Statewide Download (FGDB) (SHP)Users can also download smaller geographic areas of this feature service in ArcGIS Pro using the Copy Features geoprocessing tool. The address service contains statewide address points and related landmark name alias table and street name alias table.The New Jersey Office of Information Technology, Office of GIS (NJOGIS), in partnership with several local GIS and public safety agencies, has built a comprehensive statewide NG9-1-1 database meeting and exceeding the requirements of the National Emergency Number Association (NENA) 2018 NG9-1-1 GIS Data Standard (NENA-STA-006.1-2018). The existing New Jersey Statewide Address Point data last published in 2016 has been transformed in the NENA data model to create this new address point data.The initial address points were processed from statewide parcel records joined with the statewide Tax Assessor's (MOD-IV) database in 2015. Address points supplied by Monmouth County, Sussex County, Morris County and Montgomery Township in Somerset County were incorporated into the statewide address points using customized Extract, Transform and Load (ETL) procedures.The previous version of the address points was loaded into New Jersey's version of the NENA NG9-1-1 data model using Extract, Transform and Load (ETL) procedures created with Esri's Data Interoperability Extension. Subsequent manual and bulk processing corrections and additions have been made, and are ongoing.***NOTE*** For users who incorporate NJOGIS services into web maps and/or web applications, please sign up for the NJ Geospatial Forum discussion listserv for early notification of service changes. Visit https://nj.gov/njgf/about/listserv/ for more information.

Idaho Geological Survey's publication in the Digital Database series DD-1: Database of the Mines and Prospects of Idaho (version 1.2025) is a relational database of Idaho mines and prospects locations and attributes compatible with Access 2000, SQL Server, and ArcGIS Pro. Also published on ArcGIS Online as an interactive web map application. Mines table was used to create spatial point feature classes (shapefile, geodatabase feature classes, KMZ) included in the downloadable data package for this release. All related data in other tables. Mines contains information on over 9,400 known sites of mineral extraction and exploration activities in Idaho. This inventory and supplemental files, documents, videos, and other media and derivative resources are valuable research tools. Available sources have been used to compile and correct these data including published and unpublished reference materials. Every effort has been made to make the database complete and accurate; however, any additions or corrections should be directed to the Idaho Geological Survey. Periodic revisions of this database will be issued as new information is added.

The nine-banded Armadillo (Dasypus novemcinctus) is the only species of Armadillo in the United States and alters ecosystems by excavating extensive burrows used by many other wildlife species. Relatively little is known about its habitat use or population densities, particularly in developed areas, which may be key to facilitating its range expansion. We evaluated Armadillo occupancy and density in relation to anthropogenic and landcover variables in the Ozark Mountains of Arkansas along an urban to rural gradient. Armadillo detection probability was best predicted by temperature (positively) and precipitation (negatively). Contrary to expectations, occupancy probability of Armadillos was best predicted by slope (negatively) and elevation (positively) rather than any landcover or anthropogenic variables. Armadillo density varied considerably between sites (ranging from a mean of 4.88 – 46.20 Armadillos per km2) but was not associated with any environmental or anthropogenic variables. Methods Site Selection Our study took place in Northwest Arkansas, USA, in the greater Fayetteville metropolitan area. We deployed trail cameras (Spypoint Force Dark (Spypoint Inc, Victoriaville, Quebec, Canada) and Browning Strikeforce XD cameras (Browning, Morgan, Utah, USA) over the course of two winter seasons, December 2020-March 2021, and November 2021-March 2022. We sampled 10 study sites in year one, and 12 study sites in year two. All study sites were located in the Ozark Mountains ecoregion in Northwest Arkansas. Sites were all Oak Hickory dominated hardwood forests at similar elevation (213.6 – 541 m). Devils Eyebrow and ONSC are public natural areas managed by the Arkansas Natural heritage Commission (ANHC). Devil’s Den and Hobbs are managed by the Arkansas state park system. Markham Woods (Markham), Ninestone Land Trust (Ninestone) and Forbes, are all privately owned, though Markham has a publicly accessible trail system throughout the property. Lake Sequoyah, Mt. Sequoyah Woods, Kessler Mountain, Lake Fayetteville, and Millsaps Mountain are all city parks and managed by the city of Fayetteville. Lastly, both Weddington and White Rock are natural areas within Ozark National Forest and managed by the U.S. Forest Service. We sampled 5 sites in both years of the study including Devils Eyebrow, Markham Hill, Sequoyah Woods, Ozark Natural Science Center (ONSC), and Kessler Mountain. We chose our study sites to represent a gradient of human development, based primarily on Anthropogenic noise values (Buxton et al. 2017, Mennitt and Fristrup 2016). We chose open spaces that were large enough to accommodate camera trap research, as