Geographic Information System (GIS) analyses are an essential part of natural resource management and research. Calculating and summarizing data within intersecting GIS layers is common practice for analysts and researchers. However, the various tools and steps required to complete this process are slow and tedious, requiring many tools iterating over hundreds, or even thousands of datasets. USGS scientists will combine a series of ArcGIS geoprocessing capabilities with custom scripts to create tools that will calculate, summarize, and organize large amounts of data that can span many temporal and spatial scales with minimal user input. The tools work with polygons, lines, points, and rasters to calculate relevant summary data and combine them into a single output table that can be easily incorporated into statistical analyses. These tools are useful for anyone interested in using an automated script to quickly compile summary information within all areas of interest in a GIS dataset

Saturated thickness map of the Rush Springs aquifer in central Oklahoma. Map displays the thickness of the water level (potentiometric) surface in the Rush Springs aquifer to the base of the unit, which is defined as the base of the Marlow Formation by the OWRB for their study released in 2018. Saturated thickness ranges between 0-432 feet, with an average saturated thickness of 181 feet. In areas where the potentiometric surface rises above the top of the Rush Springs Formation into the Cloud Chief Formation, the thickness was capped at the Rush Springs/Cloud Chief contact. This calculation was done in ArcGIS 10.2.2 using a raster calculator subtracting the saturated thickness within the Cloud Chief from the total saturated thickness. Also used in the process was the Mosaic to New Raster tool to create a raster that included values from both the smaller extent Cloud Chief and the larger extent Rush Springs in one output raster with the extent of the entire Rush Springs aquifer.

This raster GIS dataset contains 5-meter-resolution cells depicting the areas of total marsh gain (value=1), lost (value=-1) and remaining (no change; value=0). Total marsh (TM) was defined as the sum of low marsh and high marsh [SLAMM category 8 + SLAMM category 7 + SLAMM category 20]. Based on SLAMM simulation outputs, we generated the gain and loss map by using the “Raster Calculator” tool under “Spatial Analyst Tools” in ArcGIS software. The methodology consists of the three steps listed below (where we use low marsh [LM] as an example). The same process can be applied to other SLAMM land cover categories.

1) Open ArcMap, add SLAMM simulation raster outputs (all SLAMM categories) for baseline year and future years.

2) In Raster Calculator, set the SLAMM codeequal to8 (low marsh = SLAMM category 8) to generate a new raster. Each individual cell in the new raster is assigned a value of “0” or “1”. “1” is low marsh and “0” is any other SLAMM land cover category. Perform this step for both the baseline year and future year.

3) In Raster Calculator, subtract the new raster for the baseline year from the new raster for the future year (formula = new future year raster - new baseline year raster). The calculation generates a new raster, in which each individual cell is assigned a value of “-1”, “0”, or “1”. Based on the calculation, “-1” means low marsh loss in the future (the cell has converted from low marsh to a different SLAMM category), “0” means low marsh is remaining (the cell stays the same), and “1” means low marsh gain in the future (the cell has converted from a different SLAMM category to low marsh).

Prior SLAMM work has been performed in the Delaware Bay, but our methods differ in that we derive results for specific marsh areas and utilize more recent, higher resolution elevation data (2015 USGS CoNED Topobathy Model: New Jersey and Delaware), the most recent SLR projections, and site-specific accretion data (through 2016). These SLAMM simulations were performed as part of a larger project by the USEPA on frameworks and methods for characterizing relative wetland vulnerabilities.

Note: additional raster files from this project are available upon request. These include files from low and high SLR scenarios and different model protection scenarios. For more information, contact Jordan West (West.Jordan@epa.gov).

This raster GIS dataset contains 5-meter-resolution cells depicting the areas of LOW marsh gain (value=1), lost (value=-1) and remaining (no change; value=0). Low marsh (LM) was defined as regularly flooded marsh [SLAMM category 8]. LM is normally inundated by tidal water at least once per day. Based on SLAMM simulation outputs, we generated the gain and loss map by using the “Raster Calculator” tool under “Spatial Analyst Tools” in ArcGIS software. The methodology consists of the three steps listed below (where we use low marsh [LM] as an example). The same process can be applied to other SLAMM land cover categories.

1) Open ArcMap, add SLAMM simulation raster outputs (all SLAMM categories) for baseline year and future years.

2) In Raster Calculator, set the SLAMM codeequal to8 (low marsh = SLAMM category 8) to generate a new raster. Each individual cell in the new raster is assigned a value of “0” or “1”. “1” is low marsh and “0” is any other SLAMM land cover category. Perform this step for both the baseline year and future year.

3) In Raster Calculator, subtract the new raster for the baseline year from the new raster for the future year (formula = new future year raster - new baseline year raster). The calculation generates a new raster, in which each individual cell is assigned a value of “-1”, “0”, or “1”. Based on the calculation, “-1” means low marsh loss in the future (the cell has converted from low marsh to a different SLAMM category), “0” means low marsh is remaining (the cell stays the same), and “1” means low marsh gain in the future (the cell has converted from a different SLAMM category to low marsh).

Prior SLAMM work has been performed in the Delaware Bay, but our methods differ in that we derive results for specific marsh areas and utilize more recent, higher resolution elevation data (2015 USGS CoNED Topobathy Model: New Jersey and Delaware), the most recent SLR projections, and site-specific accretion data (through 2016). These SLAMM simulations were performed as part of a larger project by the USEPA on frameworks and methods for characterizing relative wetland vulnerabilities.

Note: additional raster files from this project are available upon request. These include files from low and high SLR scenarios and different model protection scenarios. For more information, contact Jordan West (West.Jordan@epa.gov).

