The raster_runif data source covers the Flanders and Brussels region and has a resolution of 32 meters. The raster cells with non-missing values match the value-cells of the GRTSmaster_habitats data source with a small buffer added. Every raster cell has a random value between 0 and 1 according to the uniform distribution.

An example usage of this raster is its combination with the GRTSmaster_habitats and habitatmap_stdized data sources in order to draw an equal probability sample of habitat types in Flanders. Habitatmap_stdized contains polygons that are partially or fully covered by habitat types. The proportion of a certain type within a polygon is provided by the phab value. We can draw an equal probability sample size n for a certain habitat type as follows:

select all raster cells of GRTSmaster_habitats that overlap with the sampling frame of the target habitat type

keep the raster cells for which the raster_runif value is lower than the phab value of the habitat type within the polygon

finally select the n raster cells with the lowest GRTS ranking number.

The R-code for creating the raster_runif data source can be found in the GitHub repository 'n2khab-preprocessing' at commit ede43a4.

A reading function to return the data source in a standardized way into the R environment is provided by the R-package n2khab.

Processed results from of surface grain size analysis of the sediment grab samples recovered as part of the Long Island Sound mapping project Phase II.Sediment grab samples have been taken in summer of 2017 and 2018 using a modified van Veen grab sampler. A sub-sample of the top two centimeter was taken and stored in a jar. Dried sub-samples samples were analyzed for grain size. First the samples were treated with hydroperoxide to remove organic components. Then the sample was passed through a series of standard sieves representing Phi sizes with the smallest being 64 µm. The content of each sieve was dried and weight. If there was sufficient fine material (< 64 µm), then this fine fraction was further analyzed using a Sedigraph system. The results of sieving and sedigraph analysis have been combined and the percentages for gravel, sand, silt and clay are determined following the Wentworth scale. In addition, other statistics including mean, median, skewness and standard deviation are calculated using the USGS GSSTAT program. The results of the LDEO/Queens College grain size analysis have been combined with data collected by the LISMARC group and analyzed by USGS. ArcGIS Pro empirical kriging has been used to interpolate values for gravel, sand, silt, clay, and mud percentages as well as for mean grain size onto a 50 m raster. The interpolated raster has been clipped to fit the extent of the phase 2 survey area. The final raster data are in GeoTiff format with UTM 18 N projection.Time period of content: 2017-08-01 to 2022-11-16Attribute accuracy: The attribute accuracy has not been determined. This raster dataset shown mainly the major trends and patterns of the value distribution in the Phase 2 study area.Completeness: The dataset is complete.Positional accuracy: The raster resolution is 50 m.Attributes:clay pct raster: Interpolated clay percent of the sample mass

The SWOT Level 2 Water Mask Raster Image Data Product from the Surface Water Ocean Topography (SWOT) mission provides global surface water elevation and inundation extent derived from high rate (HR) measurements from the Ka-band Radar Interferometer (KaRIn) on SWOT. SWOT launched on December 16, 2022 from Vandenberg Air Force Base in California into a 1-day repeat orbit for the "calibration" or "fast-sampling" phase of the mission, which completed in early July 2023. After the calibration phase, SWOT entered a 21-day repeat orbit in August 2023 to start the "science" phase of the mission, which is expected to continue through 2025. Water surface elevation, area, water fraction, backscatter, geophysical information are provided in geographically fixed scenes at resolutions of 100 m and 250 m in Universal Transverse Mercator (UTM) projection. Available in netCDF-4 file format. On-demand processing available to users for different resolutions, sampling grids, scene sizes, and file formats. This dataset is the parent collection to the following sub-collections: https://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_Raster_100m_2.0 https://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_Raster_250m_2.0

The SWOT Level 2 KaRIn High Rate Raster Product (SWOT_L2_HR_Raster_D) provides rasterized estimates of water surface elevation, inundation extent, and radar backscatter derived from high-resolution radar observations by the Ka-band Radar Interferometer (KaRIn) on the SWOT satellite. This product aggregates the irregularly spaced pixel cloud data from the PIXC and PIXCVec products onto a uniform geographic grid to facilitate spatial analysis of water surface features across inland, estuarine, and coastal domains.