well as representing an array of anthropogenic noise values. Since anthropogenic noise is able to permeate into natural areas within the urban interface, introducing human disturbance that may not be detected by other layers such as impervious surface and housing unit density (Buxton et al. 2017), we used dB values for each site as an indicator of the level of urbanization. Camera Placement We sampled ten study sites in the first winter of the study. At each of the 10 study sites, we deployed anywhere between 5 and 15 cameras. Larger study areas received more cameras than smaller sites because all cameras were deployed a minimum of 150m between one another. We avoided placing cameras on roads, trails, and water sources to artificially bias wildlife detections. We also avoided placing cameras within 15m of trails to avoid detecting humans. At each of the 12 study areas we surveyed in the second winter season, we deployed 12 to 30 cameras. At each study site, we used ArcGIS Pro (Esri Inc, Redlands, CA) to delineate the trail systems and then created a 150m buffer on each side of the trail. We then created random points within these buffered areas to decide where to deploy cameras. Each random point had to occur within the buffered areas and be a minimum of 150m from the next nearest camera point, thus the number of cameras at each site varied based upon site size. We placed all cameras within 50m of the random points to ensure that cameras were deployed on safe topography and with a clear field of view, though cameras were not set in locations that would have increased animal detections (game trails, water sources, burrows etc.). Cameras were rotated between sites after 5 or 10 week intervals to allow us to maximize camera locations with a limited number of trail cameras available to us. Sites with more than 25 cameras were active for 5 consecutive weeks while sites with fewer than 25 cameras were active for 10 consecutive weeks. We placed all cameras on trees or tripods 50cm above ground and at least 15m from trails and roads. We set cameras to take a burst of three photos when triggered. We used Timelapse 2.0 software (Greenberg et al. 2019) to extract metadata (date and time) associated with all animal detections. We manually identified all species occurring in photographs and counted the number of individuals present. Because density estimation requires the calculation of detection rates (number of Armadillo detections divided by the total sampling period), we wanted to reduce double counting individuals. Therefore, we grouped photographs of Armadillos into “episodes” of 5 minutes in length to reduce double counting individuals that repeatedly triggered cameras (DeGregorio et al. 2021, Meek et al. 2014). A 5 min threshold is relatively conservative with evidence that even 1-minute episodes adequately reduces double counting (Meek et al. 2014). Landcover Covariates To evaluate occupancy and density of Armadillos based on environmental and anthropogenic variables, we used ArcGIS Pro to extract variables from 500m buffers placed around each camera (Table 2). This spatial scale has been shown to hold biological meaning for Armadillos and similarly sized species (DeGregorio et al. 2021, Fidino et al. 2016, Gallo et al. 2017, Magle et al. 2016). At each camera, we extracted elevation, slope, and aspect from the base ArcGIS Pro map. We extracted maximum housing unit density (HUD) using the SILVIS housing layer (Radeloff et al. 2018, Table 2). We extracted anthropogenic noise from the layer created by Mennitt and Fristrup (2016, Buxton et al. 2017, Table 2) and used the “L50” anthropogenic sound level estimate, which was calculated by taking the difference between predicted environmental noise and the calculated noise level. Therefore, we assume that higher levels of L50 sound corresponded to higher human presence and activity (i.e. voices, vehicles, and other sources of anthropogenic noise; Mennitt and Fristrup 2016). We derived the area of developed open landcover, forest area, and distance to forest edge from the 2019 National Land Cover Database (NLDC, Dewitz 2021, Table 2). Developed open landcover refers to open spaces with less than 20% impervious surface such as residential lawns, cemeteries, golf courses, and parks and has been shown to be important for medium-sized mammals (Gallo et al. 2017, Poessel et al. 2012). Forest area was calculated by combing all forest types within the NLCD layer (deciduous forest, mixed forest, coniferous forest), and summarizing the total area (km2) within the 500m buffer. Distance to forest edge was derived by creating a 30m buffer on each side of all forest boundaries and calculating the distance from each camera to the nearest forest edge. We calculated distance to water by combining the waterbody and flowline features in the National Hydrogeography Dataset (U.S. Geological Survey) for the state of Arkansas to capture both permanent and ephemeral water sources that may be important to wildlife. We measured the distance to water and distance to forest edge using the geoprocessing tool “near” in ArcGIS Pro which calculates the Euclidean distance between a point and the nearest feature. We extracted Average Daily Traffic (ADT) from the Arkansas Department of Transportation database (Arkansas GIS Office). The maximum value for ADT was calculated using the Summarize Within tool in ArcGIS Pro. We tested for correlation between all covariates using a Spearman correlation matrix and removed any