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...

Amazon Geoglyphs Spatial Analysis Dataset 2

DATA & Tools

Data Overview and Sources

This dataset was constructed for Phase 2 research analyzing spatial relationships between Amazon geoglyphs and environmental conditions. The analysis includes NDVI and NDMI calculations and grid-based anomaly detection.

Data sources: - Sentinel-2 Composites: forobs.jrc.ec.europa.eu/sentinel/sentinel2_composite - Pan-tropical cloud-free annual composites (2020) - jqjacobs.net: Archaeogeodesy Placemarks (Amazon geoglyph category extracted from Google Earth KML)

File Structure

amazon_geoglyphs_analysis/
├── data/
│  ├── sites_geoglyphs.gpkg      # Site locations (extracted geoglyph coordinates)
│  ├── focus_rgb_swir1_nir_red.tif  # Sentinel-2 composite (RGB: SWIR1, NIR, RED channels)
│  ├── focus_ndvi.tif         # NDVI index (vegetation greenness)
│  ├── focus_ndmi.tif         # NDMI index (vegetation moisture)
│  ├── focus_area.gpkg        # Analysis boundary (study area extent)
│  ├── amazon_grid_anomaly.gpkg    # Grid-based anomaly analysis
│  └── amazon_basin.gpkg       # Amazon basin boundaries
└── analysis_project.qgz       # QGIS project (integrated analysis workflow)

QGIS Processing Workflow

1. Satellite Data Processing (focus_rgb_swir1_nir_red.tif)

• (1) Data Source: Downloaded Sentinel-2 tiles S15_W075 and S15_W065 (2020, false color composite)
• (2) Raster → Miscellaneous → Build Virtual Raster: Merge two tiles into single virtual raster
• (3) Vector → Geoprocessing Tools → Clip Raster by Mask Layer: Clip merged raster to focus_area boundary

2. NDVI Calculation (focus_ndvi.tif)

• (1) Raster → Raster Calculator: Calculate Normalized Difference Vegetation Index Expression: ("focus_rgb_swir1_nir_red@2" - "focus_rgb_swir1_nir_red@3") / ("focus_rgb_swir1_nir_red@2" + "focus_rgb_swir1_nir_red@3")
• (2) Formula: NDVI = (NIR - RED) / (NIR + RED)
• (3) Layer Properties → Symbology: Apply RdYlGr color ramp for vegetation visualization

3. NDMI Calculation (focus_ndmi.tif)

• (1) Raster → Raster Calculator: Calculate Normalized Difference Moisture Index Expression: ("focus_rgb_swir1_nir_red@2" - "focus_rgb_swir1_nir_red@1") / ("focus_rgb_swir1_nir_red@2" + "focus_rgb_swir1_nir_red@1")
• (2) Formula: NDMI = (NIR - SWIR1) / (NIR + SWIR1)
• (3) Purpose: Monitor vegetation water content and drought conditions

4. Grid-based Anomaly Analysis (amazon_grid_anomaly.gpkg)

Layer 1: g_005_ndmi_ndvi (Fine-scale Grid Statistics)

• (1) Vector → Research Tools → Create Grid: Create 0.005° interval grid (~550m resolution)
• (2) Vector → Analysis Tools → Zonal Statistics: Calculate zonal statistics for NDVI and NDMI by grid cell
  • Statistics: Mean
  • Target rasters: focus_ndvi.tif, focus_ndmi.tif

Layer 2: g_050_anomaly_count (Anomaly Frequency Analysis)

• (1) Vector → Research Tools → Select by Expression: Identify anomalous grid cells Expression: "ndvi_mean" <= "ndvi_p10" AND "ndmi_mean" <= "ndmi_p10"
• (2) Vector → Research Tools → Create Grid: Create 0.05° interval grid (~5.5km resolution)
• (3) Vector → Analysis Tools → Join Attributes by Location (Summary): Count anomalous fine-scale grids within coarse-scale grids
• (4) Purpose: Identify areas with consistently low vegetation greenness and moisture (potential archaeological signatures)

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).

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

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

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).

Eight complexity surfaces (mean depth, standard deviation of depth, curvature, plan curvature, profile curvature, rugosity, slope, and slope of slope) were stacked and exported to create one image with several different bands (each band representing a specific metric). This image was transformed into its first three principal components using the "Principal Components Analysis" (PCA) function in ENVI 4.6. The transformation reduced the dimensionality of the dataset by removing information that was redundant among the different bands. The resulting 2x2 meter resolution, three band PCA image only contains information that uniquely described the complexity and structure of the seafloor. Coral reef habitat types were delineated and classified from this PCA image. 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).

