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.

This raster dataset 'NVIS4_1_AUST_MVS_PRE_ALB' provides summary information on Australia's estimated pre-1750 native vegetation classified into Major Vegetation Subgroups. It is in Albers Equal Area projection with a 100 m x 100 m (1 Ha) cell size.

A comparable Extant (present) vegetation raster dataset is available:

• NVIS4_1_AUST_MVS_EXT_ALB.

State and Territory vegetation mapping agencies supplied a new version of the National Vegetation Information System (NVIS) in 2009-2011. Some agencies did not supply new data for this version but approved re-use of Version 3.1 data. Summaries were derived from the best available data in the NVIS extant theme as at June 2012.

This product is derived from a compilation of data collected at different scales on different dates by different organisations. Please refer to the separate key map showing scales of the input datasets. Gaps in the NVIS database were filled by non-NVIS data, notably parts of South Australia and small areas of New South Wales such as the Curlewis area.

Eighty-five (85) Major Vegetation Subgroups identified were created in v4.1 to summarise the type and distribution of Australia's native vegetation. The classification contains an emphasis on the structural and floristic composition of the dominant stratum (as with Major Vegetation Groups), but with additional types identified according to typical shrub or ground layers occurring with a dominant tree or shrub stratum.

In a mapping sense, the subgroups reflect the dominant vegetation occurring in a map unit from a mix of several vegetation types. Less-dominant vegetation subgroups which are also present in the map unit are not shown. For example, the dominant vegetation in an area may be mapped as dominated by eucalypt open forest with a shrubby understorey, although it contains pockets of rainforest, shrubland and grassland vegetation as subdominants.

A number of other non-vegetation and non-native vegetation land cover types are also represented as Major Vegetation Subgroups. These are provided for cartographic purposes, but should not be used for analyses.

This dataset has been provided to the BA Programme for use within the programme only. The current NVIS data products are available from http://www.environment.gov.au/land/native-vegetation/national-vegetation-information-system.

Dataset History

The input vegetation data were provided from over 100 individual projects representing the majority of Australia's regional vegetation mapping over the last 50 years. State and Territory custodians translated the vegetation descriptions from these datasets into a common attribute framework, the National Vegetation Information System (ESCAVI, 2003). Scales of input mapping ranged from 1:25,000 to 1:5,000,000. These were combined into an Australia-wide set of vector data. Non-terrestrial areas were mostly removed by the State and Territory custodians before supplying the data to the Environmental Resources Information Network (ERIN), Department of Sustainability Environment Water Population and Communities (DSEWPaC).

Each NVIS vegetation description was written to the NVIS XML format file by the custodian, transferred to ERIN and loaded into the NVIS database at ERIN. A considerable number of quality checks were performed automatically by this system to ensure conformity to the NVIS attribute standards (ESCAVI, 2003) and consistency between levels of the NVIS Information Hierarchy within each description. Descriptions for non-vegetation and non-native vegetation mapping codes were transferred via CSV files.

The NVIS vector (polygon) data for Australia comprised a series of jig-saw pieces, each up to approx 500,000 polygons - the maximum tractable size for routine geoprocesssing. The spatial data was processed to conform to the NVIS spatial format (ESCAVI, 2003; other papers). Spatial processing and attribute additions were done mostly in ESRI File Geodatabases. Topology and minor geometric corrections were also performed at this stage. These datasets were then loaded into ESRI Spatial Database Engine as per the ERIN standard. NVIS attributes were then populated using database tables provided by custodians, mostly using PL/SQL Developer or in ArcGIS using the field calculator (where simple).

Each spatial dataset was joined to and checked against a lookup table for the relevant State/Territory to ensure that all mapping codes in the dominant vegetation type of each polygon (NVISDSC1) had a valid lookup description, including an allocated MVS. Minor vegetation components of each map unit (NVISDSC2-6) were not checked, but could be considered mostly complete.

Each NVIS vegetation description was allocated to a Major Vegetation Subgroup (MVS) by manual interpretation at ERIN and in consultation with data custodians. 12 new MVSs were created for version 4.1 to better represent open woodland formations, more understorey types and forests (in the NT) with no further data available. Also, a number of MVSs were redefined after creation of the new groups to give a clearer and precise description of of the Subgroup e.g. MVS 9 - 'Eucalyptus woodlands with a grassy understorey' became 'Eucalyptus woodlands with a tussock grass understorey' to distinguish it from MVS10 - 'Eucalyptus woodlands with a hummock grass understorey'.. NVIS vegetation descriptions were reallocated into these classes, if appropriate:

• Warm Temperate Rainforest

• Eucalyptus woodlands with a hummock grass understorey

• Acacia (+/- low) open woodlands and sparse shrublands with a shrubby understorey

• Mulga (Acacia aneura) open woodlands and sparse shrublands +/- tussock grass

• Eucalyptus woodlands with a chenopod or samphire understorey

• Open mallee woodlands and sparse mallee shrublands with a hummock grass understorey

• Open mallee woodlands and sparse mallee shrublands with a tussock grass understorey

• Open mallee woodlands and sparse mallee shrublands with an open shrubby understorey

• Open mallee woodlands and sparse mallee shrublands with a dense shrubby understorey

• Callitris open woodlands

• Casuarina and Allocasuarina open woodlands with a tussock grass understorey

• Casuarina and Allocasuarina open woodlands with a hummock grass understorey

• Casuarina and Allocasuarina open woodlands with a chenopod shrub understorey

• Casuarina and Allocasuarina open woodlands with a shrubby understorey

• Melaleuca open woodlands

• Other Open Woodlands

• Other sparse shrublands and sparse heathlands

• Unclassified Forest

Data values defined as cleared or non-native by data custodians were attributed specific MVS values such as 42 - naturally bare, sand, rock, claypan, mudflat; 43 - salt lakes and lagoons; 44 - freshwater lakes and dams; 46 - seas & estuaries, 90, 91, 92 & 93 - Regrowth Subgroups; 98 - Cleared, non native, buildings; and 99 - Unknown. Note: some of these MVSs are only present in Extant vegetation.

As part of the process to fill gaps in NVIS, the descriptive data from non-NVIS sources was also stored in the NVIS database, but with blank vegetation descriptions. In general, the gap-fill data comprised (a) fine scale (1:250K or better) State/Territory vegetation maps for which NVIS descriptions were unavailable and (b) coarse-scale (1:1M and 1:5M) maps from Commonwealth and other sources. MVSs were then allocated to each description from the available descriptions in accompanying publications and other sources.

Each spatial dataset with joined lookup table (including MVS_NUMBER linked via NVISDSC1) was exported to a File Geodatabase as a feature class. These were reprojected into Albers Equal Area projection (Central_Meridian: 132.000000, Standard_Parallel_1: -18.000000, Standard_Parallel_2: -36.000000, Linear Unit: Meter (1.000000), Datum GDA94, other parameters 0).

In the original extant data, parts of New South Wales, South Australia, Tasmania and the ACT have areas of vector "NoData", thus appearing as an inland sea. Where there were gaps in the spatial coverage of Australia, "artificial" estimated pre-1750 layers were created from datasets available to the ERIN Veg Team. These were managed differently based on available information and complexity of work involved. Pre-1750 vector data for other states were supplied for 4.1 or previously, and did not require modelling. The purpose of this artificial pre-1750 modelling was to ensure that the pre-1750 and extant (present) datasets are comparable in the respective MVG and MVS classifications.

Pre1750 Vector Modelling

Large areas in the original South Australia and the ACT extant vector data had 'NoData'. Pre1750 vector layers were created by filling/cutting in these areas with estimated pre-1750 data from other sources such as the Geoscience Australia (AUSLIG,1990) "Natural" vector data layer. This procedure assumes that extant native vegetation has not changed its type since European settlement. Thus, effectively, only the non-native component was modelled/estimated for pre-1750 extent.

All feature classes were then rasterised to a 100m raster with extents to a multiple of 1000 m, to ensure alignment. In some instances e.g. NSW and TAS, areas of 'NoData' had to be modelled in raster (see below).

Raster modelling

For large parts of NSW, the native component of NVIS extant data were cut into the Geoscience Australia (AUSLIG,1990) "Natural" raster data layer and in some smaller areas, existing pre1750 data layers (e.g. Tumut), using a complex series of raster operations. For Tasmania, the NVIS version 2.0 (i.e. the original NVIS with restructured attributes) pre-European layer was rasterised, and used to fill non-native areas of the extant NVIS vegetation

At the onset of the full reopening in Spring 2023 of the Difficult-to-Return Zone of Northeastern Japan following the Fukushima Daiichi Nuclear Power Plant (FDNPP) accident that took place in March 2011, several spatial layers were regrouped and compiled to facilitate environmental studies dealing with the redistribution of radiocesium fallout across landscapes.

