Geologic interpretations of an aeromagnetic map of southern New England: U.S. Geological Survey Geophysical Investigations Map GP-906, scale 1:250,000. Magnetic contour intervals are 50 and 100 gammas. Includes geologic discussion and explanatory text, 12 p., 1976,1977. This map is also available as both an ESRI and Web Map Service.

This dataset depicts the boundaries of the Southern New England Management Area in ESRI shapefile format for the NOAA Fisheries Service’s Greater Atlantic Regional Fisheries Office (GARFO). This shapefile includes boundaries for the following Regulated Areas: - Southern New England Management Area Because GIS projection and topology functions can change or generalize coordinates, these GIS files are considered to be approximate representations and are NOT an OFFICIAL record for the exact regulated area boundaries. For information on the official legal definition refer to the Use Constraints metadata section.

A regional scale structural and stratigraphic 3D model has been developed for the western
Tamworth Belt within the New England Orogen in northeastern New South Wales.
The western Tamworth Belt is bound by the crustal scale Hunter-Mooki and Peel-Manning Fault
systems, which together form a wedge of deformed Devonian to Permian rocks.
The model consists of broad lithological volumes representing Devonian, Devonian-Carboniferous,
Carboniferous and Permian rocks that are folded and offset by numerous second and third
order fault systems with minor intrusion by Permian granitoids.
The model is based on a
series of 2 dimensional cross sections developed based on the integration of surface mapping,
16 reflection seismic profiles as well as magnetic and gravity data.
Interpretation confidence volumes are provided with the model to visually represent constraint
location and constraint quality. The results of the modelling provide a basis for understanding
the regional structural architecture and controls on mineral systems. The model illustrates
the contrast in deformation style from the northern Tamworth Belt, relative to the southeast of
the belt that is more structurally complex in terms of folding and faulting. The distribution of
known hydrothermal mineral systems in the Tamworth Belt appear closely linked to the fault-architecture,
with most occurring around steep west-dipping fault zones that intersect or splay from the
Hunter-Mooki Fault at depth. Faults of this style are more common in the southeastern Tamworth Belt
than they are to the north.

This dataset contains data from a process-oriented research cruise aboard the R/V Neil Armstrong from June 18th to July 2nd. The goal of this cruise was to map the three-dimensional structure of a mid-depth salinity maximum intrusion of warm salinity slope water extending onto the continental shelf south of New England. This was done through the use of Autonomous Underwater Vehicles (two REMUS 100 vehicles and one Tethys class AUV (Long Range AUV or LRAUV)), a towed Rockland Scientific Vertical Microstructure Profiler (VMP 250), and ship-board CTD and ADCP measurements. More details about the processing, data coverage, and usage can be found in the accompanying manuscript. This cruise took place on the shelf waters south of Cape Cod, MA, extending to the shelf break, with all of the data collected between 40°N to 41°N and 71.5°W to 70°W. Attached is a data map showing the location of all data included within this dataset.

File Descriptions:

Datamap.jpg

A map of the locations of all data included within this dataset.

CTD_summer2021.mat

This file contains profiles from the ship-board CTD (SeaBird 911+). Raw data was processed and gridded into 1 decibar bins using standard procedures in Seasave V 7.26.7.121 (Look at cnv file header for details about processing). This file is organized as a structure, with each variable in the data being a different field called by dot notation and each row with the structure being a different CTD profile. Biooptical variables are not quality-controlled.

CTD.time: the time of each profile in the MATLAB datetime format (from the processed SeaBird header file) in GMT

CTD.lon: degrees longitude of the profile (from the processed SeaBird header file)

CTD.lat: degrees latitude of the profile (from the processed SeaBird header file)

CTD.pres: the pressure in decibar at each location of the profile

CTD.sal: the seawater practical salinity in psu

CTD.temp: the seawater in-situ temperature in °C

CTD.flor: seawater fluorescence in mg/ m3

CTD.depth: depth at each location within the profile in meters

CTD.density: sigmatheta (the potential seawater density with respect to a reference pressure of 0 db) in kg.m3 minus 1,000kg/m3

CTD_Darter_MMMdd.mat and CTD_Edgar_MMMdd.mat

These files contain the data from the REMUS 100 missions, with Darter and Edgar being the two different REMUS 100 vehicles.

Conductivity: conductivity in mS/cm

Depth: depth in meters

Latitude: degrees latitude

Longitude: degrees longitude

Mission_number: the number of the REMUS mission

Mission_time: time during the mission in seconds since midnight in GMT

Salinity: the seawater practical salinity in psu

Sound_speed: the sound speed in m/s

Temperature: the seawater temperature in °C

LRAUV_20210623T194917.mat and LRAUV_20210624T145829.mat

These files contain data from the Tethys Class LRAUV (Long Range AUV) missions. Each file contains 10 structure variables.

