An ESRI GRID raster data model of the Mahogany bed structure was needed to perform overburden calculations in the Uinta Basin, Utah and Colorado as part of a 2009 National Oil Shale Assessment.

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

A bare-earth, hydro-flattened, digital-elevation surface model derived from 2010 Light Detection and Ranging (LiDAR) data. Surface models are raster representations derived by interpolating the LiDAR point data to produce a seamless gridded elevation data set. A Digital Elevation Model (DEM) is a surface model generated from the LiDAR returns that correspond to the ground with all buildings, trees and other above ground features removed. The cell values represent the elevation of the ground relative to sea level. The DEM was generated by interpolating the LiDAR ground points to create a 1 foot resolution seamless surface. Cell values correspond to the ground elevation value (feet) above sea level. A proprietary approach to surface model generation was developed that reduced spurious elevation values in areas where there were no LiDAR returns, primarily beneath buildings and over water. This was combined with a detailed manual QA/QC process, with emphasis on accurate representation of docks and bare-earth within 2000ft of the water bodies surrounding each of the five boroughs.

An ESRI GRID raster data model and TIN model of the LaClede bed of the Laney Member of the Eocene Green River Formation structure was needed to perform overburden calculations in the Green River and Washakie Basins, southwestern Wyoming as part of a National Oil Shale Assessment.

SummaryThe Essential Connectivity Map shows a statewide network of 850 relatively intact Natural Landscape Blocks (ranging in size from 2,000 to about 3.7 million acres) connected by 192 Essential Connectivity Areas (Table 3.1). There are fewer Essential Connectivity Areas than Natural Landscape Blocks, because each Essential Connectivity Area serves to connect at least two, and as many as 15 Natural Landscape Blocks. Due to the broad, statewide nature of this map, and its focus on connecting very large blocks of mostly protected natural lands, the network omits many areas that are important to biological conservation. The purpose of the map is to focus attention on large areas important to maintaining ecological integrity at the broadest scale. Natural areas excluded from this broad-brush Essential Connectivity Network can therefore not be "written off" as unimportant to connectivity conservation or to sustaining California's natural heritage.DescriptionThe California Department of Transportation (Caltrans) and California Department of Fish and Game (CDFG) commissioned the California Essential Habitat Connectivity Project because a functional network of connected wildlands is essential to the continued support of California's diverse natural communities in the face of human development and climate change. The Essential Connectivity Map depicts large, relatively natural habitat blocks that support native biodiversity (Natural Landscape Blocks) and areas essential for ecological connectivity between them (Essential Connectivity Areas). This coarse-scale map was based primarily on the concept of ecological integrity, rather than the needs of particular species. Essential Connectivity Areas are placeholder polygons that can inform land-planning efforts, but that should eventually be replaced by more detailed Linkage Designs, developed at finer resolution based on the needs of particular species and ecological processes. It is important to recognize that even areas outside of Natural Landscape Blocks and Essential Connectivity Areas support important ecological values that should not be "written off" as lacking conservation value. Furthermore, because the Essential Habitat Connectivity Map was created at the statewide scale, based on available statewide data layers, and ignored Natural Landscape Blocks smaller than 2,000 acres squared, it has errors of omission that should be addressed at regional and local scales.CEHC Least Cost Corridors (LACo)Mosaic of least-cost corridor results for all Essential Connectivity Areas and clipped to the LA County Boundary. The minimum cell value was used for overlapping cells.CEHC Cost Surface (LACo)Statewide resistance surface generated for least-cost corridor models and clipped to the LA County Boundary.

An ESRI GRID raster data model of the Mahogany Zone structure was needed to perform overburden calculations in the Piceance Basin, Colorado as part of a 2009 National Oil Shale Assessment.