Standard granules cover non-overlapping 128 × 128 km² scenes in the UTM projection at 100 m and 250 m resolution, stored in NetCDF-4 format. Each file contains 2D image layers representing water surface elevation (corrected for geoid, solid Earth, load, and pole tides, as well as atmospheric and ionospheric path delays), surface area, water fraction, and sigma0, along with quality flags and uncertainty estimates. On-demand versions are available at user-specified resolutions and projections, with optional overlapping granules and GeoTIFF output via SWODLR: https://swodlr.podaac.earthdatacloud.nasa.gov/

The raster product offers a gridded alternative to the unstructured pixel cloud, supporting hydrologic and geomorphic analyses in complex flow environments such as braided rivers, floodplains, wetlands, and coastal zones. It enables consistent spatiotemporal sampling while reducing noise through spatial aggregation, making it especially suitable for applications that require map-like continuity or integration with geospatial models.
This dataset is the parent collection to the following sub-collections:
https://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_Raster_100m_D
https://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_Raster_250m_D

A SQLite database holding a realisation of a GRTS design using the principles of Reverse Randomized Quadrant-Recursive Raster method (Theobold et al 2007). The database covers a square 2D grid with 32768 (2^15) pixels in both dimensions. This allows aselect and spatially balanced sampling in Flanders (Belgium) at 10 x 10 m resolution. In the framework of the C-Mon project, selection of plots for sampling soil organic carbon (SOC) stocks over all landuses is performed based on this realisation. It is envisaged that the plots will be monitored over decades to quantify SOC stock changes over time, along with landuse changes. .

The R script used to generate the database and to sample from the database is provided. The algorithm itself is available on GitHub (10.5281/zenodo.2784016).

The C-Mon project, entitled (in Dutch): 'Actualisatie van de onderbouwing van een methodiek voor de systematische monitoring van koolstofvoorraden in de bodem' was financed by the Department Vlaams Planbureau voor Omgeving from the Flemish Government. Project-ID: OMG/VPO/BODEM/TWOL/2017/1

Wadi Hasa Sample Dataset — GRASS GIS Location
Version 1.0 (2025-09-19)

Overview
--------
This archive contains a complete GRASS GIS *Location* for the Wadi Hasa region (Jordan), including base data and exemplar analyses used in the Geomorphometry chapter. It is intended for teaching and reproducible research in archaeological GIS.

How to use
----------
1) Unzip the archive into your GRASSDATA directory (or a working folder) and add the Location to your GRASS session.
2) Start GRASS and open the included workspace (Workspace.gxw) or choose a Mapset to work in.
3) Set the computational region to the default extent/resolution for reproducibility:
g.region n=3444220 s=3405490 e=796210 w=733450 nsres=30 ewres=30 -p
4) Inspect layers as needed:
g.list type=rast,vector
r.info

Citation & License
------------------
Please cite this dataset as:

Isaac I. Ullah. 2025. *Wadi Hasa Sample Dataset (GRASS GIS Location)*. Zenodo. https://doi.org/10.5281/zenodo.17162040

All contents are released under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. The original Wadi Hasa survey dataset is available at: https://figshare.com/articles/dataset/Wadi_Hasa_Ancient_Pastoralism_Project/1404216 The original Wadi Hasa survey dataset is available at: https://figshare.com/articles/dataset/Wadi_Hasa_Ancient_Pastoralism_Project/1404216

Coordinate Reference System
---------------------------
- Projection: UTM, Zone 36N
- Datum/Ellipsoid: WGS84
- Units: meter
- Coordinate system and units are defined in the GRASS Location (PROJ_INFO/UNITS).