The current dataset is composed of 23 shapefiles including those of the delineations of different spatial zones (Intensive Contamination Survey Areas – ICAs, Special Decontamination Zones – SDZ, Difficult-to-Return Zone – DTRZ, and FNDPP location) (Evrard et al. 2019), municipalities where mushroom consumption restrictions were enforced (restricted and partially lifted restrictions), river hydrographic networks and their respective drainage areas (Mano, Niida, Ota, Takase, and Ukedo), dam reservoirs and drainage areas (Mano, Ogaki, Takanokura, and Yokokawa), multiple administrative delineations in Japan (whole Japan administrative boundaries, Prefectures, and municipalities) (GIS, 2016), and one raster file of the reconstruction of initial 137Cs fallout across eastern Japan (from Kato et al., 2019).

The current dataset provides a support to a publication submitted to the SOIL journal:

Evrard, O., Chalaux-Clergue, T., Chaboche, P.-A., Wakiyama, Y., and Thiry Y. (2023). Research and Management Challenges Following Soil and Landscape Decontamination at the Onset of the Reopening of the Difficult-To-Return Zone, Fukushima (Japan)’. SOIL 9: 479–97. https://doi.org/10.5194/soil-9-479-2023.

All map processing was carried out using QGIS 3.26.0 (QGIS, 2022) and under the EPSG:WGS 84 projection system.

The 137Cs fallout raster (in Bq m2, decay-corrected to July 2011) was generated from the point grid of Kato et al. (2019). A total of 126 tiles (0.25 x 0.25 degree) were generated by Inverse Distance Weighted (IDW) interpolation using the IDW interpolation tool with the following settings: distance coefficient P = 1.0 and pixel size (x and y) = 0.0015 degree. Tiles were then merged into a single tile using the raster Merge tool. The initial point grid footprint was manually delineated to define the spatial applicability zone of the airborne survey. A buffer zone corresponding to half plus 10% of the longest distance between two airborne points (x = 0.002, y = 0.003), i.e. 0.0017 degree, was generated using the buffer tool. The single tile was then cut according to the footprint of the buffer zone using the cut a raster according to a mask layer tool. A single-band pseudo-colour scale is provided and displays pixels with a value above 1000 Bq.kg-1 (eq. global background).

Landcover dataset created for the agricultural portion of Saskatchewan. Download: here A satellite imagery classification of Southern Saskatchewan based mainly on 1994 Landsat5 imagery. Developed by the Saskatchewan Research Council after 1997. Background: A group of Provincial and Federal Agencies formed a partnership in March of 1997 to share the cost of obtaining satellite imagery and interpreting this imagery to create a landcover dataset for the agricultural portion of Saskatchewan. The partnership included Saskatchewan Research Council (SRC), Saskatchewan Agriculture and Food (SAF), Saskatchewan Crop Insurance (SCI), Saskatchewan Property Management Corporation (SPMC), Environment Canada, the Prairie Farm Rehabilitation Administration (PFRA) and Saskatchewan Environment Resource Management (SERM). The University of Regina was also involved as an 'in kind' partner providing research services in the area of land cover classifications, accuracy assessment and data conversions. The Partnership Agreement required SRC (partner doing the bulk of data processing) to provide digital files for each of 328 1:50,000 NTS map sheets. The digital files included not only raw imagery, but also one file for each map sheet where the imagery was classified into 24 landcover types. The accuracy of this classification was to be demonstrated by SRC to be at least 90 per cent correct. In addition to the data processing done by SRC, SPMC provided the necessary positional control data (road intersection coordinates) and verified the positional accuracy of the final product. The other partners provided feedback to SRC on classification errors, which improved the overall accuracy of the final product. Classification Value No Data 0 Crop Land 1 Hay Crops (Forage) 2 Native Dominant Grass Lands 3 Tall Shrubs 4 Pasture (Seeded Grass Lands) 5 Hardwoods (Open Canopy) 6 Hardwoods (Closed Canopy) 7 Jack Pine (Closed Canopy) 8 Jack Pine (Open Canopy) 9 Spruce (Close Canopy) 10 Treed Rock 13 Recent Burns 14 Revegetating Burns 15 Cutovers 16 Water Bodies 17 Marsh 18 Herbaceous Fen 19 Mud/Sand/Saline 20 Shrub Fen (Treed Swamp) 21 Treed Bog 22 Open Bog 23 Slopes 25 Slopes 26 0. No Data 1. Crop Land - All lands dedicated to the production of annual cereal, oil seed and other specialty crops, and typically cultivated on an annual basis. 2. Hay Crops (Forage) - Alfalfa and alfalfa/tame grass mixtures. 3. Native Dominant Grass Lands - Native dominant grasslands/may contain tame grasses and herbs. 4. Tall Shrubs - Communities containing both low and tall shrub, snowberry, saskatoon, chokecherry, buffaloberry, and willow. 5. Pasture (Seeded Grass Lands) - Grassland dominated by tame grass species. 6. Hardwoods (Open Canopy) - Corresponds to Provincial Forest Inventory: over 75% hardwoods; 10-30% crown closure. 7. Hardwoods (Closed Canopy) - Corresponds to Provincial Forest Inventory: over 75% hardwoods; 30-100% crown closure. 8. Jack Pine (Closed Canopy) - Similar to Provincial Forest Inventory: 75% or greater Jack Pine; 30-100% crown closure. 9. Jack Pine (Open Canopy) - Similar to Provincial Forest Inventory: 75% or greater Jack Pine; 10-30% crown closure. 10. Spruce (Close Canopy) - Similar to Provincial Forest Inventory: 75% or greater Black and White Spruce; 10-30% crown closure. 11. Spruce: Open Canopy - Similar to Provincial Forest Inventory: 75% or greater Black and White Spruce; 10-30% crown closure. 12. Mixed Woods - All softwood/hardwood mixtures. 13. Treed Rock - Areas of exposed bedrock with generally less then 10% tree cover. Dominant species are Jack Pine and Black Spruce. 14. Recent Burns - All areas that have been recently burned over by wildfires. 15. Revegetating Burns - Burns with a regrowth of commercial timber generally 1-5 metres in height. 16. Cutovers - Areas where commercial timber has been completely or partially removed by logging operations. 17. Water Bodies - Consists of all open water - lakes, rivers, streams, ponds, and lagoons. 18. Marsh - Dominated by sedge and wetland grasses. 19. Herbaceous Fen - Fens dominated by herbaceous species. 20. Mud/Sand/Saline 21. Shrub Fen (Treed Swamp) - Fens dominated by shrubby species. 22. Treed Bog - Peat-covered or peat-filled depressions with a high water table and a surface carpet of moss, chiefly sphagnum. The bogs have 25% or more canopy by trees greater than one metre tall. The primary species is black spruce. 23. Open Bog - Peat-covered or peat-filled depressions with a high water table and a surface carpet of moss, chiefly sphagnum. 24. Farmstead - Farmstead types, towns, cities, Exposed areas with little or no vegetation or Cloud coverage. 25. Slopes - Steep Valley slopes or hill slopes where aspect and slope prohibit classification. 26. Slopes - Steep Valley slopes or hill slopes where aspect and slope prohibit classification.

