This raster layer is an aspect slope map that displays the cardinal direction that sloped areas face.White - Flat (-1)Red - North (0-22.5)Orange - Northeast (22.5-67.5)Yellow - East (67.5-112.5)Green - Southeast (112.5-157.5)Light Blue - South (157.5-202.5)Blue - Southwest (202.5-247.5)Deep Blue - West (247.5-292.5)Pink - Northwest (292.5-337.5)

Important Note: This item is in mature support as of July 2024 and will retire in December 2025. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version.ArcticDEM is a National Geospatial-Intelligence Agency (NGA) and National Science Foundation (NSF) public-private initiative to automatically produce a high-resolution, high-quality Digital Surface Model (DSM) of the Arctic using sub-meter, stereoscopic satellite imagery collected by DigitalGlobe’s satellite constellation.The Arctic DEM layer is rendered here as Aspect Map. Using the server-side aspect function, this layer provides a colorized representation of aspect. The orientation of the downward sloping surface is indicated by different colors, rotating from green (North) to blue (East), to magenta (South) to orange (West).Geographic ExtentAll land area north of 60° north latitude, including all territory of Greenland, the entire state of Alaska, and the Kamchatka Peninsula of the Russian Federation.Map ProjectionThis layer is projected to WGS 1984 EPSG Alaska Polar Stereographic.The source data is projected to WGS 84 / NSIDC Sea Ice Polar Stereographic North.NOTE: By default, opening this layer in the Map Viewer will project the layer to Web Mercator. To display in the Alaska Polar projection, use Arctic DEM: Aspect Map Web MapSpatial Resolution2-meterAccuracyWithout ground control points absolute accuracy is approximately 4 meters in horizontal and vertical planes. Uniform ground control must be applied to achieve higher accuracy. Laser altimetry data from the NASA IceSAT spacecraft has been applied to the ArcticDEM mosaic files. Users may wish to use other sources for smaller areas, particularly on ArcticDEM strip files. Strip DEM files contain IceSAT altimetry offsets within the metadata, but have not had these values applied.The accuracy of these layers will vary as a function of location and data source. Please refer to the metadata available in the layers, and follow the links to the original sources for further details. An estimate of CE90 and LE90 are included as attributes.Pixel ValuesThis layer returns 8 bit color values representing a graphic visualization, not slope values.For access to numeric aspect values, use the Arctic DEM: Aspect Degree layer, which returns orientation values from 0 to 359 degrees.For elevation height values, please reference either Arctic DEM or Arctic DEM: Height Ellipsoidal.Data Dimensions and CompositionDEM Tiles are compiled from multiple strips that have been co-registered, blended, and feathered to reduce edge-matching artifacts. Tile sizes are standardized at 50 km x 50 km.Individual DEM strips are compiled from DigitalGlobe images. DEM strip dimensions will vary according to the sensor, off-nadir angle of collection, and the corresponding stereo-pair overlap. Most strips are between 16 km and 18 km in width, and 110 km and 120 km in length. Using this layerThis colorized aspect map is appropriate for visualizing the orientation of the surface at large map scales. This layer can be added to applications or maps to enhance contextual understanding.The 8 bit color values returned by this layer represent a graphic visualization, not slope values. For access to numeric aspect values, use the Arctic DEM: Aspect Degree layer, which returns orientation values from 0 to 359 degrees.This layer can be temporally filtered by acquisition date. This layer allows query, identify, and export image requests. The layer is restricted to a 4000 x 4000 pixel limit in a single request.For additional visual context and analysis, below is the full list of layers available as Raster Functions. These can be accessed from within the service or as individual AGOL items: Arctic DEM, Hillshade Gray, Aspect Degrees, Aspect Map, Contour 25, Hillshade Multidirectional, Slope Map, Slope Degrees, Contour Smoothed 25, Hillshade Elevation Tinted, Height Ellipsoidal Additional Data SpecificationsThe ArcticDEM product is a Digital Surface Model (DSM) which includes above ground features such as man-made structures and vegetation.The data has not been edited to remove processing anomalies. Pits, spikes, false landforms, and other DEM anomalies may exist in this dataset. Polygonal hydrographic features have not been flattened and the data has not been hydrologically enforced.Since the DEM’s are optically derived, clouds, fog, shadows, and other atmospheric obstructions can obscure the ground resulting in data gaps.Since the DEM strips have not been edge-matched, visible seams and deviations between adjacent strips may be observed.The data spans multiple years and seasons. A single season/year mosaic is not possible for large areas.Mosaic tiles are displayed by default. Strips can be selected and displayed via image filtering.For quick and easy access to this and additional elevation layers, see the Elevation Layers group in ArcGIS Online.For more information on the source data, see ArcticDEM.