variable with correlation greater than 0.6. Pairwise comparisons between distance to roads and HUD and between distance to forest edge and forest area were both correlated above 0.6; therefore, we dropped distance to roads and distance to forest edge from analyses as we predicted that HUD and forest area would have larger biological impacts on our focal species (Kretser et al. 2008). Occupancy Analysis In order to better understand habitat associations while accounting for imperfect detection of Armadillos, we used occupancy modeling (Mackenzie et al. 2002). We used a single-species, single-season occupancy model (Mackenzie et al. 2002) even though we had two years of survey data at 5 of the study sites. We chose to do this rather than using a multi-season dynamic occupancy model because most sites were not sampled during both years of the study. Even for sites that were sampled in both years, cameras were not placed in the same locations each year. We therefore combined all sampling into one single-season model and created unique site by year combinations as our sampling locations and we used year as a covariate for analysis to explore changes in occupancy associated with the year of study. For each sampling location, we created a detection history with 7 day sampling periods, allowing presence/absence data to be recorded at each site for each week of the study. This allowed for 16 survey periods between 01 December 2020, and 11 March 2021 and 22 survey periods between 01 November 2021 and 24 March 2022. We treated each camera as a unique survey site, resulting in a total of 352 sites. Because not all cameras were deployed at the same time and for the same length of time, we used a staggered entry approach. We used a multi-stage fitting approach in which we

In the Prince William Sound region of Alaska, recent glacier retreat started in the mid-1800s and began to accelerate in the mid-2000s in response to warming air temperatures (Maraldo and others, 2020). Prince William Sound is surrounded by the central Chugach Mountains and consists of numerous ocean-terminating glaciers, with rapid deglaciation increasingly exposing oversteepened bedrock walls of fiords. Deglaciation may accelerate the occurrence of rapidly moving rock avalanches (RAs), which have the potential to generate tsunamis and adversely impact maritime vessels, marine activities, and coastal infrastructure and populations in the Prince William Sound region. RAs have been documented in the Chugach Mountains in the past (Post, 1967; McSaveney, 1978; Uhlmann and others, 2013), but a time series of RAs in the Chugach Mountains is not currently available. A systematic inventory of RAs in the Chugach is needed as a baseline to evaluate any future changes in RA frequency, magnitude, and mobility. This data release presents a comprehensive historical inventory of RAs in a 4600 km2 area of the Prince William Sound. The inventory was generated from: (1) visual inspection of 30-m resolution Landsat satellite images collected between July 1984 and August 2024; and (2) the use of an automated image classification script (Google earth Engine supRaglAciaL Debris INput dEtector (GERALDINE, Smith and others, 2020)) designed to detect new rock-on-snow events from repeat Landsat images from the same time period. RAs were visually identified and mapped in a Geographic Information System (GIS) from the near-infrared (NIR) band of Landsat satellite images. This band provides significant contrast between rock and snow to detect newly deposited rock debris. A total of 252 Landsat images were visually examined, with more images available in recent years compared to earlier years (Figure 1). Calendar year 1984 was the first year when 30-m resolution Landsat data were available, and thus provided a historical starting point from which RAs could be detected with consistent certainty. By 2017, higher resolution (<5-m) daily Planet satellite images became consistently available and were used to better constrain RA timing and extent. Figure 1. Diagram showing the number of usable Landsat images per year. This inventory reveals 118 RAs ranging in size from 0.1 km2 to 2.3 km2. All of these RAs occurred during the months of May through September (Figure 2). The data release includes three GIS feature classes (polygons, points, and polylines), each with its own attribute information. The polygon feature class contains the entire extent of individual RAs and does not differentiate the source and deposit areas. The point feature class contains headscarp and toe locations, and the polyline feature class contains curvilinear RA travel distance lines that connect the headscarp and toe points. Additional attribute information includes the following: location of headscarp and toe points, date of earliest identified occurrence, if and when the RA was sequestered into the glacier, presence and delineation confidence levels (see Table 1 for definition of A, B, and C confidence levels), identification method (visual inspection versus automated detection), image platform, satellite, estimated cloud cover, if the RA is lobate, image ID, image year, image band, affected area in km2, length, height, length/height, height/length, notes, minimum and maximum elevation, aspect at the headscarp point, slope at the headscarp point, and geology at the headscarp point. Topographic information was derived from 5-m interferometric synthetic aperture radar (IfSAR) Digital Elevation Models (DEMs) that were downloaded from