description: The water-budget-components geodatabase contains selected data from maps in the,"Selected Approaches to Estimate Water-Budget Components of the High Plains, 1940 through 1949 and 2000 through 2009" report (Stanton and others, 2011). Data were collected and synthesized from existing climate models including the Parameter-Elevation Regressions on Independent Slopes Model (PRISM) (Daly and others, 1994), and the Snow accumulation and ablation model (SNOW-17) (Anderson, 2006), and used in soil-water balance models to compute various components of a water budget. The methodologies used to compute the averages and volumes for the data in this geodatabase are slightly different for different components and models.; abstract: The water-budget-components geodatabase contains selected data from maps in the,"Selected Approaches to Estimate Water-Budget Components of the High Plains, 1940 through 1949 and 2000 through 2009" report (Stanton and others, 2011). Data were collected and synthesized from existing climate models including the Parameter-Elevation Regressions on Independent Slopes Model (PRISM) (Daly and others, 1994), and the Snow accumulation and ablation model (SNOW-17) (Anderson, 2006), and used in soil-water balance models to compute various components of a water budget. The methodologies used to compute the averages and volumes for the data in this geodatabase are slightly different for different components and models.

We developed a geospatial workflow that refines the distribution of a species from its extent of occurrence (EOO) to area of habitat (AOH) within the species range map. The range maps are produced with an inverse distance weighted (IDW) interpolation procedure using presence and absence points derived from primary biodiversity data (GBIF and eBird hotspots respectively). Here we provide sample data to run the geospatial workflow for nine forest species across Mexico and Central America.

The present dataset provides necessary indicators of the climate change vulnerability of Bangladesh in raster form. Geospatial databases have been created in Geographic Information System (GIS) environment mainly from two types of raw data; socioeconomic data from the Bangladesh Bureau of Statistics (BBS) and biophysical maps from various government and non-government agencies. Socioeconomic data have been transformed into a raster database through the Inverse Distance Weighted (IDW) interpolation method in GIS. On the other hand, biophysical maps have been directly recreated as GIS feature classes and eventually, the biophysical raster database has been produced. 30 socioeconomic indicators have been considered, which has been obtained from the Bangladesh Bureau of Statistics. All socioeconomic data were incorporated into the GIS database to generate maps. However, the units of some variables have been adopted directly from BBS, some have been normalized based on population, and some have been adopted as percentages. 12 biophysical system indicators have also been classified based on the collected information from different sources and literature. Biophysical maps are mainly classified in relative scales according to the intensity. These geospatial datasets have been analyzed to assess the spatial vulnerability of Bangladesh to climate change and extremes. The analysis has resulted in a climate change vulnerability map of Bangladesh with recognized hotspots, significant vulnerability factors, and adaptation measures to reduce the level of vulnerability.

Geoscience Australia has been deriving raster sediment datasets for the continental Australian Exclusive Economic Zone (AEEZ) using existing marine samples collected by Geoscience Australia and external organisations. Since seabed sediment data are collected at sparsely and unevenly distributed locations, spatial interpolation methods become essential tools for generating spatially continuous information. Previous studies have examined a number of factors that affect the performance of spatial interpolation methods. These factors include sample density, data variation, sampling design, spatial distribution of samples, data quality, correlation of primary and secondary variables, and interaction among some of these factors. Apart from these factors, a spatial reference system used to define sample locations is potentially another factor and is worth investigating. In this study, we aim to examine the degree to which spatial reference systems can affect the predictive accuracy of spatial interpolation methods in predicting marine environmental variables in the continental AEEZ. Firstly, we reviewed spatial reference systems including geographic coordinate systems and projected coordinate systems/map projections, with particular attention paid to map projection classification, distortion and selection schemes; secondly, we selected eight systems that are suitable for the spatial prediction of marine environmental data in the continental AEEZ. These systems include two geographic coordinate systems (WGS84 and GDA94) and six map projections (Lambert Equal-area Azimuthal, Equidistant Azimuthal, Stereographic Conformal Azimuthal, Albers Equal-Area Conic, Equidistant Conic and Lambert Conformal Conic); thirdly, we applied two most commonly used spatial interpolation methods, i.e. inverse distance squared (IDS) and ordinary kriging (OK) to a marine dataset projected using the eight systems. The accuracy of the methods was assessed using leave-one-out cross validation in terms of their predictive errors and, visualization of prediction maps. The difference in the predictive errors between WGS84 and the map projections were compared using paired Mann-Whitney test for both IDW and OK. The data manipulation and modelling work were implemented in ArcGIS and R. The result from this study confirms that the little shift caused by the tectonic movement between WGS84 and GDA94 does not affect the accuracy of the spatial interpolation methods examined (IDS and OK). With respect to whether the unit difference in geographical coordinates or distortions introduced by map projections has more effect on the performance of the spatial interpolation methods, the result shows that the accuracies of the spatial interpolation methods in predicting seabed sediment data in the SW region of AEEZ are similar and the differences are considered negligible, both in terms of predictive errors and prediction map visualisations. Among the six map projections, the slightly better prediction performance from Lambert Equal-Area Azimuthal and Equidistant Azimuthal projections for both IDS and OK indicates that Equal-Area and Equidistant projections with Azimuthal surfaces are more suitable than other projections for spatial predictions of seabed sediment data in the SW region of AEEZ. The outcomes of this study have significant implications for spatial predictions in environmental science. Future spatial prediction work using a data density greater than that in this study may use data based on WGS84 directly and may not have to project the data using certain spatial reference systems. The findings are applicable to spatial predictions of both marine and terrestrial environmental variables.