WORKING VERSION. All layers are visible in this linked webgis app along with estimated error. The layers available in this dataset are in a WGS84 geographic coordinate reference system (EPSG:4326) where latitude and longitude coordinates at 0.0008983 degrees ground sampling distance per cell, which corresponds to about 1 ha, i.e. ~100 m x ~100 m at the equator, but decreases in area with increasing latitude as the coordinate system is not equal-area, e.g. ~70 m at 45° latitude and ~50 m at 60° latitude. Aspect.tif, slope.tif and elevation.tif represent Earth surface morphology biomass2020fireres.tif - Biomass values at year 2020 Mg/ha CanopyBulkDensity.tif - Amount of canopy biomass per volume of canopy (kg/m³) CanopyBaseHeight.tif - Height of lower canopy from the ground (m) CanopyHeight.tif - Total height of canopy from the ground (m) Fuel Model FuelModelClasses_ScottBurgan.tif - the category of Fuel Model according to Scott&Burgan 2005 FuelModelClasses_Aragonese.tif - the category of Fuel Model according to Aragonese et al. 2023 DOI: 10.5194/essd-15-1287-2023 - values are from 1 to 24, with a Look Up Table for correspondence (values are ordered matching the order in table 1 of the article) . FuelModelClasses_ScottBurgan.clr/qml CLR/QML - style file for QGIS FuelModelClasses_Aragonese.clr/qml CLR/QML - style file for QGIS FuelModelPercent - the percent of fuel model category belonging to that pixel, between 0 and 100 FuelModelAllPerc - multi-band raster with percent of each fuel model category to belong to each pixel.

ETOPO5 was generated from a digital data base of land and sea- floor elevations on a 5-minute latitude/longitude grid

LiDAR (Light Detection and Ranging) is a remote sensing technology, i.e. the technology is not in direct contact with what is being measured. From satellite, aeroplane or helicopter, a LiDAR system sends a light pulse to the ground. This pulse hits the ground and returns back to a sensor on the system. The time is recorded to measure how long it takes for this light to return. Knowing this time measurement scientists are able to create topography maps.LiDAR data are collected as points (X,Y,Z (x & y coordinates) and z (height)). The data is then converted into gridded (GeoTIFF) data to create a Digital Terrain Model and Digital Surface Model of the earth. This LiDAR data was collected on 25th March 2015.An ordnance datum (OD) is a vertical datum used as the basis for deriving heights on maps. This data is referenced to the Malin Head Vertical Datum which is the mean sea level of the tide gauge at Malin Head, County Donegal. It was adopted as the national datum in 1970 from readings taken between 1960 and 1969 and all heights on national grid maps are measured above this datum. Digital Terrain Models (DTM) are bare earth models (no trees or buildings) of the Earth’s surface.Digital Surface Models (DSM) are earth models in its current state. For example, a DSM includes elevations from buildings, tree canopy, electrical power lines and other features. Hillshading is a method which gives a 3D appearance to the terrain. It shows the shape of hills and mountains using shading (levels of grey) on a map, by the use of graded shadows that would be cast by high ground if light was shining from a chosen direction.This data shows the hillshade of the DSM.This data was collected by New York University. All data formats are provided as GeoTIFF rasters. Raster data is another name for gridded data. Raster data stores information in pixels (grid cells). Each raster grid makes up a matrix of cells (or pixels) organised into rows and columns. NYU data has a grid cell size of 1meter by 1meter. This means that each cell (pixel) represents an area of 1meter squared.