CTD_Seabird: structure containing the bin median temperature in °C and salinity in PSU.

depth: the depth at each data point in meters.

fix_residual_percent_distance_traveled: underwater dead-reckoned navigation error (based on GPS fix when on surface) as a percentage of distance traveled

latitude: Latitude at each data point (not corrected for vehicle drift in underwater current)

latitude_fix: latitude of GPS fix (vehicle surfaced)

longitude: Longitude at each data point (not corrected for vehicle drift in underwater current)

longitude_fix: longitude of GPS fix (vehicle surfaced)

platform_battery_charge: The battery charge in ampere-hour

time_fix: time in seconds since January 1, 1970 (epoch time)

VMPtransact_YYYYMMdd.mat

Vertical Microstructure Profiler (Rockland Scientific VMP 250)

These files contain the processed data for each Vertical Microstructure Profiler (Rockland Scientific VMP 250) transect, consisting of multiple profiles. Data has been gridded on a 1 decibar equidistant grid using standard procedures in Rockland Scientific’s processing software. Note: Bio-optical variables and dissipation rates have not been quality-controlled.

Time: Time in MATLAB datenum format (days since 0000-00-00 00:00:00) in GMT

z: Pressure in decibar

T: in-situ temperature in degC

cnd: conductivity in mS/cm

Chl: Chlorophyll from fluorescence in mg/ m3

turb: Turbidity in NTU

eps: dissipation rate inferred from microstructure shear in m^2/s^3. (Note: Dissipation estimates come from standard fitting of microstructure data within a 1 decibar bin to a turbulence spectrum within Rockland Scientific’s standard processing. The dissipation data in the provided files has not been quality-controlled.

VMP-data was georeferenced by comparing the time stamps of VMP and processed ADCP files.

ADCP_ar50_wh300.mat

This file contains the data from the shipboard ADCP (Teledyne WH300 kHz). ADCP data was processed aboard using standard procedures in UHDAS/CODAS (University of Hawaii Technical Services Program, servicing UNOLS vessels (https://currents.soest.hawaii.edu/docs/adcp_doc/index.html). Vertical bin size is 2 m. u: zonal (positive towards east) velocity component in m/s

v: meridional (positive towards north) component in m/s

txy: time, longitude, and latitude of the velocity profiles. Time is in decimal days, with noon of Jan 1 being 0.5 decimal days and noon of January 20th being 19.5 decimal days of the reference year. For another example, 6am on June 18, 2021, is decimal day 168.25. All times are in GMT.

refyear: The reference year from which the decimal days are calculated.

depth: vertical coordinate of the velocity bin center

pgood: percent good, a quality parameter showing the fraction of good pings within an ensemble average.

spd_u: zonal ship speed in m/s

spd_v: meridional ship speed in m/s

tr_temp: ADCP transducer temperature in deg C

amp: backscatter amplitude in relative units

This storymap visualizes data from Piping Plovers that were tagged at nesting areas in southern New England and tracked during fall migration using the Motus network (www.motus.org). The storymap is available at the following link: https://storymaps.arcgis.com/stories/5bab01fc5fa445f58ee54c062b4d2f3dExplore the map below to see how Piping Plovers take flight and make their away across the Atlantic--sometimes flying as fast as 80 km an hour. For migrating plovers, wind and weather conditions play an important role in their flight departures; and stopover sites in the Mid-Atlantic provide critical habitat for rest and refueling. Here in this map, you can look at how nano-tagged Piping Plovers from Rhode Island and Massachusetts timed their migration flights with wind conditions.The storymap is available at the following link: https://storymaps.arcgis.com/stories/5bab01fc5fa445f58ee54c062b4d2f3dStory Map Created by Alex Cook, USFWS Directorate Fellowship Program 2020 Cohort

The widespread influence of land use and natural disturbance on population, community, and landscape dynamics and the long-term legacy of disturbance on modern ecosystems requires that a historical, broad-scale perspective become an integral part of modern ecological studies and conservation assessment and planning. In previous studies, the Harvard Forest Long Term Ecological Research (LTER) program has developed an integrated approach of paleoecological and historical reconstruction, meteorological modeling, air photo interpretation, GIS analyses, and field studies of vegetation and soils, to address fundamental ecological questions concerning the rates, direction, and causes of vegetation change, to evaluate controls over modern species and community distributions and landscape patterns, and to provide critical background for conservation and restoration planning. In the current study, we extend this approach to investigate the link between landscape history and the abundance, distribution, and dynamics of species, communities and landscapes of the Cape Cod to Long Island coastal region, including the islands of Martha's Vineyard, Nantucket, and Block Island. The study region includes many areas of high conservation priority that are linked geographically, historically, and ecologically. This data package includes GIS layers digitized by Harvard Forest researchers from copies of the US Coastal Survey “T-Sheet” maps available from the National Archives in College Park, Maryland. The US Coastal Survey, and then the US Coast and Geodetic Survey mapped the region, or specific parts of it, several times between 1832 and the 1960s. In this project we digitized the earliest T-Sheet available for each location. The original maps were surveyed between 1832 and 1886, with most of them made between 1835 to 1855. The original maps showed features such as roads, farm walls, railroads, buildings, some industrial buildings, saltworks, wharfs, and land cover including woodlands, sandplains, grasslands, open agricultural fields, cultivated areas, fruit tree orchards, wetlands, etc. Many sheets had symbols which differentiated conifer trees from hardwoods. There were some inconsistencies in what features were mapped or how they were drawn between the original T-Sheets. Since we digitized the maps over the course of several different research projects, we did not always digitize all of the same features in each geographic area, therefore users of this data are encouraged to look at scans of the original T-Sheets for their specific areas of interest (links below). We always digitized land cover and roads and occasionally buildings and fences as mentioned in the datasets below.

