Integrated terrain models covering 16,357 square kilometers of the Massachusetts coastal zone and offshore waters were built to provide a continuous elevation and bathymetry terrain model for ocean planning purposes. The area is divided into the following four geographical areas to reduce file size and facilitate publishing: Massachusetts Bay from the Massachusetts-New Hampshire border south to Provincetown and Scituate and east to Stellwagen Bank; Cape Cod Bay from Provincetown to Scituate and south to Hyannis; Buzzards Bay from the Cape Cod Canal southwest to the State border including the Elizabeth Islands and extending north to Fall River and Mount Hope Bay; and Nantucket and Vineyard Sounds, from Hyannis south to the border of the Massachusetts Coastal zone approximately 8 kilometers south of Nantucket. A Triangulated Irregular Network was created from public-domain bathymetric and LiDAR data using the ArcGIS terrain-model framework and then interpolated into a 32-bit GeoTiff of 10 meter resolution. The grids for each of the four geographical areas are referenced to the Universal Transverse Mercator, Zone 19, North American Datum of 1983 coordinate system, and the North American Vertical Datum of 1988. A polygon shapefile recording the source datasets accompanies each of the four grids.

This polygon layer represents the ten USA and Mexico states addressed by the U.S.-Mexico Border Program. States are sourced from Esri's ArcGIS Online: USA States (Generalized) Living Atlas Layer: https://www.arcgis.com/home/item.html?id=99fd67933e754a1181cc755146be21ca These data support the U.S.-Mexico Border Program Map, which highlights the projects funded through the Border 2020 Program (2013-2020) in both Region 9 and Region 6 of the U.S. EPA, including U.S. Federally recognized Tribal communities and states of Texas, New Mexico, Chihuahua, Nuevo Leon, Tamaulipas, Coahuila, California, Baja California, Sonora, and Arizona within 62 miles (100 kilometers) of the U.S.-Mexico Border. The projects stem from the Border 2020 framework that has five goals to reduce air pollution, improve access to clean water, promote materials and waste management, improve emergency preparedness, and enhance environmental stewardship, and fundamental strategies that includes children's health and environmental education and outreach. This layer is tied to the U.S.-Mexico Border Program with EPA and is relevant to past and future programs.

This feature represents the entire area considered as the U.S.-Mexico border region for the U.S.-Mexico Border Program. The extent of the area was agreed upon between the United States and Mexico under the 1983 La Paz Agreement. The framework of this agreement stated that the border area is defined as "the area situated 62 miles (100 kilometers) on either side of the inland and maritime boundaries". This layer was generated by buffering the Homeland Infrastructure Foundation-Level Data (HIFLD) "Mexico and US Border" layer (https://hifld-geoplatform.opendata.arcgis.com/datasets/geoplatform::mexico-and-us-border/about) by [60] Miles. These data support the U.S.-Mexico Border Program Map, which highlights the projects funded through the U.S.-Mexico Border Program (2013-2020) in both Region 9 and Region 6 of the U.S. EPA, including U.S. Federally recognized Tribal communities and states of Texas, New Mexico, Chihuahua, Nuevo Leon, Tamaulipas, Coahuila, California, Baja California, Sonora, and Arizona within 62 miles (100 kilometers) of the U.S.-Mexico Border. The projects stem from the Border 2020 framework that has five goals to reduce air pollution, improve access to clean water, promote materials and waste management, improve emergency preparedness, and enhance environmental stewardship, and fundamental strategies that includes children's health and environmental education and outreach. For more information about Border 2020 and/or current U.S.-Mexico Border program visit this website: https://www.epa.gov/usmexicoborder

This sub layer displays the proposal to: “Create 361 acres of cutback borders by cutting or girdling trees to feather edges of the Elleber range allotment and Ramshorn area. Provide habitat for federally-endangered Indiana bats by retaining a minimum of six snags per acre across the habitat complexes.”

Purpose:

This data was created by the U.S. Forest Service staff for use in analysis of the project’s likely environmental impacts.