You can also purchase hard copies of Geoscience Australia data and other products at http://www.ga.gov.au/products-services/how-to-order-products/sales-centre.html

Results from a New Mexico county based gravity model measuring geographic accessibility using 2015 population and physician data. Both Euclidean and road distance measures were used. The relative difference between the Euclidean and road distance measures is presented. An IDW interpolation for road distance results is presented in addition choropleth maps. The 2015 census population estimates are from UNM-GPS and the 2015 primary care physician estimates were obtained from the New Mexico Health Care Workforce Committee, 2016 Annual Report: (http://hsc.unm.edu/assets/doc/economic-development/nmhcwc-presentation-2016.PDF).Additional results from a New Mexico Census Tract based gravity model measuring geographic accessibility using 2002 population and physician data. Both Euclidean and road distance measures were used. The relative difference between the Euclidean and road distance measures is presented. An IDW interpolation for road distance results is presented in addition choropleth maps. The 2015 census population estimates are from UNM-GPS and the 2002 primary care physicians estimates were from the Division of Government Research, UNM as part of work performed for the New Mexico Health Policy Commission from 1998 through 2002.Note: both choropleth and IDW interpolation examples are presented.More information at: (http://www.unm.edu/~lspear/health_stuff.html).

This map presents NASC values (Nautical Area Scattering Coefficient) attributed to anchovy (Engraulis encrasicolus) in 2019, 2020, 2021 and 2022, highlighting the regional abundance gradient of this species. The map was created through IDW interpolation applied to the results of the MEDIAS surveys (MEDiteranean International Acoustic Survey) conducted in the Mediterranean. Detailed information on the methodology for acquiring and processing acoustic data is available in the MEDIAS handbook. Additionally, details on the interpolation method used are provided in the report of the 16th working group meeting.

This map presents NASC values (Nautical Area Scattering Coefficient) attributed to sardine (Sardina pilchardus) in 2019, 2020, 2021 and 2022, highlighting the regional abundance gradient of this species. The map was created through IDW interpolation applied to the results of the MEDIAS surveys (MEDiteranean International Acoustic Survey) conducted in the Mediterranean. Detailed information on the methodology for acquiring and processing acoustic data is available in the MEDIAS handbook. Additionally, details on the interpolation method used are provided in the report of the 16th working group meeting.