Curvature was calculated from the bathymetry surface for each raster cell using the ArcGIS 3D Analyst "Curvature" Tool. Curvature describes the rate of change of curvature (in 1/100 z units) within a square 3x3 cell window. A negative value denotes concavity, while a positive value denotes convexity. The 2x2 meter resolution 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 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).

Reason for SelectionMany Southeastern ecosystems rely on regular, low-intensity fires to maintain habitat, encourage native plant growth, and reduce wildfire risk. Historically in the South, “fires burned as often as once a year or more in Coastal Plain pine systems or as infrequently as every 50 years or more on north-facing or cove sites in the mountains”, typically started by lightning or by Indigenous Americans using fire to manage open savannas. As a result, the forests and grasslands of the South contain many species that not only tolerate fire but require it. Fire suppression during the 20th century led to the loss and deterioration of many fire-adapted ecosystems and their associated wildlife and plant species. Today, “prescribed burning is an important tool throughout Southern forests, grasslands, and croplands” (Waldrop and Goodrick 2012).Input DataBase Blueprint 2022 extent2019 National Land Cover Database (NLCD): Land coverFloodplain Inundation Frequency Southeast version, available on request (email yvonne_allen@fws.gov)Landsat 8 Burned Area Products (ver. 2.0, Oct 2021)Monitoring Trends in Burn Severity (2020 data release, released 4-22-2022): National Burned Areas Boundaries Dataset Base Blueprint 2022 subregionsSoutheast Blueprint 2023 extentMapping StepsIdentify burns using the annual burn frequency rasters in the Landsat 8 Burned Area (LBA) Products. Note: This LBA data source differs from the burned area probability raster used by Southeast FireMap 1.0 (Beta). The burn probability data was found to greatly overestimate the extent of burned area across the full region of the Southeast. Currently Southeast FireMap is limited to the historic range of longleaf pine. Sum the annual LBA rasters to calculate the number of times a pixel has been burned from 2013-2021 using the ArcPy Spatial Analyst Cell Statistics “SUM” function. Reclassify to a value of 0 the burned areas that are most likely to be false positives. Assign a value of 0 to pixels classified in the 2019 NLCD as one of the following land cover types: Cultivated crops, barren (31), all urban (21, 22, 23, 24), woody wetlands (90) and open water (11). Fire in these pixels was often either not natural or likely misclassified. Clusters of pixels in barren landcover were often industrial sites and quarries. Assign a value of 0 to areas with inundation frequency values from 5 to 100. Inundated vegetation is often misclassified as burned area since they have similar spectral signatures in remote sensing.Identify burns using the annual Monitoring Trends in Burn Severity (MTBS) data. These data are very robust, but only capture large fires on a subset of public lands. Therefore, we use them in conjunction with the LBA data. Sum the annual MTBS rasters to calculate the number of times a pixel has been burned from 2013-2021 using the ArcPy Spatial Analyst Cell Statistics “SUM” function. Combine LBA and MTBS results using ArcPy Spatial Analyst Cell Statistics to calculate the maximum number of times a pixel was classified as burned using the LBA and MTBS datasets. Reclassify the resulting raster so that all values of 3+ receive the maximum value of 3 in the final indicator, as shown below.Clip to the spatial extent of Base Blueprint 2022.Use the Base Blueprint 2022 subregions to mask out the “Marine Shelf and Extension” and “Marine Gulf Stream” subregions from the indicator. These two subregions were not evaluated for fire frequency because they are outside the scope of this terrestrial indicator.As a final step, clip to the spatial extent of Southeast Blueprint 2023. Note: For more details on the mapping steps, code used to create this layer is available in the Southeast Blueprint Data Download under > 6_Code. Final indicator valuesIndicator values are assigned as follows:3 = Burned 3+ times from 2013-20212 = Burned 2 times from 2013-20211 = Burned 1 time from 2013-20210 = Not burned from 2013-2021 or row cropKnown IssuesThe LBA data layers overestimate fire frequency in open areas with wet soils. Wet soils can be much darker than dry soils and may be misclassified as burned areas. This misclassification was improved by removing areas classified as cultivated crops. A mask built upon the combination of Floodplain Inundation Frequency and NLCD woody wetlands was also used to reduce misclassifications.This indicator overestimates fire frequency in places with major impediments to burned area detection/mapping. Impediments include rapid green-up following a burn, cloud cover and shadows obscuring burn signatures, difficulty detecting or differentiating a low intensity burn signature beneath tree canopies, and the satellite product resolution often being too coarse to capture fine-scale differences or small burns.Disclaimer: Comparing with Older Indicator VersionsThere are numerous problems with using Southeast Blueprint indicators for change analysis. Please consult Blueprint staff if you would like to do this (email hilary_morris@fws.gov).Literature CitedAllen, Y. 2016. Landscape Scale Assessment of Floodplain Inundation Frequency Using Landsat Imagery. River Research and Applications 32:1609–1620. [https://doi.org/10.1002/rra.2987]. Hawbaker, T.J., Vanderhoof, M.K., Schmidt, G.L., Beal, Y., Picotte, J.J., Takacs, J.D., Falgout, J.T., and Dwyer, J.L, 2020, The Landsat Burned Area products for the conterminous United States (ver. 2.0, October 2021): U.S. Geological Survey data release, [https://doi.org/10.5066/P9QKHKTQ]. Monitoring Trends in Burn Severity. Burned Areas Boundaries Dataset. 2020 Data Release. Published April 28, 2022. [https://www.mtbs.gov/]. Teske, Casey, Melanie K. Vanderhoof, Todd J. Hawbaker, Joe Noble, and John K. Hiers. 2021. Using the Landsat Burned Area Products to Derive Fire History Relevant for Fire Management and Conservation in the State of Florida, Southeastern USA, Fire, 4, no. 2: 26. [https://doi.org/10.3390/fire4020026]. U.S. Geological Survey (USGS). Published June 2021. National Land Cover Database (NLCD) 2019 Land Cover Conterminous United States. Sioux Falls, SD. [https://doi.org/10.5066/P9KZCM54]. Waldrop, Thomas A.; Goodrick, Scott L. 2012. (Slightly revised 2018). Introduction to prescribed fires in Southern ecosystems. Science Update SRS-054. Asheville, NC: U.S. Department of Agriculture Forest Service, Southern Research Station. 80 p. [https://www.srs.fs.usda.gov/pubs/su/su_srs054.pdf]. Yang, L., Jin, S., Danielson, P., Homer, C., Gass, L., Case, A., Costello, C., Dewitz, J., Fry, J., Funk, M., Grannemann, B., Rigge, M. and G. Xian. 2018. A New Generation of the United States National Land Cover Database: Requirements, Research Priorities, Design, and Implementation Strategies, ISPRS Journal of Photogrammetry and Remote Sensing, 146, pp.108-123. [https://doi.org/10.1016/j.isprsjprs.2018.09.006].