This map provides a colorized representation of aspect, generated dynamically using the server-side aspect function on the Terrain layer. The orientation of the downward sloping terrain (0° – 360°) is indicated by different colors, rotating from green (North) to blue (East), to magenta (South) to orange (West). Flat areas having no down slope direction are given a value of 361° and rendered as gray. This service can be used for visualization or analysis. Note: If you require access to numeric (float) aspect values, use the Terrain - Aspect layer, which returns orientation values from 0 to 360 degrees. Units: DegreesUpdate Frequency: QuarterlyCoverage: World/GlobalData Sources: This layer is compiled from a variety of best available sources from several data providers. To see the coverage and extents of various datasets comprising this service in an interactive map, see World Elevation Coverage Map.What can you do with this layer?Use for Visualization: Yes. This colorized aspect map is appropriate for visualizing the downslope direction of the terrain. This layer can be added to applications or maps to enhance contextual understanding.Use for Analysis: Yes. 8 bit color values returned by this service represent integer aspect values. For float values, use the Terrain - Aspect layer.For more details such as Data Sources, Mosaic method used in this layer, please see the Terrain layer. This layer allows query, identify, and export image requests. The layer is restricted to a 5,000 x 5,000 pixel limit in a single export image request.

This layer is part of a larger collection of elevation layers that you can use to perform a variety of mapping analysis tasks.

This layer provides aspect values calculated dynamically using the server-side aspect function applied on the Terrain layer. The values are float, and represent the orientation of the downward sloping terrain in degrees (0 to 360), clockwise from north. Cells in the input raster that are flat with zero slope are assigned an aspect of -1.Units: DegreesUpdate Frequency: QuarterlyCoverage: World/GlobalData Sources: This layer is compiled from a variety of best available sources from several data providers. To see the coverage and extents of various datasets comprising this service in an interactive map, see World Elevation Coverage Map.WARNING: Aspect is computed in the projection specified by the client software. The server resamples the data to the required projection and then computes aspect. The default projection for web applications is Mercator in which scale increases equally in x and y by latitude, so aspect computations are not affected. Using geographic coordinates will give distorted results. It is advised to check the client application projection prior to obtaining aspect values. What can you do with this layer?Use for Visualization: No. This layer provides numeric values indicating terrain characteristics, and is not generally appropriate for visual interpretation, unless the client application applies an additional color map. For visualization use the Terrain - Aspect Map.Use for Analysis: Yes. This layer provides numeric values indicating the orientation of the terrain within a raster cell, calculated based on the defined cell size. There is a limit of 5000 rows x 5000 columns. For more details such as Data Sources, Mosaic method used in this layer, please see the Terrain layer. This layer allows query, identify, and export image requests. The layer is restricted to a 5,000 x 5,000 pixel limit in a single export image request.This layer is part of a larger collection of elevation layers that you can use to perform a variety of mapping analysis tasks.

An aspect map (slope direction) derived from Digital Elevation Models (DEMs) with a 3ft. grid cell size. Compass direction is rendered using the following colors: red (north), magenta (northwest), blue (west), cyan (southwest), light cyan (south), light green (southeast), light orange (east), orange (northeast). Data used to create the DEMs was derived from LiDAR collected by the NC Floodplain Mapping Program and processed by NC Department of Public Safety - Division of Emergency Management.Download county-based DEMs from the NC OneMap Direct Data Downloads. Data should not be downloaded using the map on the dataset's item page.