The U.S. Geological Survey, in partnership with the National Park Service's Colonial National Historic Park (COLO), used commercially available satellite data and soil samples from around Jamestown Island to evaluate vegetative health and soil conditions on the island to further understand the extent and severity of conditions that threaten archaeological sites and vegetation. 50 sites were initially selected for sampling, however, only 48 of the sites were accessible in either June 2021 or March 2022. The soil samples were collected from 2 depths at 48 different sites around the island. The first sample was collected just below the land surface in the O horizon, and the second sample was collected from a minimum of 0.34 ft below the land surface in the A horizon. Two soil sampling efforts were conducted, one in June 2021 and a second in March 2022 to represent drier and wetter times of the year. Measurements of temperature in degrees Celsius, moisture content in percent volume, and soil conductivity in millisiemens per centimeter, were made using a Dynamax WET-2 sensor. Soil pH was also measured using the U.S. Environmental Protection Agency's 9045D method. Satellite imagery, multispectral and panchromatic images, used in the project come from the GeoEYE, QuickBird 2, WorldView 2, and WorldView 3 satellites operated by the European Space Agency and Digital Globe . USGS used panchromatic and multispectral images of Jamestown Island taken from 2010 – 2018 to create Normalized Difference Vegetative Index (NDVI) and difference of NDVI rasters to evaluate vegetative stress across Jamestown Island over time. The images used were acquired using the USGS's Commercial Remote Sensing Space Policy (CRSSP) Imagery Derived Requirements (CIDR) tool. The search terms used for the CIDR request were for multispectral and panchromatic images of Jamestown Island, VA at a standard (2A) processing level with an image resolution of 1-4m, a max cloud cover of 20%, from 05/01/2008 - 07/28/2022. The search returned 12 images, or scenes, of which 4 were used for the associated publication. The collection dates, satellite platform and panchromatic and multispectral ground sample distances (GSD) respectively are as follows: - 11/28/2010 at 16:24 from WorldView 2; GSD 1.509 ft and 5.906 ft - 06/25/2011 at 15:56 by GeoEye; GSD 1.345ft and 5.413 ft - 10/10/2011 by QuickBird 2; GSD 2.001 ft and 7.874 ft - 2/12/2018 at 16:08 by WorldView 3; GSD 1.017 ft and 4.068 ft The multispectral images were pan-sharpened to increase the resolution for visual light rasters of Jamestown Island using ESRI ArcGIS Pro's Pan-Sharpen tool utilizing the Graham-Schmidt method. Additionally, the 4 multispectral images were used to create normalized difference vegetative index rasters using the ESRI ArcGIS Pro NDVI tool. For images with multiple near-infrared (NIR) bands, the first NIR band was used to create the NDVI rasters. A difference of NDVI raster was created using the Raster Calculator tool in ArcGIS Pro to show change in vegetative heath over time. The 11/28/2010 WorldView 2 and 12/12/2018 WorldView 3 NDVI rasters with water removed from the rasters were used to create the difference of NDVI raster. The GSD for the difference of NDVI raster is 5.906 ft. The original multispectral and panchromatic images could not be published in this data release as the rights for those images belong to European Space Agency or Digital Globe. As such only the derived products, the pan-sharpened image, NDVI rasters, and difference of NDVI raster have been published in this data release.

Abstract

The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

This resource contains raster datasets created using ArcGIS to analyse groundwater levels in the Namoi subregion.

This is an update to some of the data that is registered here: http://data.bioregionalassessments.gov.au/dataset/7604087e-859c-4a92-8548-0aa274e8a226

Purpose

These data layers were created in ArcGIS as part of the analysis to investigate surface water - groundwater connectivity in the Namoi subregion. The data layers provide several of the figures presented in the Namoi 2.1.5 Surface water - groundwater interactions report.

Dataset History

Extracted points inside Namoi subregion boundary. Converted bore and pipe values to Hydrocode format, changed heading of 'Value' column to 'Waterlevel' and removed unnecessary columns then joined to Updated_NSW_GroundWaterLevel_data_analysis_v01\NGIS_NSW_Bore_Join_Hydmeas_unique_bores.shp clipped to only include those bores within the Namoi subregion.

Selected only those bores with sample dates between >=26/4/2012 and <31/7/2012. Then removed 4 gauges due to anomalous ref_pt_height values or WaterElev values higher than Land_Elev values.

Then added new columns of calculations:

WaterElev = TsRefElev - Water_Leve

DepthWater = WaterElev - Ref_pt_height

Ref_pt_height = TsRefElev - LandElev

Alternatively - Selected only those bores with sample dates between >=1/5/2006 and <1/7/2006

2012_Wat_Elev - This raster was created by interpolating Water_Elev field points from HydmeasJune2012_only.shp, using Spatial Analyst - Topo to Raster tool. And using the alluvium boundary (NAM_113_Aquifer1_NamoiAlluviums.shp) as a boundary input source.

12_dw_olp_enf - Select out only those bores that are in both source files.

Then using depthwater in Topo to Raster, with alluvium as the boundary, ENFORCE field chosen, and using only those bores present in 2012 and 2006 dataset.

2012dw1km_alu - Clipped the 'watercourselines' layer to the Namoi Subregion, then selected 'Major' water courses only. Then used the Geoprocessing 'Buffer' tool to create a polygon delineating an area 1km around all the major streams in the Namoi subregion.

selected points from HydmeasJune2012_only.shp that were within 1km of features the WatercourseLines then used the selected points and the 1km buffer around the major water courses and the Topo to Raster tool in Spatial analyst to create the raster.