This collection contains the following datasets: 1) Classifications of water and non-water for individual Landsat image dates. Water: value = 1 Non-water: value = 2 NoData: clouds, shadows, urban, elevations >70m, areas outside of the Central Valley Joint Venture Boundary Each filename contains the original Landsat sceneID, which stored information about the sensor, the path/row, year date (YYYYDDD), and base station that downloaded the data from the satellite. See the Landsat surface reflectance documentation for more detail about the naming conventions 2) Inputs: 2a) Python scripts used to produce water/non-water classifications 2b) Landsat scene list containing the SceneIDs for all processed images Image Processing Methods: All available Landsat surface reflectance images from spring (Feb - May) 1983-2015 for the Sacramento Valley (path/row 044/033) were downloaded from . An optimized spring threshold to separate water from non-water was applied to the mid-infrared band for each image. Values <0.69 were classified as water, while values >0.69 were classified as non-water. See Schaffer-Smith et al. for more detail regarding the threshold optimization approach. A series of masking and clipping operations were performed to produce the final maps to excludeclouds, shadows, urban areas, and steep topography. Cloud and shadow regions are identified in the cfmask band of the surface reflectance dataset (Zhu et al. 2015). Values = 2, 4 were reclassified to NoData, while all others were classified as 1. urban regions were identified from the USDA National Agricultural Statistics Service (USDA NASS) cropland data layer (2014, Value = 23, 24, 25). Pixels in steep mountainous areas(>70m) elevation were also excluded based on the 10-m National Elevation Dataset (USGS). Finally, regions outside of the Central Valley Joint Venture (CVJV) boundary, which is based on watersheds, were masked out (Ducks Unlimited 2014). For SLC-off Landsat 7 images, which have artifacts due to slide line corrector failure, additional processing was required. See Schaffer-Smith et al. for more information about this issue. Inverse distance weighted (IDW) interpolation was applied to the thresholded water/non-water map, guided by county land use survey boundaries from the California Department of Water Resources (2016). The cfmask band of each SLC-off Landsat 7 surface reflectance dataset is also affected by these artifacts. To fill gaps in the cloud and shadow mask before final masking and clipping steps, IDW interpolation was also applied to the cloud and shadow mask. For more detail regarding image processing and analysis methods, see Schaffer-Smith et al. ... [Read More]

This data release comprises four datasets that represent Phosphorus concentration in the A and C horizon of soils within the conterminous United States. The source dataset is a slight modification of the data published in the report "Geochemical and mineralogical data for soils of the conterminous United States", http://pubs.usgs.gov/ds/801/. Data were interpolated for use in models associated with the National Water Quality Assessment program of the USGS. Two datasets are of Phosphorus in soil A horizon and C horizon, interpolated using Inverse Distance Weight (IDW) algorithm. Two datasets are of Phosphorus in soil A horizon and C horizon, interpolated using IDW, and then aggregated to Geologic Mapping Units (GMUs).

Esri ArcGIS Online (AGOL) Map Image Layer for accessing the Maryland Coast Smart - Climate Ready Action Boundary (CRAB) Inundated Zones data product.Maryland Coast Smart - Climate Ready Action Boundary (CRAB) Inundated Zones consists of polygon geometric features which represent the geographic areas throughout the State of Maryland that are impacted by CRAB inundation (0 to 1ft, 1 to 2ft, and 2ft or more).The Maryland Coast Smart - Climate Ready Action Boundary (CRAB) Inundated Zones data product was created using a GIS spatial analysis model, unique for each county in the State of Maryland. Coastal counties follow an analysis methodology that incorporates FEMA Stillwater wave action as it is understood from the FEMA identified VE zones. A Water Surface Elevation (WSE) and Still Water Elevation (SWEL) rasters are used as the baseline to identify existing water depths within each county. For all flood zones that are not classified as VE the WSE three feet was added to reflect a three-foot rise in the base flood elevations. For those WSEs falling within a FEMA floodplain identified V Zone, six feet was added (three feet for the increase in flood elevations for the CS-CRAB, and 3 feet to compensate for the minimum of 3 foot wave action typically mapped by FEMA) / wave heights greater than 3 feet were reduced to the 3 foot minimum for consistency across the shoreline. The newly calculated WSE plus three datasets were then converted to points and merged. Next, an Inverse Distance Weighted (IDW) Interpolation was used to compute the proportional weighted values between the WSE point locations based on proximity. The DEM for each county is then subtracted from the new IDW raster in order to show precise water locations as they relate to the land elevation, producing a freeboard depth grid representing the depth of flood waters above the existing ground elevation given a 3 foot increase in water level. A course resolution QAQC was applied to remove “islands” of data associated with DEM inaccuracies and other elevation anomalies. The analysis was run at a1 ft x 1 ft raster resolution. The DEM accuracy for each county varies based what is currently available. Here the breakdown of DEM accuracy for each county used in this project: Anne Arundel County DEM year is 2017 and horizontal resolution is 1ft. Baltimore County DEM year is 2015 and horizontal resolution is 2.5ft. Baltimore City DEM year is 2015 and horizontal resolution is 0.7m. Calvert County DEM year is 2017 and horizontal resolution is 1ft. Caroline County DEM year is 2013 and horizontal resolution is 3.125ft. Cecil county DEM year is 2013 and horizontal resolution is 0.6m. Charles County DEM year is 2014 and horizontal resolution is 0.9m. Dorchester County DEM year is 2013 and horizontal resolution is 0.9m. Harford County DEM is 2013 and horizontal accuracy is 1.5m. Kent County DEM year is 2015 and horizontal resolution is 0.7m. Prince George’s County DEM year is 2014 and horizontal resolution is 0.7m. Queen Anne’s County DEM year is 2013 and horizontal resolution is 0.6m. Somerset County DEM year is 2012 and horizontal accuracy is 1m. St Mary’s County DEM year is 2014 and Horizontal accuracy is 0.9m. Talbot County DEM year is 2015 and Horizontal accuracy is 0.7m. Wicomico County DEM year is 2012 and horizontal accuracy is 1m. Worchester County DEM year is 2011 and horizontal accuracy is 1m.The Maryland Coast Smart - Climate Ready Action Boundary (CRAB) Inundated Zones data product was created by the Maryland Environmental Service (MES) in partnership with the Maryland Department of Environment (MDE) and the Coast Smart Council, under the guidance of the Maryland Department of Natural Resource (DNR).For additional information, contact MDOT SHA OIT Enterprise Information Services:Email: GIS@mdot.maryland.gov