Source & Date:

Deer Creek Integrated Resource Project Scoping Information: 4.4. Data was downloaded from the project website ( https://www.fs.usda.gov/project/?project=60882 ) on 11/17/2022.

Processing:

ABRA symbolized the layer using the project’s scoping maps as a guide. This and other project layers were published together from ArcMap as a Feature Service.

Symbology:

• Cutback Borders: Blue dashed polygon

The Inflation Reduction Act of 2022 (IRA) became law on August 8, 2022. Under the law, new qualifying renewable and/or carbon-free electricity generation projects constructed in certain areas of the US, called energy communities, are eligible for bonus worth an additional 10% to the value of the production tax credit or a 10 percentage point increase in the value of the investment tax credit. The IRA does not explicitly map or list these specific communities. Instead, eligible communities are defined by a series of qualifications:

1. a brownfield site,
2. a metropolitan statistical area (MSA) or non-metropolitan statistical area with either (a) 0.17% or greater employment or (b) 25% or greater local tax revenues related to the extraction, processing, transport, or storage of coal, oil, or natural gas; and an unemployment rate at or above the national average for the previous year, or
3. a census tract containing or adjacent to (a) a coal mine closed after December 31, 1999 or (b) a coal-fired electric generating unit retired after December 31, 2009.

These maps and data layers contain GIS data for coal mines, coal-fired power plants, fossil energy related employment, and brownfield sites. Each record represents a point, tract or metropolitan statistical area and non-metropolitan statistical area with attributes including plant type, operating information, GEOID, etc. The input data used includes:

1. Brownfields – Source: EPA. No analysis was performed on this data layer. However, tract polygon layers have a column denoting brownfield presence (0 for no brownfield site, 1 if the tract contains a brownfield somewhere within the polygon).
2. Eligible Employment MSAs (“Final_Employment_Qualifying_MSAs”) – Source: US Census County Business Patterns. MSAs and non-MSA regions with employment over 0.17% in the fossil fuel industry (defined here as NAICS codes 211, 2121, 213, 23712, 324, 4247, and 486) and unemployment greater than or equal to 3.9% (the average national unemployment rate in 2021, according to the Bureau of Labor Statistics).

--Possibly Eligible MSAs (“FossilFuel_Employment_Qualifying_MSAs”) are MSA and non-MSA regions that meet or exceed the 0.17% employment in the fossil fuel industry threshold but do not exceed the unemployment threshold.

--Relevant columns include:

a) SUM_nhgis0: Total employment in 2020.

b) SUM_nhgis1: Total unemployment in 2020.

c) P_Unemp: Percent unemployment in 2020.

d) Q_Unemp: Boolean column indicating if the MSA or non-MSA’s unemployment rate is at or above the national average of 3.9%.

e) FF_Qual: Boolean column indicating if the MSA or non-MSA had employment in the fossil fuel industry at or above 0.17% in the past 11 years.

f) final_Qual: Boolean column indicating if an MSA or non-MSA qualifies for both unemployment rate and fossil fuel employment under the IRA.

1. Retired Power Plants – Source: EIA via HFLID. Qualifying power plants were selected by use of coal in at least one generator, and if they were retired (RET_DATE) on or after January 1, 2010. This data goes through December 2021.

--Adjacent tract data was derived by Cecelia Isaac using ESRI ArcGIS Pro.

1. Abandoned Coal Mines – Source: MSHA. Mines labeled “Abandoned”, “Abandoned and Sealed” or “NonProducing” between January 1, 2000 and September 2022.

--Adjacent tract data was derived by Cecelia Isaac using ESRI ArcGIS Pro.

5) US State Borders– Source: IPUMS NHGIS.

Also included here are polygon shapefiles for Onshore Wind and Solar Candidate Project Areas from Princeton REPEAT. These files have been updated to include columns related to the energy communities.

