Catholic Carbon Footprint Story Map Map:DataBurhans, Molly A., Cheney, David M., Gerlt, R.. . “PerCapita_CO2_Footprint_InDioceses_FULL”. Scale not given. Version 1.0. MO and CT, USA: GoodLands Inc., Environmental Systems Research Institute, Inc., 2019.Map Development: Molly BurhansMethodologyThis is the first global Carbon footprint of the Catholic population. We will continue to improve and develop these data with our research partners over the coming years. While it is helpful, it should also be viewed and used as a "beta" prototype that we and our research partners will build from and improve. The years of carbon data are (2010) and (2015 - SHOWN). The year of Catholic data is 2018. The year of population data is 2016. Care should be taken during future developments to harmonize the years used for catholic, population, and CO2 data.1. Zonal Statistics: Esri Population Data and Dioceses --> Population per dioceses, non Vatican based numbers2. Zonal Statistics: FFDAS and Dioceses and Population dataset --> Mean CO2 per Diocese3. Field Calculation: Population per Diocese and Mean CO2 per diocese --> CO2 per Capita4. Field Calculation: CO2 per Capita * Catholic Population --> Catholic Carbon FootprintAssumption: PerCapita CO2Deriving per-capita CO2 from mean CO2 in a geography assumes that people's footprint accounts for their personal lifestyle and involvement in local business and industries that are contribute CO2. Catholic CO2Assumes that Catholics and non-Catholic have similar CO2 footprints from their lifestyles.Derived from:A multiyear, global gridded fossil fuel CO2 emission data product: Evaluation and analysis of resultshttp://ffdas.rc.nau.edu/About.htmlRayner et al., JGR, 2010 - The is the first FFDAS paper describing the version 1.0 methods and results published in the Journal of Geophysical Research.Asefi et al., 2014 - This is the paper describing the methods and results of the FFDAS version 2.0 published in the Journal of Geophysical Research.Readme version 2.2 - A simple readme file to assist in using the 10 km x 10 km, hourly gridded Vulcan version 2.2 results.Liu et al., 2017 - A paper exploring the carbon cycle response to the 2015-2016 El Nino through the use of carbon cycle data assimilation with FFDAS as the boundary condition for FFCO2."S. Asefi‐Najafabady P. J. Rayner K. R. Gurney A. McRobert Y. Song K. Coltin J. Huang C. Elvidge K. BaughFirst published: 10 September 2014 https://doi.org/10.1002/2013JD021296 Cited by: 30Link to FFDAS data retrieval and visualization: http://hpcg.purdue.edu/FFDAS/index.phpAbstractHigh‐resolution, global quantification of fossil fuel CO2 emissions is emerging as a critical need in carbon cycle science and climate policy. We build upon a previously developed fossil fuel data assimilation system (FFDAS) for estimating global high‐resolution fossil fuel CO2 emissions. We have improved the underlying observationally based data sources, expanded the approach through treatment of separate emitting sectors including a new pointwise database of global power plants, and extended the results to cover a 1997 to 2010 time series at a spatial resolution of 0.1°. Long‐term trend analysis of the resulting global emissions shows subnational spatial structure in large active economies such as the United States, China, and India. These three countries, in particular, show different long‐term trends and exploration of the trends in nighttime lights, and population reveal a decoupling of population and emissions at the subnational level. Analysis of shorter‐term variations reveals the impact of the 2008–2009 global financial crisis with widespread negative emission anomalies across the U.S. and Europe. We have used a center of mass (CM) calculation as a compact metric to express the time evolution of spatial patterns in fossil fuel CO2 emissions. The global emission CM has moved toward the east and somewhat south between 1997 and 2010, driven by the increase in emissions in China and South Asia over this time period. Analysis at the level of individual countries reveals per capita CO2 emission migration in both Russia and India. The per capita emission CM holds potential as a way to succinctly analyze subnational shifts in carbon intensity over time. Uncertainties are generally lower than the previous version of FFDAS due mainly to an improved nightlight data set."Global Diocesan Boundaries:Burhans, M., Bell, J., Burhans, D., Carmichael, R., Cheney, D., Deaton, M., Emge, T. Gerlt, B., Grayson, J., Herries, J., Keegan, H., Skinner, A., Smith, M., Sousa, C., Trubetskoy, S. “Diocesean Boundaries of the Catholic Church” [Feature Layer]. Scale not given. Version 1.2. Redlands, CA, USA: GoodLands Inc., Environmental Systems Research Institute, Inc., 2016.Using: ArcGIS. 10.4. Version 10.0. Redlands, CA: Environmental Systems Research Institute, Inc., 2016.Boundary ProvenanceStatistics and Leadership DataCheney, D.M. “Catholic Hierarchy of the World” [Database]. Date Updated: August 2019. Catholic Hierarchy. Using: Paradox. Retrieved from Original Source.Catholic HierarchyAnnuario Pontificio per l’Anno .. Città del Vaticano :Tipografia Poliglotta Vaticana, Multiple Years.The data for these maps was extracted from the gold standard of Church data, the Annuario Pontificio, published yearly by the Vatican. The collection and data development of the Vatican Statistics Office are unknown. GoodLands is not responsible for errors within this data. We encourage people to document and report errant information to us at data@good-lands.org or directly to the Vatican.Additional information about regular changes in bishops and sees comes from a variety of public diocesan and news announcements.GoodLands’ polygon data layers, version 2.0 for global ecclesiastical boundaries of the Roman Catholic Church:Although care has been taken to ensure the accuracy, completeness and reliability of the information provided, due to this being the first developed dataset of global ecclesiastical boundaries curated from many sources it may have a higher margin of error than established geopolitical administrative boundary maps. Boundaries need to be verified with appropriate Ecclesiastical Leadership. The current information is subject to change without notice. No parties involved with the creation of this data are liable for indirect, special or incidental damage resulting from, arising out of or in connection with the use of the information. We referenced 1960 sources to build our global datasets of ecclesiastical jurisdictions. Often, they were isolated images of dioceses, historical documents and information about parishes that were cross checked. These sources can be viewed here:https://docs.google.com/spreadsheets/d/11ANlH1S_aYJOyz4TtG0HHgz0OLxnOvXLHMt4FVOS85Q/edit#gid=0To learn more or contact us please visit: https://good-lands.org/Esri Gridded Population Data 2016DescriptionThis layer is a global estimate of human population for 2016. Esri created this estimate by modeling a footprint of where people live as a dasymetric settlement likelihood surface, and then assigned 2016 population estimates stored on polygons of the finest level of geography available onto the settlement surface. Where people live means where their homes are, as in where people sleep most of the time, and this is opposed to where they work. Another way to think of this estimate is a night-time estimate, as opposed to a day-time estimate.Knowledge of population distribution helps us understand how humans affect the natural world and how natural events such as storms and earthquakes, and other phenomena affect humans. This layer represents the footprint of where people live, and how many people live there.Dataset SummaryEach cell in this layer has an integer value with the estimated number of people likely to live in the geographic region represented by that cell. Esri additionally produced several additional layers World Population Estimate Confidence 2016: the confidence level (1-5) per cell for the probability of people being located and estimated correctly. World Population Density Estimate 2016: this layer is represented as population density in units of persons per square kilometer.World Settlement Score 2016: the dasymetric likelihood surface used to create this layer by apportioning population from census polygons to the settlement score raster.To use this layer in analysis, there are several properties or geoprocessing environment settings that should be used:Coordinate system: WGS_1984. This service and its underlying data are WGS_1984. We do this because projecting population count data actually will change the populations due to resampling and either collapsing or splitting cells to fit into another coordinate system. Cell Size: 0.0013474728 degrees (approximately 150-meters) at the equator. No Data: -1Bit Depth: 32-bit signedThis layer has query, identify, pixel, and export image functions enabled, and is restricted to a maximum analysis size of 30,000 x 30,000 pixels - an area about the size of Africa.Frye, C. et al., (2018). Using Classified and Unclassified Land Cover Data to Estimate the Footprint of Human Settlement. Data Science Journal. 17, p.20. DOI: http://doi.org/10.5334/dsj-2018-020.What can you do with this layer?This layer is unsuitable for mapping or cartographic use, and thus it does not include a convenient legend. Instead, this layer is useful for analysis, particularly for estimating counts of people living within watersheds, coastal areas, and other areas that do not have standard boundaries. Esri recommends using the Zonal Statistics tool or the Zonal Statistics to Table tool where you provide input zones as either polygons, or raster data, and the tool will summarize the count of population within those zones. https://www.esri.com/arcgis-blog/products/arcgis-living-atlas/data-management/2016-world-population-estimate-services-are-now-available/