This is a collection of all GPS- and computer-generated geospatial data specific to the Alpine Treeline Warming Experiment (ATWE), located on Niwot Ridge, Colorado, USA. The experiment ran between 2008 and 2016, and consisted of three sites spread across an elevation gradient. Geospatial data for all three experimental sites and cone/seed collection locations are included in this package. ––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Geospatial files include cone collection, experimental site, seed trap, and other GPS location/terrain data. File types include ESRI shapefiles, ESRI grid files or Arc/Info binary grids, TIFFs (.tif), and keyhole markup language (.kml) files. Trimble-imported data include plain text files (.txt), Trimble COR (CorelDRAW) files, and Trimble SSF (Standard Storage Format) files. Microsoft Excel (.xlsx) and comma-separated values (.csv) files corresponding to the attribute tables of many files within this package are also included. A complete list of files can be found in this document in the “Data File Organization” section in the included Data User's Guide. Maps are also included in this data package for reference and use. These maps are separated into two categories, 2021 maps and legacy maps, which were made in 2010. Each 2021 map has one copy in portable network graphics (.png) format, and the other in .pdf format. All legacy maps are in .pdf format. .png image files can be opened with any compatible programs, such as Preview (Mac OS) and Photos (Windows). All GIS files were imported into geopackages (.gpkg) using QGIS, and double-checked for compatibility and data/attribute integrity using ESRI ArcGIS Pro. Note that files packaged within geopackages will open in ArcGIS Pro with “main.” preceding each file name, and an extra column named “geom” defining geometry type in the attribute table. The contents of each geospatial file remain intact, unless otherwise stated in “niwot_geospatial_data_list_07012021.pdf/.xlsx”. This list of files can be found as an .xlsx and a .pdf in this archive. As an open-source file format, files within gpkgs (TIFF, shapefiles, ESRI grid or “Arc/Info Binary”) can be read using both QGIS and ArcGIS Pro, and any other geospatial softwares. Text and .csv files can be read using TextEdit/Notepad/any simple text-editing software; .csv’s can also be opened using Microsoft Excel and R. .kml files can be opened using Google Maps or Google Earth, and Trimble files are most compatible with Trimble’s GPS Pathfinder Office software. .xlsx files can be opened using Microsoft Excel. PDFs can be opened using Adobe Acrobat Reader, and any other compatible programs. A selection of original shapefiles within this archive were generated using ArcMap with associated FGDC-standardized metadata (xml file format). We are including these original files because they contain metadata only accessible using ESRI programs at this time, and so that the relationship between shapefiles and xml files is maintained. Individual xml files can be opened (without a GIS-specific program) using TextEdit or Notepad. Since ESRI’s compatibility with FGDC metadata has changed since the generation of these files, many shapefiles will require upgrading to be compatible with ESRI’s latest versions of geospatial software. These details are also noted in the “niwot_geospatial_data_list_07012021” file.

In addition to this statewide 3DEP hydro-flattened base-earth DEM, the IGIO has also published this data as an Esri Imagery Service, allowing users to conveniently visualize the DEM data by applying visual enhancements to create dynamic hillshade, shaded relief, slope, and aspect-ratio raster maps.This image service has a series of image processing templates (or raster functions) integrated and ready to use out of the box. By default, the DEM is symbolized as a shaded relief - a hillshade with an elevation color ramp applied on top of it. If users would like to view the data differently in ArcGIS Online or desktop applications, they can select from one of the other raster functions. These raster functions include the bare-earth DEM, grayscale hillshade, slope (RGB), slope (degrees), aspect (RGB), and aspect (numeric values).

This dynamic World Elevation Terrain layer returns float values representing ground heights in meters and compiles multi-resolution data from many authoritative data providers from across the globe. Heights are orthometric (sea level = 0), and water bodies that are above sea level have approximated nominal water heights.Height units: MetersUpdate Frequency: QuarterlyCoverage: World/GlobalData Sources: This layer is compiled from a variety of best available sources from several data providers. To see the coverage and extents of various datasets comprising this service in an interactive map, see World Elevation Coverage Map.What can you do with this layer?Use for Visualization: This layer is generally not optimal for direct visualization. By default, 32 bit floating point values are returned, resulting in higher bandwidth requirements. Therefore, usage should be limited to applications requiring elevation data values. Alternatively, client applications can select from numerous additional functions, applied on the server, that return rendered data. For visualizations such as multi-directional hillshade, hillshade, elevation tinted hillshade, and slope, consider using the appropriate server-side function defined on this service.Use for Analysis: Yes. This layer provides data as floating point elevation values suitable for use in analysis. There is a limit of 5000 rows x 5000 columns.Note: This layer combine data from different sources and resamples the data dynamically to the requested projection, extent and pixel size. For analyses using ArcGIS Desktop, it is recommended to filter a dataset, specify the projection, extent and cell size using the Make Image Server Layer geoprocessing tool. The extent is factor of cell size and rows/columns limit. e.g. if cell size is 10 m, the extent for analysis would be less than 50,000 m x 50,000 m.Server Functions: This layer has server functions defined for the following elevation derivatives. In ArcGIS Pro, server function can be invoked from Layer Properties - Processing Templates.