This dataset contains data from a process-oriented research cruise aboard the R/V Neil Armstrong from June 18th to July 2nd. The goal of this cruise was to map the three-dimensional structure of a mid-depth salinity maximum intrusion of warm salinity slope water extending onto the continental shelf south of New England. This was done through the use of Autonomous Underwater Vehicles (two REMUS 100 vehicles and one Tethys class AUV (Long Range AUV or LRAUV)), a towed Rockland Scientific Vertical Microstructure Profiler (VMP 250), and ship-board CTD and ADCP measurements. More details about the processing, data coverage, and usage can be found in the accompanying manuscript. This cruise took place on the shelf waters south of Cape Cod, MA, extending to the shelf break, with all of the data collected between 40°N to 41°N and 71.5°W to 70°W. Attached is a data map showing the location of all data included within this dataset.

File Descriptions: Datamap.jpg A map of the locations of all data included within this dataset.

CTD_summer2021.mat
This file contains profiles from the ship-board CTD (SeaBird 911+). Raw data was processed and gridded into 1 decibar bins using standard procedures in Seasave V 7.26.7.121 (Look at cnv file header for details about processing). This file is organized as a structure, with each variable in the data being a different field called by dot notation and each row with the structure being a different CTD profile. Biooptical variables are not quality-controlled.
CTD.time: the time of each profile in the MATLAB datetime format (from the processed SeaBird header file) in GMT CTD.lon: degrees longitude of the profile (from the processed SeaBird header file) CTD.lat: degrees latitude of the profile (from the processed SeaBird header file) CTD.pres: the pressure in decibar at each location of the profile
CTD.sal: the seawater practical salinity in psu
CTD.temp: the seawater in-situ temperature in °C
CTD.flor: seawater fluorescence in mg/ m3 CTD.depth: depth at each location within the profile in meters CTD.density: sigmatheta (the potential seawater density with respect to a reference pressure of 0 db) in kg.m3 minus 1,000kg/m3

CTD_Darter_MMMdd.mat and CTD_Edgar_MMMdd.mat These files contain the data from the REMUS 100 missions, with Darter and Edgar being the two different REMUS 100 vehicles.
Conductivity: conductivity in mS/cm
Depth: depth in meters Latitude: degrees latitude
Longitude: degrees longitude Mission_number: the number of the REMUS mission Mission_time: time during the mission in seconds since midnight in GMT
Salinity: the seawater practical salinity in psu
Sound_speed: the sound speed in m/s
Temperature: the seawater temperature in °C

LRAUV_20210623T194917.mat and LRAUV_20210624T145829.mat These files contain data from the Tethys Class LRAUV (Long Range AUV) missions. Each file contains 10 structure variables.
CTD_Seabird: structure containing the bin median temperature in °C and salinity in PSU.
depth: the depth at each data point in meters.
fix_residual_percent_distance_traveled: underwater dead-reckoned navigation error (based on GPS fix when on surface) as a percentage of distance traveled latitude: Latitude at each data point (not corrected for vehicle drift in underwater current)
latitude_fix: latitude of GPS fix (vehicle surfaced) longitude: Longitude at each data point (not corrected for vehicle drift in underwater current) longitude_fix: longitude of GPS fix (vehicle surfaced) platform_battery_charge: The battery charge in ampere-hour
time_fix: time in seconds since January 1, 1970 (epoch time)

VMPtransact_YYYYMMdd.mat Vertical Microstructure Profiler (Rockland Scientific VMP 250) These files contain the processed data for each Vertical Microstructure Profiler (Rockland Scientific VMP 250) transect, consisting of multiple profiles. Data has been gridded on a 1 decibar equidistant grid using standard procedures in Rockland Scientific’s processing software. Note: Bio-optical variables and dissipation rates have not been quality-controlled.
Time: Time in MATLAB datenum format (days since 0000-00-00 00:00:00) in GMT z: Pressure in decibar T: in-situ temperature in degC cnd: conductivity in mS/cm
Chl: Chlorophyll from fluorescence in mg/ m3 turb: Turbidity in NTU eps: dissipation rate inferred from microstructure shear in m^2/s^3. (Note: Dissipation estimates come from standard fitting of microstructure data within a 1 decibar bin to a turbulence spectrum within Rockland Scientific’s standard processing. The dissipation data in the provided files has not been quality-controlled. VMP-data was georeferenced by comparing the time stamps of VMP and processed ADCP files.