This dataset is a series of digital map-posters accompanying the AdaptNRM Guide: Helping Biodiversity Adapt: supporting climate adaptation planning using a community-level modelling approach.

These represent supporting materials and information about the community-level biodiversity models applied to climate change. Map posters are organised by four biological groups (vascular plants, mammals, reptiles and amphibians), two climate change scenario (1990-2050 MIROC5 and CanESM2 for RCP8.5), and five measures of change in biodiversity.

The map-posters present the nationally consistent data at locally relevant resolutions in eight parts – representing broad groupings of NRM regions based on the cluster boundaries used for climate adaptation planning (http://www.environment.gov.au/climate-change/adaptation) and also Nationally.

Map-posters are provided in PNG image format at moderate resolution (300dpi) to suit A0 printing. The posters were designed to meet A0 print size and digital viewing resolution of map detail. An additional set in PDF image format has been created for ease of download for initial exploration and printing on A3 paper. Some text elements and map features may be fuzzy at this resolution.

Each map-poster contains four dataset images coloured using standard legends encompassing the potential range of the measure, even if that range is not represented in the dataset itself or across the map extent.

Most map series are provided in two parts: part 1 shows the two climate scenarios for vascular plants and mammals and part 2 shows reptiles and amphibians. Eight cluster maps for each series have a different colour theme and map extent. A national series is also provided. Annotation briefly outlines the topics presented in the Guide so that each poster stands alone for quick reference.

An additional 77 National maps presenting the probability distributions of each of 77 vegetation types – NVIS 4.1 major vegetation subgroups (NVIS subgroups) - are currently in preparation.

Example citations:

Williams KJ, Raisbeck-Brown N, Prober S, Harwood T (2015) Generalised projected distribution of vegetation types – NVIS 4.1 major vegetation subgroups (1990 and 2050), A0 map-poster 8.1 - East Coast NRM regions. CSIRO Land and Water Flagship, Canberra. Available online at www.AdaptNRM.org and https://data.csiro.au/dap/.

Williams KJ, Raisbeck-Brown N, Harwood T, Prober S (2015) Revegetation benefit (cleared natural areas) for vascular plants and mammals (1990-2050), A0 map-poster 9.1 - East Coast NRM regions. CSIRO Land and Water Flagship, Canberra. Available online at www.AdaptNRM.org and https://data.csiro.au/dap/.

This dataset has been delivered incrementally. Please check that you are accessing the latest version of the dataset. Lineage: The map posters show case the scientific data. The data layers have been developed at approximately 250m resolution (9 second) across the Australian continent to incorporate the interaction between climate and topography, and are best viewed using a geographic information system (GIS). Each data layers is 1Gb, and inaccessible to non-GIS users. The map posters provide easy access to the scientific data, enabling the outputs to be viewed at high resolution with geographical context information provided.

Maps were generated using layout and drawing tools in ArcGIS 10.2.2

A check list of map posters and datasets is provided with the collection.

Map Series: 7.(1-77) National probability distribution of vegetation type – NVIS 4.1 major vegetation subgroup pre-1750 #0x

