributed in plain and valley between rivers, surrounded by residential land and cultivated land.
Glaciers and permanent snow cover 44 are mainly distributed on the top of (over 4000) mountains.
The beach land 46 is mainly distributed in the valley, piedmont, plain lowland, the edge of river lake basin and so on.
Residential land: urban land 51 is mainly distributed in plain, mountain basin, slope and gully platform.
Rural residential land 52 is mainly distributed in oasis, cultivated land and roadside, tableland, slope, etc.
Industrial and mining land and traffic land 53 are generally distributed in the periphery of cities and towns, more developed traffic areas and industrial mining areas.
Unused land: sand 61 is mostly distributed in the basin, both sides of the river, the river bay and the periphery of the mountain front Gobi.
Gobi 62 is mainly distributed in the Piedmont belt with strong wind erosion and sediment transport.
Salt alkali 63 is mainly distributed in relatively low and easy to accumulate water, dry lakes and lakeside.
Swamp 64 is mainly distributed in relatively low and easy to accumulate water.
Bare soil 65 is mainly distributed in the arid areas (mountain steep slopes, hills, Gobi), and the vegetation coverage is less than 5%.
Bare rock 66 is mainly distributed in the extremely dry stone mountain area (windy, light rain).
The other 67 are mainly distributed in the exposed rocks formed by freezing and thawing over 4000 meters, also known as alpine tundra. Projection parameters: Projection ALBERS Units METERS Spheroid Krasovsky Parameters: 25 00 0.000 /* 1st standard parallel 47 00 0.000 /* 2nd standard parallel 105 00 0.000 /* central meridian 0 0 0.000 /* latitude of projection's origin 0.00000 /* false easting (meters) 0.00000 /* false northing (meters)