Slope Degrees Slope Percent Aspect Ellipsoidal height Hillshade Multi-Directional Hillshade Dark Multi-Directional Hillshade Elevation Tinted Hillshade Slope Map Aspect Map Mosaic Method: This image service uses a default mosaic method of "By Attribute”, using Field 'Best' and target of 0. Each of the rasters has been attributed with ‘Best’ field value that is generally a function of the pixel size such that higher resolution datasets are displayed at higher priority. Other mosaic methods can be set, but care should be taken as the order of the rasters may change. Where required, queries can also be set to display only specific datasets such as only NED or the lock raster mosaic rule used to lock to a specific dataset.Accuracy: Accuracy will vary as a function of location and data source. Please refer to the metadata available in the layer, and follow the links to the original sources for further details. An estimate of CE90 and LE90 are included as attributes, where available.This layer allows query, identify, and export image requests. The layer is restricted to a 5,000 x 5,000 pixel limit in a single request.This layer is part of a larger collection of elevation layers that you can use to perform a variety of mapping analysis tasks.

Graphing topological features of the ocean seafloor provides insight for diverse scientific disciplines. Here, we provide visual representation of the combined slope and aspect of the swath sonar bathymetry during the POLARSTERN cruise PS101 (ARK-XXX/3). This dataset contains two raster grids in GeoTiff format (color-rgb type and value type), a symbology classification layer file, and an overview map with legend. The value-type GeoTIFF has an accompanying symbology layer file that can be applied in ArcGIS. Each pixel value has an associated color (aspect) and hue (slope) classification. This combination of aspect and slope makes this layer uniquely informative and indended for visualization purposes; see "PS101_aspect_slope_map.png (hdl:10013/epic.51997.d001)" for a map preview. The orientation aspect values are from 0 to 359 degrees and the slope values are 5, 15, 30, and 45 percent rise. Values less than 5 percent are flat areas and have no associated orientation values. The GeoTiff has a pixel size of 100 m x 100 m and was created using ArcGIS 10.5 software, in stereographic polar projection, datum WGS 84.

The zip file contains: 1. PS101_aspect_slope.tif 2. PS101_aspect_slope_value.tif 3. PS101_aspect_slope_symbology.lyr 4. PS101_aspect_slope_map.png (hdl:10013/epic.51997.d001)

Yearly effective energy and mass transfer (EEMT) (MJ m−2 yr−1) was calculated for the Valles Calders, upper part of the Jemez River basin by summing the 12 monthly values. Effective energy and mass flux varies seasonally, especially in the desert southwestern United States where contemporary climate includes a bimodal precipitation distribution that concentrates in winter (rain or snow depending on elevation) and summer monsoon periods. This seasonality of EEMT flux into the upper soil surface can be estimated by calculating EEMT on a monthly basis as constrained by solar radiation (Rs), temperature (T), precipitation (PPT), and the vapor pressure deficit (VPD): EEMT = f(Rs,T,PPT,VPD). Here we used a multiple linear regression model to calculate the monthly EEMT that accounts for VPD, PPT, and locally modified T across the terrain surface. These EEMT calculations were made using data from the PRISM Climate Group at Oregon State University (www.prismclimate.org). Climate data are provided at an 800-m spatial resolution for input precipitation and minimum and maximum temperature normals and at a 4000-m spatial resolution for dew-point temperature (Daly et al., 2002). The PRISM climate data, however, do not account for localized variation in EEMT that results from smaller spatial scale changes in slope and aspect as occurs within catchments. To address this issue, these data were then combined with 10-m digital elevation maps to compute the effects of local slope and aspect on incoming solar radiation and hence locally modified temperature (Yang et al., 2007). Monthly average dew-point temperatures were computed using 10 yr of monthly data (2000–2009) and converted to vapor pressure. Precipitation, temperature, and dew-point data were resampled on a 10-m grid using spline interpolation. Monthly solar radiation data (direct and diffuse) were computed using ArcGIS Solar Analyst extension (ESRI, Redlands, CA) and 10-m elevation data (USGS National Elevation Dataset [NED] 1/3 Arc-Second downloaded from the National Map Seamless Server at seamless.usgs.gov). Locally modified temperature was used to compute the saturated vapor pressure, and the local VPD was estimated as the difference between the saturated and actual vapor pressures. The regression model was derived using the ISOHYS climate data set comprised of approximately 30-yr average monthly means for more than 300 weather stations spanning all latitudes and longitudes (IAEA).