Default Region (computational extent & resolution)
--------------------------------------------------
- North: 3444220
- South: 3405490
- East: 796210
- West: 733450
- Resolution: 30 (NS), 30 (EW)
- Rows x Cols: 1291 x 2092 (cells: 2700772)

Directory / Mapset Structure
----------------------------
This Location contains the following Mapsets (data subprojects), each with its own raster/vector layers and attribute tables (SQLite):
- Boolean_Predictive_Modeling: 8 raster(s), 4 vector(s)
- ISRIC_soilgrid: 31 raster(s), 0 vector(s)
- Landsat_Imagery: 3 raster(s), 0 vector(s)
- Landscape_Evolution_Modeling: 41 raster(s), 0 vector(s)
- Least_Cost_Analysis: 13 raster(s), 4 vector(s)
- Machine_Learning_Predictive_Modeling: 70 raster(s), 11 vector(s)
- PERMANENT: 4 raster(s), 2 vector(s)
- Sentinel2_Imagery: 4 raster(s), 0 vector(s)
- Site_Buffer_Analysis: 0 raster(s), 2 vector(s)
- Terrain_Analysis: 27 raster(s), 2 vector(s)
- Territory_Modeling: 14 raster(s), 2 vector(s)
- Trace21k_Paleoclimate_Downscale_Example: 4 raster(s), 2 vector(s)
- Visibility_Analysis: 11 raster(s), 5 vector(s)

Data Content (summary)
----------------------
- Total raster maps: 230
- Total vector maps: 34

Raster resolutions present:
- 10 m: 13 raster(s)
- 30 m: 183 raster(s)
- 208.01 m: 2 raster(s)
- 232.42 m: 30 raster(s)
- 1000 m: 2 raster(s)

Major content themes include:
- Base elevation surfaces and terrain derivatives (e.g., DEMs, slope, aspect, curvature, flow accumulation, prominence).
- Hydrology, watershed, and stream-related layers.
- Visibility analyses (viewsheds; cumulative viewshed analyses for Nabataean and Roman towers).
- Movement and cost-surface analyses (isotropic/anisotropic costs, least-cost paths, time-to-travel surfaces).
- Predictive modeling outputs (boolean/inductive/deductive; regression/classification surfaces; training/test rasters).
- Satellite imagery products (Landsat NIR/RED/NDVI; Sentinel‑2 bands and RGB composite).
- Soil and surficial properties (ISRIC SoilGrids 250 m products).
- Paleoclimate downscaling examples (CHELSA TraCE21k MAT/AP).

Vectors include:
- Archaeological point datasets (e.g., WHS_sites, WHNBS_sites, Nabatean_Towers, Roman_Towers).
- Derived training/testing samples and buffer polygons for modeling.
- Stream network and paths from least-cost analyses.

Important notes & caveats
-------------------------
- Mixed resolutions: Analyses span 10 m (e.g., Sentinel‑2 composites, some derived surfaces), 30 m (majority of terrain and modeling rasters), ~232 m (SoilGrids products), and 1 km (CHELSA paleoclimate). Set the computational region appropriately (g.region) before processing or visualization.
- NoData handling: The raw SRTM import (Hasa_30m_SRTM) reports extreme min/max values caused by nodata placeholders. Use the clipped/processed DEMs (e.g., Hasa_30m_clipped_wshed*) and/or set nodata with r.null as needed.
- Masks: MASK rasters are provided for analysis subdomains where relevant.
- Attribute tables: Vector attribute data are stored in per‑Mapset SQLite databases (sqlite/sqlite.db) and connected via layer=1.

Provenance (brief)
------------------
- Primary survey points and site datasets derive from the Wadi Hasa projects (see Figshare record above).
- Base elevation and terrain derivatives are built from SRTM and subsequently processed/clipped for the watershed.
- Soil variables originate from ISRIC SoilGrids (~250 m).
- Paleoclimate examples use CHELSA TraCE21k surfaces (1 km) that are interpolated to higher resolutions for demonstration.
- Satellite imagery layers are derived from Landsat and Sentinel‑2 scenes.

Reproducibility & quick commands
--------------------------------
- Restore default region: g.region n=3444220 s=3405490 e=796210 w=733450 nsres=30 ewres=30 -p
- Set region to a raster: g.region raster=

Change log
----------
- v1.0: Initial public release of the teaching Location on Zenodo (CC BY 4.0).