ADCP_ar50_wh300.mat This file contains the data from the shipboard ADCP (Teledyne WH300 kHz). ADCP data was processed aboard using standard procedures in UHDAS/CODAS (University of Hawaii Technical Services Program, s…

The Maine Silver Jackets team developed a set of coastal flood risk data layers to support local resilience planning. The USACE New England District modeled three different storm intensities (10-yr, 25-yr, and 100-yr) based on historic analogues (Patriot's Day Storm of April 15-17, 2007; Bomb Cyclone of Jan 4-6, 2018; and the Blizzard of Feb 4-10, 1978), and four different sea level heights (0, 1.5, 3.0, 3.9, and 8.8 feet above current mean higher-high water, NTDE 1983-2001). Tides, sea level rise scenarios, and riverine discharges were added as boundary conditions to the Coastal Modeling System framework (CMS-Flow), a hydrodynamic model solving for depth-averaged circulation. This was coupled with a spectral wave transformation model, CMS-Wave, to compute wave statistics and account for wave setup within the model domain. The resulting model information was provided as sets of nodes, or points, representing grid cell centroids (easting and northing) and maximum water level values referenced to NAVD88. These sets of model nodes were post-processed by NOAA to generate a single set of clean, consistent, merged, and spatially-referenced points. The points were used to interpolate raster-based water surface elevation data which became the basis for inundation mapping. High resolution lidar-derived elevation data were compiled with breaklines representing shorelines and waterfront infrastructure to generate a physical representation against which the storm-generated water surface elevation data were compared. Inundation depth grids and flood extent polygons were produced as final data layers.

This storymap visualizes data from Piping Plovers that were tagged at nesting areas in southern New England and tracked during fall migration using the Motus network (www.motus.org). The storymap is available at the following link: https://storymaps.arcgis.com/stories/5bab01fc5fa445f58ee54c062b4d2f3dExplore the map below to see how Piping Plovers take flight and make their away across the Atlantic--sometimes flying as fast as 80 km an hour. For migrating plovers, wind and weather conditions play an important role in their flight departures; and stopover sites in the Mid-Atlantic provide critical habitat for rest and refueling. Here in this map, you can look at how nano-tagged Piping Plovers from Rhode Island and Massachusetts timed their migration flights with wind conditions.The storymap is available at the following link: https://storymaps.arcgis.com/stories/5bab01fc5fa445f58ee54c062b4d2f3dStory Map Created by Alex Cook, USFWS Directorate Fellowship Program 2020 Cohort

The 2022 cartographic boundary shapefiles are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. Divisions are groupings of states within a census geographic region, established by the Census Bureau for the presentation of census data. The current nine divisions (East North Central, East South Central, Middle Atlantic, Mountain, New England, Pacific, South Atlantic, West North Central, and West South Central) are intended to represent relatively homogeneous areas that are subdivisions of the four census geographic regions.

This storymap visualizes data from Piping Plovers that were tagged at nesting areas in southern New England and tracked during fall migration using the Motus network (www.motus.org). The storymap is available at the following link: https://storymaps.arcgis.com/stories/5bab01fc5fa445f58ee54c062b4d2f3dExplore the map below to see how Piping Plovers take flight and make their away across the Atlantic--sometimes flying as fast as 80 km an hour. For migrating plovers, wind and weather conditions play an important role in their flight departures; and stopover sites in the Mid-Atlantic provide critical habitat for rest and refueling. Here in this map, you can look at how nano-tagged Piping Plovers from Rhode Island and Massachusetts timed their migration flights with wind conditions.The storymap is available at the following link: https://storymaps.arcgis.com/stories/5bab01fc5fa445f58ee54c062b4d2f3dStory Map Created by Alex Cook, USFWS Directorate Fellowship Program 2020 Cohort