A 6-in resolution 8-class land cover dataset derived from the 2017 Light Detection and Ranging (LiDAR) data capture. This dataset was developed as part of an updated urban tree canopy assessment and therefore represents a ''top-down" mapping perspective in which tree canopy overhanging features is assigned to the tree canopy class. The eight land cover classes mapped were: (1) Tree Canopy, (2) Grass\Shrubs, (3) Bare Soil, (4) Water, (5) Buildings, (6) Roads, (7) Other Impervious, and (8) Railroads. The primary sources used to derive this land cover layer were 2017 LiDAR (1-ft post spacing) and 2016 4-band orthoimagery (0.5-ft resolution). Object based image analysis was used to automate land-cover features using LiDAR point clouds and derivatives, orthoimagery, and vector GIS datasets -- City Boundary (2017, NYC DoITT) Buildings (2017, NYC DoITT) Hydrography (2014, NYC DoITT) LiDAR Hydro Breaklines (2017, NYC DoITT) Transportation Structures (2014, NYC DoITT) Roadbed (2014, NYC DoITT) Road Centerlines (2014, NYC DoITT) Railroads (2014, NYC DoITT) Green Roofs (date unknown, NYC Parks) Parking Lots (2014, NYC DoITT) Parks (2016, NYC Parks) Sidewalks (2014, NYC DoITT) Synthetic Turf (2018, NYC Parks) Wetlands (2014, NYC Parks) Shoreline (2014, NYC DoITT) Plazas (2014, NYC DoITT) Utility Poles (2014, ConEdison via NYCEM) Athletic Facilities (2017, NYC Parks) For the purposes of classification, only vegetation > 8 ft were classed as Tree Canopy. Vegetation below 8 ft was classed as Grass/Shrub. To learn more about this dataset, visit the interactive "Understanding the 2017 New York City LiDAR Capture" Story Map -- https://maps.nyc.gov/lidar/2017/ Please see the following link for additional documentation on this dataset -- https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_LandCover.md

This datalayer is part of a group of layers used for research in the Ipswich River Watershed. This is Digital Elevation Model data for the study area, in a 30-meter grid. The source elevation tile data was provided on the MassGIS website www.state.ma.us/mgis/massgis.htm in ESRI-format shapefile format and imported into IDRISI software using the ShapeIdr command. The resulting vector elevation files were converted to raster format using successive Lineras macro commands. This has the effect of mosaicing the tiles as well. The raster image was filtered once using a low-pass (mean) filter, then masked to the Ipswich study area parameters (extent). This datalayer was produced as part of a research project concerning the Ipswich River Watershed.

This submission includes maps of the spatial distribution of basaltic, and felsic rocks in the Oregon Cascades. It also includes a final Play Fairway Analysis (PFA) model, with the heat and permeability composite risk segments (CRS) supplied separately. Metadata for each raster dataset can be found within the zip files, in the TIF images

Dataset organized by the Open-Earth-Monitor (OEMC) project within the context of Hackathon 2023.

The dataset contains monthly mean FAPAR values aggregated by each ground station. FAPAR represents the fraction of the incoming (photosynthetic active) radiation that is absorbed by vegetation, and is given in the range 0-1. It is a measure of vegetation health and ecosystem functioning, and a key parameter in light use efficiency models that model primary productivity.

For each monthly FAPAR value, a set of covariates / features were extracted from 32 raster spatial layers, including including satellite (spectral bands and indices) and temperature images (land surface temperature), climate images (precipitation) and digital terrain model (slope and elevation). The features are organized by columns, unique data points in time are identified by the sample_id column, and data points points belonging to the same location are identified by station_number.

Column names:

sample_id: unique identifier of datapoint

station: ground station number

fapar: monthly mean FAPAR

month: month of measurement

modis_{..}: NDVI, EVI, reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared) based on MOD13Q1

modis_lst_day_p{..}: Land surface temperatures daytime of percentiles 5th, 50th and 95th based on MOD11A2

modis_lst_night_p{..}: Land surface temperatures nighttime of percentiles 5th, 50th and 95th based on MOD11A2

wv_yearly_p{..}: Water vapour aggregated yearly by percentiles 25th, 50th and 75th based on derived from MCD19A2

wv_monthly_lt_p{..}: Water vapour aggregated long-term monthly by percentiles 25th, 50th and 75th based on MCD19A2

wv_monthly_lt_sd: Water vapour aggregated long-term monthly standard deviation based on MCD19A2

wv_monthly_ts_raw: Water vapour monthly time series based on MCD19A2

wv_monthly_ts_smooth: Water vapour monthly time series smoothed using the Whittaker method based on MCD19A2

accum_pr_monthly: Monthly accumulated precipitation based on CHELSA timeseries

dtm_{..}: Several DTM derivatives (Elevation, Slope, aspect (sine, cosine), curvature (up- and downslope), openness (negative, positive), compound topographic index (cti), valley bottom flatness (vbf)) based on MERIT DEM