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. We followed methods in Anderson and Merrill (1998) for combining gradient layers into an “ecological land units” map (also referred to as a “biophysical units” map). Our goal was to use this information to create sampling strata that capture the range of environments observed. The Anderson and Merrill (1998) method (implemented as a set of GIS scripts by F. Biasi (2001)) builds an ecological units map by classifying and combining individual environmental gradient maps in a GIS. Maps of aspect, moisture, slope, and slope shape are reclassified and assembled to produce maps of landform units. These landform units are then combined with reclassified elevation and geologic maps to produce a final ecological land units or “ELU” map. We used these methods as a guide to building an ecological land units map for Shenandoah National Park, adapting the procedures for local conditions. Individual steps in the process and maps resulting from intermediate and final stages are described in the report.

Yearly effective energy and mass transfer (EEMT) (MJ m−2 yr−1) was calculated for the Catalina Mountains by summing the 12 monthly values. Effective energy and mass flux varies seasonally, especially in the desert southwestern United States where contemporary climate includes a bimodal precipitation distribution that concentrates in winter (rain or snow depending on elevation) and summer monsoon periods. This seasonality of EEMT flux into the upper soil surface can be estimated by calculating EEMT on a monthly basis as constrained by solar radiation (Rs), temperature (T), precipitation (PPT), and the vapor pressure deficit (VPD): EEMT = f(Rs,T,PPT,VPD). Here we used a multiple linear regression model to calculate the monthly EEMT that accounts for VPD, PPT, and locally modified T across the terrain surface. These EEMT calculations were made using data from the PRISM Climate Group at Oregon State University (www.prismclimate.org). Climate data are provided at an 800-m spatial resolution for input precipitation and minimum and maximum temperature normals and at a 4000-m spatial resolution for dew-point temperature (Daly et al., 2002). The PRISM climate data, however, do not account for localized variation in EEMT that results from smaller spatial scale changes in slope and aspect as occurs within catchments. To address this issue, these data were then combined with 10-m digital elevation maps to compute the effects of local slope and aspect on incoming solar radiation and hence locally modified temperature (Yang et al., 2007). Monthly average dew-point temperatures were computed using 10 yr of monthly data (2000–2009) and converted to vapor pressure. Precipitation, temperature, and dew-point data were resampled on a 10-m grid using spline interpolation. Monthly solar radiation data (direct and diffuse) were computed using ArcGIS Solar Analyst extension (ESRI, Redlands, CA) and 10-m elevation data (USGS National Elevation Dataset [NED] 1/3 Arc-Second downloaded from the National Map Seamless Server at seamless.usgs.gov). Locally modified temperature was used to compute the saturated vapor pressure, and the local VPD was estimated as the difference between the saturated and actual vapor pressures. The regression model was derived using the ISOHYS climate data set comprised of approximately 30-yr average monthly means for more than 300 weather stations spanning all latitudes and longitudes (IAEA).