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).

This image represents a 2x2 meter resolution bathymetry surface of the moderate-depth portion of the NPS's Virgin Islands Coral Reef National Monument, south of St. John, US Virgin Islands. The depth values contained in this surface are in meters. 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).

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.

Categorised depth to groundwater in the Great Artesian Basin.

Categories are: (Depth indicated is meters below surface, surface elevation taken from 3 Second SRTM DEM-s)

1 = 0 - 5m

2 = 5 - 10m

3 = 10 - 15m

4 = 15 - 20m

5 = 20 - 25m

6 = 25 - 30m

7 = 30 - 35m

8 = 35 - 40m

9 = +40m

Dataset History

Grid derived from subtracting the GAB waterlevel elevation surface from the GA 3second SRTM DEM-s

1. Depth to water table grid created using Raster Calculator. \[GA 3sec SRTM DEM-s\] - \[GAB water table grid\], Grid: float, produced.

2. QA/QC on float grid to remove values < 0 (water table above surface)

3. Artefacts still exist with high values up to 750m. This issue is resolved by classifying the grid (using the Spatial Analyst, Reclassify tool) into 5m intervals up to 40m than a final category of >40m. This final output is the available grid within this dataset.

Dataset Citation

Bioregional Assessment Programme (XXXX) Classified depth to water table for the Great Artesian Basin. Bioregional Assessment Derived Dataset. Viewed 30 September 2016, http://data.bioregionalassessments.gov.au/dataset/6eabd9e1-0018-4314-bba4-87d5d1ebc8c3.

Dataset Ancestors

• Derived From Geoscience Australia, 3 second SRTM Digital Elevation Model (DEM) v01

• Derived From Water table elevation of the Great Artesian Basin (GABWRA)

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).