Files

train.csv: Training set with 3,461 rows and 36 columns, including sample id (sample_id - index column), ground station (station), reference month (month), measured FAPAR (fapar), and 32 features / covariates

test.csv: Test set with 4,939 rows and 34 columns, including sample id (sample_id - index column), ground station (station), reference month (month) and 32 features / covariates

sample_submission.csv: a sample submission file with 4,939 rows and 2 columns, including sample id (sample_id - index column) and measured FAPAR (fapar)

6 inch resolution raster image of New York City, classified by landcover type.

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

The present dataset is part of the Alaiz Experiment-2017 (ALEX17). The information is divided into two groups based on their source. 1)Two raster-tpye geotif files containing the Digital Elevation and Digital Surface Models (DEM and DSM) data of the ALEX17 domain. The models were built by TRACASA ( https://tracasa.es/all-about-us/) which is a company part of the Navarra Government. The original dataset is cropped to fit the ALEX17 experimental domain with the following spatial coverage: 607700, 4720300 628010, 4738800 The datasets are generated through lidar airborne scans taken during years 2011 and 2012 and updated by photogrammetry with orthophotos of year 2014. The original lidar scans (2011-2012) have a density of 1pnt/m^2 . The raw data are then processed and converted to orthometric heights (from the original ellipsoidal heights ) and later projected into a 2x2m grid with spatial reference EPSG:25830. The conversion from ellipsoidal to orthometric height is carried out with the EGM2008_REDNAP model, generated by the Spanish Geographic National Institute available in: ftp://ftp.geodesia.ign.es/geoide/ 2)The second dataset is also a raster-type file which contains the approximate annual mean of aerodynamic roughness length in meters. The maps was created with two data sources: Visual estimation of the roughness length values & zones. The Corine Land Cover (CLC) 2006 data. 2.1) The visual estimations of roughness values w carried out with the use of both, orthophotos gathered from the National Geographic Institute of Spain (IGN) as well as site visits. These values were assigned to the Alaiz mountain region while the 2.2) CLC-derived roughness was set to the rest of the domain area. The orthophotos are obtained from the National Plan for Aerial Orthophotogrpy (PNOA) program (available at http://www.ign.es/ign/layoutIn/faimgsataerea.do ). These photos have a pixel size of 50cm and were taken in summer 2014. On the other hand, the Corine Land Cover (CLC) 2006 raster dataset have a 100 m grid size. These data are available at http://www.eea.europa.eu/data-and-maps/data/corine- land-cover-2006-raster-3 (g100_06.zip file). The roughness values were derived from the Land Cover data mostly based on the relation between CLC and the aerodynamic roughness length applied by the Finnish wind atlas (http://www.tuuliatlas.fi/modelling/mallinnus_3.html ). The final composed roughness raster map was built by interpolation (nearest-neighbor) of the two data sources onto a 10x10 meters grid . The map is also projected with the same spatial reference as the DEM/DSM data described above.

This is a raster file in .e00 file that has a number of values that represent a range of elevations across Interior Alaska.