Contact
-------
For questions, corrections, or suggestions, please contact Isaac I. Ullah

The UK Compiled Topsoil Dataset (UKTS) is the most extensive topsoil geochemistry dataset for the UK available at the time of release (August 2024). The dataset consists of 82 georeferenced TIFF raster images (GeoTIFF format) with a cell size of 500 x 500 m, displaying the predicted concentrations for 41 chemical elements in UK topsoil and their respective standard error. The dataset is based on the geochemical analyses of 57,966 topsoil samples collected between 1978 and 2014 and analysed by X-Ray Fluorescence spectrometry (XRF). The UKTS was brought together by combining data from the following sources: i. the British Geological Survey’s (BGS) Geochemical Baseline Survey of the Environment (G-BASE) rural and urban topsoil dataset (which accounts for 76.4% of the topsoil samples included in the UKTS) ii. the Geological Survey of Northern Ireland (GSNI) TellusNI rural and urban topsoil geochemical survey dataset (13.8% of the UKTS samples) iii. the BGS-Rothamsted Research X-ray Fluorescence Spectrometry (XRF) rural soil dataset (RR-BGS XRF), based on sub-samples held at Rothamsted Research from the National Soil Inventory (NSI) of England and Wales sample archive, National Soil Resources Institute, Cranfield University (9.8% of the UKTS samples). An atlas of the compiled topsoil concentrations for the UK is available to download (https://nora.nerc.ac.uk/id/eprint/535963) and all maps are available to view within the UK Soil Observatory website (https://www.ukso.org). The dataset covers England, Wales and Northern Ireland, and the Clyde Basin in Scotland. The GeoTIFF raster image maps were produced from the interpolation by ordinary kriging of the concentration values in the source data points, using the geostatistical wizard in the geostatistical analyst toolbox of ESRI ArcGIS 10.8.

Ths data set consists Bailey's Ecoregions polygons for the continental United States, sampled into raster grids at spatial resolutions of .1 and 1.0 degrees.

Here, the master data of the standardized 88 km sampling grid of recent fluvial sediments across the Weiße Elster catchment are provided. We arranged a grid of 88 km cells over the entire Weiße Elster catchment, yielding a total of 111 raster cells. In each raster cell, we sampled with a spade recent streambed deposits from Weiße Elster tributaries.

The Windmill Detection on French Aerial Images Dataset was curated from 11 raster samples extracted from the latest French aerial images of 2021, each containing a minimum of 20 windmills per sample. These samples were cropped to a size of 20482048 pixels for training and validation purposes. During the selection of these areas, the authors delineated the regions of interest (ROI), defined as the union of the bounding boxes of the raster images. Initially, the raster images were in JPEG2000 format but were converted to JPEG to facilitate labeling using the YBAT tool. YBAT, an open-source labeling tool, was chosen due to its lack of size constraints and ease of installation, as it is built with pure HTML and JavaScript. Raster sampling was conducted using the sampling and generation functions of Odeon landcover, resulting in a training folder comprising the 11 raster samples divided into smaller patches. These patches were labeled and subsequently split into training and validation datasets.

The data are 475 thematic land cover raster’s at 2m resolution. Land cover classification was to the land cover classes: Tree (1), Water (2), Barren (3), Other Vegetation (4) and Ice & Snow (8). Cloud cover and Shadow were sometimes coded as Cloud (5) and Shadow (6), however for any land cover application would be considered NoData. Some raster’s may have Cloud and Shadow pixels coded or recoded to NoData already. Commercial high-resolution satellite data was used to create the classifications. Usable image data for the target year (2010) was acquired for 475 of the 500 primary sample locations, with 90% of images acquired within ±2 years of the 2010 target. The remaining 25 of the 500 sample blocks had no usable data so were not able to be mapped. Tabular data is included with the raster classifications indicating the specific high-resolution sensor and date of acquisition for source imagery as well as the stratum to which that sample block belonged. Methods for this classification are described in Pengra et al. (2015). A 1-stage cluster sampling design was used where 500 (475 usable), 5 km x 5 km sample blocks were the primary sampling units (note; the nominal size was 5km x 5km blocks, but some have deviations in dimensions due only partial coverage of the sample block with usable imagery). Sample blocks were selected using stratified random sampling within a sample frame stratified by a modification of the Köppen Climate/Vegetation classification and population density (Olofsson et al., 2012). Secondary sampling units are each of the classified 2m pixels of the raster. This design satisfies the criteria that define a probability sampling design and thus serves as the basis to support rigorous design-based statistical inference (Stehman, 2000).