New columns include:

1. CoalPlantTract: Boolean column indicating if the CPA is within a tract that qualifies because of a retired coal plant.
2. CoalMineTract: Boolean column indicating if the CPA is within a tract that qualifies because of a closed coal mine.
3. FossilFuelEmp: Boolean column indicating if the CPA is within an MSA or non-MSA with greater than or equal to 0.17% employment in the fossil fuel industry.
4. UnempQualification: Boolean column indicating if the CPA is within an MSA or non-MSA with greater than or equal to 0.17% employment in the fossil fuel industry.
5. MSA_non_to: The code of the MSA or non-MSA area that contains the CPA.
6. P_Unemp: The percent unemployment of the MSA or non-MSA that contains the CPA in 2021.

Integrated terrain models covering 16,357 square kilometers of the Massachusetts coastal zone and offshore waters were built to provide a continuous elevation and bathymetry terrain model for ocean planning purposes. The area is divided into the following four geographical areas to reduce file size and facilitate publishing: Massachusetts Bay from the Massachusetts-New Hampshire border south to Provincetown and Scituate and east to Stellwagen Bank; Cape Cod Bay from Provincetown to Scituate and south to Hyannis; Buzzards Bay from the Cape Cod Canal southwest to the State border including the Elizabeth Islands and extending north to Fall River and Mount Hope Bay; and Nantucket and Vineyard Sounds, from Hyannis south to the border of the Massachusetts Coastal zone approximately 8 kilometers south of Nantucket. A Triangulated Irregular Network was created from public-_domain bathymetric and LiDAR data using the ArcGIS terrain-model framework and then interpolated into a 32-bit GeoTiff of 10 meter resolution. The grids for each of the four geographical areas are referenced to the Universal Transverse Mercator, Zone 19, North American Datum of 1983 coordinate system, and the North American Vertical Datum of 1988. A polygon shapefile recording the source datasets accompanies each of the four grids.

Integrated terrain models covering 16,357 square kilometers of the Massachusetts coastal zone and offshore waters were built to provide a continuous elevation and bathymetry terrain model for ocean planning purposes. The area is divided into the following four geographical areas to reduce file size and facilitate publishing: Massachusetts Bay from the Massachusetts-New Hampshire border south to Provincetown and Scituate and east to Stellwagen Bank; Cape Cod Bay from Provincetown to Scituate and south to Hyannis; Buzzards Bay from the Cape Cod Canal southwest to the State border including the Elizabeth Islands and extending north to Fall River and Mount Hope Bay; and Nantucket and Vineyard Sounds, from Hyannis south to the border of the Massachusetts Coastal zone approximately 8 kilometers south of Nantucket. A Triangulated Irregular Network was created from public-domain bathymetric and LiDAR data using the ArcGIS terrain-model framework and then interpolated into a 32-bit GeoTiff of 10 meter resolution. The grids for each of the four geographical areas are referenced to the Universal Transverse Mercator, Zone 19, North American Datum of 1983 coordinate system, and the North American Vertical Datum of 1988. A polygon shapefile recording the source datasets accompanies each of the four grids.

Abstract This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. The Border Rivers Gwydir and …Show full descriptionAbstract This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. The Border Rivers Gwydir and Namoi Regional Vegetation Map is a subset of the statewide vegetation mapping and classification program undertaken by the NSW Office of Environment and Heritage (OEH Regional Scale State Vegetation Map) and covers the two former Catchment Management Authority Regions.

The primary thematic data layer in this dataset is a map of regional scale Plant Community Types (PCT's). The map was developed from a process using vegetation surveys, remote sensing derivations, visual interpretation and spatial distribution models.

The full dataset comprises the following data layers as delivered in an ArcGIS 9.3 File Geo-database:

PLANT COMMUNITY TYPE: The primary map of Plant Community Types developed from an ensemble of visual interpretation of high resolution imagery and spatial distribution models.

WOODY EXTENT LAYER: A map of woody vegetation derived from classification of 5m SPOT-5 imagery.

KEITH CLASS: A map based on aerial photo interpretation and spatial distribution models.

MAP SOURCE: A map of the various sources of information used including spatial models, visual interpretation and existing map products.

SURVEY DENSITY ALL: A map of the density of all survey sites used.

SURVEY DENSITY FULL FLORISTICS: A map of the density of only full floristic survey sites used.

MODELLING CONFIDENCE: A map of the confidence outcomes achieved.