the USGS National Elevation Dataset website (U.S. Geological Survey, 2015) and were mosaicked together in ArcGIS Pro. The aspect and slope layers were generated from the downloaded 5-m DEM with the “Aspect” and “Slope” tools in ArcGIS Pro. Aspect and slope at the headscarp mid-point were then recorded in the attribute table. A shapefile of Alaska state geology was downloaded from Wilson and others (2015) and was used to determine the geology at the headscarp location. The 118 identified RAs have the following confidence level breakdown for presence: 66 are A-level, 51 are B-level, and 1 is C-level. The 118 identified RAs have the following confidence level breakdown for delineation: 39 are A-level and 79 are B-level. Please see the provided attribute table spreadsheet for more detailed information. Figure 2. Diagram showing seasonal timing of mapped rock avalanches. Table 1. Rock avalanche presence and delineation confidence levels Category Grade Justification Presence A Feature is clearly visible in one or more satellite images. B Feature is clearly visible in one or more satellite images but has low contrast with the surroundings and may be surficial debris from rock fall, rather than from a rock avalanche. C Feature presence is possible but uncertain due to poor quality of imagery (e.g., heavy cloud cover or shadows) or lack of multiple views. Delineation A Exact outline of the feature from headscarp to toe is clear. B General shape of the feature is clear but the exact headscarp or toe location is unclear (e.g., due to clouds or shadows). Disclaimer: Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. References Maraldo, D.R., 2020, Accelerated retreat of coastal glaciers in the Western Prince William Sound, Alaska: Arctic, Antarctic, and Alpine Research, v. 52, p. 617-634, https://doi.org/10.1080/15230430.2020.1837715 McSaveney, M.J., 1978, Sherman glacier rock avalanche, Alaska, U.S.A. in Voight, B., ed., Rockslides and Avalanches, Developments in Geotechnical Engineering, Amsterdam, Elsevier, v. 14, p. 197–258. Post, A., 1967, Effects of the March 1964 Alaska earthquake on glaciers: U.S. Geological Survey Professional Paper 544-D, Reston, Virgina, p. 42, https://pubs.usgs.gov/pp/0544d/ Smith, W. D., Dunning, S. A., Brough, S., Ross, N., and Telling, J., 2020, GERALDINE (Google Earth Engine supRaglAciaL Debris INput dEtector): A new tool for identifying and monitoring supraglacial landslide inputs: Earth Surface Dynamics, v. 8, p. 1053-1065, https://doi.org/10.5194/esurf-8-1053-2020 Uhlmann, M., Korup, O., Huggel, C., Fischer, L., and Kargel, J. S., 2013, Supra-glacial deposition and flux of catastrophic rock-slope failure debris, south-central Alaska: Earth Surface Processes and Landforms, v. 38, p. 675–682, https://doi.org/10.1002/esp.3311 U.S. Geological Survey, 2015, USGS NED Digital Surface Model AK IFSAR-Cell37 2010 TIFF 2015: U.S. Geological Survey, https://elevation.alaska.gov/#60.67183:-147.68372:8 Wilson, F.H., Hults, C.P., Mull, C.G, and Karl, S.M, compilers, 2015, Geologic map of Alaska: U.S. Geological Survey Scientific Investigations Map 3340, pamphlet p. 196, 2 sheets, scale 1:1,584,000, https://pubs.usgs.gov/publication/sim3340

Rising sea levels (SLR) will cause coastal groundwater to rise in many coastal urban environments. Inundation of contaminated soils by groundwater rise (GWR) will alter the physical, biological, and geochemical conditions that influence the fate and transport of existing contaminants. These transformed products can be more toxic and/or more mobile under future conditions driven by SLR and GWR. We reviewed the vulnerability of contaminated sites to GWR in a US national database and in a case comparison with the San Francisco Bay region to estimate the risk of rising groundwater to human and ecosystem health. The results show that 326 sites in the US Superfund program may be vulnerable to changes in groundwater depth or flow direction as a result of SLR, representing 18.1 million hectares of contaminated land. In the San Francisco Bay Area, we found that GWR is predicted to impact twice as much coastal land area as inundation from SLR alone, and 5,297 state-managed sites of contamination may be vulnerable to inundation from GWR in a 1-meter SLR scenario. Increases of only a few centimeters of elevation can mobilize soil contaminants, alter flow directions in a heterogeneous urban environment with underground pipes and utility trenches, and result in new exposure pathways. Pumping for flood protection will elevate the salt water interface, changing groundwater salinity and mobilizing metals in soil. Socially vulnerable communities are more exposed to this risk at both the national scale and in a regional comparison with the San Francisco Bay Area. Methods Data Dryad This data set includes data from the California State Water Resources Control Board (WRCB), the California Department of Toxic Substances Control (DTSC), the USGS, the US EPA, and the US Census. National Assessment Data Processing: For this portion of the project, ArcGIS Pro and RStudio software applications were used. Data processing for superfund site contaminants in the text and supplementary materials was done in RStudio using R programming language. RStudio and R were also used to clean population data from the American Community Survey. Packages used include: Dplyr, data.table, and tidyverse to clean and organize data from the EPA and ACS. ArcGIS Pro was used to compute spatial data regarding sites in the risk