Then used the alluvium boundary to truncate the raster, to limit to the area of interest.

12_minus_06 - Select out bores from the 2006 dataset that are also in the 2012 dataset. Then create a raster using depth_water in topo to raster, with ENFORCE field chosen to remove sinks, and alluvium as boundary. Then, using Map Algebra - Raster Calculator, subtract the raster just created from 12_dw_olp_enf

Dataset Citation

Bioregional Assessment Programme (2017) Namoi bore analysis rasters - updated. Bioregional Assessment Derived Dataset. Viewed 10 December 2018, http://data.bioregionalassessments.gov.au/dataset/effa0039-ba15-459e-9211-232640609d44.

Dataset Ancestors

• Derived From Bioregional Assessment areas v02

• Derived From Gippsland Project boundary

• Derived From Bioregional Assessment areas v04

• Derived From Upper Namoi groundwater management zones

• Derived From Natural Resource Management (NRM) Regions 2010

• Derived From Bioregional Assessment areas v03

• Derived From Victoria - Seamless Geology 2014

• Derived From GIS analysis of HYDMEAS - Hydstra Groundwater Measurement Update: NSW Office of Water - Nov2013

• Derived From Bioregional Assessment areas v01

• Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb)

• Derived From GEODATA TOPO 250K Series 3

• Derived From NSW Catchment Management Authority Boundaries 20130917

• Derived From Geological Provinces - Full Extent

• Derived From Hydstra Groundwater Measurement Update - NSW Office of Water, Nov2013

Abstract The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. This dataset is comprised of derived thickness grids and contours for the Bandanna and Eromanga Basins within Galilee subregion. The dataset also contains annotation text and masks for the Eromanga basin. ## Purpose The annotation text is used for labeling contour values and the cartographic mask is used to clear unwanted features from report maps. ## Dataset History Bandanna data: Bandanna_thk = DEM (GUID: ebcf6ca2-513a-4ec7-9323-73508c5d7b93) - Top_Bandanna(Steph Hostetler) This GRID was produced using Arc Catalogue raster calculator tool Eromanga data: Eromanga_not_clipped_contour: derived from Grid (Eromanga_thickness_not_clipped) Eromanga_thickness_not_clipped = DEM (GUID: ebcf6ca2-513a-4ec7-9323-73508c5d7b93) - *layer10 (ESRI GRD)_clp* [GUID: 11dcfa96-2a3e-402d-b6f2-1e90290842f7] This GRID was produced using Arc Catalogue raster calculator tool Bandanna_contour: derived from the grid (Bandanna_thk) using topo to raster tool in ArcMap. Annotation text and masks were created using the ArcGIS tool 'FeatureOutlineMasks' ## Dataset Citation Bioregional Assessment Programme (2015) Eromanga grids contours. Bioregional Assessment Derived Dataset. Viewed 07 December 2018, http://data.bioregionalassessments.gov.au/dataset/ac342830-f8e2-4c63-91e8-1a86d63beeac. ## Dataset Ancestors * Derived From Australian Geological Provinces, v02 * Derived From Layer 10 Great Artesian Basin base of Jurassic-Cretaceous sequence surface (Great Artesian Basin Water Resource Assessment) * Derived From GEODATA 9 second Digital Elevation Model Version 3 Hillshade * Derived From GEODATA 9 second DEM and D8: Digital Elevation Model Version 3 and Flow Direction Grid 2008

