aAgricultural components (i.e. crop fields, pasture) were combined into one class for general comparisons across the broader land cover classes.

The Historical Urban Ecological (HUE) data project was created for exploring and analyzing the urban health environments of seven major United States cities - Baltimore, Boston, Brooklyn, Chicago, Cincinnati, Manhattan, and Philidelphia - from 1830 through 1930. The data for each city includes ward boundary changes, street networks, and ward-level data on disease, mortality, crime, and other variables reported by municipal departments. The HUE data set was produced for the "Early Indicators of Later Work Levels, Disease and Death" project, funded by the National Institute of Aging. This collection represents the GIS data for each of the seven American cities, and in addition to ward boundary changes and street networks, includes in-street sewer and water sanitation systems coverage. All cities except Cincinnati include sanitation infrastructure data, and for Baltimore only water infrastructure is available. The city of Chicago includes supplemental GIS layers which reflect a reconstruction of two of Homer Hoyt's maps of average land value (1933 dollars) in the City of Chicago for 1873 and 1892. The square mile areas defined by Hoyt using Chicago's system of mile streets have been fit to the HUE street centerlines for Chicago. The Excel data tables include information about deaths in each ward broken down by cause of death, age, race, gender, as well as information about live births and deliveries.

This dataset consists of current and future species distribution models generated using 4 Representative Concentration Pathways (RCPs) carbon emission scenarios, 18 global climate models (GCMs), and 8 time steps between 2015 and 2085, for Grey-headed Robin (Heteromyias albispecularis).

Urban green spaces are closely related to the abundance and biodiversity of birds by providing important habitats and together contribute to ecosystem health. This project aims to guide the University of British Columbia Botanical Garden to create Bird-friendly green spaces by using LiDAR data to analyze and map UBCBG's bird habitat suitability and create a 3D digital twin model of UBCBG in the open source game engine Minetest to increase 3D visualization and aid in landscape planning. By extracting the Canopy Height Model (CHM) using LiDAR data and performing individual tree segmentation, the derived metrics were used to identify trees with the highest bird habitat suitability index. The results showed that the suitability index ranges from -0.0016 to 0.5187, with a mean value of 0.2051. There are 68 trees with high suitability above the 0.4 intervals which have significance to bird populations and are worthy of being protected, accounting for only 3.38% of the total trees. They usually have a low vertical complexity index and foliage height diversity but are characterized by very tall trees with relatively large tree crowns. The Digital Elevation Model (DEM), Canopy Height Model (CHM) generated by LiDAR data were visualized in Minetest's UBCBG's 3D digital twin model using real terrain mod as topography and vegetation layers, while bird habitat suitability was used to symbolize the tree canopy layer. This study is highly relevant for landscape adaptation and planning in conjunction with other management considerations to support bird-friendly green spaces. The digital twin model can be used for educational and promotional purposes, and for landscape planning and aesthetic design with the consideration of bird conservation.

This dataset consists of current and future species distribution models generated using 4 Representative Concentration Pathways (RCPs) carbon emission scenarios, 18 global climate models (GCMs), and 8 time steps between 2015 and 2085, for Fairy Martin (Petrochelidon (Petrochelidon) ariel).

This dataset consists of current and future species distribution models generated using 4 Representative Concentration Pathways (RCPs) carbon emission scenarios, 18 global climate models (GCMs), and 8 time steps between 2015 and 2085, for Grey-headed Honeyeater (Lichenostomus (Ptilotula) keartlandi).

This teaching data subset contains1. a subset of spatial data (gis layers for the California Madera County and NEON SOAP and SJER sites). 2. Some other general spatial boundary layers from natural earth3. NEON lidar data and insitu measurements for SOAP and SJER sites. The data are used in both the Earth Analytics R and python courses. The Lidar data can be used to teach uncertainty given there are ground measurements available. We have recently added an additional vector layer so that cropping raster data can be taught using this data set as well.

Environmental pollution is a persistent problem in terrestrial ecosystems, including remote mountain areas. This study investigates the extent and patterns of littering on three popular hiking trails among mountaineers and tourists in the Dolomites range located in northeastern Italy. The data was collected adopting a citizen science approach with the participation of university students surveying the trails and recording the macroscopic waste items through a GPS-based offline platform. The waste items were categorized according to their material type, usage, and geographical location, and the sorted data was applied to Esri GIS ArcMapTM 10.8.1.