At the onset of the full reopening in Spring 2023 of the Difficult-to-Return Zone of Northeastern Japan following the Fukushima Daiichi Nuclear Power Plant (FDNPP) accident that took place in March 2011, several spatial layers were regrouped and compiled to facilitate environmental studies dealing with the redistribution of radiocesium fallout across landscapes.

The current dataset is composed of 23 shapefiles including those of the delineations of different spatial zones (Intensive Contamination Survey Areas – ICAs, Special Decontamination Zones – SDZ, Difficult-to-Return Zone – DTRZ, and FNDPP location) (Evrard et al. 2019), municipalities where mushroom consumption restrictions were enforced (restricted and partially lifted restrictions), river hydrographic networks and their respective drainage areas (Mano, Niida, Ota, Takase, and Ukedo), dam reservoirs and drainage areas (Mano, Ogaki, Takanokura, and Yokokawa), multiple administrative delineations in Japan (whole Japan administrative boundaries, Prefectures, and municipalities) (GIS, 2016), and one raster file of the reconstruction of initial 137Cs fallout across eastern Japan (from Kato et al., 2019).

The current dataset provides a support to a publication submitted to the SOIL journal:

Evrard, O., Chalaux-Clergue, T., Chaboche, P.-A., Wakiyama, Y., and Thiry Y. (2023). Research and Management Challenges Following Soil and Landscape Decontamination at the Onset of the Reopening of the Difficult-To-Return Zone, Fukushima (Japan)’. SOIL 9: 479–97. https://doi.org/10.5194/soil-9-479-2023.

All map processing was carried out using QGIS 3.26.0 (QGIS, 2022) and under the EPSG:WGS 84 projection system.

The 137Cs fallout raster (in Bq m2, decay-corrected to July 2011) was generated from the point grid of Kato et al. (2019). A total of 126 tiles (0.25 x 0.25 degree) were generated by Inverse Distance Weighted (IDW) interpolation using the IDW interpolation tool with the following settings: distance coefficient P = 1.0 and pixel size (x and y) = 0.0015 degree. Tiles were then merged into a single tile using the raster Merge tool. The initial point grid footprint was manually delineated to define the spatial applicability zone of the airborne survey. A buffer zone corresponding to half plus 10% of the longest distance between two airborne points (x = 0.002, y = 0.003), i.e. 0.0017 degree, was generated using the buffer tool. The single tile was then cut according to the footprint of the buffer zone using the cut a raster according to a mask layer tool. A single-band pseudo-colour scale is provided and displays pixels with a value above 1000 Bq.kg-1 (eq. global background).