S is a probability of cultivation based on a series of environmental conditions on a global scale. Here, S is created to compare settlement locations throughout Utah to explain initial Euro-American settlement of the region. S is one of two proxies created specifically for Utah for comparison of environmental productivity throughout the state. The data are presented as a raster file where any one pixel represents the probability of cultivation from zero to one, normalized on a global scale (Ramankutty et al., 2002). Because S is normalized on a global scale, the range of values of S for Utah U.S.A does not cover the global spectrum of S, thus the highest S value in the data is 0.51. S was originally created by Ramankutty et al. (2002) on a global scale to understand probability of cultivation based on a series of environmental factors. The Ramankutty et al. (2002) methods were used to build a regional proxy of agricultural suitability for the state of Utah. Adapting the methods in Ramankutty et al. (2002), we created a higher resolution dataset of S specific to the state of Utah. S is composed of actual and potential evapotranspiration rates from 2000-2013, growing degree days, soil carbon density, and soil pH. The Moisture Index is calculated as: MI = ETact /PET Where ETact is the actual evapotranspiration and PET is the potential evapotranspiration. This calculation results in a zero to one index representing global variation in moisture. MI was calculated for the study area (Utah) using a raster of annual actual ETact and PET evapotranspiration data from 2000 to 2013 derived from the MODIS instrumentation (Mu, Zhao, & Running, 2011; Mu, Zhao, & Running, 2013; Numerical Terradynamic Simulation Group, 2013). Using ArcMap 10.3.1 Raster Calculator (Spatial Analyst), a raster dataset is created at a resolution of 2.6 kilometers.containing values representative of the average Moisture Index for Utah over a period of fourteen years (ESRI, 2015). The data were collected remotely by satellite (MODIS) and represents reflective surfaces (urban areas, lakes, and the Utah Salt Flats) as null values in the dataset. Areas of null values that were not bodies of water were interpolated using Inverse Distance Weighting (3d Analyst) in ArcMap 10.3.1 (ESRI, 2015). The probability of cultivation (S) is calculated as a normalized product of growing degree days (GDD), available moisture (MI), soil carbon density (Csoil), and soil pH (pHsoil). The equation is divided into two general components: S = Sclim * Ssoil where Sclim = f1(GDD) f2(MI) and Ssoil = g1(Csoil) g2(pHsoil) Climate suitability (Sclim) is calculated as a normalized probability density function of cropland area to Growing Degree-days (f1[GDD]) and probability density function of cropland area to Moisture Index (f2[MI]) (Ramankutty et al. 2002). Soil suitability (Ssoil) is calculated using a sigmoidal function of the soil carbon density and soil acidity/alkalinity. The optimum soil carbon range is from 4 to 8 kg of C/m2 and the optimum range of soil pH is from 6 to 7 (Ramankutty et al. 2002). The resulting S value varies from zero to one indicating the probability of agricultural on a global scale. To implement the equation for S, growing degree-days (GDD) are calculated using usmapmaker.pl Growing Degree-days calculator and PRISM climate maps with a minimum temperature threshold of 50 degrees Fahrenheit (Coop, 2010; Daly, Gibson, Taylor, Johnson, & Pasteris, 2002; Willmott & Robeson, 1995; “US Degree-Day Map Maker,” n.d.). Moisture Index data is calculated as described above. To calculate the overall climate suitability (Sclim), the resulting raster datasets of Growing Degree-days and Moisture Index are combined in ArcMap 10.3.1 using the Raster Calculator (Spatial Analyst) to create climate suitability (Sclim) raster dataset with a resolution of 2.6 kilometers sq. To calculate soil suitability, the functions provided by Ramankutty et al. (2002) are applied to soil data derived from the SSURGO soil dataset compiled using NRCS Soil Data Viewer 6.1 to create thematic maps of average soil pH within the top 30 centimeters and average carbon density within the top 30 centimeters ( Soil Survey Staff, 2015; NRCS Soils, n.d.). However, there are missing values in the SSURGO soil dataset for the state of Utah, resulting in datasets using soil pH to have null values in portions of the state (Soil Survey Staff, 2015). The resulting raster datasets of soil pH and carbon density are combined in ArcMap 10.3.1 using the Raster Calculator (Spatial Analyst) to create a soil suitability (Ssoil) raster dataset with a resolution of 9.2 kilometers sq (ESRI, 2015). The climate suitability raster dataset and soil suitability raster dataset are combined in ArcMap 10.3.1 using the Raster Calculator (Spatial Analyst) generating a S raster dataset with a resolution of 9.2 kilometers (ESRI, 2015). Projection: GCS_WGS_1984 Citations Coop, L. B. (2010). U. S. degree-day...

description: This dataset includes DRASTIC (Aller and others, 1987) model results for Upper Floridan aquifer vulnerability to contamination. The DRASTIC value serves as an intrinsic vulnerability index for assessing the transport of contaminants from the surface. The DRASTIC model setup requires the input of raster data for depth to groundwater, aquifer recharge, aquifer media, soil media, topography, vadose zone media, and aquifer hydraulic conductivity. These variables were entered into the DRASTIC equation using the raster calculator tool in ArcGIS.; abstract: This dataset includes DRASTIC (Aller and others, 1987) model results for Upper Floridan aquifer vulnerability to contamination. The DRASTIC value serves as an intrinsic vulnerability index for assessing the transport of contaminants from the surface. The DRASTIC model setup requires the input of raster data for depth to groundwater, aquifer recharge, aquifer media, soil media, topography, vadose zone media, and aquifer hydraulic conductivity. These variables were entered into the DRASTIC equation using the raster calculator tool in ArcGIS.

This raster GIS dataset contains 5-meter-resolution cells depicting the areas of HIGH marsh. High marsh (HM) was defined as the aggregation of irregularly-flooded marsh [SLAMM category 7] and transitional salt marsh [SLAMM category 20]. HM is covered by water only sporadically (once per day or less). Based on SLAMM simulation outputs, we generated the gain and loss map by using the “Raster Calculator” tool under “Spatial Analyst Tools” in ArcGIS software. The methodology consists of the three steps listed below (where we use low marsh [LM] as an example). The same process can be applied to other SLAMM land cover categories. 1) Open ArcMap, add SLAMM simulation raster outputs (all SLAMM categories) for baseline year and future years. 2) In Raster Calculator, set the SLAMM code equal to 8 (low marsh = SLAMM category 8) to generate a new raster. Each individual cell in the new raster is assigned a value of “0” or “1”. “1” is low marsh and “0” is any other SLAMM land cover category. Perform this step for both the baseline year and future year. 3) In Raster Calculator, subtract the new raster for the baseline year from the new raster for the future year (formula = new future year raster - new baseline year raster). The calculation generates a new raster, in which each individual cell is assigned a value of “-1”, “0”, or “1”. Based on the calculation, “-1” means low marsh loss in the future (the cell has converted from low marsh to a different SLAMM category), “0” means low marsh is remaining (the cell stays the same), and “1” means low marsh gain in the future (the cell has converted from a different SLAMM category to low marsh). Prior SLAMM work has been performed in the Delaware Bay, but our methods differ in that we derive results for specific marsh areas and utilize more recent, higher resolution elevation data (2015 USGS CoNED Topobathy Model: New Jersey and Delaware), the most recent SLR projections, and site-specific accretion data (through 2016). These SLAMM simulations were performed as part of a larger project by the USEPA on frameworks and methods for characterizing relative wetland vulnerabilities. Note: additional raster files from this project are available upon request. These include files from low and high SLR scenarios and different model protection scenarios. For more information, contact Jordan West (West.Jordan@epa.gov).