Rasterized water surface elevation and inundation extent in geographically fixed tiles at resolutions of 100 m and 250 m in a Universal Transverse Mercator projection grid. Provides rasters with water surface elevation, area, water fraction, backscatter, geophysical information. On-demand processing available to users for different resolutions, sampling grids, scene sizes, and file formats. Gridded scene (approx 128x128 km2, georeferenced); full swath. Available in netCDF-4 file format.

The SWOT Level 2 Water Mask Raster Image Data Product from the Surface Water Ocean Topography (SWOT) mission provides global surface water elevation and inundation extent derived from high rate (HR) measurements from the Ka-band Radar Interferometer (KaRIn) on SWOT. SWOT launched on December 16, 2022 from Vandenberg Air Force Base in California into a 1-day repeat orbit for the "calibration" or "fast-sampling" phase of the mission, which completed in early July 2023. After the calibration phase, SWOT entered a 21-day repeat orbit in August 2023 to start the "science" phase of the mission, which is expected to continue through 2025. Water surface elevation, area, water fraction, backscatter, geophysical information are provided in geographically fixed scenes at resolutions of 100 m and 250 m in Universal Transverse Mercator (UTM) projection. Available in netCDF-4 file format. On-demand processing available to users for different resolutions, sampling grids, scene sizes, and file formats. This dataset is the parent collection to the following sub-collections: https://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_Raster_100m_2.0 https://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_Raster_250m_2.0

Coastwide vegetation surveys have been conducted multiple times over the past 50 years (e.g., Chabreck and Linscombe 1968, 1978, 1988, 1997, 2001, and 2013) by the Louisiana Department of Wildlife and Fisheries (LDWF) in support of coastal management activities. The last survey was conducted in 2013 and was funded by the Louisiana Coastal Protection and Restoration Authority (CPRA) and the U.S. Geological Survey (USGS) as a part of the Coastal Wetlands Planning, Protection, and Restoration Act (CWPPRA) monitoring program. These surveys provide important data that have been utilized by federal, state, and local resource managers. The surveys provide information on the condition of Louisiana’s coastal marshes by mapping plant species composition and vegetation change through time. During the summer of 2021, the U.S. Geological Survey, Louisiana State University, and the Louisiana Department of Wildlife and Fisheries jointly completed a helicopter survey to collect data on 2021 vegetation types using the same field methodology at previously sampled data points. Plant species were identified and their abundance classified at each point. Based on species composition and abundance, each marsh sampling station was assigned a marsh type: fresh, intermediate, brackish, or saline marsh. The field point data were interpolated to classify marsh vegetation into polygons and map the distribution of vegetation types. We then used the 2021 polygons with additional remote sensing data to create the final raster dataset. We used the polygon marsh type zones (available in this data release), as well as National Land Cover Database (NLCD; https://www.usgs.gov/centers/eros/science/national-land-cover-database) and NOAA Coastal Change Analysis Program (CCAP; https://coast.noaa.gov/digitalcoast/data/ccapregional.html) datasets to create a composite raster dataset. The composite raster was created to provide more detail, particularly with regard to “Other”, “Swamp”, and “Water” categories, than is available in the polygon dataset. The overall boundary of the raster product was extended beyond past surveys to better inform swamp, water, and other boundaries across the coast. A majority of NLCD and CCAP classification during a 2010-2019 period was used, rather than creating a raster classification specific to 2021, as there was a desire to use published datasets. Users are cautioned that the raster dataset is generalized but more specific than the polygon dataset. This data release includes 3 datasets: the point field data collected by the helicopter survey team, the polygon data developed from the point data, and the raster data developed from the polygon data plus additional remote sensing data as described above.

The sampling locations provided here were selected as a two-stage Generalized Random Tessellation Stratified (GRTS) sample (Stevens & Olsen 2004). The first stage of the GRTS draw used a master sample developed by the North American Bat Monitoring Program (Loeb et al. 2015) from a 10 x 10 km grid placed over the conterminous U.S., Canada, and Mexico. Each 10 x 10 km grid cell (hereafter, master cell) was assigned a GRTS rank by NABat. The rank represents the priority order in which master cells should ideally be sampled. For the second stage of the draw, sampling points within a master cell were selected. Each point was defined as a 30 x 30 m cell of the GIS raster that defined monarch-relevant habitat. Sampling points within each master cell were assigned to 5 land-use sectors of interest. For the western U.S., 3 categories of estimated milkweed habitat suitability were used instead of land-use sectors.