LiDAR (Light Detection and Ranging) is a remote sensing technology, i.e. the technology is not in direct contact with what is being measured. From satellite, aeroplane or helicopter, a LiDAR system sends a light pulse to the ground. This pulse hits the ground and returns back to a sensor on the system. The time is recorded to measure how long it takes for this light to return. Knowing this time measurement scientists are able to create topography maps.LiDAR data are collected as points (X,Y,Z (x & y coordinates) and z (height)). The data is then converted into gridded (GeoTIFF) data to create a Digital Terrain Model and Digital Surface Model of the earth. This LiDAR data was collected between June and October 2018.An ordnance datum (OD) is a vertical datum used as the basis for deriving heights on maps. This data is referenced to the Malin Head Vertical Datum which is the mean sea level of the tide gauge at Malin Head, County Donegal. It was adopted as the national datum in 1970 from readings taken between 1960 and 1969 and all heights on national grid maps are measured above this datum. Digital Terrain Models (DTM) are bare earth models (no trees or buildings) of the Earth’s surface.Digital Surface Models (DSM) are earth models in its current state. For example, a DSM includes elevations from buildings, tree canopy, electrical power lines and other features.Hillshading is a method which gives a 3D appearance to the terrain. It shows the shape of hills and mountains using shading (levels of grey) on a map, by the use of graded shadows that would be cast by high ground if light was shining from a chosen direction.This data shows the hillshade of the DTM.This data was collected by BlueSky and GeoAeroSpace and provided to the Geological Survey Ireland. All data formats are provided as GeoTIFF rasters but are at different resolutions. Data resolution is 1m.Both a DTM and DSM are raster data. Raster data is another name for gridded data. Raster data stores information in pixels (grid cells). Each raster grid makes up a matrix of cells (or pixels) organised into rows and columns. This data has a grid cell size of 1 meter by 1 meter. This means that each cell (pixel) represents an area of 1 meter squared.

Data were derived from 2014 6" resolution Island County lidar data using ArcGIS and Spatial Analyst Tools. The resulting raster was then converted to polygons. Polygons spanning elevation differences <10' were deleted.

An index of TIFs received from the Rural Water Association. The TIFs are comparable to the Unconsolidated Aquifers and the Surficial Geologic Matetrials shapefiles. Data exists for the following towns: Ancram, Austerlitz, Chatham, Claverack, Copake, Germantown, Ghent, Hillsdale, Stuyvesant, and Taghkanic.

TIF Data current as of March 2016.

Soil surface clay percent is one of 19 attributes of soils chosen to underpin the land suitability assessment of the Flinders and Gilbert Agricultural Resource Assessment (FGARA) project through the digital soil mapping process (DSM). This raster data (in GeoTIFF format) represents a modelled surface of clay in the soil surface measured as a percent and is derived from laboratory, MIR and environmental covariates. The data is used in assessment of soil physical factors eg water infiltration, seedling establishment and machinery workability. The attribute data file is named "ClayPredictions.tif". Also included are data reflecting confidence of the main dataset. This file is named "Clay_SD.tif". "SD" represents "standard deviation". The DSM process is described in the technical report: Bartley R, Thomas MF, Clifford D, Phillip S, Brough D, Harms D, Willis R, Gregory L, Glover M, Moodie K, Sugars M, Eyre L, Smith DJ, Hicks W and Petheram C (2013) Land suitability: technical methods. A technical report to the Australian Government for the Flinders and Gilbert Agricultural Resource Assessment (FGARA) project, CSIRO. This raster data provides improved soil information to identify opportunities and promote detailed investigation for a range of sustainable development options and was created within the “Land Suitability” component of FGARA projects. Lineage: This data has been created from a range of inputs and processing steps. Below is an overview. Broadly, the steps were to: 1. Collate existing data (data related to: climate, topography, soils, natural resources, remotely sensed etc of various formats; reports, spatial vector, spatial raster etc.). 2. Select additional soil and attribute site data by Latin hypercube statistical sampling method applied across the covariate space. 3. Carry out fieldwork to collect additional soil and attribute data and understand geomorphology and landscapes. 4. Build models from selected input data and covariate data using predictive learning via rule ensembles in the RuleFit3 software. 5. Create Soil Surface Clay Percenct Digital Soil Mapping (DSM) key attribute output data. DSM is the creation and population of a geo-referenced database, generated using field and laboratory observations, coupled with environmental data through quantitative relationships. It applies pedometrics - the use of mathematical and statistical models that combine information from soil observations with information contained in correlated environmental variables, remote sensing images and some geophysical measurements. Quality assessment of the attribute data is mapped spatially as a function of the model output by evaluating the rigour of the DSM attribute data using non-parametric bootstrapping of the DSM modelling. For more information refer to “Land suitability: technical methods. A technical report to the Australian Government for the Flinders and Gilbert Agricultural Resource Assessment (FGARA) project”.