Moisture Index (MI) for the state of Utah is calculated from a spatial raster of annual actual (ETact) and potential (PET) evapotranspiration data from 2000 to 2013 derived from the MODIS instrumentation (Mu, Zhao, & Running, 2011; Mu, Zhao, & Running, 2013; Numerical Terradynamic Simulation Group, 2013). Moisture Index (MI) was created to compare the suitability of settlement locations throughout Utah to explain initial Euro-American settlement of the region. MI is one of two proxies created specifically for Utah for comparison of environmental productivity throughout the state. Moisture index (MI) was originally used by Ramankutty et al. (2002) on a global scale to understand probability of cultivation based on a series of environmental factors. The Ramankutty et al. (2002) methods were used to build a regional proxy of agricultural suitability for the state of Utah. Adapting the methods in Ramankutty et al. (2002), we were able to create a higher resolution dataset of MI specific to the state of Utah. Unlike S, MI only accounts for evapotranspiration rates.The Moisture Index is calculated as: MI = ETact / PET Where ETact is the actual evapotranspiration and PET is the potential evapotranspiration. This calculation results in a zero to one index representing global variation in moisture. MI is calculated for the study area (Utah) using a raster of annual actual (ETact) and potential (PET) evapotranspiration data from 2000 to 2013 derived from the MODIS instrumentation (Mu, Zhao, & Running, 2011; Mu, Zhao, & Running, 2013; Numerical Terradynamic Simulation Group, 2013). Using the ArcMap 10.3.1 Raster Calculator (Spatial Analyst), a raster dataset is created at a resolution of 2.6 kilometer square, which contain values representative of the average Moisture Index for Utah over a fourteen year period (ESRI, 2015). The data were collected remotely by satellite (MODIS) and represents reflective surfaces (urban areas, lakes, and the Utah Salt Flats) as null values in the dataset. Areas of null values that were not bodies of water are interpolated using Inverse Distance Weighting (3d Analyst) in ArcMap 10.3.1 (ESRI, 2015). Download the moisture index (MI) data below. If you have any questions or concerns, please contact me at PYaworsky89@gmail.com. Citations ESRI. (2015). ArcGIS Desktop: Release (Version 10.3.1). Redlands, CA: Environmental Systems Research Institute. Mu, Q., Zhao, M., & Running, S. W. (2013). MODIS Global Terrestrial Evapotranspiration (ET) Product (NASA MOD16A2/A3). Algorithm Theoretical Basis Document, Collection, 5. Retrieved from http://www.ntsg.umt.edu/sites/ntsg.umt.edu/files/MOD16_ATBD.pdf Mu, Q., Zhao, M., & Running, S. W. (2011). Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sensing of Environment, 115(8), 1781–1800. Numerical Terradynamic Simulation Group. (2013, July 29). MODIS Global Evapotranspiration Project (MOD16). University of Montana. Ramankutty, N., Foley, J. A., Norman, J., & Mcsweeney, K. (2002). The global distribution of cultivable lands: current patterns and sensitivity to possible climate change. Global Ecology and Biogeography, 11(5), 377–392. http://doi.org/10.1046/j.1466-822x.2002.00294.x

File-based data for download:https://www.sciencebase.gov/catalog/item/6556549dd34ee4b6e05c4822This layer calculated changes between the first and last time steps from the Sagebrush Conservation Design dataset. Calculations were done by adding the first and second time step rasters using the Raster Calculator tool in ArcGIS Pro. The later raster was reclassified with the following values Non-Rangeland Areas = 0, Core Sagebrush Areas = 10, Growth Opportunity Areas = 20, Other Rangeland Areas = 30. This created a raster showing change with the following values. Non-Rangeland to Non-Rangeland = 0Core to Non-Rangeland =1, Growth to Non-Rangeland = 2,Other to Non-Rangeland = 3Non-Rangeland to Core = 10Core to Core = 11Growth to Core = 12Other to Core = 13Non-Rangeland to Growth = 20Core to Growth = 21Growth to Growth = 22Other to Growth = 23Non-Rangeland to Other = 30Core to Other = 31Growth to Other = 32Other to Other = 33The purpose of these data are to provide a biome-wide, consistent, quantitative information about changes in sagebrush core habitat and growth areas. These data may be used to enable better prioritization of landscapes for conservation, and to inform which treatments or other conservation actions are appropriate in specific areas.Original Data cited as:Doherty, K., Theobald, D.M., Holdrege, M.C., Wiechman, L.A., and Bradford, J.B., 2022, Biome-wide sagebrush core habitat and growth areas estimated from a threat-based conservation design: U.S. Geological Survey data release, https://doi.org/10.5066/P94Y5CDV.Supporting literature for original dataset:Doherty, K., Theobald, D.M., Bradford, J.B., Wiechman, L.A., Bedrosian, G., Boyd, C.S., Cahill, M., Coates, P.S., Creutzburg, M.K., Crist, M.R., Finn, S.P., Kumar, A.V., Littlefield, C.E., Maestas, J.D., Prentice, K.L., Prochazka, B.G., Remington, T.E., Sparklin, W.D., Tull, J.C., Wurtzebach, Z., and Zeller, K.A., 2922, A sagebrush conservation design to proactively restore America’s sagebrush biome: U.S. Geological Survey Open-File Report 2022–1081, 38 p., https://doi.org/10.3133/ofr20221081.