The High Resolution Digital Elevation Model (HRDEM) product is derived from airborne LiDAR data (mainly in the south) and satellite images in the north. The complete coverage of the Canadian territory is gradually being established. It includes a Digital Terrain Model (DTM), a Digital Surface Model (DSM) and other derived data. For DTM datasets, derived data available are slope, aspect, shaded relief, color relief and color shaded relief maps and for DSM datasets, derived data available are shaded relief, color relief and color shaded relief maps. The productive forest line is used to separate the northern and the southern parts of the country. This line is approximate and may change based on requirements. In the southern part of the country (south of the productive forest line), DTM and DSM datasets are generated from airborne LiDAR data. They are offered at a 1 m or 2 m resolution and projected to the UTM NAD83 (CSRS) coordinate system and the corresponding zones. The datasets at a 1 m resolution cover an area of 10 km x 10 km while datasets at a 2 m resolution cover an area of 20 km by 20 km. In the northern part of the country (north of the productive forest line), due to the low density of vegetation and infrastructure, only DSM datasets are generally generated. Most of these datasets have optical digital images as their source data. They are generated at a 2 m resolution using the Polar Stereographic North coordinate system referenced to WGS84 horizontal datum or UTM NAD83 (CSRS) coordinate system. Each dataset covers an area of 50 km by 50 km. For some locations in the north, DSM and DTM datasets can also be generated from airborne LiDAR data. In this case, these products will be generated with the same specifications as those generated from airborne LiDAR in the southern part of the country. The HRDEM product is referenced to the Canadian Geodetic Vertical Datum of 2013 (CGVD2013), which is now the reference standard for heights across Canada. Source data for HRDEM datasets is acquired through multiple projects with different partners. Since data is being acquired by project, there is no integration or edgematching done between projects. The tiles are aligned within each project. The product High Resolution Digital Elevation Model (HRDEM) is part of the CanElevation Series created in support to the National Elevation Data Strategy implemented by NRCan. Collaboration is a key factor to the success of the National Elevation Data Strategy. Refer to the “Supporting Document” section to access the list of the different partners including links to their respective data.

In addition to this statewide base-earth DEM, the IGIO has also published this data as an Esri Imagery Service, allowing users to conveniently visualize the DEM data by applying visual enhancements to create dynamic hillshade, shaded relief, slope, and aspect-ratio raster maps.This image service has a series of image processing templates (or raster functions) integrated and ready to use out of the box. By default, the DEM is symbolized as a shaded relief - a hillshade with an elevation color ramp applied on top of it. If users would like to view the data differently in ArcGIS Online or desktop applications, they can select from one of the other raster functions. These raster functions include the bare-earth DEM, grayscale hillshade, slope (RGB), slope (degrees), aspect (RGB), and aspect (numeric values).

This image service is available through CTECO, a partnership between UConn CLEAR and CT DEEP. The Apsect service is created by applying the aspect function to the bare earth Elevation service.

Aspect and hillshade was derived primarily from the 2017 Washoe County 3DEP LIDAR with some inputs in the Tahoe Basin from the 2010 Tahoe Basin LIDAR. The two DEMs were combined into a single DEM and a hillshade and slope map was derived from that DEM. The data were then smoothed at 25x25 meters and polygons were generated to represent 45 degree increments. The polygons are visible when zooming in closer than 1:5,000 map scale.

In addition to this statewide 3DEP hydro-flattened base-earth DEM, the IGIO has also published this data as an Esri Imagery Service, allowing users to conveniently visualize the DEM data by applying visual enhancements to create dynamic hillshade, shaded relief, slope, and aspect-ratio raster maps.This image service has a series of image processing templates (or raster functions) integrated and ready to use out of the box. By default, the DEM is symbolized as a shaded relief - a hillshade with an elevation color ramp applied on top of it. If users would like to view the data differently in ArcGIS Online or desktop applications, they can select from one of the other raster functions. These raster functions include the bare-earth DEM, grayscale hillshade, slope (RGB), slope (degrees), aspect (RGB), and aspect (numeric values).

Map of the abundance and distribution of terrestrial vegetation of the Windmill Islands. Point data represent regions of vegetation occurrence.

The data are available in two formats. One is as a basic text document, and the other is as a GIS shapefile. See the URLs for more details.

The fields in this dataset are: Location Date Time Collector Substrate Setting Ext_region (See additional information) Exposure Aspect Water_prox (See additional information) Bird_nutri (See additional information) Species Notes DQI (Data Quality Indicator) Latitude Longitude

This is a dataset download, not a document. The Open Document button will start the download.