Landforms are large recognizable features such as mountains, hills and plains; they are an important determinant of ecological character, habitat definition and terrain analysis. Landforms are important to the distribution of life in natural systems and are the basis for opportunities in built systems, and therefore landforms play a useful role in all natural science fields of study and planning disciplines.Dataset SummaryPhenomenon Mapped: LandformsUnits: MetersCell Size: 231.91560581932 metersSource Type: ThematicPixel Type: 8-bit unsigned integerData Coordinate System: WGS 1984Mosaic Projection: Web Mercator Auxiliary SphereExtent: GlobalSource: EsriPublication Date: May 2016ArcGIS Server URL: https://landscape7.arcgis.com/arcgis/In February 2017, Esri updated the World Landforms - Improved Hammond Method service with two display functions: Ecological Land Units landform classes and Ecological Facets landform classes. This layer represents Ecological Facets landform classes. You can view the Ecological Land Units landform classes by choosing Image Display, and changing the Renderer. This layer was produced using the Improved Hammond Landform Classification Algorithm produced by Esri in 2016. This algorithm published and described by Karagulle et al. 2017: Modeling global Hammond landform regions from 250-m elevation data in Transactions in GIS.The algorithm, which is based on the most recent work in this area by Morgan, J. & Lesh, A. 2005: Developing Landform Maps Using Esri’s Model Builder., Esri converted Morgan’s model into a Python script and revised it to work on global 250-meter resolution GMTED2010 elevation data. Hammond’s landform classification characterizes regions rather than identifying individual features, thus, this layer contains sixteen classes of landforms:Nearly flat plainsSmooth plains with some local reliefIrregular plains with moderate relief Irregular plains with low hillsScattered moderate hillsScattered high hillsScattered low mountainsScattered high mountainsModerate hillsHigh hills Tablelands with moderate reliefTablelands with considerable reliefTablelands with high relief Tablelands with very high relief Low mountainsHigh mountainsTo produce these classes, Esri staff first projected the 250-meter resolution GMTED elevation data to the World Equidistant Cylindrical coordinate system. Each cell in this dataset was assigned three characteristics: slope based on 3-km neighborhood, relief based on 6 km neighborhood, and profile based on 6-km neighborhood. The last step was to overlay the combination of these three characteristics with areas that are exclusively plains. Slope is the percentage of the 3-km neighborhood occupied by gentle slope. Hammond specified 8% as the threshold for gentle slope. Slope is used to define how flat or steep the terrain is. Slope was classified into one of four classes: Percent of neighborhood over 8% of slopeSlope Classes0 - 20%40021% -50%30051% - 80%200>81% 100Local Relief is the difference between the maximum and minimum elevation within in the 6-km neighborhood. Local relief is used to define terrain how rugged or the complexity of the terrain's texture. Relief was assigned one of six classes:Change in elevationRelief Class ID0 – 30 meters1031 meter – 90 meters2091 meter – 150 meters30151 meter – 300 meters40301 meter – 900 meters50>900 meters60The combination of slope and relief begin to define terrain as mountains, hills and plains. However, the difference between mountains or hills and tablelands cannot be distinguished using only these parameters. Profile is used to determine tableland areas. Profile identifies neighborhoods with upland and lowland areas, and calculates the percent area of gently sloping terrain within those upland and lowland areas. A 6-km circular neighborhood was used to calculate the profile parameter. Upland/lowland is determined by the difference between average local relief and elevation. In the 6-km neighborhood window, if the difference between maximum elevation and cell’s elevation is smaller than half of the local relief it’s an upland. If the difference between maximum elevation and cell’s elevation is larger than half of the local relief it’s a lowland. Profile was assigned one of five classes:Percent of neighborhood over 8% slope in upland or lowland areasProfile ClassLess than 50% gentle slope is in upland or lowland0More than 75% of gentle slope is in lowland150%-75% of gentle slope is in lowland250-75% of gentle slope is in upland3More than 75% of gentle slope is in upland4Early reviewers of the resulting classes noted one confusing outcome, which was that areas were classified as "plains with low mountains", or "plains with hills" were often mostly plains, and the hills or mountains were part of an adjacent set of exclusively identified hills or mountains. To address this areas that are exclusively plains were produced, and used to override these confusing areas. The hills and mountains within those areas were converted to their respective landform class.The combination of slope, relief and profile merged with the areas of plains, can be better understood using the following diagram, which uses the colors in this layer to show which classes are present and what parameter values produced them:What can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop. This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks.The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Geonet is a good resource for learning more about landscape layers and the Living Atlas of the World. To get started see the Living Atlas Discussion Group.The Esri Insider Blog provides an introduction to the Ecophysiographic Mapping project.