8.1 Generalised projected distribution of vegetation types (NVIS subgroups) (1990 and 2050)

9.1 Revegetation benefit (cleared natural areas) for plants and mammals (1990-2050)

9.2 Revegetation benefit (cleared natural areas) for reptiles and amphibians (1990-2050)

10.1 Need for assisted dispersal for vascular plants and mammals (1990-2050)

10.2 Need for assisted dispersal for reptiles and amphibians (1990-2050)

11.1 Refugial potential for vascular plants and mammals (1990-2050)

11.1 Refugial potential for reptiles and amphibians (1990-2050)

12.1 Climate-driven future revegetation benefit for vascular plants and mammals (1990-2050)

12.2 Climate-driven future revegetation benefit for vascular reptiles and amphibians (1990-2050)

The USGS Elevation Contours service from The National Map displays contours generated for the United States at various scales. Small-scale contours were created by USGS TNM from 1 arc-second data with 100-meter contours, and are visible at 1:600,000 and smaller scales. Medium-scale contours were created by USGS EROS from 1/3-arc-second data with 100-foot intervals, and are visible between 1:150,000 and 1:600,000. Additional medium-scale contours were created by USGS EROS from 1/3-arc-second data with 50-foot intervals, and are visible between 1:50,000 and 1:150,000. Large scale contours are updated every quarter, and are created by USGS TNM for the 7.5' 1:24,000-scale US Topo digital map series. These contours are derived from 1/3 arc-second or better resolution data, and are visible at scales 1:50,000 and larger. Large scale contour intervals are variable across the United States depending on complexity of topography, and as contours are generated per US Topo quadrangle, lines may not match across quad boundaries. The National Map download client allows free downloads of public domain contour data in either Esri File Geodatabase or Shapefile formats. The 3D Elevation Program (3DEP) provides elevation data for The National Map and basic elevation information for earth science studies and mapping applications. Scientists and resource managers use elevation data for global change research, hydrologic modeling, resource monitoring, mapping and visualization, and many other applications. For additional information on 3DEP, go to https://www.usgs.gov/3d-elevation-program. See https://apps.nationalmap.gov/help/ for assistance with The National Map viewer, download client, services, or metadata.

In recent decades India has undergone substantial land use/land cover change as a result of population growth and economic development. Historical land use/land cover maps are necessary to quantify the impact of change at global and regional scales, improve predictions about the quantity and location of future change and support planning decisions. Here, a regional land change model driven by district-level inventory data is used to generate an annual time series of high-resolution land use/land cover maps for the Indian subcontinent between 1960-2010. The allocation procedure is based on statistical analysis of the relationship between contemporary land use/land cover and various spatially explicit covariates, with the derived statistical models used to predict the suitability of individual pixels to the respective land use/land cover types. A comparison of the simulated map for 1985 against remotely-sensed land use/land cover maps for 1985 and 2005 reveals substantial disagreement between the simulated and remote sensing maps, much of which arises due to differences in the amount of change aggregated to the country level.

This part of DS 781 presents data for the geologic and geomorphic map of the Offshore of Fort Ross map area, California. The vector data file is included in "Geology_OffshoreFortRoss.zip," which is accessible from http://pubs.usgs.gov/ds/781/OffshoreFortRoss/data_catalog_OffshoreFortRoss.html. The morphology and the geology of the offshore part of the Offshore of Fort Ross map area result from the interplay between local sedimentary processes, oceanography, sea-level rise, and tectonics. The nearshore seafloor in the northern half of the map area is characterized by rocky outcrops of Tertiary sedimentary rocks (units Tgr and Tsm). This rugged nearshore zone and the inner shelf (to water depths of about 50 m) typically dip seaward about 1.5 to 2.5 degrees, whereas the mid-shelf within State Waters (about 50 to 85 m) dips more gently, about 0.4 degrees. In contrast, the nearshore to mid shelf in the southern half of the map area lies directly offshore of the mouth of the Russian River and has a more gentle, uniform dip, about 0.45 to 0.55 degrees, out to water depths of about 70 m at the outer limit of State Waters. A significant amount of the Russian River sediment load, estimated at about 900,000 metric tons/yr by Farnsworth and Warrick (2007) is deposited offshore of the river mouth, contributing to the noted north-to-south contrast in bathymetric slope. On a larger geomorphic scale, sea level has risen about 125 to 130 m over about the last 21,000 years (for example, Lambeck and Chappell, 2001; Peltier and Fairbanks, 2005), leading to broadening of the continental shelf, progressive eastward migration of the shoreline and wave-cut platform, and associated transgressive erosion and deposition. Tectonic influences impacting shelf geomorphology and geology are primarily related to the active San Andreas Fault system (see below). Given exposure to high wave energy, modern nearshore to inner-shelf sediments north of the mouth of the Russian River are mostly sand (unit Qms) and a mix of sand, gravel, and cobbles (units Qmsc and Qmsd). The more coarse-grained sands and gravels (units Qmsc and Qmsd) are primarily recognized on the basis of bathymetry and high backscatter. Both Qmsc and Qmsd typically have abrupt landward contacts with bedrock (units Tgr, Tsm, Tkfs, fsr) and form irregular to lenticular exposures that are commonly elongate in the shore-normal direction. Contacts between units Qmsc and Qms are typically gradational. Unit Qmsd forms erosional lags in scoured depressions that are bounded by relatively sharp and less commonly diffuse contacts with unit Qms horizontal sand sheets. These depressions are typically a few tens of centimeters deep and range in size from a few 10's of sq m to more than one sq km. Similar Qmsd scour depressions are common along this stretch of the California coast (see, for example, Cacchione and others, 1984; Hallenbeck and others, 2012) where surficial offshore sandy sediment is relatively thin (thus unable to fill the depressions) due to both lack of sediment supply and to erosion and transport of sediment during large northwest winter swells. Such features have been referred to as "rippled-scour depressions" (see, for example, Cacchione and others, 1984) or "sorted bedforms" (see, for example, Goff and others, 2005; Trembanis and Hume, 2011). Although the general areas in which both Qmsd scour depressions and surrounding mobile sand sheets occur are not likely to change substantially, the boundaries of the individual Qmsd depressions are likely ephemeral, changing seasonally and during significant storm events. Unit Qmsf lies offshore of unit Qms, and consists primarily of mud and muddy sand and is commonly extensively bioturbated. The water depth of the transition from sand-dominated marine sediment (unit Qms) to mud-dominated marine sediment (Qmsf) increases from about 45 to 50 m directly offshore of the mouth of the Russian River to as much as about 60 m adjacent to the rocky outcrops along the northern map boundary. This change is clearly related to the large amount of fine sediment load carried by the Russian River, which feeds a widespread, mid-shelf, mud belt that extends along the mid-shelf from Point Arena to Point Reyes (Klise, 1983; Drake and Cacchione, 1985; Demirpolat, 1991). Map unit polygons were digitized over underlying 2-meter base layers developed from multibeam bathymetry and backscatter data (see Bathymetry--Offshore Fort Ross, California and Backscattter A to C--Offshore Fort Ross, California, DS 781, for more information). The bathymetry and backscatter data were collected between 2006 and 2009. References Cited Cacchione, D.A., Drake, D.E., Grant, W.D., and Tate, G.B., 1984, Rippled scour depressions of the inner continental shelf off central California: Journal of Sedimentary Petrology, v. 54, p. 1,280-1,291. Demirpolat, S., 1991, Surface and near-surface sediments from the continental shelf off the Russian River, northern California: Marine Geology, v. 99, p. 163-173. Drake, D.E., and Cacchione, D.A., 1985, Seasonal variation in sediment transport on the Russian River shelf, California: Continental Shelf Research, v. 14, p. 495-514. Farnsworth, K.L., and Warrick, J.A., 2007, Sources, dispersal, and fate of fine sediment supplied to coastal California: U.S. Geological Survey Scientific Investigations Report 2007-5254, 77 p. Goff, J.A., Mayer, L.A., Traykovski, P., Buynevich, I., Wilkens, R., Raymond, R., Glang, G., Evans, R.L., Olson, H., and Jenkins, C., 2005, Detailed investigations of sorted bedforms or "rippled scour depressions", within the Marthaâ s Vineyard Coastal Observatory, Massachusetts: Continental Shelf Research, v. 25, p. 461-484. Hallenbeck, T.R., Kvitek, R.G., and Lindholm, J., 2012, Rippled scour depressions add ecologically significant heterogeneity to soft-bottom habitats on the continental shelf: Marine Ecology Progress Series, v. 468, p. 119-133. Klise, D.H., 1983, Modern sedimentation on the California continental margin adjacent to the Russian River: M.S. thesis, San Jose State University, 120 p. Lambeck, K., and Chappell, J., 2001, Sea level change through the last glacial cycle: Science, v. 292, p. 679-686, doi: 10.1126/science.1059549. Peltier, W.R., and Fairbanks, R.G., 2005, Global glacial ice volume and Last Glacial Maximum duration from an extended Barbados sea level record: Quaternary Science Reviews, v. 25, p. 3,322-3,337. Trembanis, A.C., and Hume, T.M., 2011, Sorted bedforms on the inner shelf off northeastern New Zealand-Spatiotemporal relationships and potential paleo-environmental implications: Geo-Marine Letters, v. 31, p. 203-214.