While much of the aerial photo interpretation employed was undertaken at around 1:8000, PCT attribution is generally at a much coarser scale. The Map Source layer (as described above) can be used as a guide to how vegetation attribution was derived. We recommend that the highest resolution appropriate for this product be 1:15000.

Validation Summary:

PCT Map: Based on 100% of the survey data (modelling and hand mapping), the final mapped product has an accuracy in the range 68%-70% for prediction of the three most likely PCTs. Be aware that these accuracies are highly variable across each PCT. Some PCT's utilised more site data than others. Keith Class reached a 76% accuracy using the independent test data. Modelled PCT and modelled top 3 PCT overall accuracies were 53% and 68% respectively. Woody Extent received a 92% overall accuracy.

Accompanying documents: BRGNamoi Technical Notes.pdf - Technical Report BRGN_PCT_KC_LUT.xls - A look-up table listing the relationship between PCT, Keith Class and Keith Formation classifications.\ BRGNv2_Spatial_Layer_Descriptors.txt BRGN_V2.mxd Border Rivers Gwydir / Namoi Regional Native Vegetation Mapping Technical Notes Version 1.0. Reference: NSW Office of Environment and Heritage, 2015. BRG-Namoi Regional Native Vegetation Mapping. Technical Notes, NSW Office of Environment and Heritage, Sydney, Australia.

The download package contains a "quick view" map composite of the study area only. The quick view maps are of PCT, Keith Class, Keith Form, Map Source and Modelling Confidence. They also show the broad-scale line work. For more detailed line work and woody percent per polygon, please refer to the full dataset.

For access queries regarding the full dataset, please contact: data.broker@environment.nsw.gov.au

BRG_Namoi_v2_0_E_4204. \ VIS_ID 4204 Purpose This dataset was developed as part of the OEH State Vegetation Map to provide government and community with regional-scale information about native vegetation. Dataset History A summary of the product's lineage is below. Please refer to the Technical Notes v1.0 for a detailed description of the methodologies and source datasets.

The PCT map was derived primarily using a spatial modeling approach augmented with high resolution aerial imagery (50cm ADS40) for visual interpretation and automated line-work derivation. \ In summary the process for PCT attribution involved the following:

1. Vegetation Survey and Classification: Existing floristic plot data comprised 9054 existing sites after data cleaning. A large number of gaps in existing survey coverage were evident and required further survey information. Stratification based on archive broad vegetation type mapping (Regional Vegetation Types; Eco Logical Australia 2008b) and gap analysis was undertaken to select locations for additional plot data collection. A total of 6013 additional rapid data points were collected. To allocate survey sites to PCTs, full floristic plots were analysed using a UPGMA clustering approach in Primer with significant groups identified using SIMPROF and species contributions for each resulting group calculated using SIMPER. The existing plot data were allocated across 258 PCTs.

2. Pattern Derivation: A multi-resolution segmentation algorithm was used to create image objects with low internal variation. Image objects represent patches of vegetation that can later be classified based on attributes such as crown cover, spectral response, or soil type. The segmentation parameters and scale was derived iteratively based on visual inspection. Vegetation patterns from existing stereoscopic aerial photo interpretation and those recognised in high spatial resolution imagery (ADS40) were used as a reference point. Segmentation was performed using ADS40, SPOT 5 and SRTM derived topographic indices. this process provided the line work for subsequent PCT attribution.

3. Visual attribution of Landscape Class: The purpose of attributing Landscape classes to polygons is to predetermine broad vegetation types for modelling purposes using remote sensing. These classes reduce the PCT options for any one polygon making the modeling more effective in its attribution with commensurate less computing effort/time. A landscape class was attributed to every polygon in the study area. Landscape classes were aided by reference to existing mapping. Corrections were made based on ADS40 with on-screen attribution. Every polygon was visually checked by an expert interpreter.