Crowther_Nature_Files.zip This description pertains to the original download. Details on revised (newer) versions of the datasets are listed below. When more than one version of a file exists in Figshare, the original DOI will take users to the latest version, though each version technically has its own DOI. -- Two global maps (raster files) of tree density. These maps highlight how the number of trees varies across the world. One map was generated using biome-level models of tree density, and applied at the biome scale. The other map was generated using ecoregion-level models of tree density, and applied at the ecoregion scale. For this reason, transitions between biomes or between ecoregions may be unrealistically harsh, but large-scale estimates are robust (see Crowther et al 2015 and Glick et al 2016). At the outset, this study was intended to generate reliable estimates at broad spatial scales, which inherently comes at the cost of fine-scale precision. For this reason, country-scale (or larger) estimates are generally more robust than individual pixel-level estimates. Additionally, due to data limitations, estimates for Mangroves and Tropical coniferous forest (as identified by WWF and TNC) were generated using models constructed from Topical moist broadleaf forest data and Temperate coniferous forest data, respectively. Because we used ecological analogy, the estimates for these two biomes should be considered less reliable than those of other biomes . These two maps initially appeared in Crowther et al (2015), with the biome map being featured more prominently. Explicit publication of the data is associated with Glick et al (2016). As they are produced, updated versions of these datasets, as well as alternative formats, will be made available under Additional Versions (see below).

Methods: We collected over 420,000 ground-sources estimates of tree density from around the world. We then constructed linear regression models using vegetative, climatic, topographic, and anthropogenic variables to produce forest tree density estimates for all locations globally. All modeling was done in R. Mapping was done using R and ArcGIS 10.1.

Viewing Instructions: Load the files into an appropriate geographic information system (GIS). For the original download (ArcGIS geodatabase files), load the files into ArcGIS to view or export the data to other formats. Because these datasets are large and have a unique coordinate system that is not read by many GIS, we suggest loading them into an ArcGIS dataframe whose coordinate system matches that of the data (see File Format). For GeoTiff files (see Additional Versions), load them into any compatible GIS or image management program.

Comments: The original download provides a zipped folder that contains (1) an ArcGIS File Geodatabase (.gdb) containing one raster file for each of the two global models of tree density – one based on biomes and one based on ecoregions; (2) a layer file (.lyr) for each of the global models with the symbology used for each respective model in Crowther et al (2015); and an ArcGIS Map Document (.mxd) that contains the layers and symbology for each map in the paper. The data is delivered in the Goode homolosine interrupted projected coordinate system that was used to compute biome, ecoregion, and global estimates of the number and density of trees presented in Crowther et al (2015). To obtain maps like those presented in the official publication, raster files will need to be reprojected to the Eckert III projected coordinate system. Details on subsequent revisions and alternative file formats are list below under Additional Versions.----------

Additional Versions: Crowther_Nature_Files_Revision_01.zip contains tree density predictions for small islands that are not included in the data available in the original dataset. These predictions were not taken into consideration in production of maps and figures presented in Crowther et al (2015), with the exception of the values presented in Supplemental Table 2. The file structure follows that of the original data and includes both biome- and ecoregion-level models.

Crowther_Nature_Files_Revision_01_WGS84_GeoTiff.zip contains Revision_01 of the biome-level model, but stored in WGS84 and GeoTiff format. This file was produced by reprojecting the original Goode homolosine files to WGS84 using nearest neighbor resampling in ArcMap. All areal computations presented in the manuscript were computed using the Goode homolosine projection. This means that comparable computations made with projected versions of this WGS84 data are likely to differ (substantially at greater latitudes) as a product of the resampling. Included in this .zip file are the primary .tif and its visualization support files.