The USGS, in cooperation with NOAA, is producing detailed maps of the seafloor off southern New England. The current phase of this cooperative research program is directed toward analyzing how bathymetric relief relates to the distribution of sedimentary environments and benthic communities. As part of this program, digital terrain models (DTMs) from bathymetry collected as part of NOAA's hydrographic charting activities are converted into ESRI raster grids and imagery, verified with bottom sampling and photography, and used to produce interpretations of seabed geology and hydrodynamic processes. Although each of the 7 continuous-coverage, completed surveys individually provides important benthic environmental information, many applications require a geographically broader perspective. For example, the usefulness of individual surveys is limited for the planning and construction of cross-Sound infrastructure, such as cables and pipelines, or for the testing of regional circulation models. To address this need, we integrated the 7 contiguous multibeam bathymetric DTMs into one dataset that covers much of Block Island Sound. The new dataset is adjusted to mean lower low water, is provided in UTM Zone 19 NAD83 and geographic WGS84 projections, and is gridded to 4-m resolution. This resolution is adequate for seafloor-feature and process interpretation, but small enough to be queried and manipulated with standard GIS programs and to allow for future growth. Natural features visible in the grid include boulder lag deposits of submerged moraines, sand-wave fields, and scour depressions that reflect the strength of the oscillating tidal currents. Bedform asymmetry allows interpretations of net sediment transport. Together the merged data reveal a larger, more continuous perspective of bathymetric topography than previously available, providing a fundamental framework for research and resource management activities off this portion of the Rhode Island coast.

This Feature Class was created in 2014 as a part of the State of Connecticut’s Policy Intergovernmental Policy Division grant to the Southern Connecticut Regional Council of Governments for the Regional Web-Based GIS program.The development of the parcel layer was started in 1998-1999 by East Coast Mapping of New Hampshire. East Coast created CAD Drawings for the Town of Wallingford generated through the digitization of Town of Wallingford’s Tax Maps. By use of stereoscopic techniques East Coast created a seamless parcel base from a 2000 aerial flight’s orthophoto’s (1x600ft scale). The CAD files and base were then given to the Wallingford’s Town Engineer who maintained the base. New England Geosystems of Middletown, CT received the CAD files from Wallingford in 2014 and converted the files to GIS format to create the parcel layer. Last Updated: April 2019

This map shows the boundaries of regions, shires and municipalities as from 1 January 1967. The map is annotated to show the areas transferred from the Namoi to the New England Region.

The scale is 48 miles = 7/8 inch.


(SR Map No.52734). 1 map.

Note:
This description is extracted from Concise Guide to the State Archives of New South Wales, 3rd Edition 2000.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. Mapping vegetation of the Appalachian National Scenic Trail (APPA, also referred to as the “AT corridor” for the Appalachian Trail corridor) involved the following six primary steps: (1) preliminary map classification with a vegetation primer for each APPA project area, (2) field reconnaissance for each APPA project area, (3) map classification by each APPA project area, (4) aerial image interpretation and mapping by each APPA project area, (5) compilation of a final classification and map layer covering the entire AT corridor following accuracy assessment (AA), and (6) database development of the map layer. Although these steps proceeded sequentially, they overlap to some degree. Steps 1–4 proceeded sequentially by APPA project area starting in the Southern Blue Ridge (SBR) project area in 2010, moving north to the Central Appalachian (CAP) project area in 2011, then to the Lower New England (LNE) project area in 2012, and ending in the Northern Appalachian (NAP) project area in 2013. (See Figures 6–9 in the “Introduction and Project Overview” section of this report for detailed locations of the four APPA project areas.) Steps 5 and 6 compiled all APPA project areas into a contiguous map classification and map layer. Summary reports generated from the vegetation map layer of the map classes representing USNVC natural (including ruderal) vegetation types apply to 28,242 polygons (92.9% of polygons) and cover 106,413.0 ha (95.9%) of the map extent for APPA. The map layer indicates APPA to be 92.4% forest and woodland (102,480.8 ha), 1.7% shrubland (1866.3 ha), and 1.8% herbaceous cover (2,065.9 ha). Map classes representing park-special vegetation (undefined in the USNVC) apply to 58 polygons (0.2% of polygons) and cover 404.3 ha (0.4%) of the map extent. Map classes representing USNVC cultural types apply to 1,777 polygons (5.8% of polygons) and cover 2,516.3 ha (2.3%) of the map extent. Map classes representing nonvegetated water (non-USNVC) apply to 332 polygons (1.1% of polygons) and cover 1,586.2 ha (1.4%) of the map extent.

This resource is available as a downloadable file, ESRI Service, and as a Web Map service. Bedrock map, Tectonic map, Cross Section image, and a text on the Bedrock geology for the the Lunenburg-Brunswick-Guildhall area, Vermont.Abstract: The Lunenburg-Brunswick-Guildhall area is situated in the southern half of Essex County in northeastern Vermont and belongs to the western portion of the White Mountain section of the New England physiographic province. About 70 per cent of the area's bedrock consists of various metasediments, clearly indicating increased intensity of regional metamorphism toward the northwest. Metasedimentary rock units of both the 'New Hampshire sequence 'and 'Vermont sequence 'are present. They are separated by the controversial Monroe fault which strikes northeasterly from Granby to the Connecticut River in southern Brunswick.