4. Modelling Envelopes:As a further constraint to modelling outcomes, spatial envelopes were used to constrain PCTs to a certain geographic range, reducing the amount of types competing within the model at any particular location. The constraints used were applied at different stages in the mapping process. The Keith Class (Keith 2004) models were constrained to particular IBRA (Interim Bioregionalisation of Australia v7; Commonwealth of Australia 2012) subregions, selected based on review of the literature and expert opinion. The type models were constrained to particular ranges of a topographic position index, again based on literature review and expert opinion. Not all types were constrained by topographic envelopes, as some were considered to be less correlated with particular topographic positions.

5. Spatial Distribution Modelling of Keith Classes and Plant Community Types. Modelling of Keith Class and PCT used a combination (ensemble) of Generalised Dissimilarity Model (GDM), Boosted Regression Trees (BRT), and a simple Nearest Neighbour model.A suite of candidate environmental predictor variables, including climate, geology, soil, geophysical data, and terrain indices, were compiled for use in the GDM and BRT models. A comprehensive list of these predictor variables can be found in the Technical Notes v1.0.

6. Uplifted API and Expert Editing: Vegetation communities from the Gwydir Wetlands and Floodplain Vegetation Map 2008 (Bowen & Simpson 2010) were spatially translated into the current line-work via a majority extent per polygon algorithm. The vegetation community mapping resulting from the aforementioned procedures was extensively edited on screen to correct attribution where there may have been for example existing API, missed vegetation, ecological anomalies, incorrect assignments, modelling noise and inclusion of late site data. The extent of each attribution source is delineated by the Map Source data layer provided in this dataset.

For further details on methodology and validation please refer to the Border Rivers Gwydir / Namoi Regional Native Vegetation Mapping Technical Notes Version 1.0. Reference: NSW Office of Environment and Heritage, 2015. BRG-Namoi Regional Native Vegetation Mapping. Technical Notes, NSW Office of Environment and Heritage, Sydney, Australia. Dataset Citation NSW Office of Environment and Heritage (2015) Border Rivers Gwydir / Namoi Regional Native Vegetation Map Version 2.0. VIS_ID 4204. Bioregional Assessment Source Dataset. Viewed 11 December 2018, http://data.bioregionalassessments.gov.au/dataset/b3ca03dc-ed6e-4fdd-82ca-e9406a6ad74a.

Ecological Response Units (ERUs) facilitate landscape analyses and planning. The framework represents all major ecosystem types of the southwest region, and represents a stratification of biophysical themes. ERUs are used to define historic/reference conditions within a mapping unit by integrating site potential (soil physical and chemical properties, geology, geomorphology, aspect, slope, climate variables, and geographic location), fire regime (historic and contemporary), neighboring vegetation communities, and seral state sequence.The process by which this dataset was created is as follows:The input data were forest Terrestrial Ecological Unit Inventory (TEUI) survey data that were crosswalked to the ERU list, ERU version 4, corrections to ERU version 4 utilizing a climate gradient to identify anomalous attribution, a collaborative review product with the University of Arizona’s Ecologist Jim Malusa, Integrated Landscape Assessment Project (ILAP) data, Regional Riparian Mapping Project (RMAP) data, and subclass information derived from an ILAP grid analysis. The data layers listed above were assembled in a hierarchical order starting with the ERU version 4 data through a series of geoprocessing updates. Highest confidence was placed on TEUI data and RMAP data so those datasets were burned into the product last.During key review periods by resource specialists, there were metrics built to analyze percent of ERUs that lay on the border of a climate gradient or a Southwest Biotic Community (Brown and Lowe). These metrics were used to inform attribution for ERUs that needed evaluation by the resource specialist. Additional processing was performed using the Eliminate tool to reduce the amount of “noise” in the dataset brought on by fragmented polygons smaller than a parametric minimum map unit. The minimum map unit was defined as 1 hectare for grassland, shrubland, forest, woodland, and great plains system types and 1 acre for riparian types. By virtue of the Eliminate tool, polygons that did not meet the minimum map unit were merged to a neighboring polygon that shared the largest boundary. RMAP data was used as an exclusion dataset and no changes were made to that via the Eliminate processing. Multiple QA/QC reviews were performed at many stages of the building of the layer and as products were being finalized. During these reviews, updates were made to the layer as needed.