zone and vulnerable populations. DEM data processed for each state removed any elevation data above 10m, keeping anything 10m and below. The Intersection tool was used to identify superfund sites within the 10m sea level rise risk zone. The Calculate Geometry tool was used to calculate the area within each coastal state that was occupied by the 10m SLR zone and used again to calculate the area of each superfund site. Summary Statistics were used to generate the total proportion of superfund site surface area / 10m SLR area for each state. To generate population estimates of socially vulnerable households in proximity to superfund sites, we followed methods similar to that of Carter and Kalman (2020). First, we generated buffers at the 1km, 3km, and 5km distance of superfund sites. Then, using Tabulate Intersection, the estimated population of each census block group within each buffer zone was calculated. Summary Statistics were used to generate total numbers for each state. Bay Area Data Processing: In this regional study, we compared the groundwater elevation projections by Befus et al (2020) to a combined dataset of contaminated sites that we built from two separate databases (Envirostor and GeoTracker) that are maintained by two independent agencies of the State of California (DTSC and WRCB). We used ArcGIS to manage both the groundwater surfaces, as raster files, from Befus et al (2020) and the State’s point datasets of street addresses for contaminated sites. We used SF BCDC (2020) as the source of social vulnerability rankings for census blocks, using block shapefiles from the US Census (ACS) dataset. In addition, we generated isolines that represent the magnitude of change in groundwater elevation in specific sea level rise scenarios. We compared these isolines of change in elevation to the USGS geological map of the San Francisco Bay region and noted that groundwater is predicted to rise farther inland where Holocene paleochannels meet artificial fill near the shoreline. We also used maps of historic baylands (altered by dikes and fill) from the San Francisco Estuary Institute (SFEI) to identify the number of contaminated sites over rising groundwater that are located on former mudflats and tidal marshes. The contaminated sites' data from the California State Water Resources Control Board (WRCB) and the Department of Toxic Substances (DTSC) was clipped to our study area of nine-bay area counties. The study area does not include the ocean shorelines or the north bay delta area because the water system dynamics differ in deltas. The data was cleaned of any duplicates within each dataset using the Find Identical and Delete Identical tools. Then duplicates between the two datasets were removed by running the intersect tool for the DTSC and WRCB point data. We chose this method over searching for duplicates by name because some sites change names when management is transferred from DTSC to WRCB. Lastly, the datasets were sorted into open and closed sites based on the DTSC and WRCB classifications which are shown in a table in the paper's supplemental material. To calculate areas of rising groundwater, we used data from the USGS paper “Projected groundwater head for coastal California using present-day and future sea-level rise scenarios” by Befus, K. M., Barnard, P., Hoover, D. J., & Erikson, L. (2020). We used the hydraulic conductivity of 1 condition (Kh1) to calculate areas of rising groundwater. We used the Raster Calculator to subtract the existing groundwater head from the groundwater head under a 1-meter of sea level rise scenario to find the areas where groundwater is rising. Using the Reclass Raster tool, we reclassified the data to give every cell with a value of 0.1016 meters (4”) or greater a value of 1. We chose 0.1016 because groundwater rise of that little can leach into pipes and infrastructure. We then used the Raster to Poly tool to generate polygons of areas of groundwater rise.

The state of Tennessee is divided into 805 individual 7.5-minute topographic quadrangle maps. The Tennessee Department of Environment and Conservation (TDEC) maintains an archive of paper maps that were utilized for estimating groundwater well locations. Each well location was plotted by hand and marked with corresponding water well data. These hand-plotted locations represent the most accurate spatial information for each well but exist solely in paper format. To create the shapefile of the well location data for this data release, individual paper maps were scanned and georeferenced. From these georeferenced map images (GRI), the hand-plotted well locations were digitized into a shapefile of point data using ArcGIS Pro. The shapefile is contained in "TN_waterwell.zip," which contains locations for 8,826 points from the first 200 7.5-minute quadrangles in Tennessee (sorted alphabetically) from Adair 438NW through Harriman 123NE. While some spring locations are included in this dataset, it does not provide a comprehensive collection of spring data. Attribute data includes quad name, drawing number, and hand-written identification data that was transcribed from the topographic maps. Latitude and longitude coordinates (decimal degrees) were populated. Data projection is USA Contiguous Albers Equal Area Conic USGS (meters). A table of attribute data is included in this data release as "TN_waterwells_table.xlsx." Detailed descriptions of the attributes can be found in the accompanying metadata file named "TN_waterwells_metadata.xml."