Profile curvature was calculated from the bathymetry surface for each raster cell using the ArcGIS 3D Analyst "Curvature" Tool. Profile curvature describes the rate of change of curvature (parallel to the slope direction) within a square 3x3 cell neighborhood. A negative value denotes concavity, while a positive value denotes convexity. The 2x2 meter resolution profile curvature GeoTIFF was exported and added as a new map layer to aid in benthic habitat classification. Please see ESRI's online support center for more information about Profile Curvature. Acoustic imagery was acquired for the VICRNM on two separate missions onboard the NOAA ship, Nancy Foster. The first mission took place from 2/18/04 to 3/5/04. The second mission took place from 2/1/05 to 2/12/05. On both missions, seafloor depths between 14 to 55 m were mapped using a RESON SeaBat 8101 ER (240 kHz) MBES sensor. This pole-mounted system measured water depths across a 150 degree swath consisting of 101 individual 1.5 degree x 1.5 degree beams. The beams to the port and starboard of nadir (i.e., directly underneath the ship) overlapped adjacent survey lines by approximately 10 m. The vessel survey speed was between 5 and 8 kn. In 2004, the ship's location was determined by a Trimble DSM 132 DGPS system, which provided a RTCM differential data stream from the U.S. Coast Guard Continually Operating Reference Station (CORS) at Port Isabel, Puerto Rico. Gyro, heave, pitch and roll correctors were acquired using an Ixsea Octans gyrocompass. In 2005, the ship's positioning and orientation were determined by the Applanix POS/MV 320 V4, which is a GPS aided Inertial Motion Unit (IMU) providing measurements of roll, pitch and heading. The POS/MV obtained its positions from two dual frequency Trimble Zephyr GPS antennae. An auxiliary Trimble DSM 132 DGPS system provided a RTCM differential data stream from the U.S. Coast Guard CORS at Port Isabel, Puerto Rico. For both years, CTD (conductivity, temperature and depth) measurements were taken approximately every 4 hours using a Seabird Electronics SBE-19 to correct for the changing sound velocities in the water column. In 2004, raw data were logged in .xtf (extended triton format) using Triton ISIS software 6.2. In 2005, raw data were logged in .gsf (generic sensor format) using SAIC ISS 2000 software. Data from 2004 were referenced to the WGS84 UTM 20 N horizontal coordinate system, and data from 2005 were referenced to the NAD83 UTM 20 N horizontal coordinate system. Data from both projects were referenced to the Mean Lower Low Water (MLLW) vertical tidal coordinate system. The 2004 and 2005 MBES bathymetric data were both corrected for sensor offsets, latency, roll, pitch, yaw, static draft, the changing speed of sound in the water column and the influence of tides in CARIS Hips & Sips 5.3 and 5.4, respectively. The 2004 data was then binned to create a 1 x 1 m raster surface, and the 2005 data was binned to a create 2 x 2 m raster surface. After these final surfaces were created, the datum for the 2004 bathymetric surfaces was transformed from WGS84 to NAD83 using the "Project Raster" function in ArcGIS 9.1. The 2004 surface was transformed so that it would have the same datum as the 2005 surface. The 2004 bathymetric surface was then down sampled from 1 x 1 to 2 x 2 m using the "Resample" function in ArcGIS 9.1. The 2004 surface was resampled so it would have the same spatial resolution as the 2005 surface. Having the same coordinate systems and spatial resolutions, the final 2004 and 2005 bathymetry rasters were then merged using the Raster Calculator function "Merge" in ArcGIS's Spatial Analyst Extension to create a seamless bathymetry surface for the entire VICRNM area south of St. John. For a complete description of the data acquisition and processing parameters, please see the data acquisition and processing reports (DAPRs) for projects: NF-04-06-VI and NF-05-05-VI (Monaco & Rooney, 2004; Battista & Lazar, 2005).

Terminal lakes are lakes with no hydrologic surface outflows and with losses of water occurring only through surface evaporation and groundwater discharge. We quantified the extent of the littoral zones (areas where 1% or more of surface irradiation reaches the lake bottom) and open water zones (areas where less than 1% of surface irradiation reaches the lake bottom) in 18 terminal lakes. Additionally, we quantified habitat usage and diets of the fish species inhabiting these lakes. This dataset contains includes seven lakes from North America (Atitlan, Crater, Eagle, Mann, Pyramid, Summit, Walker), one from South America (Titicaca), five from Eurasia (Caspian, Issyk-Kul, Neusiedl, Qinghai, Van), and five from Africa (Abijatta, Manyara, Nakuru, Shala, Turkana). Methods Measurements of the surface areas of the littoral and open water zones were performed using ArcGIS Pro Version 2.9. First, we generated year-specific digital elevation models (DEMs) of the lake’s bathymetry by a) using existing bathymetry raster data or b) by digitizing published depth contours of the lake’s bathymetry and interpolating a bathymetry raster using a natural neighbor interpolation. For several lakes that showed significant changes in lake level and where data regarding lake level change were available, we were able to produce a second year closer to the present by using the Raster Calculator function in ArcGIS Pro and then clipping the bathymetry raster to the lower lake level. This was possible for 5 of the 18 lakes (Mann Lake, Eagle Lake, Lake Abijatta, Walker Lake, and Lake Turkana), allowing us to map changes in the littoral zone size between the two years. For the lakes containing two years of data, we used only the most recent year in all subsequent analyses. We defined the portions of the littoral zone of the lake as the portions where the intensity of photosynthetically active radiation (PAR) reaching the lake bottom is 1% or greater relative to the intensity at the surface. For lakes where 1% PAR depth was not published, we calculated 1% PAR depth from published light profiles using the Lambert-Beer Law: 0.01 = e-u*z where µ is the light attenuation coefficient (meters-1) and z is 1% PAR depth (meters). For lakes where neither 1% PAR depth nor light profiles were published, we approximated the 1% PAR depth by multiplying the Secchi depth of the lake by a coefficient of 2.5. We sought the most recently collected Secchi depth to make these calculations. We then used the Raster Calculator function in ArcGIS PRO 2.9 to determine the portions of the lake where depth was less than or greater than the 1% PAR depth to map the open water and littoral zones, respectively. Fish species inventories and information regarding each species’ habitat and diet was compiled from 1) published peer-reviewed primary literature, 2) non-peer-reviewed literature (books, reports by government agencies or private firms), 3) online databases (i.e., FishBase (https://www.fishbase.de/home.htm), California Fish Website (www.calfish.ucdavis.edu)), and/or 4) experts studying the ecology of the species or lake ecosystem. We employed a conservative view regarding species taxonomy (i.e., ‘lumping’ rather than ‘splitting’). We classified species’ habitats with respect to three categories: 1) littoral zone (occurring in parts of the lake where 1% or more of the surface radiation reaches the lake bottom), 2) open water zone (occurring in parts of the lake where less than 1% of the surface radiation reaches the lake bottom), and 3) littoral & open water zone (occurring in both lake zones). These habitat classifications were based on adult habitat use only, and habitat use during larval and juvenile stages was not considered. We classified diets with respect to seven categories: 1) plankton only, 2) periphyton only, 3) periphyton and macroinvertebrates, 4) periphyton, macroinvertebrates, and plankton, 5) periphyton, macroinvertebrates, and fish, 6) fish OR fish and plankton, and 7) fish, plankton, periphyton, and macroinvertebrates.