References:

Crowther, T. W., Glick, H. B., Covey, K. R., Bettigole, C., Maynard, D. S., Thomas, S. M., Smith, J. R., Hintler, G., Duguid, M. C., Amatulli, G., Tuanmu, M. N., Jetz, W., Salas, C., Stam, C., Piotto, D., Tavani, R., Green, S., Bruce, G., Williams, S. J., Wiser, S. K., Huber, M. O., Hengeveld, G. M., Nabuurs, G. J., Tikhonova, E., Borchardt, P., Li, C. F., Powrie, L. W., Fischer, M., Hemp, A., Homeier, J., Cho, P., Vibrans, A. C., Umunay, P. M., Piao, S. L., Rowe, C. W., Ashton, M. S., Crane, P. R., and Bradford, M. A. 2015. Mapping tree density at a global scale. Nature, 525(7568): 201-205. DOI: http://doi.org/10.1038/nature14967Glick, H. B., Bettigole, C. B., Maynard, D. S., Covey, K. R., Smith, J. R., and Crowther, T. W. 2016. Spatially explicit models of global tree density. Scientific Data, 3(160069), doi:10.1038/sdata.2016.69.

Download: Density of indicative threatened ecological community distributions (arcgis.com)Web service: species/ec_density (ImageServer)The density of indicative threatened ecological community distributions is derived from the Department's ecological communities of national environmental significance data. Threatened Ecological Communities (TEC) distributions contain three categories to indicate where their habitat is known, likely or may occur across Australia. The spatial input data was filtered using the following criteria: 1. Distributions for EPBC Act (1999) listed TECs that are Matters of National Environmental Significance (critically endangered or endangered).2. Contains ‘known’ and/or ‘likely to occur’ habitat categories. 3. Marine TECs are includedThe number of overlaps for each distribution in the selected feature set were counted and gridded to a 0.01 decimal degree (~1km) cell size. Note projecting the data will alter the cell size. The source distribution for each TEC is determined independently of others and is indicative in nature. As such, a count higher than one may indicate:• TECs have been mapped in the same habitat or • TECs are mapped adjacent within the same 1km grid cell or • TECs distributions have been mapped at different scales or levels of detail Given the indicative nature of the source data which includes data of a range of quality and currency, this output should be used as a guide to the location of TECs across the country.The selection of TEC distributions for inclusion in the count is based on the EPBC Act list of TECs and spatial data in the Department enterprise GIS as at the revision date in the metadata. Current EPBC Act listed TECs are described in the Species Profiles and Threats application (SPRAT: https://www.environment.gov.au/cgi-bin/sprat/public/sprat.pl).

This dataset combines the work of several different projects to create a seamless data set for the contiguous United States. Data from four regional Gap Analysis Projects and the LANDFIRE project were combined to make this dataset. In the northwestern United States (Idaho, Oregon, Montana, Washington and Wyoming) data in this map came from the Northwest Gap Analysis Project. In the southwestern United States (Colorado, Arizona, Nevada, New Mexico, and Utah) data used in this map came from the Southwest Gap Analysis Project. The data for Alabama, Florida, Georgia, Kentucky, North Carolina, South Carolina, Mississippi, Tennessee, and Virginia came from the Southeast Gap Analysis Project and the California data was generated by the updated California Gap land cover project. The Hawaii Gap Analysis project provided the data for Hawaii. In areas of the county (central U.S., Northeast, Alaska) that have not yet been covered by a regional Gap Analysis Project, data from the Landfire project was used. Similarities in the methods used by these projects made possible the combining of the data they derived into one seamless coverage. They all used multi-season satellite imagery (Landsat ETM+) from 1999-2001 in conjunction with digital elevation model (DEM) derived datasets (e.g. elevation, landform) to model natural and semi-natural vegetation. Vegetation classes were drawn from NatureServe's Ecological System Classification (Comer et al. 2003) or classes developed by the Hawaii Gap project. Additionally, all of the projects included land use classes that were employed to describe areas where natural vegetation has been altered. In many areas of the country these classes were derived from the National Land Cover Dataset (NLCD). For the majority of classes and, in most areas of the country, a decision tree classifier was used to discriminate ecological system types. In some areas of the country, more manual techniques were used to discriminate small patch systems and systems not distinguishable through topography. The data contains multiple levels of thematic detail. At the most detailed level natural vegetation is represented by NatureServe's Ecological System classification (or in Hawaii the Hawaii GAP classification). These most detailed classifications have been crosswalked to the five highest levels of the National Vegetation Classification (NVC), Class, Subclass, Formation, Division and Macrogroup. This crosswalk allows users to display and analyze the data at different levels of thematic resolution. Developed areas, or areas dominated by introduced species, timber harvest, or water are represented by other classes, collectively refered to as land use classes; these land use classes occur at each of the thematic levels. Raster data in both ArcGIS Grid and ERDAS Imagine format is available for download at http://gis1.usgs.gov/csas/gap/viewer/land_cover/Map.aspx Six layer files are included in the download packages to assist the user in displaying the data at each of the Thematic levels in ArcGIS. In adition to the raster datasets the data is available in Web Mapping Services (WMS) format for each of the six NVC classification levels (Class, Subclass, Formation, Division, Macrogroup, Ecological System) at the following links. http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Class_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Subclass_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Formation_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Division_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Macrogroup_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_Ecological_Systems_Landuse/MapServer