Methods:This lidar derivative provides information about the bare surface of the earth. The 2-foot resolution hillshade raster was produced from the 2020 Digital Terrain Model using the hillshade geoprocessing tool in ArcGIS Pro.QL1 airborne lidar point cloud collected countywide (Sanborn)Point cloud classification to assign ground points (Sanborn)Ground points were used to create over 8,000 1-foot resolution hydro-flattened Raster DSM tiles. Using automated scripting routines within LP360, a GeoTIFF file was created for each tile. Each 2,500 x 2,500 foot tile was reviewed using Global Mapper to check for any surface anomalies or incorrect elevations found within the surface. (Sanborn)1-foot hydroflattened DTM tiles mosaicked together into a 1-foot resolution mosaiced hydroflattened DTM geotiff (Tukman Geospatial)1-foot hydroflattened DTM (geotiff) resampled to 2-foot hydro-flattened DTM using Bilinear interpolation and clipped to county boundary with 250-meter buffer (Tukman Geospatial)2-foot hillshade derived from DTM using the ESRI Spatial Analyst ‘hillshade’ function The data was developed based on a horizontal projection/datum of NAD83 (2011), State Plane, Feet and vertical datum of NAVD88 (GEOID18), Feet. Lidar was collected in early 2020, while no snow was on the ground and rivers were at or below normal levels. To postprocess the lidar data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Sanborn Map Company, Inc., utilized a total of 25 ground control points that were used to calibrate the lidar to known ground locations established throughout the project area. An additional 125 independent accuracy checkpoints, 70 in Bare Earth and Urban landcovers (70 NVA points), 55 in Tall Grass and Brushland/Low Trees categories (55 VVA points), were used to assess the vertical accuracy of the data. These check points were not used to calibrate or post process the data.Uses and Limitations: The hillshade provides a raster depiction of the ground returns for each 2x2 foot raster cell across Santa Clara County. The layer is useful for hydrologic and terrain-focused analysis and is a helpful basemap when analyzing spatial data in relief.Related Datasets: This dataset is part of a suite of lidar of derivatives for Santa Clara County. See table 1 for a list of all the derivatives. Table 1. lidar derivatives for Santa Clara CountyDatasetDescriptionLink to DataLink to DatasheetCanopy Height ModelPixel values represent the aboveground height of vegetation and trees.https://vegmap.press/clara_chmhttps://vegmap.press/clara_chm_datasheetCanopy Height Model – Veg Returns OnlySame as canopy height model, but does not include lidar returns labelled as ‘unclassified’ (uses only returns classified as vegetation)https://vegmap.press/clara_chm_veg_returnshttps://vegmap.press/clara_chm_veg_returns_datasheetCanopy CoverPixel values represent the presence or absence of tree canopy or vegetation greater than or equal to 15 feet tall.https://vegmap.press/clara_coverhttps://vegmap.press/clara_cover_datasheetCanopy Cover – Veg Returns OnlySame as canopy height model, but does not include lidar returns labelled as ‘unclassified’ (uses only returns classified as vegetation)https://vegmap.press/clara_cover_veg_returnshttps://vegmap.press/clara_cover_veg_returns_datasheet HillshadeThis depicts shaded relief based on the Hillshade. Hillshades are useful for visual reference when mapping features such as roads and drainages and for visualizing physical geography. https://vegmap.press/clara_hillshadehttps://vegmap.press/clara_hillshade_datasheetDigital Terrain ModelPixel values represent the elevation above sea level of the bare earth, with all above-ground features, such as trees and buildings, removed. The vertical datum is NAVD88 (GEOID18).https://vegmap.press/clara_dtmhttps://vegmap.press/clara_dtm_datasheetDigital Surface ModelPixel values represent the elevation above sea level of the highest surface, whether that surface for a given pixel is the bare earth, the top of vegetation, or the top of a building.https://vegmap.press/clara_dsmhttps://vegmap.press/clara_dsm_datasheet

World Terrestrial Ecosystems are areas of climate, landform and land cover that form the basic components of terrestrial ecosystem structure. This map is the first-of-its-kind effort to characterize and map global terrestrial ecosystems at a much finer spatial resolution (250 m) than existing ecoregionalizations, and a much finer thematic resolution than existing global land cover products.This pro package was updated on February 26, 2024 to distinguish between Boreal and Polar climate regions in the terrestrial ecosystems. This map is important because the ecologically relevant distinctions are authoritatively defined and modeled using globally consistent objectively derived data.World Terrestrial Ecosystems map was produced by adopting and modifying the Intergovernmental Panel on Climate Change (IPCC) approach on the definition of Terrestrial Ecosystems and development of standardized (default) global climate regions using the values of environmental moisture regime and temperature regime. We then combined the values of Global Climate Regions, Landforms and matrix-forming vegetation assemblage or land use, using the ArcGIS Combine tool (Spatial Analyst) to produce World Ecosystems Dataset. This combination resulted of 431 World Ecosystems classes.In this ArcGIS Pro Package you will see three sources of authoritative information:The World Climate Regions, which establish the macroclimate regimeWorld Landforms, which modify the macroclimates into mesoclimates and microclimatesWorld Vegetation/Land Cover, which identify the major plant formations occurring in a place in response to the climate and landforms.This map allows you to query of any 250 m pixel on the land surface of the Earth, and returns the values of all the input parameters and the name of the World Terrestrial Ecosystem at that location.Each combination was assigned a color using an algorithm that blended traditional color schemes for each of the four components. Values for each of the four input layers are listed in the table below. Every point in this map is symbolized by a combination of values for each of these fields.This layer provides access to a cached map service created by Esri in partnership with U.S. Geological Survey's Climate and Land Use Change Program and The Nature Conservancy. The work from this collaboration is documented in the publication:Sayre et al. 2020. An assessment of the representation of ecosystems in global protected areas using new maps of World Climate Regions and World Ecosystems - Global Ecology and Conservation. You can access and view World Terrestrial Ecosystems Image File. You can access and have an high-level understanding of this dataset from the Introduction to World Terrestrial Ecosystems Story Map.