This data layer is an element of the Oregon GIS Framework. This data layer represents the Existing Vegetation data element. This statewide grid was created by combining four independently-generated datasets: one for western Oregon (USGS zones 2 and 7), and two for eastern Oregon (USGS zones 8 and 9; forested and non-forested lands), and selected wetland types from the Oregon Wetlands geodatabase. The landcover grid for zones 2 and 7 was produced using a modification of Breiman's Random Forest classifier to model landcover. Multi-season satellite imagery (Landsat ETM+, 1999-2003) and digital elevation model (DEM) derived datasets (e.g. elevation, landform, aspect, etc.) were utilized to build two predictive models for the forested landcover classes, and the nonforested landcover classes. The grids resulting from the models were then modified to improve the distribution of the following classes: volcanic systems and wetland vegetation. Along the eastern edge, the sagebrush systems were modified to help match with the map for the adjacent region. Additional classes were then layered on top of the modified models from other sources. These include disturbed classes (harvested and burned), cliffs, riparian, and NLCD's developed, agriculture, and water classes. A validation for forest classes was performed on a withheld of the sample data to assess model performance. Due to data limitations, the nonforest classes were evaluated using the same data that were used to build the original nonforest model. Two independent grids were combined to map landcover in adjacent zones 8 and 9. Tree canopy greater than 10% (from NLCD 2001), complemented with a disturbance grid, served as a mask to delineate forested areas. A grid of non-forested areas was extracted from a larger, regional grid (Sagemap) created using decision tree classifier and other techniques. Multi-season satellite imagery (Landsat ETM+, 1999-2003) and digital elevation model (DEM) derived datasets (e.g. elevation, landform, aspect, etc.) were utilized to derive rule sets for the various landcover classes. Eleven mapping areas, each characterized by similar ecological and spectral characteristics, were modeled independently of one another and mosaicked. An internal validation for modeled classes was performed on a withheld 20% of the sample data to assess model performance. The portion of this original grid corresponding to USGS map zones 8 and 9 was extracted and split into three mapping areas (one for USGS zone 8, two for USGS zone 9: Northern Basin and Range in the south, Blue Mountains in the north) and modified to improve the distribution of the following classes: cliffs, subalpine zone, dunes, lava flows, silver sagebrush, ash beds, playas, scabland, and riparian vegetation. Agriculture and urban areas were extracted from NLCD 2001. A forest grid was generated using Gradient Nearest Neighbor (GNN) imputation process. GNN uses multivariate gradient modeling to integrate data from regional grids of field plots with satellite imagery and mapped environmental data. A suite of fine-scale plot variables is imputed to each pixel in a digital map, and regional maps can be created for most of the same vegetation attributes available from the field plots. However, due to lack of sampling plots in the southern half of zone 9, the GNN model proved unreliable there; forest data from Landfire were used instead. To compensate for known under-representation of wetlands, selected wetland types from the Oregon Wetlands Geodatabase (version 2009-1030) were converted to raster and overlaid (replaced) pixel value assignments from the previous steps just detailed. See Process Steps for more information. The ecological systems were crosswalked to landcover (based on Oregon landcover standard, modified from NLCD 2001) and to wildlife habitats (based on integrated habitats used in the Oregon, Washington, and Idaho Dept of Fish & Wildlife conservation strategies). These codes and names are included in the value attribute table provided with the ecological systems grid.

The 'Stems' data are from an individual tree segmentation (Swetnam and Falk 2014) derived from the 2010 snow-off lidar and biomass-carbon allometric equations. The purpose of the dataset is to evaluate the distribution of aboveground carbon across an elevation gradient in temperature and precipitation.

The '10m Topo points' data are derived from a bare earth digital elevation model (DEM) generated from the 2010 snow-off lidar flight, these include the topographic metrics and the biomass-carbon for each pixel derived from the sum of STEMS. The purpose of the dataset is to evaluate the distribution of aboveground carbon across an elevation gradient in temperature and precipitation.

A total of three catchments in Boulder Creek were analyzed: Como Creek, Gordon Gulch, and Betasso Preserve.

Significance Statement: Forest carbon reservoirs in complex terrain along an elevation-climate gradient spanning an 11 Celsius range in mean annual temperature (MAT) and a 50 cm yr-1 range in mean annual precipitation (MAP) did not exhibit the expected response of increasing in size with greater MAP and idealized MAT. Within catchments, the distribution of mean and peak carbon storage doubled in size for valleys versus ridges. These results suggest spatial variations in carbon storage relate more to topographically mediated water availability, as well as aspect (energy-balance) and topographic curvature (a proxy for soil depth and depth to ground water), than elevation-climate gradients. Consequently, lateral redistribution of precipitation across topographic position may either moderate or exacerbate regional climatic controls over ecosystem productivity and tree-level responses during drought.