These are the data from the publication in Journal of Applied Ecology in 2011, See References for full reference and doi link. The dataset contains a readme file, a csv file with data and a shp file (collection of four separate files to be opened in a GIS program) showing the locations of sampling sites. Please consult the readme for information regarding data structure, and the journal article for sampling information and scientific context.

The Washington State Department of Ecology has defined a facility/site as an operation at a fixed location that is of interest to the agency because it has an active or potential impact upon the environment.Ecology recognizes that this definition is broad and generic; but the agency has found that such a definition is required in order to encompass all the facilities and sites in Washington that are within the purview of its programs. These programs cover a wide variety of environmental aspects and conditions including air quality, water quality, shorelands, water resources, toxics cleanup, hazardous waste, toxics reduction, and nuclear waste. The definitions of a facility and/or a site vary significantly across these programs, both in practice and law.Examples of facilities/sites include:Operation that pollutes the air or waterSpill cleanup siteHazardous waste management facilityHazardous waste generatorLicensed laboratorySUPERFUND siteFarm which draws water from a wellSolid waste recycling centerFacility/Site Interactions is a point feature service representing the facility/site locations stored in the Facility/Site database. This feature service queries directly the Facility/Site publication database which is updated nightly from the production transactional database.GIS Metadata: https://www.ecy.wa.gov/services/gis/data/environment/facsite.htmFor more information, contact Christina Kellum, Washington State Department of Ecology GIS Manager, gis@ecy.wa.gov.

This file contains the ranking and distance of the closest educational establishment for each of the 49 research sites. The ranking criteria is as follows: 0 = >1000m; 1 = 401m to 1000m; 2 = 101m to 400m; 3 = ≤100m.

This dataset consists of current and future species distribution models generated using 4 Representative Concentration Pathways (RCPs) carbon emission scenarios, 18 global climate models (GCMs), and 8 time steps between 2015 and 2085, for Frill-necked Monarch (Arses lorealis).

New-ID: NBI16

Agro-ecological zones datasets is made up of AEZBLL08, AEZBLL09, AEZBLL10.

The Africa Agro-ecological Zones Dataset documentation

Files: AEZBLL08.E00 Code: 100025-011 AEZBLL09.E00 100025-012 AEZBLL10.E00 100025-013

Vector Members The E00 files are in Arc/Info Export format and should be imported with the Arc/Info command Import cover In-Filename Out-Filename.

The Africa agro-ecological zones dataset is part of the UNEP/FAO/ESRI Database project that covers the entire world but focuses on Africa. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. Most data for the database were provided by Food and Agriculture Organization (FAO), the Soil Resources, Management and Conservation Service Land and Water Development Division, Italy. The daset was developed by United Nations Environment Program (UNEP), Kenya. The base maps that were used were the UNESCO/FAO Soil Map of the world (1977) in Miller Oblated Stereographic projection, the Global Navigation and Planning Charts (various 1976-1982) and the National Geographic Atlas of the World (1975). basemap and the source maps. The digitizing was done with a spatial resolution of 0.002 inches. The maps were then transformed from inch coordinates to latitude/longitude degrees. The transformation was done by an unpublished algorithm (by US Geological Survey and ESRI) to create coverages for one-degree graticules. This edit step required appending the country boundaries from Administrative Unit map and then producing the computer plot.