Version 3 of the Named Landforms of the World (NLWv3) is an update of version 2 of the Named Landforms of the World (NLWv2). NLWv2 will remain available as the compilation that best matches the work of E.M. Bridges and Richard E. Murphy. In NLWv3, we added attributes that describe each landform's volcanism based on data from the Smithsonian Institution's Global Volcanism Program (GVP). We designed NLWv3 layers for two purposes:To label maps with broadly accepted names for physiographic features. To add landform attributes to other layers. For example, species observation data or other small features to enable rich and relevant descriptions for how those features relate to landforms. To accomplish this, typically, we use overlay tools such as Identity. For background, version 2 provided features with the physiographic and geomorphologic characteristics for the world's named landforms. This means it was more than just showing the land versus water or mountains versus plains; it also included the underlying structure and processes that created the landforms. We begin with the largest landform regions, which are continents, followed by tectonic plates, then divisions, provinces, sections, and finally, individual landforms. In adding the GVP volcanic landforms to NLWv3, we learned that volcanoes are relatively short-lived as landforms, with most not enduring for two million years. For context, the age of the rocks in most of the Earth's mountain ranges is in the tens to hundreds of millions of years. The full collection of layers and maps for NLWv3 are available in an ArcGIS Online Group named Named Landforms Of the World v3 (NLWv3) Layers and Maps. The GVP included two inventories--one for the Holocene Epoch, which are the volcanoes that formed during most recent 11,700 years (since the last ice age). The other is for the Pleistocene Epoch, which precedes the Holocene, and lasted about 2.6 million years. While the Pleistocene epoch is 222 times longer than the Holocene, it only has 7.8% more volcanoes. Most of the volcanoes that formed during the Pleistocene have disappeared through natural erosional and depositional processes. In NLWv3, volcanic landforms include calderas, clusters and complexes, shields, stratovolcanoes, and minor volcanic features such as cinder cones, lava domes, and fissure vents. Not all the GVP features, particularly fissure vents and remnants of calderas, are large enough to be mapped as polygons in NLWv3. Similarly, complexes and volcanic fields typically had greater areas and included many individual cinder cones and calderas. ContinentCount of Volcanic LandformsArea km2 of Volcanic Landforms (% of land area)Europe7822,888 (0.23%)Antarctica4234,035 (0.27%)Australia14757,422 (0.65%)South America37081,475 (0.46%)Small Volcanic Islands559124,310 (8.52%)Africa282147,116 (0.50%)Asia698227,486 (0.53%)North America622295,340 (1.23%)Global Totals2,7981,000,073 (0.67%)This table shows the distribution of volcanic landforms and their surface areas. Overview of UpdatesCorresponding landform polygons now include attributes for the GVP's ID, name, province, and region. Details are provided below in the volcanic attributes section. Additionally, a text description of volcanism for each GVP feature was derived from these attributes to provide a reader-friendly characterization of each volcanic landform.Landforms of Antarctica. Given recent analysis of Antarctica and the use of GVP data, rudimentary landform features for Antarctica have been added. See details in the Antarctica section below.Refined the definition of Murphy's Isolated Volcanics classification. If the volcanic landform occurred outside of an orogenic, rifting, or subducting zone, only then did we consider it isolated. The areas along tectonic plate boundaries are where volcanoes typically occur. Only volcanoes occurring in areas with no tectonic activity are considered isolated. These typically occur in mid-continent or mid-tectonic plate. See details in the Isolated Volcanic Areas section.Edits to tectonic process attributes in selected areas. The GVP point locations for volcanoes include an attribute for the underlying tectonic process. The concept matched the existing tectonic process in the NLWv2, and we compared the values. When the values differed, we reviewed research and made changes. See details in the Tectonic Process section below.Minor boundary changes at the province, section, and landform level in the western mountains of North and South America. Details are provided below in the Boundary Change Locations section. Technical CharacteristicsThe NLWv2 and NLWv3 are derived from the same raster datasets used to produce the 2018 version of the World Terrestrial Ecosystems (WTEs), which, when combined, have a lowest-common-denominator resolution (minimum mapping unit) of 1 km. Some features, such as very small islands, were not included in NLWv3, and complex coastlines were simplified and were only included if the 