The mapping is based of secondary data sources for bottom fishing pressure (OSPAR), sediment type and organic carbon (OC) content (Smeaton et al 2021), and sediment lability as a function of grain size (Smeaton et al 2022). A new calculation methodology has been created to estimate the potential vulnerability of OC to bottom fishing induced disturbance as a function of sediment grain size and resettling speed. This allows for the potential OC lost through lateral transportation, consumption, or remineralisation as a result of bottom fishing disturbance to be estimated. By using fuzzy set theory, the potential vulnerability of sedimentary OC is estimated and mapped for the UK EEZ. All calculations and modelling were carried out within the ESRI ArcGIS software package using the spatial overlay, raster calculator and the zonal statistics tools. Full details of the study and methodology can be found in Black et al. (2022) and relevant supporting documents.

Mean depth was calculated from the bathymetry surface for each cell using the ArcGIS Spatial Analyst Focal Statistics "Mean" parameter. Mean depth represents the average depth value (in meters) within a square 3x3 cell window. The 2x2 meter resolution mean depth GeoTIFF was exported and added as a new map layer to aid in benthic habitat classification. Acoustic imagery was acquired for the VICRNM on two separate missions onboard the NOAA ship, Nancy Foster. The first mission took place from 2/18/04 to 3/5/04. The second mission took place from 2/1/05 to 2/12/05. On both missions, seafloor depths between 14 to 55 m were mapped using a RESON SeaBat 8101 ER (240 kHz) MBES sensor. This pole-mounted system measured water depths across a 150 degree swath consisting of 101 individual 1.5 degree x 1.5 degree beams. The beams to the port and starboard of nadir (i.e., directly underneath the ship) overlapped adjacent survey lines by approximately 10 m. The vessel survey speed was between 5 and 8 kn. In 2004, the ship's _location was determined by a Trimble DSM 132 DGPS system, which provided a RTCM differential data stream from the U.S. Coast Guard Continually Operating Reference Station (CORS) at Port Isabel, Puerto Rico. Gyro, heave, pitch and roll correctors were acquired using an Ixsea Octans gyrocompass. In 2005, the ship's positioning and orientation were determined by the Applanix POS/MV 320 V4, which is a GPS aided Inertial Motion Unit (IMU) providing measurements of roll, pitch and heading. The POS/MV obtained its positions from two dual frequency Trimble Zephyr GPS antennae. An auxiliary Trimble DSM 132 DGPS system provided a RTCM differential data stream from the U.S. Coast Guard CORS at Port Isabel, Puerto Rico. For both years, CTD (conductivity, temperature and depth) measurements were taken approximately every 4 hours using a Seabird Electronics SBE-19 to correct for the changing sound velocities in the water column. In 2004, raw data were logged in .xtf (extended triton format) using Triton ISIS software 6.2. In 2005, raw data were logged in .gsf (generic sensor format) using SAIC ISS 2000 software. Data from 2004 were referenced to the WGS84 UTM 20 N horizontal coordinate system, and data from 2005 were referenced to the NAD83 UTM 20 N horizontal coordinate system. Data from both projects were referenced to the Mean Lower Low Water (MLLW) vertical tidal coordinate system. The 2004 and 2005 MBES bathymetric data were both corrected for sensor offsets, latency, roll, pitch, yaw, static draft, the changing speed of sound in the water column and the influence of tides in CARIS Hips & Sips 5.3 and 5.4, respectively. The 2004 data was then binned to create a 1 x 1 m raster surface, and the 2005 data was binned to a create 2 x 2 m raster surface. After these final surfaces were created, the datum for the 2004 bathymetric surfaces was transformed from WGS84 to NAD83 using the "Project Raster" function in ArcGIS 9.1. The 2004 surface was transformed so that it would have the same datum as the 2005 surface. The 2004 bathymetric surface was then down sampled from 1 x 1 to 2 x 2 m using the "Resample" function in ArcGIS 9.1. The 2004 surface was resampled so it would have the same spatial resolution as the 2005 surface. Having the same coordinate systems and spatial resolutions, the final 2004 and 2005 bathymetry rasters were then merged using the Raster Calculator function "Merge" in ArcGIS's Spatial Analyst Extension to create a seamless bathymetry surface for the entire VICRNM area south of St. John. For a complete description of the data acquisition and processing parameters, please see the data acquisition and processing reports (DAPRs) for projects: NF-04-06-VI and NF-05-05-VI (Monaco & Rooney, 2004; Battista & Lazar, 2005).

