This entry provides access to surficial geology maps that have been published by the Geological survey of Canada. Two series of maps are available: "A Series" maps, published from 1909 to 2010 and "Canadian Geoscience Maps", published since 2010. Three types of CGM-series maps are available: 1)Surficial Geology: based on expert-knowledge full air photo interpretation (may include interpretive satellite imagery, Digital Elevation Models (DEM)), incorporating field data and ground truthing resulting from extensive, systematic fieldwork across the entire map area. Air photo interpretation includes map unit/deposit genesis, texture, thickness, structure, morphology, depositional or erosional environment, ice flow or meltwater direction, age/cross-cutting relationships, landscape evolution and associated geological features, complemented by additional overlay modifiers, points and linear features, selected from over 275 different geological elements in the Surficial Data Model. Wherever possible, legacy data is also added to the map. 2)Reconnaissance Surficial Geology: based on expert-knowledge full air photo interpretation (may include interpretive satellite imagery, DEMs), with limited or no fieldwork. Air photo interpretation includes map unit/deposit genesis, texture, thickness, structure, morphology, depositional or erosional environment, ice flow or meltwater direction, age/cross-cutting relationships, landscape evolution and associated geological features, complemented by additional overlay modifiers, points and linear features, selected from over 275 different geological elements in the Surficial Data Model. Wherever possible, legacy data is also added to the map. 3)Predictive Surficial Geology: derived from one or more methods of remote predictive mapping (RPM) using different satellite imagery, spectral characteristics of vegetation and surface moisture, machine processing, algorithms etc., DEMs, where raster data are converted to vector, with some expert-knowledge air photo interpretation (training areas or post-verification areas), varying degrees of non-systematic fieldwork, and the addition of any legacy data available. Each map is based on a version of the Geological Survey of Canada's Surficial Data Model (https://doi.org/10.4095/315021), thus providing an easily accessible national surficial geological framework and context in a standardized format to all users. "A series" maps were introduced in 1909 and replaced by CGM maps in 2010. The symbols and vocabulary used on those maps was not as standardized as they are in the CGM maps. Some "A series" maps were converted into, or redone, as CGM maps, Both versions are available whenever that is the case. In addition to CGM and "A series" maps, some surficial geology maps are published in the Open File series. Those maps are not displayed in this entry, but can be found and accessed using the NRCan publications website, GEOSCAN:(https://geoscan.nrcan.gc.ca).