The Maine Silver Jackets team developed a set of coastal flood risk data layers to support local resilience planning. The USACE New England District modeled three different storm intensities (10-yr, 25-yr, and 100-yr) based on historic analogues (Patriot's Day Storm of April 15-17, 2007; Bomb Cyclone of Jan 4-6, 2018; and the Blizzard of Feb 4-10, 1978), and four different sea level heights (0, 1.5, 3.0, 3.9, and 8.8 feet above current mean higher-high water, NTDE 1983-2001). Tides, sea level rise scenarios, and riverine discharges were added as boundary conditions to the Coastal Modeling System framework (CMS-Flow), a hydrodynamic model solving for depth-averaged circulation. This was coupled with a spectral wave transformation model, CMS-Wave, to compute wave statistics and account for wave setup within the model domain. The resulting model information was provided as sets of nodes, or points, representing grid cell centroids (easting and northing) and maximum water level values referenced to NAVD88. These sets of model nodes were post-processed by NOAA to generate a single set of clean, consistent, merged, and spatially-referenced points. The points were used to interpolate raster-based water surface elevation data which became the basis for inundation mapping. High resolution lidar-derived elevation data were compiled with breaklines representing shorelines and waterfront infrastructure to generate a physical representation against which the storm-generated water surface elevation data were compared. Inundation depth grids and flood extent polygons were produced as final data layers.

The surficial geologic map of the Eastern and Central United States depicts the areal distribution of surficial geologic deposits and other materials that accumulated or formed during the past 2+ million years, the period that includes all activities of the human species. These materials are at the surface of the earth. They make up the "ground" on which we walk, the "dirt" in which we dig foundations, and the “soil” in which we grow crops. Most of our human activity is related in one way or another to these surface materials that are referred to collectively by many geologists as regolith, the mantle of fragmental and generally unconsolidated material that overlies the bedrock foundation of the continent. The map is based on 31 published maps in the U.S. Geological Survey's Quaternary Geologic Atlas of the United States map series (U.S. Geological Survey Miscellaneous Investigations Series I-1420). It was compiled at 1:1,000,000 scale, to be viewed as a digital map at 1:2,000,000 nominal scale and to be printed as a conventional paper map at 1:2,500,000 scale. This map is not a map of soils as recognized and classified in agriculture. Rather, it is a generalized map of soils as recognized in engineering geology, or of substrata or parent materials in which agricultural, agronomic, or pedologic soils are formed. Where surficial deposits or materials are thick, agricultural soils are developed only in the upper part of the engineering soils. Where they are very thin, agricultural soils are developed through the entire thickness of a surficial deposit or material. The surficial geologic map provides a broad overview of the areal distribution of surficial deposits and materials. It identifies and depicts more than 150 types of deposits and materials. In general, the map units are divided into two major categories, surface deposits and residual materials. Surface deposits are materials that accumulated or were emplaced after component particles were transported by ice, water, wind, or gravity. The glacial sediments that cover the surface in much of the northern United States east of the Rocky Mountains are in this category, as are the gravel, sand, silt, and clay that were deposited in past and present streams, lakes, and oceans. In contrast, residual materials formed in place, without significant transport of component particles by ice, water, wind, or gravity. They are products of modification or alteration of pre-existing surficial deposits, surficial materials, or bedrock. For example, intense weathering of solid rock, or even stream deposits, by chemical processes may produce a residual surficial material that is greatly transformed from its original physical and chemical state. In recent years, surficial deposits and materials have become the focus of much interest by scientists, environmentalists, governmental agencies, and the general public. They are the foundations of ecosystems, the materials that support plant growth and animal habitat, and the materials through which travels much of the water required for our agriculture, our industry, and our general well being. They also are materials that easily can become contaminated by pesticides, fertilizers, and toxic wastes. In this context, the value of the surficial geologic map is evident The map and its digital database provide information about four major aspects of the surficial materials, through description of more than 150 types of materials and depiction of their areal distribution. The map unit descriptions provide information about (1) genesis (processes of origin) or environments of deposition (for example, deposits related to glaciation (glacial deposits), flowing water (alluvial deposits), lakes (lacustrine deposits), wind (eolian deposits), or gravity (mass-movement deposits)), (2) age (for example, how long ago the deposits accumulated or were emplaced or how long specific processes have been acting on the materials), (3) properties (the chemical, physical, and mechanical or engineering characteristics of the materials), and (4) thickness or depth to underlying deposits or materials or to bedrock. This approach provides information appropriate for a broad user base. The map is useful to national, state, and other governmental agencies, to engineering and construction companies, to environmental organizations and consultants, to academic scientists and institutions, and to the layman who merely wishes to learn more about the materials that conceal the bedrock. The map can facilitate regional and national overviews of (1) geologic hazards, including areas of swelling clay and areas of landslide deposits and landslide-prone materials, (2) natural resources, including aggregate for concrete and road building, peat, clay, and shallow sources for groundwater, and (3) areas of special environmental concern, i... Visit https://dataone.org/datasets/d863e647-d00d-4994-89bc-be4be9d4adf0 for complete metadata about this dataset.