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.

This dataset contains an integrated GeoTIFF with 1x1 meter cell size representing the 2014 Long Island Sound Benthic Habitat Priority Area of Interest between Bridgeport, CT, and Port Jefferson, NY. This integrated bathymetric raster is a mosaic of surveys from NOAA Ship Thomas Jefferson (S-222) and its two inshore launch vessels, NOAA Ship Rude (S-590), as well as surveys conducted by the Stony Brook University R/V Pritchard in coordination with the NOAA Biogeography Branch and the Office of Coastal Services between in the year 2012. Bathymetry data was collected using multibeam sonars and integrated into a seamless 32 bit raster using ArcGIS 10.1 raster calculator by the Biogeography Branch by a NOAA contractor.

Click to view Metadata CLOCAGWIaa183 grid represents sum of ugsaa with ussaa to obtain total groundwater infiltration (gwiaa). This 2007 draft data set was generated using the PRMS surface water model by Earthfx for CLOCA's Tier 1 water budget contract/source water protection program (2007-2008). Values are in mm/a transient (long-term annual averages) for a 19-year period of record of 1981-1999. Annual and monthly average grids are also available (summed daily means). Spatial resolution is a 25m grid covering CLOCA watersheds. The south-west corner of the data set is omitted due to the original grid definition. Zonal Statistics (mean) for CLOCA Watershed was calculated and CLOCAgwiaa183 was extracted. Raster calculator was then used to extract 1.15 or 15% above the mean for the jurisdiction.

The Viewshed analysis layer is used to identify visible areas. You specify the places you are interested in, either from a file or interactively, and the Viewshed service combines this with Esri-curated elevation data to create output polygons of visible areas. Some questions you can answer with the Viewshed task include:What areas can I see from this location? What areas can see me?Can I see the proposed wind farm?What areas can be seen from the proposed fire tower?The maximum number of input features is 1000.Viewshed has the following optional parameters:Maximum Distance: The maximum distance to calculate the viewshed.Maximum Distance Units: The units for the Maximum Distance parameter. The default is meters.DEM Resolution: The source elevation data; the default is 90m resolution SRTM. Other options include 30m, 24m, 10m, and Finest.Observer Height: The height above the surface of the observer. The default value of 1.75 meters is an average height of a person. If you are looking from an elevation location such as an observation tower or a tall building, use that height instead.Observer Height Units: The units for the Observer Height parameter. The default is meters.Surface Offset: The height above the surface of the object you are trying to see. The default value is 0. If you are trying to see buildings or wind turbines add their height here.Surface Offset Units: The units for the Surface Offset parameter. The default is meters.Generalize Viewshed Polygons: Determine if the viewshed polygons are to be generalized or not. The viewshed calculation is based upon a raster elevation model which creates a result with stair-stepped edges. To create a more pleasing appearance, and improve performance, the default behavior is to generalize the polygons. This generalization will not change the accuracy of the result for any location more than one half of the DEM's resolution.By default, this tool currently works worldwide between 60 degrees north and 56 degrees south based on the 3 arc-second (approximately 90 meter) resolution SRTM dataset. Depending upon the DEM resolution pick by the user, different data sources will be used by the tool. For 24m, tool will use global dataset WorldDEM4Ortho (excluding the counties of Azerbaijan, DR Congo and Ukraine) 0.8 arc-second (approximately 24 meter) from Airbus Defence and Space GmbH. For 30m, tool will use 1 arc-second resolution data in North America (Canada, United States, and Mexico) from the USGS National Elevation Dataset (NED), SRTM DEM-S dataset from Geoscience Australia in Australia and SRTM data between 60 degrees north and 56 degrees south in the remaining parts of the world (Africa, South America, most of Europe and continental Asia, the East Indies, New Zealand, and islands of the western Pacific). For 10m, tool will use 1/3 arc-second resolution data in the continental United States from USGS National Elevation Dataset (NED) and approximately 10 meter data covering Netherlands, Norway, Finland, Denmark, Austria, Spain, Japan Estonia, Latvia, Lithuania, Slovakia, Italy, Northern Ireland, Switzerland and Liechtenstein from various authoritative sources.To learn more, read the developer documentation for Viewshed or follow the Learn ArcGIS exercise called I Can See for Miles and Miles. To use this Geoprocessing service in ArcGIS Desktop 10.2.1 and higher, you can either connect to the Ready-to-Use Services, or create an ArcGIS Server connection. Connect to the Ready-to-Use Services by first signing in to your ArcGIS Online Organizational Account:Once you are signed in, the Ready-to-Use Services will appear in the Ready-to-Use Services folder or the Catalog window:If you would like to add a direct connection to the Elevation ArcGIS Server in ArcGIS for Desktop or ArcGIS Pro, use this URL to connect: https://elevation.arcgis.com/arcgis/services. You will also need to provide your account credentials. ArcGIS for Desktop:ArcGIS Pro:The ArcGIS help has additional information about how to do this:Learn how to make a ArcGIS Server Connection in ArcGIS Desktop. Learn more about using geoprocessing services in ArcGIS Desktop.This tool is part of a larger collection of elevation layers that you can use to perform a variety of mapping analysis tasks.