The Tamalpais Lands Collaborative (One Tam; https://www.onetam.org/), the network of organizations that manage lands on Mount Tamalpais in Marin County, initiated the countywide mapping project with their interest in creating a seamless, comprehensive map depicting vegetation communities across the landscape. With support from their non-profit partner the Golden Gate National Parks Conservancy (https://www.parksconservancy.org/) One Tam was able to build a consortium to fund and implement the countywide fine scale vegetation map.Development of the Marin fine-scale vegetation map was managed by the Golden Gate National Parks Conservancy and staffed by personnel from Tukman Geospatial (https://tukmangeospatial.com/) Aerial Information Systems (AIS; http://www.aisgis.com/), and Kass Green and Associates. The fine-scale vegetation map effort included field surveys by a team of trained botanists. Data from these surveys, combined with older surveys from previous efforts, were analyzed by the California Native Plant Society (CNPS) Vegetation Program (https://www.cnps.org/vegetation) with support from the California Department of Fish and Wildlife Vegetation Classification and Mapping Program (VegCAMP; https://wildlife.ca.gov/Data/VegCAMP) to develop a Marin County-specific vegetation classification.High density lidar data was obtained countywide in the early winter of 2019 to support the project. The lidar point cloud, and many of its derivatives, were used extensively during the process of developing the fine-scale vegetation and habitat map. The lidar data was used in conjunction with optical data. Optical data used throughout the project included 6-inch resolution airborne 4-band imagery collected in the summer of 2018, as well as 6-inch imagery from 2014 and various dates of National Agriculture Imagery Program (NAIP) imagery.In 2019, a 26-class lifeform map was produced which serves as the foundation for the much more floristically detailed fine-scale vegetation and habitat map. The lifeform map was developed using expert systems rulesets in Trimble Ecognition®, followed by manual editing.In 2019, Tukman Geospatial staff and partners conducted countywide reconnaissance fieldwork to support fine-scale mapping. Field-collected data were used to train automated machine learning algorithms, which produced a fully automated countywide fine-scale vegetation and habitat map. Throughout 2020, AIS manually edited the fine-scale maps, and Tukman Geospatial and AIS went to the field for validation trips to inform and improve the manual editing process. In the spring of 2021, draft maps were distributed and reviewed by Marin County's community of land managers and by the funders of the project. Input from these groups was used to further refine the map. The countywide fine-scale vegetation map and related data products were made public in June 2021. In total, 107 vegetation classes were mapped with a minimum mapping size of one fifth to one acre, varying by class.Accuracy assessment plot data were collected in 2019, 2020, and 2021. Accuracy assessment results were compiled and analyzed in the summer of 2021. Overall accuracy of the lifeformmap is 95%. Overall accuracy of the fine-scale vegetation map is 77%, with an overall 'fuzzy' accuracy of 81%.The Marin County fine-scale vegetation map was designed for a broad audience for use at many floristic and spatial scales. At its most floristically resolute scale, the fine-scale vegetation map depicts the landscape at the National Vegetation Classification alliance level - which characterizes stands of vegetation generally by the dominant species present. This product is useful to managers interested in specific information about vegetation composition. For those interested in general land use and land cover, the lifeform map may be more appropriate. Tomake the information contained in the map accessible to the most users, the vegetation map is published as a suite of GIS deliverables available in a number of formats. Map products are being made available wherever possible by the project stakeholders, including the regional data portal Pacific Veg Map (http://pacificvegmap.org/data-downloads).

These are products of the Soil and Landscape Grid of Australia Facility generated through disaggregation of the Western Australian soil mapping. There are 9 soil attribute products available from the Soil Facility: Available Water Holding Capacity - Volumetric (AWC); Bulk Density - Whole Earth (BDw); Bulk Density - Fine Earth (BDf); Clay (CLY); Course Fragments (CFG); Electrical Conductivity (ECD); pH Water (pHw); Sand (SND); Silt (SLT).

Each soil attribute product is a collection of 6 depth slices. Each depth raster has an upper and lower uncertainty limit raster associated with it. The depths provided are 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm & 100-200cm, consistent with the Specifications of the GlobalSoilMap.

The DSMART tool (Odgers et al. 2014) tool was used in a downscaling process to translate legacy soil landscape mapping to 3” resolution (approx. 100m cell size) raster predictions of soil classes (Holmes et al. Submitted). The soil class maps were then used to produce corresponding soil property surfaces using the PROPR tool (Odgers et al. 2015; Odgers et al. Submitted). Legacy mapping was compiled for the state of WA from surveys ranging in map scale from 1:20,000 to 1:2,000,000 (Schoknecht et al., 2004). The polygons are attributed with the soils and proportions of soils within polygons however individual soils were not explicitly spatially defined. These new disaggregated map products aim to incorporate expert soil surveyor knowledge embodied in legacy polygon soil maps, while providing re-interpreted soil spatial information at a scale that is more suited to on-ground decision making.

Note: The DSMART-derived dissagregated legacy soil mapping products provide different spatial predictions of soil properties to the national TERN Soil Grid products derived by Cubist (data mining) and kriging based on site data by Viscarra Rossel et al. (Submitted). Where they overlap, the national prediction layers and DSMART products can be considered complementary predictions. They will offer varying spatial reliability (/ uncertainty) depending on the availability of representative site data (for national predictions) and the scale and expertise of legacy mapping. The national predictions and DSMART disaggregated layers have also been merged as a means to present the best available (lowest statistical uncertainty) data from both products (Clifford et al. In Prep).

Previous versions of this collection contained Depths layers. These have been removed as the units do not comply with Global Soil Map specifications. Lineage: The soil attribute maps are generated using novel spatial modelling and digital soil mapping techniques to disaggregate legacy soil mapping.