Urban area boundaries for 1900, part of the Baltimore-Washington Spatial Dynamics and Human Impacts dataset. The Baltimore-Washington Spatial Dynamics and Human Impacts dataset is an integrated and flexible temporal urban land characteristics database for the Baltimore-Washington metropolitan area. The compilation of this data is a collaborative effort led by the U.S. Geological Survey and the University of Maryland Baltimore County. The database provides visual and historical perspective of the urban growth experienced in the area between 1792 and 1992. Data on built-up areas exists as separate geographic layers for the dates: 1792, 1801, 1822, 1850, 1878, 1900, 1925, 1938, 1953, 1966, 1972, 1982, and 1992. Temporal urban mapping reconstructs past landscapes by incorporating historic maps, census statistics, and commerce records to generate a progressive geo-referenced picture of the past changes in a region. Contemporary mapping focuses on the use of remotely sensed data, existing digital land use data, digital census information, and a variety of earth science infrastructure data, such as Digital Line Graphs, Digital Elevation Models, and key ancillary demographic information. Different procedures were used for different time periods, more fully described for each file in the Process Step Section 2.5.2. The resulting database of temporal urban land use/land cover and demographic changes provides an ideal source of test data and information for both urban geographers and global change research scientists. While this dataset was developed by the University of Maryland Baltimore County final quality control and metadata generation was performed by the University of Vermont's Spatial Analysis Lab. Two significant problems were noted regarding this dataset. The first anomoly is that the 1801, 1822, and 1878 layers have a much smaller extent, and contain data only for Baltimore City. The second discrepancy is that there are also some very obvious positional errors causing misalignments between layers of different dates (i.e. urban areas become non-urban in a very short time period, an unlikely occurance). This is part of a collection of 221 Baltimore Ecosystem Study metadata records that point to a geodatabase. The geodatabase is available online and is considerably large. Upon request, and under certain arrangements, it can be shipped on media, such as a usb hard drive. The geodatabase is roughly 51.4 Gb in size, consisting of 4,914 files in 160 folders. Although this metadata record and the others like it are not rich with attributes, it is nonetheless made available because the data that it represents could be indeed useful. This is part of a collection of 221 Baltimore Ecosystem Study metadata records that point to a geodatabase. The geodatabase is available online and is considerably large. Upon request, and under certain arrangements, it can be shipped on media, such as a usb hard drive. The geodatabase is roughly 51.4 Gb in size, consisting of 4,914 files in 160 folders. Although this metadata record and the others like it are not rich with attributes, it is nonetheless made available because the data that it represents could be indeed useful.

[From The Landmap Project: Introduction, "http://www.landmap.ac.uk/background/intro.html"]

 A joint project to provide orthorectified satellite image mosaics of Landsat,
 SPOT and ERS radar data and a high resolution Digital Elevation Model for the
 whole of the UK. These data will be in a form which can easily be merged with
 other data, such as road networks, so that any user can quickly produce a
 precise map of their area of interest.

 Predominately aimed at the UK academic and educational sectors these data and
 software are held online at the Manchester University super computer facility
 where users can either process the data remotely or download it to their local
 network.

 Please follow the links to the left for more information about the project or
 how to obtain data or access to the radar processing system at MIMAS. Please
 also refer to the MIMAS spatial-side website,
 "http://www.mimas.ac.uk/spatial/", for related remote sensing materials.

The Climate Adaptation Science Centers (CASCs) partner with natural and cultural resource managers, tribes and indigenous communities, and university researchers to provide science that helps fish, wildlife, ecosystems, and the communities they support adapt to climate change. The CASCs provide managers and stakeholders with information and decision-making tools to respond to the effects of climate change. While each CASC works to address specific research priorities within their respective region, CASCs also collaborate across boundaries to address issues within shared ecosystems, watersheds, and landscapes. These shapefiles represent the 9 CASC regions and the national CASC that comprise the CASC network, highlighting the consortium institutions that make up each region.The shapefiles were produced in ArcGIS Pro but any geospatial software can be used to view the shapefiles (ArcGIS, QGIS, etc).

This mapping completed the Larsemann Hills photogrammetric mapping project. The project was commenced on 14 December 2001 and completed in April 2003. It includes the integration of newly mapped data with dataset gis136. (Larsemann Hills - Mapping from Landsat 7 imagery captured January 2000)

A report on the project is available at the url given below.