Investigating the bioaccessibility of harmful inorganic elements in soil is crucial for understanding their behavior in the environment and accurately assessing the environmental risks associated with soil. Traditional batch experimental methods and linear models, however, are time-consuming and often fall short in precisely quantifying bioaccessibility. In this study, using 937 data points gathered from 56 journal articles, we developed machine learning models for three harmful inorganic elements, namely, Cd, Pb, and As. After thorough analysis, the model optimized through a boosting ensemble strategy demonstrated the best performance, with an average R2 of 0.95 and an RMSE of 0.25. We further employed SHAP values in conjunction with quantitative analysis to identify the key features that influence bioaccessibility. By utilizing the developed integrated models, we carried out predictions for 3002 data points across China, clarifying the bioaccessibility of cadmium (Cd), lead (Pb), and arsenic (As) in the soils of various sites and constructed a comprehensive spatial distribution map of China using the inverse distance weighting (IDW) interpolation method. Based on these findings, we further derived the soil environmental standards for metallurgical sites in China. Our observations from the collected data indicate a reduction in the number of sites exceeding the standard levels for Cd, Pb, and As in mining/smelting sites from 5, 58, and 14 to 1, 24, and 7, respectively. This research offers a precise and scientific approach for cross-regional risk assessment at the continental scale and lays a solid foundation for soil environmental management.

Change classes for total basal area, total aboveground biomass, and species diversity across the plots between 2004 and 2011, where 2004 and 2011 are the centroid-year of first (1997–2010) and last (2003–2018) inventory intervals, respectively (N = 1,432).

Spatial predictions of the fractions of mud, sand and gravel as continuous response variables for the north-west European continental shelf. Mud, sand and gravel fractions range from 0-1 (i.e. 0-100%). These fractions were generated from two additive log-ratios (ALR), ALRs and ALRm which are independent, unconstrained response variables. These raw predictions as rasters are also included presented in the attached dataset. Predicted fractions have been combined to predict the likely sediment classification based on the EUNIS level 3 sediment classification for broadscale habitats, Folk 5, Folk 7, Folk 11 and Folk 15 classification schemes. These are available as raster tif files with an ArcGIS layer file indicating the appropriate class for each raster value. For all predictions an accompanying map of the spatial distribution of error/accuracy is also included as a separate raster. For the three components of the sediment fraction a smoothed Root-Mean-Squared-Error layer is available. For the classification maps a smoothed local accuracy map is available. Spatial predictions of mud, sand and gravel were generated for the north-west European continental shelf. Based on these fractions sediment classification maps were also generated for the study site. To support the interpretation of these layers maps of the spatial distribution of error/accuracy were also generated. In short, analysis combined the eight continuous predictive layers (Bathymetry, Bathymetric position index at a 50-pixel radii, Bathymetric position index at a 434-pixel radii, Distance from coast, Current speed at the seabed, Wave peak orbital velocity at the seabed, and suspended inorganic particulate matter for summer and winter as two separate variables) with sediment observation data in a statistical regression model to make spatial predictions of the fractions of mud, sand and gravel. Spatial predictions were generated based on two additive log-ratios that could then be back transformed to produce spatial predictions for each fraction. From these spatial predictions any classification scheme based on the percentages of mud, sand and gravel can be applied. Included here are the five classification shemes generated from these maps. The maps of accuracy were also generated to support interpretation. For the maps of the fractions of mud, sand and gravel map error was calculated based on the Root-Mean-Squared-Error of the observed vs predicted fractions from the test samples. A smoothed surface of local RMSE was then generated using the Inverse Distance Weighted (IDW) technique in ArcGIS. Each pixels’ RMSE was determined based on the closest 50 points (up to a maximum distance of 200 km). A weighting power function was applied in the IDW tool (set at 0.3) so nearer points contributed more to the pixel than distant points. For the classified maps spatial accuracy was calculated using a locally constrained confusion matrix. The IDW technique was applied to calculate a local thematic accuracy value. As above, this was applied based on the closest 50 points (maximum distance of 200 km) with a weighting power function of 0.3.