AbstractCatchment Scale Land Use of Australia (CLUM) depicted as five broad agricultural industries - Grazing native vegetation, Grazing modified pastures, Cropping, Horticulture, and Intensive plant and animal industries (and other non-agricultural uses).The Catchment Scale Land Use of Australia – Update December 2023 version 2 dataset is the national compilation of catchment scale land use data available for Australia, as at December 2023. It replaces the Catchment Scale Land Use of Australia – Update December 2020.The Catchment Scale Land Use of Australia – Update December 2023 version 2 dataset is the national compilation of catchment scale land use data available for Australia (CLUM), as at December 2023. It replaces the Catchment Scale Land Use of Australia – Update December 2020.Land use is classified according to the Australian Land Use and Management (ALUM) Classification version 8. It has been compiled from vector land use datasets collected as part of state and territory mapping programs and other authoritative sources, through the Australian Collaborative Land Use and Management Program (ACLUMP). Catchment scale land use data was produced by combining land tenure and other types of land use information including, fine-scale satellite data, ancillary datasets, and information collected in the field.The date of mapping (2008 to 2023) and scale of mapping (1:5,000 to 1:250,000) vary, reflecting the source data, capture date and scale. Date and scale of mapping are provided in supporting datasets.CurrencyDate modified: June 2024Modification frequency: As requiredData extentSpatial extentNorth: -8.03°South: -45.5°East: 161.5°West: 105.7°Source informationData, Metadata, Maps and Interactive views are available from Catchment Scale Land Use of Australia - Update December 2023Catchment Scale Land Use of Australia - Update December 2023 – Descriptive metadataThe data was obtained from Department of Agriculture, Fisheries and Forestry - Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES). ABARES is providing this data to the public under a Creative Commons Attribution 4.0 license.Lineage statementABARES has produced this raster dataset from vector catchment scale land use data provided by state and territory agencies, as follows:Catchment Scale Land Use Mapping for the Australian Capital Territory 20122017 NSW Land Use v1.5Land Use Mapping Project of the Northern Territory, 2016 – 2022 (LUMP)Land use mapping – 2021 – Great Barrier Reef NRM regionsLand use mapping – 1999 to Current – Queensland (June 2019)[South Australia] Land Use (ACLUMP) (2017)Tasmanian Land Use 2022Victorian Land Use Information System [VLUIS] 2021-22Catchment Scale Land Use Mapping for Western Australia 2018Australian Tree Crops, Australian Protected Cropping Structures and Queensland Soybean Crops maps (as at 30 November 2023)Applied Agricultural Remote Sensing Centre (AARSC), University of New England.Links to land use mapping datasets and metadata are available at the ACLUMP data download page at agriculture.gov.au.State and territory vector catchment scale land use data were produced by combining land tenure and other types of land use information, fine-scale satellite data and information collected in the field, as outlined in 'Guidelines for land use mapping in Australia: principles, procedures and definitions, 4th edition' (ABARES 2011). The Northern Territory, Queensland, South Australia, Tasmania, Victoria and Western Australia were mapped to version 8 of the ALUM classification (‘The Australian Land Use and Management Classification Version 8’, ABARES 2016).The Australian Capital Territory was mapped to version 7 of the ALUM classification and converted to version 8 using a look-up table based on Appendix 1 of ABARES (2016).The following agricultural (excluding intensive uses) classes were included from the Queensland Great Barrier Reef NRM Regions 2021 modified ALUM classification schema dataset:2.2.0 Grazing native vegetation3.2.0 Grazing modified pastures3.3.0 Cropping3.3.5 Sugar3.4.0 Perennial horticulture3.4.1 Tree fruits3.5.0 Seasonal horticulture3.6.0 Land in transition4.2.0 Grazing irrigated modified pastures4.3.0 Irrigated cropping4.3.5 Irrigated sugar4.4.0 Irrigated perennial horticulture4.4.1 Irrigated tree fruits4.5.0 Irrigated seasonal horticulture4.6.0 Irrigated land in transitionFixes to known issues include:In Western Australia, ALUM classes 4.0.0 Production from Irrigated Agriculture and Plantations, 5.0.0 Intensive Uses and 6.0.0 Water have been attributed to secondary level by visual interpretation using satellite data.In South Australia, through consultation with the South Australian Department of Environment and Water, the mining area (ALUM class 5.8.0 Mining) within mining tenements is more accurately delineated. The area within mining tenements that is not used for mining is now attributed as grazing of native vegetation (ALUM class 2.1.0) within pastoral