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

Maxent software (http://www.cs.princeton.edu/~schapire/maxent) is frequently used for presence-only species distribution modeling. Maxent requires, however, that input ASCII raster files be aligned with one another and have the same spatial extent. This tool pre-processes raster data in preparation for Maxent modeling to ensure that all rasters have the same extent, same cell size, and aren't missing data. There are two version of this geoprocessing modeling. The advanced version is for the ArcGIS Advanced license. The basic version is the the ArcGIS Advanced license. Both versions require Spatial Analyst. The difference between the two is that the advanced version creates a polygon shapefile that shows the difference between the template raster and the processed raster. Ideally, this should generate a polygon with empty output, but if it doesn't you can use it to diagnose problems. The tool first resamples the raster, then uses a focalmean (3x3 and 5x5) to fill gaps, and mosaics the resampled, 3x3, and 5x5 rasters together, and converts to ASCII.Recommended citation format: Dilts, T.E. (2015) Prepare Rasters for Maxent Tool for ArcGIS 10.1. University of Nevada Reno. Available at: http://www.arcgis.com/home/item.html?id=11bf7e689c92413f8d31933b3e1f56b1

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 of the overburden material above the Mahogany Zone was needed to perform 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.

We developed habitat suitability models for four invasive plant species of concern to Department of Interior land management agencies. We generally followed the modeling workflow developed in Young et al. 2020, but developed models both for two data types, where species were present and where they were abundant. We developed models using five algorithms with VisTrails: Software for Assisted Habitat Modeling [SAHM 2.1.2]. We accounted for uncertainty related to sampling bias by using two alternative sources of background samples, and constructed model ensembles using the 10 models for each species (five algorithms by two background methods) for four different thresholds. This data bundle contains the presence and abundance merged data sets to create models for medusahead rye, red brome, venanata and bur buttercup, the eight raster files associated with each species/ data type (presence or abundance), and tabular summaries by management unit (including each species/ data type combin ...

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.

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

This dataset consists of hydrologically enforced digital elevation model rasters for each 4-digit Hydrologic Unit Code (HUC) area in Maine (0101, 0102, 0103, 0104, 0105, and 0106). The cell size of each raster is 10 meters, and the elevation values are expressed in centimeters. The elevation rasters may be used along with the accompanying data layers in this release to delineate watersheds within the HUC-4 areas.

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

ESRI GRID raster datasets were created to display and quantify nahcolite resources for eight oil shale zones in the Piceance Basin, Colorado as part of a 2009 National Oil Shale and Nahcolite Assessment. The zones in descending order are: L-5, R-5, L-4, R-4, L-3, R-3, L-2, and R-2. Each raster cell represents a one-acre square of the land surface and contains values for nahcolite tonnage. The gridnames follow the naming convention _n, where "" can be replaced by the name of the oil shale zone.

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

Tabular and raster data containing spatial capture recapture records for male and female black bears (Ursus americanus) in Glacier National Park and surrounding landscape collected from June - October 2004 and associated tabular data files required for analysis of data with spatial capture connectivity models and raster data describing the ouput from SCR models. Associated tables and rasters include details for traps, the state space and connectivity space required modeling and associated spatial covariates tested in models, as well as rasters describing black bear population density, habitat use, and population connectivity.

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.

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.

High resolution land cover data set for New York City. This is the 3ft 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.

Learn to appropriately symbolize rasters based on their attributes and intended use, modify raster properties to support better visualization and interpretation, and apply out-of-the-box appearance functions to enhance the viewing experience.GoalsChoose appropriate tools to help with better visualization and interpretation of rasters and imagery.

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

ESRI GRID raster datasets were created to display and quantify oil shale resources for eighteen zones in the Uinta Basin, Utah and Colorado as part of a 2010 National Oil Shale Assessment. The oil shale zones in descending order are: Bed 76, Bed 44, A Groove, Mahogany Zone, B Groove, R-6, L-5, R-5, L-4, R-4, L-3, R-3, L-2, R-2, L-1, R-1, L-0, and R-0. Each raster cell represents a one-acre square of the land surface and contains values for either oil yield in barrels per acre, gallons per ton, or isopach thickness, in feet, as defined by the grid name: _b (barrels per acre), _g (gallons per ton), and _i (isopach thickness) where "" can be replaced by the name of the oil shale zone.

ESRI GRID raster datasets were created to display and quantify oil shale resources for seventeen zones in the Piceance Basin, Colorado as part of a 2009 National Oil Shale Assessment. The oil shale zones in descending order are: Bed 44, A Groove, Mahogany Zone, B Groove, R-6, L-5, R-5, L-4, R-4, L-3, R-3, L-2, R-2, L-1, R-1, L-0, and R-0. Each raster cell represents a one-acre square of the land surface and contains values for either oil yield in barrels per acre, gallons per ton, or isopach thickness, in feet, as defined by the grid name: _b (barrels per acre), _g (gallons per ton), and _i (isopach thickness) where "" can be replaced by the name of the oil shale zone.