1-km cell contained at least 50% land. Because the coastlines in the raster datasets varied by as much as 3 km from the actual coastline, nearly always due to missing land. Many of the worst such cases in NLWv2 were manually corrected using the 12-30-meter resolution World Hillshade layer as a guide. In NLWv3, we continued this work by adding 247 volcanic islands, some of which were smaller than 1 km in area. We estimate that these islands comprise about one percent of the world's smaller islands. In NLWv3, we also refined the coastlines of volcanic coastal areas, particularly in Oceania and Japan. For NLWv4, we plan to continue this refinement work, intending that future versions of NLW will have a progressively refined, medium-resolution coastline. However, we do not intend to capture the full detail of the Global Islands dataset, which was produced from 30-m Landsat data. Detailed Description of Updates Volcanic AttributesThe GVP Excel spreadsheets for the Holocene and Pleistocene epochs, which contained the coordinates and attributes for each volcano, were combined. A column for the geologic age was added before saving the spreadsheet as a .CSV file and importing into ArcGIS Pro. The XY Table to Points tool was used to create point features. Nearly ten percent of the point locations that lacked sufficient precision to fall within the correct landform polygon were revised manually in order to assign the correct Volcano ID to each polygon.2,394 of the 2,662 GVP volcanic features were assigned to landform polygons. 198 GVP features were not assigned because they represented undersea features, and 75 GVP features did not have apparent corresponding landform polygons because they were either too small or indistinguishable from surrounding topography. Of the 2,394 assigned GVP features, 48% are Holocene Age features and 52% are Pleistocene epoch features. 225 GVP features did not fall within within a landform feature that represented topographically a volcanic landform feature, such as a caldera or stratovolcano. This was usually due to insufficient precision of the GVP coordinates, which sometimes were rounded to the nearest integer of latitude and longitude and could therefore be over 50km away from the landform's location. AttributeDescriptionVolcano ID (SI)The six-digit unique ID for the Global Volcanism Program features.Volcano Name (SI)The Name of the volcanic feature as provided by the Global Volcanism Program. Volcanic Region (SI)The Name of the volcanic region as provided by the Global Volcanism Program. Volcanic Province (SI)The Name of the volcanic province as provided by the Global Volcanism Program. VolcanismA consistently formatted description volcanism for the landform feature based on the age, last eruption, landform type, and type of material. This information was not consistently available from the Global Volcanism Program, and we used a Python script to determine the condition of the Global Volcanism Program"s data and then include whatever information was available. AntarcticaSeveral recent analyses of Antarctica complemented the GVP point features. In particular, the British Antarctic Survey's 2019 Deep glacial troughs and stabilizing ridges unveiled beneath the margins of the Antarctic ice sheet show sufficiently detailed land surface elevation beneath the ice sheets to support identifying topographic landform classes. We georeferenced the elevation image and combined it with Bridge's geomorphological divisions and provinces to divide the continent into different landform polygons. Additional work is needed to make these landform polygons as rich and accurately defined as those in NLWv2. Isolated Volcanic AreasThere are 333 Isolated Volcanic landforms in NLWv2. We intentionally expanded on Murphy"s map which could not show many of the smaller landforms and areas due to the 1:50,000,000 scale (poster sized map of the world). Murphy"s map only included isolated volcanic areas in three locations: north-central Africa, Hawaii, and Iceland. In NLWv2, we used the Global Lithological Map to identify several areas on each continent and used the example of Hawaii to include many other known volcanic islands. In most ways, Isolated Volcanics denoted geographic isolation from other mountain systems. NLWv3 includes 2,798 volcanic landform features, and 185 have been assigned Murphy's Isolated Volcanic structure class because they do not occur within a region with the tectonic process of orogenic, subduction, or rifting. These Isolated Volcanic landform features are located mostly in mid-tectonic plate regions of Africa, the Arabian Peninsula, and on islands, particularly in the southern hemisphere, with a few in North America and Asia. NLWv3 contains 2,603 volcanic landform features, occurring on all continents and on islands within all oceans. Tectonic ProcessThe GVP data included a tectonic setting attribute that was compiled independently of the NLWv2 tectonic setting variable. When these