This datalayer is part of a group of layers used for research in the Ipswich River Watershed. This layer contains the single pixel locations of each water nutrient sampling site within the Ipswich River Watershed. This layer is the basis for further analysis of possible influences on nutrient sample site data.

Processed results from of surface x-ray florescence (XRF) analysis of the sediment grab samples recovered as part of the Long Island Sound mapping project Phase II. Sediment grab samples have been collected in the summers of 2017 and 2018 using a modified van Veen grab sampler. A subsample of the top two centimeter was taken for further lab analysis. Dried and homogenized splits of the samples were analyzed for chemical composition using an Innov-X Alpha series 4000 XRF (Innov-X Systems, Woburn, MA). The results of the measurements are presented as ppm. The XRF analytical protocol included the following elements: P, S, Cl, K, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn As, Se, Br, Rb, Sr, Zr, Mo, Ag, Cd, Sn, Sb, I, Ba, Hg, Pb, Bi, Th, and U. However, only Cl, K, Ca, Ti, Cr, Mn, Fe, Co, Cu, Zn, As, Br, Rb, Sr, Zr and Pb were consistently present at levels above background detection in surficial sediments collected in the LIS Phase II area. ArcGIS Pro empirical kriging has been used to interpolate values for selected elements onto a 50 m raster. The interpolated raster has been clipped to fit the extent of the phase 2 survey area. The final raster data are in GeoTiff format with UTM 18 N projection.Time period of content: 2017-08-01 to 2022-11-16Attribute accuracy: The attribute accuracy has not been determined. This raster dataset shown mainly the major trends and patterns of the value distribution in the Phase 2 study area.Completeness: The dataset is complete.Positional accuracy: The raster resolution is 50 m.Attributes: XRF Rb raster: Interpolated Rubidium (Rb) distribution (in ppm)

This database was prepared using a combination of materials that include aerial photographs, topographic maps (1:24,000 and 1:250,000), field notes, and a sample catalog. Our goal was to translate sample collection site locations at Yellowstone National Park and surrounding areas into a GIS database. This was achieved by transferring site locations from aerial photographs and topographic maps into layers in ArcMap. Each field site is located based on field notes describing where a sample was collected. Locations were marked on the photograph or topographic map by a pinhole or dot, respectively, with the corresponding station or site numbers. Station and site numbers were then referenced in the notes to determine the appropriate prefix for the station. Each point on the aerial photograph or topographic map was relocated on the screen in ArcMap, on a digital topographic map, or an aerial photograph. Several samples are present in the field notes and in the catalog but do not corresp ...

After fast mean shift (FMS) clustering, the whole research area was divided to 10 subareas, so the new samples can characterize the geographical features of each subarea were collected through field investigations. Because of our limited human and material resources, it is difficult to conduct a mass of sampling in each subarea. In order to make the most of our limited resources, we need to conduct reasonable field sampling strategy. For the first two large subareas, we collected 70 field samples respectively, and labeled them as the first sample set and the second sample set that will be used to build their own GWR models for extend prediction of unobserved points in each area, i.e. local extension prediction; while the remaining 8 small subareas took moderate amounts of samples according to their size, if one subarea owns the size of raster points more than 5000, 16 samples will be collected from it, otherwise, take 12 samples. In this way, a total of 112 samples are put together as the third sample set, and the third GWR model is constructed to achieve the global extension prediction of 8 subareas. In addition, three sample sets were divided into training set and test set, respectively. For the first two sample sets, the ratio of sample size of training set and test set are all 5:2, i.e. training set contains 50 samples, test set has 20 samples. Because of the third sample set composed of samples from 8 subareas, we divided the samples of each subarea into training set and test set according to the ratio of 3:1. In the other word, the sample number of training set from third to tenth subarea is 12, 9, 9, 12, 9, 12, 12 and 9 respectively, and 84 training sample in total; and the sample number of test set from eight subarea is 4, 3, 3, 4, 3, 4, 4 and 3 respectively, a total of 28 samples.

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.