The Mohorovicic discontinuity or 'Moho' maps the boundary between the earth's crust and mantle and is defined by an abrupt change in seismic velocity due to changes in the density of rocks between the crust and mantle. GeoTIFF grids that map depth to Moho (crustal thickness) for the United States and Canada, and for Australia are provided in this report and were used as evidential layers in developing prospectivity models for basin-hosted Pb-Zn mineralization (Lawley and others, 2022). A composite grid of Moho depths across the United States and Canada was created using data from Shen and Ritzwoller (2016) for the conterminous United States, from Zhang and others (2019) for Alaska, and from Schetselaar and Snyder (2017) for Canada. For this study, data covering North America were gridded onto a 0.25 degree grid and merged to create the composite grid across the United States and Canada. A grid of depth to Moho for Australia is from Kennett and others (2011). The GeoTiff grid of Mo ...

This dataset provides field observations of thaw depth and dominant vegetation types, a LiDAR-derived elevation map, and permafrost distribution and probability maps for an area on the coastal plain of the Yukon-Kuskokwim Delta (YKD), in western Alaska, USA. Field data were collected during July 8-17, 2016 to parameterize and to validate the derived permafrost maps. The YKD is in the sporadic to isolated permafrost zone where permafrost forms extensive elevated plateaus on abandoned floodplains. The region is extremely flat and vulnerable to eustatic sea-level rise and inland storm surges. These high-resolution permafrost maps support landscape change analyses and assessments of the impacts of climate change on permafrost in this region of high biological productivity, critical wildlife habitats, and subsistence-based human economy.

The Chesapeake Bay Estuary is the largest estuary in the United States and provides habitats for diverse wildlife and aquatic species, protects communities against flooding, reduces pollution to waterways, and supports local economies through commercial and recreational activities. In the Spring of 2018, the U.S. Geological Survey (USGS) Coastal National Elevation Database (CoNED) Applications Project at the USGS Earth Resources Observation and Science (EROS) Center and the Virginia Institute of Marine Science (VIMS) Center for Coastal Resources Management (CCRM) initiated collaborative work. The goal of this collaboration is to evaluate how various remote sensing technologies can be employed to model estuarine riverbank topography and measure volumetric change in riverbanks for downstream sediment transport modeling for Chesapeake Bay. Additional science interests for this USGS CoNED and VIMS CCRM collaboration include understanding the spatial extent and variation within tidal wetland boundaries, comparing microtopographic changes of protected/stabilized living shorelines versus natural shorelines, and examining riverine and estuarine land/water interface transitions between topography and bathymetry. The remote sensing technologies investigated in this collaboration include airborne lidar, ground based lidar (GBL), Structure from Motion (SfM) processing of high-resolution imagery, and Satellite Derived Bathymetry (SDB) produced from Landsat 8/9, Sentinel-2, and/or WorldView imagery. Long-term field study sites have been established by VIMS CCRM along the James, Severn, and York Rivers in the Chesapeake Bay Region, with the goal of returning to the sites biannually. The following child pages describe and contain the field data collected during these biannual efforts.

This project combines data extraction, predictive modeling, and geospatial mapping to analyze housing trends in Mercer County, New Jersey. It consists of three core components: Census Data Extraction: Gathers U.S. Census data (2012–2022) on median house value, household income, and racial demographics for all census tracts in the county. It accounts for changes in census tract boundaries between 2010 and 2020 by approximating values for newly defined tracts. House Value Prediction: Uses an LSTM model with k-fold cross-validation to forecast median house values through 2025. Multiple feature combinations and sequence lengths are tested to optimize prediction accuracy, with the final model selected based on MSE and MAE scores. Data Mapping: Visualizes historical and predicted housing data using GeoJSON files from the TIGERWeb API. It generates interactive maps showing raw values, changes over time, and percent differences, with customization options to handle outliers and improve interpretability. This modular workflow can be adapted to other regions by changing the input FIPS codes and feature selections.

NOTE: A more current version of the Protected Areas Database of the United States (PAD-US) is available: PAD-US 3.0 https://doi.org/10.5066/P9Q9LQ4B. The USGS Protected Areas Database of the United States (PAD-US) is the nation's inventory of protected areas, including public land and voluntarily provided private protected areas, identified as an A-16 National Geospatial Data Asset in the Cadastre Theme (https://communities.geoplatform.gov/ngda-cadastre/). The PAD-US is an ongoing project with several published versions of a spatial database including areas dedicated to the preservation of biological diversity, and other natural (including extraction), recreational, or cultural uses, managed for these purposes through legal or other effective means. The database was originally designed to support biodiversity assessments; however, its scope expanded in recent years to include all public and nonprofit lands and waters. Most are public lands owned in fee (the owner of the property has full and irrevocable ownership of the land); however, long-term easements, leases, agreements, Congressional (e.g. 'Wilderness Area'), Executive (e.g. 'National Monument'), and administrative designations (e.g. 'Area of Critical Environmental Concern') documented in agency management plans are also included. The PAD-US strives to be a complete inventory of public land and other protected areas, compiling “best available” data provided by managing agencies and organizations. The PAD-US geodatabase maps and describes areas using over twenty-five attributes and five feature classes representing the U.S. protected areas network in separate feature classes: Fee (ownership parcels), Designation, Easement, Marine, Proclamation and Other Planning Boundaries. Five additional feature classes include various combinations of the primary layers (for example, Combined_Fee_Easement) to support data management, queries, web mapping services, and analyses. This PAD-US Version 2.1 dataset includes a variety of updates and new data from the previous Version 2.0 dataset (USGS, 2018 https://doi.org/10.5066/P955KPLE ), achieving the primary goal to "Complete the PAD-US Inventory by 2020" (https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/science/pad-us-vision) by addressing known data gaps with newly available data. The following list summarizes the integration of "best available" spatial data to ensure public lands and other protected areas from all jurisdictions are represented in PAD-US, along with continued improvements and regular maintenance of the federal theme. Completing the PAD-US Inventory: 1) Integration of over 75,000 city parks in all 50 States (and the District of Columbia) from The Trust for Public Land's (TPL) ParkServe data development initiative (https://parkserve.tpl.org/) added nearly 2.7 million acres of protected area and significantly reduced the primary known data gap in previous PAD-US versions (local government lands). 2) First-time integration of the Census American Indian/Alaskan Native Areas (AIA) dataset (https://www2.census.gov/geo/tiger/TIGER2019/AIANNH) representing the boundaries for federally recognized American Indian reservations and off-reservation trust lands across the nation (as of January 1, 2020, as reported by the federally recognized tribal governments through the Census Bureau's Boundary and Annexation Survey) addressed another major PAD-US data gap. 3) Aggregation of nearly 5,000 protected areas owned by local land trusts in 13 states, aggregated by Ducks Unlimited through data calls for easements to update the National Conservation Easement Database (https://www.conservationeasement.us/), increased PAD-US protected areas by over 350,000 acres. Maintaining regular Federal updates: 1) Major update of the Federal estate (fee ownership parcels, easement interest, and management designations), including authoritative data from 8 agencies: Bureau of Land Management (BLM), U.S. Census Bureau (Census), Department of Defense (DOD), U.S. Fish and Wildlife Service (FWS), National Park Service (NPS), Natural Resources Conservation Service (NRCS), U.S. Forest Service (USFS), National Oceanic and Atmospheric Administration (NOAA). The federal theme in PAD-US is developed in close collaboration with the Federal Geographic Data Committee (FGDC) Federal Lands Working Group (FLWG, https://communities.geoplatform.gov/ngda-govunits/federal-lands-workgroup/); 2) Complete National Marine Protected Areas (MPA) update: from the National Oceanic and Atmospheric Administration (NOAA) MPA Inventory, including conservation measure ('GAP Status Code', 'IUCN Category') review by NOAA; Other changes: 1) PAD-US field name change - The "Public Access" field name changed from 'Access' to 'Pub_Access' to avoid unintended scripting errors associated with the script command 'access'. 2) Additional field - The "Feature Class" (FeatClass) field was added to all layers within PAD-US 2.1 (only included in the "Combined" layers of PAD-US 2.0 to describe which feature class data originated from). 3) Categorical GAP Status Code default changes - National Monuments are categorically assigned GAP Status Code = 2 (previously GAP 3), in the absence of other information, to better represent biodiversity protection restrictions associated with the designation. The Bureau of Land Management Areas of Environmental Concern (ACECs) are categorically assigned GAP Status Code = 3 (previously GAP 2) as the areas are administratively protected, not permanent. More information is available upon request. 4) Agency Name (FWS) geodatabase domain description changed to U.S. Fish and Wildlife Service (previously U.S. Fish & Wildlife Service). 5) Select areas in the provisional PAD-US 2.1 Proclamation feature class were removed following a consultation with the data-steward (Census Bureau). Tribal designated statistical areas are purely a geographic area for providing Census statistics with no land base. Most affected areas are relatively small; however, 4,341,120 acres and 37 records were removed in total. Contact Mason Croft (masoncroft@boisestate) for more information about how to identify these records. For more information regarding the PAD-US dataset please visit, https://usgs.gov/gapanalysis/PAD-US/. For more information about data aggregation please review the Online PAD-US Data Manual available at https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/pad-us-data-manual .