areas and residual native cover (ALUM class 1.3.3) outside of pastoral areas.NODATA voids in Adelaide, South Australia were filled with data from mesh block land use attributes (Australian Bureau of Statistics 2021) according to Table 8. All other NODATA voids were filled using the ESRI ArcGIS focal statistics command.For the purposes of web viewing, the data was reprojected to EPSG:3857 - Web Mercator.Land use classificationThe Australian Land Use and Management (ALUM) Classification version 8 is a three-tiered hierarchical structure. There are five primary classes, identified in order of increasing levels of intervention or potential impact on the natural landscape. Water is included separately as a sixth primary class. Primary and secondary levels relate to the principal land use. Tertiary classes may include additional information on commodity groups, specific commodities, land management practices or vegetation information. The primary, secondary and tertiary codes work together to provide increasing levels of detail about the land use. Land may be subject to concurrent uses. For example, while the main management objective of a multiple-use production forest may be timber production, it may also provide conservation, recreation, grazing and water catchment land uses. In these cases, production forestry is commonly identified in the ALUM code as the prime land use.Table 1: Agricultural Industries classification symbology as RGB and hexadecimal colour valuesVALUE (ALUM)AGINDRedGreenBlueHex210Grazing native vegetation217214207#D9D6CF320; 321; 322; 323; 324; 325; 360; 361; 362; 363; 364; 365; 420; 421; 422; 423; 424; 460; 461; 462; 463; 464; 465Grazing modified pastures20521370#CDD546330; 331; 332; 333; 334; 335; 336; 337; 338; 430; 431; 432; 433; 434; 435; 436; 437; 438; 439Cropping11413626#72881A340; 341; 342; 343; 344; 345; 346; 347; 348; 350; 351; 352; 353; 440; 441; 442; 443; 444; 445; 446; 447; 448; 449; 450; 451; 452; 453; 454Horticulture23000#E60000510; 511; 512; 513; 514; 515; 520; 521; 522; 523; 524; 525; 526; 527; 528Intensive plant and animal industries115223255#73DFFF110; 111; 112; 113; 114; 115; 116; 117; 120; 121; 122; 123; 124; 125; 130; 131; 132; 133; 134; 220; 221; 222, 310; 311; 312; 313; 314; 410; 411; 412; 413; 414; 530; 531; 532; 533; 534; 535; 536; 537; 538; 540; 541; 550; 551; 552; 553; 554; 555; 560; 561; 562; 563; 564; 565; 566; 567; 570; 571; 572; 573; 574; 575; 542; 543; 544; 545; 580; 581; 582; 583; 584; 590; 591; 592; 593; 594; 595; 610; 611; 612; 613; 614; 620; 621; 622; 623; 630; 631; 632; 633; 640; 641; 642; 643; 650; 651; 652; 653; 654; 660; 661; 662; 663Other uses255255255#FFFFFFNote: Codes refer to the Australian Land Use and Management (ALUM) Classification, version 8. Data dictionaryAttribute nameDescriptionOIDInternal feature number that uniquely identifies each row.VALUEALUM code as a three digit integer. First digit is primary code, second digit is secondary code, and third digit is tertiary code.COUNTCount of the number of raster cells in each class of VALUE.LU_CODEV8ALUM code as a string.LU_V8NALUM code as a three digit integer. First digit is primary code, second digit is secondary code, and third digit is tertiary code.TERTV8ALUM tertiary code and description as a string.SECV8ALUM secondary code and description as a string.PRIMV8ALUM primary code and description as a string.SIMPNCode for simplified land use classification.SIMPDescription of the simplified land use classification.AGINDDescription of agricultural industries.Red, Green, BlueRGB values for classification colours ContactDepartment of Agriculture, Fisheries and Forestry (ABARES), info.ABARES@aff.gov.au

The uncertainty around the impacts of changing climate poses a significant challenge to sustaining forest ecosystems in the Northeast. Important work has been done downscaling projected changes in climate conditions, modeling shifts in suitable habitat, and mapping disturbance patterns across the region. The goal of this project is to aggregate these valuable but disparate spatial data sets to quantify relative exposure to climate change impacts at the species, and community level. The resulting climate exposure maps provide insight to how the degree of potential risk exposure vary across the landscape an across species. Results indicate that at the stand level, highest overall exposure to climate, disturbance, and limitations in suitable habitat for current species distributions occurs in mountainous regions throughout the region and southeastern Maine. Across the region relative exposure increases by 4 percent between low and high emission scenarios. Much of our current management is guided by the outcomes of decades of silviculture research, yet many of the conditions under which those results were generated are rapidly changing. These relative exposure maps can inform where climate adaptation management applications may be most necessary over time.