Spatial data layers and analytical code used for the analyses and summaries presented by Bradstock et al. 2021. The base data layers comprise: Raster data in GeoTIFF format (.tif file extension) for long-term fire history, 2019/20 fire occurrence and severity, logging history, native vegetation (SVTM forest formations), tenure, topography. Raster layers for predicted habitat suitability of selected fauna species. Vector data (mostly polygon features) for study area and tenure and forest management boundaries, together with fire history, logging history and vegetation layers used to derive the corresponding raster data layers used for analyses. All code used for analyses is provided as Rmarkdown documents. Each document contains code in the R statistical language, together with explanatory text. Documents can be opened in the freely available RStudio software (https://www.rstudio.com) to run the code, or in any text editor to read code and text. See file 00_README.txt for further description of files and folders. Explore Metadata

Reason for Selection Low-urban historic landscapes indicate significant cultural landscapes whose cultural context has been less impacted by urban development. Cultural landscapes are “properties [that] represent the combined works of nature and of man” (UNESCO 2012). Loss of natural habitat within these cultural landscapes reduces their overall historic and cultural value. Input Data

Southeast Blueprint 2023 subregions: Caribbean
Southeast Blueprint 2023 extent
2020 LANDFIRE Existing Vegetation Type (EVT) (v2.2.0) for Puerto Rico and the U.S. Virgin Islands; access the data for U.S. Insular Areas
The following The National Register of Historic Places data for Puerto Rico provided by Eduardo Cancio, Information Systems Specialist with the Puerto Rico State Historic Preservation Office (SHPO) on 2-21-2023 (contact ecancio@prshpo.pr.gov for more information):NRHP_PR_individual_properties.shp
    NRHP_PR_lineal_districts.shp
    NRHP_PR_polygonal_districts.shp 

The National Register of Historic Places reflects what Americans value in their historic built environment. It is the collection of our human imprint on the landscape that records through time our changing relationship with the landscape, bridging between modern life and our history by providing, as closely as possible, experiences that evoke our empathy and understanding of previous eras.