This science transfer project used a multi-phase approach that included deep learning techniques and geospatial rules to create a habitat map of the St. Louis River estuary. The Project The St. Louis River estuary, which runs along the boundary between Wisconsin and Minnesota in the Duluth-Superior metro area, is a vital resource ecologically, culturally, and economically. The St. Louis River is the largest U.S. tributary to Lake Superior and the second largest tributary in the Lake Superior watershed. In 1987, the Great Lakes Water Quality Agreement designated the 12,000-acre freshwater estuary a Great Lakes Area of Concern because legacy contaminants and disturbances had led to nine key impairments, including loss of fish and wildlife habitat. This project addressed a need identified by the St. Louis River Habitat Workgroup to better understand current conditions to support the identification and prioritization of areas for future restoration and conservation. The project approach included the transfer of a repeatable habitat mapping process developed by the Lake Superior Reserve and partners to a larger area encompassing 57,000 acres of wetlands and adjacent uplands spanning the lower twenty-one miles of the St. Louis River below the Fond du Lac dam. The team applied accessible image classification methods-including use of common machine learning classifiers and freely available, non-proprietary data-to create a reproducible approach that could be adopted in other locations and redeployed at regular intervals to illuminate change over time. In addition to the habitat map and reproducible workflow, the team also produced a change analysis report comparing the 2024 habitat map to the previous St. Louis River Estuary map from 2002.

Taken from sections of the Report:

The 2002-03 Mapping and Geographic Information Program (MAGIP) field season was undertaken from Davis Station. Nigel Peters from Sinclair Knight Merz undertook this season's fieldwork, the results of which are described in the following report.

The main objective for this season was to provide photo control mapping in the Rauer Group, with photo control also required at Davis Station and Marine Plains. A number of other tasks were undertaken in support of various scientific and engineering programs.

The tasks outlined in the surveyors brief are varied and numerous and have been included to provide the surveyor with a full and appropriate work program. The tasks are prioritised, usually with one or two major tasks with a number of minor tasks listed to be undertaken if the opportunity arises. This season's Survey Brief has been included in Appendix A with a summary of achievements listed in Appendix B.

The following report covers the fieldwork undertaken by myself during the 2002/2003 ANARE Summer Field Season. Data collected in support of other scientific programs has been included in this report primarily as a record of work undertaken by the mapping program. These data have been supplied to the various scientists for inclusion in their studies.

Sequence of Events

4th November - 12th November 2002 - Pre-Departure Training

• Field training for expeditioners at Bronte Park prior to the departure of V2.
• Survey briefing at Antarctic Division by Mapping Officer, Mr Henk Brolsma

20th November -5th December 2002 - Voyage 2

• Final preparation and checking and replacement of damaged equipment
• The Aurora Australis departed Hobart on the evening of 22nd November en route for Zhongshan, Davis and Mawson
• The Aurora Australis arrived of Zhongshan on the 3rd December where Chinese personal were deployed
• The Aurora Australis stopped approximately 1km off shore from Davis on the evening of the 4th December and arrived at Davis Station 5th December

6th December - 10th December 2002 - Davis Station

• Davis Resupply involving unloading and storage of food and equipment

11th December - 31st December 2002 - Davis Station

• Down loading Tide Gauge at Davis Station
• GPS measurements AUS303
• Coordination and levelling building Heights
• Coordination of control points Rauer Group
• Coordination of control points Davis

1st January - 20th January 2003 - Davis Station

• Coordination of control points Rauer Group
• Antenna Farm levelling
• Surveys at Brooks, Bandits and Watts huts

21st January - 26th January 2003 - Law Base

• Law Base Tide gauge downloading
• GPS connections to Davis

27th January - 9th February 2003 - Davis Station

• Tarbuck Crag repeater survey
• Skyline Survey Antenna Farm
• Establish new Tide Pole at Deep Lake
• Station duties loading equipment on to Ice Bird

10th February - 22nd February 2003 - Voyage 5

• Depart Davis
• Arrive Hobart 22nd February

Scope of Work

The Antarctic Mapping Officer Mr Henk Brolsma provided the scope of works within the Surveyors Brief for the 2002- 2003 field survey program (Appendix A). The following is a summation of the survey requirements for this season.