Goddard’s LiDAR, Hyperspectral, and Thermal Imager (G-LiHT) mission is a portable, airborne imaging system that aims to simultaneously map the composition, structure, and function of terrestrial ecosystems. G-LiHT primarily focuses on a broad diversity of forest communities and ecoregions in North America, mapping aerial swaths over the coterminous United States (CONUS), Alaska, Puerto Rico, and Mexico. The purpose of G-LiHT’s LiDAR Point Cloud data product (GLLIDARPC) is to provide high-density individual LiDAR return data, including 3D coordinates, classified ground returns, Above Ground Level (AGL) heights, and LiDAR apparent reflectance. GLLIDARPC data are processed as a LAS Version 1.1 binary format specified by the American Society for Photogrammetry and Remote Sensing (ASPRS). The point cloud includes a density of more than 10 points per square meter. A low resolution browse is also provided showing the LiDAR Point Cloud as an Inverse Data Weighted (IDW) interpolation in PNG format.

This dataset is a broad overview of individual fish species distribution and biomass across the Northeast U.S. Continental Shelf ecosystem . In 2014, the Marine Geospatial Ecology Lab (MGEL) of Duke University began work with the Northeast Regional Ocean Council (NROC), the NOAA National Centers for Coastal Ocean Science (NCCOS), the NOAA Northeast Fisheries Science Center (NEFSC) and Loyola University Chicago, as part of the Marine-life Data Analysis Team (MDAT), to characterize and map marine life in the Northeast region in support of the Regional Ocean Plan. In 2015, the MidAtlantic Regional Council on the Ocean (MARCO) contracted with MDAT to build upon and expand this effort into the Mid-Atlantic planning area, and in support of the Mid-Atlantic Regional Ocean Plan. These research groups collaborated to produce “base layer” predictive model products with associated uncertainty products for marine mammal species or species guilds and avian species, and three geospatial products for fish species. Periodic updates to these base layer models and data are produced by the individual institutions in the MDAT team based on schedules set by the funders of each modeling effort. MDAT member Northeast Fisheries Science Center (NEFSC) summarized fish biomass and distribution from coastal fishery independent trawl data which spans Cape Hatteras, North Carolina to the Gulf of Maine. NEFSC provided three data products: (1) bubble plot of raw observations, (2) hexagon plot showing the mean, and (3) a 10km x 10km inverse-distance weighted (IDW) interpolation plot which smoothed over multiple observations and interpolated in regions with few observations. All units are natural log kilograms per tow. These products were created for three sources of fisheries independent trawl data, across multiple time spans: North East Areas Monitoring and Assessment Program (NEAMAP) 2007-2014; Massachusetts Division of Marine Fisheries (MDMF) 1978-2014; 2005-2014; Maine & New Hampshire state trawls (ME/NH) 2000-2014; 20052014 Survey samples for all data sources were collected primarily in September and October, with some in November and a small number in December. v2019_06_01 Much more detail about the NEFSC Ecosystem Assessment Program, along with additional data sets, can be found here: https://www.nefsc.noaa.gov/ecosys/ In 2019, MDAT member The Nature Conservancy (TNC) produced fish biomass and distribution products in partnership with OceanAdapt (a collaboration between the Pinksy Lab at Rutgers University and the National Marine Fisheries Service). These products are also bubble plots of raw observations and IDW surfaces at a 2km x 2km resolution for bottom trawl data from NEFSC during 2010-2017 (fall) and 2010-2016 (spring). All units are kilograms per tow. Survey samples for fall trawls were collected primarily in September and October, with some in November and a small number in December. Spring survey samples were collected from February to April.View Dataset on the Gateway

Tungsten Raster for TellusSW. Part of the Geochemical Baseline Survey of the Environment (G-BASE) for south-west England.

This map is based on analysis of 3720 stream sediment samples collected at an average density of one per 2.5 km2 during the field campaigns of 2004 and 2012.

The image was generated in ArcGIS 10.1 using inverse distance weighting (IDW) of the nearest 12 samples within a variable search radius capped at 10 km. Cell size is 500 m. Detailed information on sampling and analytical methods are provided in the following published geochemical atlas:

Regional geochemistry of Wales and west-central England: stream sediment and soil. 2000. British Geological Survey, Keyworth, Nottingham. ISBN 0 85272 378 4.

These data are delivered under the terms of the Open Government Licence, subject to the following acknowledgement accompanying the reproduced BGS materials: "Contains British Geological Survey materials © UKRI [year]".