Contact: UNEP/GRID-Nairobi, P O Box 30552 Nairobi, Kenya FAO, Soil Resources, Management and Conservation Service, 00100, Rome, Italy ESRI, 380 New York Street, Redlands, CA 92373, USA

The AEZBLL08 data covers North-West of African continent The AEZBLL09 data covers North-East of African continent The AEZBLL10 data covers South of African continent

References:

ESRI. Final Report UNEP/FAO world and Africa GIS data base (1984). Internal Publication by ESRI, FAO and UNEP

FAO/UNESCO. Soil Map of the World (1977). Scale 1:5000000. UNESCO, Paris

Defence Mapping Agency. Global Navigation and Planning Charts for Africa (various dates:1976-1982). Scale 1:5000000. Washington DC.

G.M. Grosvenor. National Geographic Atlas of the World (1975). Scale 1:8500000. National Geographic Society, Washington DC.

FAO. Statistical Data on Existing Animal Units by Agro-ecological Zones for Africa (1983). Prepared by Todor Boyadgiev of the Soil Resources, Management and Conservation Services Division.

FAO. Statistical Data on Existing and Potential Populations by Agro-ecological Zones for Africa (1983). Prepared by Marina Zanetti of the Soil Resources, Management and Conservation Services Division. FAO. Report on the Agro-ecological Zones Project. Vol.I (1978), Methodology & Result for Africa. World Soil Resources No.48.

Source : UNESCO/FAO Soil Map of the World, scale 1:5000000 Publication Date : Dec 1984 Projection : Miller Type : Polygon Format : Arc/Info Export non-compressed Related Datasets : All UNEP/FAO/ESRI Datasets, Landuse (100013/05, New-ID: 05 FAO Irrigable Soils Datasets and Water balance (100050/53)

These data show sample locations for various abiotic data collected on Konza Prairie (rain gauges, soil moisture, and stream data). Included in these data are the locations for 12 rain gauges (GIS300) on Konza Prairie. The Konza headquarters weather station formerly consisted of two gauges which were operated year-round. The Konza headquarters weather station currently consists of one Otto-Pluvio2 gauge which is operated year-round. The remaining Konza-operated gauges run from April 1 to November 1. These data are to be used in conjunction with the APT01 (precipitation) dataset. GIS305 defines the locations where measurements of soil moisture (%volume) are taken on Konza Prairie. These data are to be used in conjunction with the ASM01 (soil moisture) dataset. GIS309 defines the locations within watershed N4D of soil sampler nests. In Jan 2020, we separated the original GIS310 file 'Wells in N4D' into GIS310 'Wells in N4D' and GIS309 'Soil Sampler Nests'. Prior to then, soil sampler nests and wells were combined in GIS310. GIS310 defines the locations within watershed N4D where samples are taken for analyzing the belowground water chemistry of the watershed. These data are to be used in conjunction with the AGW01 dataset. GIS311 defines the locations of 14 wells at two sites along Kings Creek. Depth and nutrient content of groundwater is measured at these sites. These data are to be used in conjunction with the AGW02 dataset. GIS315 defines the locations of stream sampling stations within multiple Konza watersheds. These data are to be used in conjunction with the NWC, ASS, ASD, and ASW datasets. GIS320 defines the locations of the rainfall collectors used to collect the samples analyzed as a part of the National Atmospheric Deposition Program. These data are to be used in conjunction with the ANA01 dataset. These data are available to download as zipped shapefiles (.zip), compressed Google Earth KML layers (.kmz).

This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. No Abstract Available Resources in this dataset:Resource Title: Website Pointer to html file. File Name: Web Page, url: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-sgs&identifier=802 Webpage with information and links to data files for download

Web Application that enables you to search the ecological tapestry of any part of the world. If you click on the map the following are returned:BioclimatesLandformsRock TypeLand Cover