Legacy soil mapping: Polygon-based soil mapping for Western Australia’s agricultural zone was developed via WA’s Department of Agriculture and Food (Schoknecht et al., 2004). Seventy-three soil classes (termed ‘WA soil groups’ Schoknecht and Pathan, 2013) have been defined to capture the range of variation in soil profiles across this area. While legacy soil mapping does not explicitly map the distribution of these soil classes, estimates of their percentage composition and associated soil properties are available for each soil landscape map unit (polygon).

Disaggregation of soil classes: The DSMART algorithm (version 1, described in Odgers et al. 2014) was used to produce fine-resolution raster predictions for the probability of occurrence of each soil class. This uses random virtual sampling within each map unit (with sampling weighted by the expected proportions of each soil class) to build predictions for the distribution of soil classes based on relationships with environmental covariate layers (e.g. elevation, terrain attributes, climate, remote sensing vegetation indices, radiometrics). The algorithm was run 100 times then averaged to create probabilistic estimates for soil class spatial distributions.

Soil property predictions: The PROPR algorithm (Odgers et al. 2015) was used to generate soil property maps (and their associated uncertainty) using reference soil property data and the soil class probability maps create through the above DSMART disaggregation step.

Western Australia’s expert defined typical range of soil properties by soil class was used to provide reference soil properties to PROPR. These estimates were made separately for each physiographic zone across WA, and are based on available profile data and surveyor experience. Uncertainty bounds were determined by the minimum and maximum soil properties at the ‘qualified soil group’ level, and the property value of the most common soil in the map unit was used to define the typical soil property. This methodology was previously developed to meet the specifications of McKenzie et al. (2012) and provides expert soil surveyor estimates for map unit area composition and representative profile properties. Depth averaging was applied to the regional variant profile data to obtain property values at the specified GlobalSoilMap depth intervals. Then area-weighted soil property averages were calculated for each subgroup soil class. This process is documented further in Odgers et al. (Submitted).

Landslide Susceptibility Map of the Republic of Srpska is prepared using the expert AHP methodology and it shows a proposal of the terrain ranking in terms of spatial expectance of landslides in some areas. These maps are very suitable clear maps, comprehensible to experts from different fields, primarily spatial planning, urbanization etc. The preparation of this map, using the modern GIS tools, is a very useful way of prevention and the prerequisite for rational and meaningful struggle against landslides and their negative effects.

"Pre-European Vegetation Map of Boorowa Shire and surrounds.; Vegetation map based on classified vegetation survey data, and modelling layers, derived from a 25 metre Digital Elevation Model, and a composite geology map derived from Department of Minerals geology data. Data derived from the following sources: Digital elevation model in integer format, 25 m grid cells, produced 1997, Land Information Centre; Catchment variables derived from DEM, using Arcview 3.2; Geology data from 1:250 K Geology Map, Department of Mineral Resources of NSW; Derived Elevation, Slope Steepness, Drainage from DEM; Combined Geology and sub-catchments within Boorowa Shire; Derivation of individual grid layers for each map unit; Compilation of individual map units, using merge request function in Arcview 3.2; Derivation of vegetation mask, using Landsat ETM band 5 to create a native forest/woodland cover map; Intersection of pre-european vegetation map with M305 native woody vegetation map to produce extant layer.; ; Method used was based on expert modelling of vegetation types, based on consultant EcoGIS's (Nic Gellie) knowledge of distribution of similar vegetation types in relation to lithology and broad landscape variables. To reduce possible error in expert models, modelling zones based on a combination of lithology classes and sub-catchments were produced from expert examination of the spread and patterns of each vegetation group. The modelling zones helped to reduce the number of vegetation groups to be modelled down to 2-3 groups; Careful inspection of sites within each vegetation group helped to determine the broad environmental niche of each vegetation group. A table of possible relationships between vegetation groups and environmental variables was drawn up to help with the modelling process. It was clear that the patterns of vegetation in the study area were more influenced by geochemistry of the lithology classes and topographic position in the landscape, rather than the conventional aspect and moisture relationships found in coastal higher rainfall environments. This conclusion helped to determine the development of terrain variables that could separate vegetation groups that occurred predominantly on ridges and hillslopes from those vegetation groups that occurred in valley bottoms. A neighbourhood variable, using stream pattern derived from the watershed models within Arcview, helped to distinguish hillslopes from valley bottoms.; ; The modelling process enabled a complete audit of all vegetation types mapped in the study area and allowed a transparent and flexible process of mapping to be documented. In the event that detailed inspection of the results of the model or field validation resulted in possible changes to the map, individual modelling zones could be remodelled with the new knowledge, or new site data. This approach also prevented grid layers from spreading to areas where the vegetation groups would logically not occur in. When all modelling zones had been modelled, the resultant grid layers were then compiled into a single Arcview view. The data layers were then sorted into an agreed order of precedence that enabled each grid layer to be displayed on the final vegetation map. Reclassification and merge request functions within Arcview Spatial Analyst then produced a pre-European vegetation map. The final pre-European vegetation map was then masked with an extant vegetation cover to produce an extant vegetation map."; ; VIS_ID 1626; ; ANZLIC: ANZNS0208000216