Rauer Group

• Photo control are required throughout the Rauer Group at specified locations

Davis

• Down Load Tide gauge
• Timed water level measurements
• Levelling between tide gauge benchmarks, including GPS observation
• Update station map and determine levels for all building floors, roof levels and the ground at the corner of every building
• Photo control for orthophoto at Davis and at Heideman Bay

Zhongshan

• Download tide gauge
• Timed water level measurements
• Height connection from Law Base to tide gauge bench mark
• Level between tide gauge benchmarks
• Check existing marks established for tide gauge location

Vestfold Hills

• Deep Lake depth pole
• Take pole readings
• Repair depth pole
• Lake levelling
• Location of bench marks

Land Tenure Mapping via Satellite Market Outlook

According to our latest research, the global Land Tenure Mapping via Satellite market size reached USD 1.62 billion in 2024, driven by rapid advancements in satellite imaging and geospatial analytics. The market is projected to expand at a robust CAGR of 10.7% from 2025 to 2033, reaching an estimated USD 4.04 billion by 2033. This growth is fueled by increasing demand for transparent land ownership records, enhanced agricultural productivity, and the integration of advanced satellite technologies into land management systems worldwide.

One of the primary growth factors for the Land Tenure Mapping via Satellite market is the increasing global emphasis on land rights and transparent property ownership. Governments and international organizations are recognizing the importance of clear land tenure documentation to reduce conflicts, improve land administration, and foster sustainable development. The adoption of satellite-based mapping solutions enables accurate, up-to-date, and large-scale land records that are crucial for legal security and economic empowerment, particularly in developing regions where traditional land surveys are often incomplete or outdated. This shift is further supported by policy initiatives and funding from multilateral agencies, which are investing in digital transformation projects for land governance.

Technological advancements in satellite imagery and data analytics are also playing a pivotal role in driving market expansion. The proliferation of high-resolution optical, radar, and multispectral satellites has dramatically improved the precision and frequency of land mapping activities. These technologies allow for the detection of subtle changes in land use, boundaries, and environmental conditions, which are essential for accurate tenure mapping. Additionally, the integration of artificial intelligence and machine learning algorithms into data processing workflows is enabling automated analysis and visualization, reducing the time and cost associated with manual interpretation. This technological leap is making satellite-based land tenure mapping more accessible and cost-effective for a wide range of end-users, from government agencies to private enterprises.

The growing need for sustainable land management in the face of climate change and rapid urbanization is another significant driver for the market. Accurate land tenure mapping via satellite supports policy makers in managing natural resources, planning urban expansion, and monitoring environmental changes. In agriculture, for example, clear tenure records are essential for investment, credit access, and implementation of sustainable practices. Similarly, urban planners rely on up-to-date land maps to guide infrastructure development and prevent illegal encroachments. As environmental monitoring becomes a global priority, satellite-based solutions provide the granular, real-time data required to track deforestation, land degradation, and other ecological risks, further boosting market demand.

Regionally, North America and Europe are at the forefront of adopting satellite-based land tenure mapping solutions, driven by advanced technological infrastructure and robust regulatory frameworks. However, significant growth is anticipated in Asia Pacific, Latin America, and Africa, where land tenure issues are often more acute and the benefits of digital mapping are increasingly recognized. Governments in these regions are launching ambitious land reform and digitization programs, often in collaboration with international partners. This regional diversification is expected to shape the competitive dynamics and innovation landscape of the global Land Tenure Mapping via Satellite market over the coming decade.

Solution Type Analysis

The Land Tenure Mapping via Satellite market is segmented by solution type into Imagery Acquisition, Data Processing & Analytics, Mapping & Visualization, and Others. Imagery Acquisition forms the corner

1. Abstract Landslides are damaging and deadly, and they occur in every U.S. state. However, our current ability to understand landslide hazards at the national scale is limited, in part because spatial data on landslide occurrence across the U.S. varies greatly in quality, accessibility, and extent. Landslide inventories are typically collected and maintained by different agencies and institutions, usually within specific jurisdictional boundaries, and often with varied objectives and information attributes or even in disparate formats. The purpose of this data release is to provide an openly accessible, centralized map of existing information about landslide occurrence across the entire U.S. This data release is an update of previous versions 1 (Jones and others, 2019) and 2 (Belair and others, 2022). Changes relative to version 2 are summarized in us_ls_v3_changes.txt. It provides an integrated database of the landslides from these inventories (refer to US_Landslide_v3_gpkg) wi ...