  OpenStreetMap data “multipolygons” layer, accessed 3-14-2023 

A polygon from this dataset is considered a historic site if the “historic” tag is not null. In OpenStreetMap, a historic feature refers to “features that still exist or of which traces are observable, and that are of historic interest, or where the feature class is generally of historical interest”. We only used historic polygons if the name tag is also not null. OpenStreetMap® is open data, licensed under the Open Data Commons Open Database License (ODbL) by the OpenStreetMap Foundation (OSMF). Additional credit to OSM contributors. Read more on the OSM copyright page.

  Select USVI historic districts: Polygon boundaries for the Christiansted National Historic District on St. Thomas and Charlotte Amalie Historic and Architectural Historic District on St. Croix, provided by Nikita Beck with the University of the Virgin Islands on 3-6-2023 (contact nikita.beck@uvi.edu for more information)

Mapping Steps

Identify urban areas using the following classes from 2020 LANDFIRE EVT: Developed-High Intensity, Developed-Low Intensity, Developed-Medium Intensity, Developed-Open Space, Developed-Roads. Classify all urban pixels as 1 and all other pixels as 0.
Calculate the percent urban in a 270 m radius circle for each pixel using the Focal Statistics tool in ArcGIS. Since the LANDFIRE data resolution is 30 m, 270 m (9 pixels) approximates a 250 m radius. Retain all pixels that are <50% urban within a 270 m radius. 
Create a historic places layer by combining the following vector datasets as follows:Buffer National Register point data from the Puerto Rico SHPO by 100 m.
    Combine National Register polygons from the Puerto Rico SHPO, select USVI historic districts, and OpenStreetMap polygons. Only use OpenStreetMap polygons if both the historic and name columns are null. Buffer the polygons by 30 m.
    Buffer line data from the Puerto Rico SHPO by 30 m.
    Merge all buffered point, polygon, and line data into one layer and convert to a 30 m raster representing historic places.

Use the historic places raster to remove areas that fall outside of the historic places.
Reclassify the above raster into 3 classes, seen in the final indicator values below.
Clip to the Caribbean Blueprint 2023 subregion.
As a final step, clip to the spatial extent of Southeast Blueprint 2023. 

Note: For more details on the mapping steps, code used to create this layer is available in the Southeast Blueprint Data Download under > 6_Code. Final indicator values Indicator values are assigned as follows: 2 = Historic place with nearby low-urban buffer 1 = Historic place with nearby high-urban buffer 0 = Not identified as a historic place Known Issues

There are likely spatial mapping errors for some of the historic areas. 
Some historic areas with cultural importance are not captured in the National Register of Historic Places.
The approach to measuring urban development doesn’t capture degradation to historic places that were historically in larger cities (e.g., courthouses and other downtown buildings). It also doesn’t distinguish between historic places that have always been urban and historic places that used to be low-urban.
This layer likely underrepresents some historic areas in the U.S. Virgin Islands compared to Puerto Rico because we were unable to incorporate historic places data from the USVI SHPO during the timeline of this Blueprint update. As a result, some sites on the National Register of Historic Places are not depicted in this indicator.
OpenStreetMap is a crowdsourced dataset. While members of the OpenStreetMap community often verify map features to check for accuracy and completeness, there is the potential for spatial errors (e.g., misrepresenting the boundary of a historic site) or incorrect tags (e.g., labelling an area as a historic site that does not have historic value). However, using a crowdsourced dataset gives on-the-ground experts, Blueprint users, and community members the power to fix errors and add new historic sites to improve the accuracy and coverage of this indicator in the future.
Because open water is considered a non-urban landcover for the purposes of this analysis, this indicator is likely overprioritizing some urbanized historic areas that are close to water, such as marinas and bridges.

Disclaimer: Comparing with Older Indicator Versions There are numerous problems with using Southeast Blueprint indicators for change analysis. Please consult Blueprint staff if you would like to do this (email hilary_morris@fws.gov). Literature Cited OpenStreetMap. Historic. Data extracted through Geofabrik downloads. Accessed March 14, 2023. [https://wiki.openstreetmap.org/wiki/Key:historic].

LANDFIRE, Earth Resources Observation and Science Center (EROS), U.S. Geological Survey. Published August 1, 2022. LANDFIRE 2020 Existing Vegetation Type (EVT) Puerto Rico US Virgin Islands. LF 2020, raster digital data. Sioux Falls, SD. [https://www.landfire.gov].UNESCO (2012) Operational Guidelines for the Implementation of the World Heritage Convention [1]. UNESCO World Heritage Centre. Paris. Page 14. [https://whc.unesco.org/archive/opguide12-en.pdf].