This part of DS 781 presents data for the geologic and geomorphic map of the Offshore of Salt Point map area, California. The vector data file is included in "Geology_OffshoreSaltPoint.zip," which is accessible from http://pubs.usgs.gov/ds/781/OffshoreSaltPoint/data_catalog_OffshoreSaltPoint.html. The morphology and the geology of the Offshore of Salt Point map area result from the interplay between local sea-level rise, sedimentary processes, oceanography, and tectonics. The offshore part of the map area extends from the shoreline to water depths of about 90 to 100 m on the mid-continental shelf; the shelfbreak occurs about 20 km farther offshore at water depths of about 200 m. The nearshore and inner shelf (to water depths of about 50 to 60 m) typically dips seaward about 1.0 to 1.5 degrees; the mid to outer shelf dips more gently, generally less than 0.5 degrees. Sea level has risen about 125 to 130 m over about the last 21,000 years (for example, Lambeck and Chappell, 2001; Peltier and Fairbanks, 2005), leading to broadening of the continental shelf, progressive eastward migration of the shoreline and wave-cut platform, and associated transgressive erosion and deposition. Land-derived sediment was carried into this dynamic setting, then subjected to full Pacific Ocean wave energy and strong currents before deposition or offshore transport. Tectonic influences impacting shelf morphology and geology are related to local faulting, folding, uplift, and subsidence (see below). Bedrock of the Eocene and Paleocene German Rancho Formation (unit Tgr) underlies much of the inner shelf, extending to water depths of as much as 60 m. Although onshore coastal outcrops of this unit are well bedded, seafloor outcrops imaged on high-resolution bathymetry have a hackly surface texture and abundant fractures. Embayments in the outer margin of the seafloor bedrock outcrops are commonly paired with the mouths of coastal watersheds and are inferred to have formed by fluvial erosion during the last sealevel lowstand. One of the more prominent embayments occurs about one kilometer north of Salt Point at the mouth of Miller Creek (fig. 1-2). These coastal watersheds are relatively small and steep, extending to a drainage divide just 2 to 3 km east of the shoreline, and are inferred sources of coarse-grained sediments. Immediately east of this onshore topographic divide, drainage along this part of the coast is captured by the northwest-flowing South Fork of the Gualala River (fig. 1-2), which runs parallel to the coast along the trace of the San Andreas fault. Given relatively shallow water depths (0 to about 50 m) and exposure to high wave energy, modern nearshore to mid-shelf sediments are mostly sand (unit Qms) and a mix of sand, gravel, and cobbles (units Qmsc and Qmsd). The more coarse-grained sands and gravels (units Qmsc and Qmsd) are primarily recognized on the basis of bathymetry and high backscatter. Both Qmsc and Qmsd typically have abrupt landward contacts with bedrock (unit Tgr) and form irregular to lenticular exposures that are commonly elongate in the shore-normal direction. Contacts between units Qmsc and Qms are typically gradational. Unit Qmsd forms erosional lags in scoured depressions that are bounded by relatively sharp and less commonly diffuse contacts with unit Qms horizontal sand sheets. These depressions are typically a few tens of centimeters deep and range in size from a few 10's of sq m to more than one sq km. Similar unit Qmsd scour depressions are common along this stretch of the California coast (see, for example, Cacchione and others, 1984; Hallenbeck and others, 2012) where surficial offshore sandy sediment is relatively thin (thus unable to fill the depressions) due to both lack of sediment supply and to erosion and transport of sediment during large northwest winter swells. Such features have been referred to as "rippled-scour depressions" (see, for example, Cacchione and others, 1984) or "sorted bedforms" (see, for example, Goff and others, 2005; Trembanis and Hume, 2011). Although the general areas in which both unit Qmsd scour depressions and surrounding mobile sand sheets occur are not likely to change substantially, the boundaries of the individual Qmsd depressions are likely ephemeral, changing seasonally and during significant storm events. The offshore decrease in slope at mid-shelf water depths (about 60 m) approximately coincides with a transition to more fine-grained marine sediments (unit Qmsf), which extends to the outer (3-nautical-mile) limit of California's State Waters. Unit Qmsf consists primarily of mud and muddy sand and is commonly extensively bioturbated. These fine-grained sediments are inferred to have been derived from from the Russian River, which has its mouth about 15 km south of the map area. Both Drake and Cacchione (1985) and Sherwood and others (1994) have documented seasonal, mid-shelf, northwest-directed, bottom currens capable of transporting fine-grained, suspended sediment from the Russian River to the Offshore of Salt Point map area. Map unit polygons were digitized over underlying 2-meter base layers developed from multibeam bathymetry and backscatter data (see Bathymetry--Offshore Salt Point, California and Backscattter--Offshore Salt Point, California, DS 781, for more information). The bathymetry and backscatter data were collected between 2006 and 2010. References Cited Cacchione, D.A., Drake, D.E., Grant, W.D., and Tate, G.B., 1984, Rippled scour depressions of the inner continental shelf off central California: Journal of Sedimentary Petrology, v. 54, p. 1,280-1,291. Drake, D.E., and Cacchione, D.A., 1985, Seasonal variation in sediment transport on the Russian River shelf, California: Continental Shelf Research, v. 14, p. 495-514. Goff, J.A., Mayer, L.A., Traykovski, P., Buynevich, I., Wilkens, R., Raymond, R., Glang, G., Evans, R.L., Olson, H., and Jenkins, C., 2005, Detailed investigations of sorted bedforms or "rippled scour depressions", within the Marthaâ s Vineyard Coastal Observatory, Massachusetts: Continental Shelf Research, v. 25, p. 461-484. Hallenbeck, T.R., Kvitek, R.G., and Lindholm, J., 2012, Rippled scour depressions add ecologically significant heterogeneity to soft-bottom habitats on the continental shelf: Marine Ecology Progress Series, v. 468, p. 119-133. Lambeck, K., and Chappell, J., 2001, Sea level change through the last glacial cycle: Science, v. 292, p. 679-686, doi: 10.1126/science.1059549. Manson, M.W., Huyette, C.M., Wills, C.J., Huffman, M.E., Smelser, G.G., Fuller, M.E., Domrose, C., and Gutierrez, C., 2006, Landslides in the Highway 1 corridor between Bodega Bay and Fort Ross, Sonoma County, California: California Geological Survey Special Report 196, 26 p., 2 plates, 38 maps, scale 1:12,000. Peltier, W.R., and Fairbanks, R.G., 2005, Global glacial ice volume and Last Glacial Maximum duration from an extended Barbados sea level record: Quaternary Science Reviews, v. 25, p. 3,322-3,337. Sherwood, C.R., Butman, B., Cacchione, D.A., Drake, D.E., Gross, T.F., Sternberg, R.W., Wiberg, P.L., and Williams, A.J., III, 1994, Sediment transport events on the northern California continental shelf during the 1990-1991 STRESS experiment: Continental Shelf Research, v. 14, p. 1063-1099. Trembanis, A.C., and Hume, T.M., 2011, Sorted bedforms on the inner shelf off northeastern New Zealand-Spatiotemporal relationships and potential paleo-environmental implications: Geo-Marine Letters, v. 31, p. 203-214.

The lithosphere-asthenosphere boundary (LAB), calculated from calibrated surface wave tomography models, is marked by an abrupt change in seismic velocity between the earth's cooler lithosphere (higher seismic velocities) and the warmer and more ductile asthenosphere (lower seismic velocities). GeoTIFF grids that were extracted from global compilations (Hoggard and others, 2020) that map depth to the LAB for the United States and Canada, and for Australia are provided in this report. Previous studies have identified locations of sediment-hosted Pb-Zn deposits occur along a gradient in the depth of the lithosphere-asthenosphere boundary. The LAB gradient is interpreted to represent a change from thicker to thinner lithosphere which has localized the development of basins prospective for Pb-Zn mineralization (Hoggard and others, 2020). The GeoTIFF grids were used as evidential layers in developing prospectivity models for basin-hosted Pb-Zn mineralization (Lawley and others, 2022). ...