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 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.

This dataset was superseded by the State Vegetation Type Map (https://datasets.seed.nsw.gov.au/dataset/nsw-state-vegetation-type-map) on 24.06.2022.

Please note, Upper Hunter v1.0. VIS_ID 4894 web service and zipped dataset will be archived and will no longer be available on line after 31st March 2025.

The NSW Office of Environment and Heritage (OEH) is producing a new map of the State’s native vegetation. This seamless map of NSW’s native vegetation types will enable government, industry and the community to better understand the composition and the relative significance of the native vegetation in their local area. The State Vegetation Type Map (SVTM) (http://www.environment.nsw.gov.au/vegetation/state-vegetation-type-map.htm) is constructed from the best available imagery, site survey records, and environmental information.

The primary thematic layer in this dataset is a regional scale map of Plant Community Type (PCT) - "quickview" map.

Where spatially coincident, this map of Upper Hunter (v1.0) supersedes the Greater Hunter Native Vegetation Mapping v4.0. VIS ID 3855 and was generated sourcing the following improvements:

• A comprehensive revision of vegetation plot allocation to Plant Community Types (PCT), superseding GHM v4 Map Units.
• Addition of 463 vegetation plots.
• Comprehensive revision of aerial photo interpretation of Vegetation Photo Patterns (VPP) at 1:10,000. A relevant selection of PCT’s were nested and modelled within each VPP.
• Utilisation of Boosted Regression Tree modelling in place of Generalised Dissimilarity Modelling
• All manual aerial photo interpretation of VPP’s modelled PCT’s performed using high resolution 50cm ADS-40 aerial imagery in place of SPOT-5 2.5m imagery.
• Semi-automated line work generated using high resolution 50cm ADS-40 aerial imagery in place of SPOT-5 2.5m imagery.
• Climatic and topographic rule based envelopes were generated to constrain the maximum spatial envelope for each PCT. Each envelope was further manually edited.
• Dry Sclerophyll communities further constrained by exposure and landform envelopes.
• Selective integration of the following pre-existing maps to PCT: VIS1849, VIS3863, VIS3913, VIS4184, VIS4778
• 312 vegetation communities mapped as PCT’s compared to 185 GHMv4 map units over this region.

QuickView map fields:

• PCTID – Plant Community Type identifier.
• PCTName – Plant Community Type common names
• vegClass – The PCT’s Keith Class
• vegFormation – The PCT’s Keith Formation
• mapSource - The source of the polygon’s PCT attribution.
• MapName – The 100k sheet map name

Note that this is a dissolved surface and does not highlight the fine internal line-work within each map unit. Please refer to the 100k full data sheets for the complete editable internal linework, which are available by request to Data.Broker@environment.nsw.gov.au.

The data are provided in an ArcGIS 10.4 compatible file geodatabase.

Fields in the undissolved 100k sheet fine scale linework:

• polygonID – Unique map polygon identifier
• PCTID – Plant Community Type identifier
• PCTName – Plant Community Type common name
• vegetationClass – The PCT’s Keith Class
• vegetationFormation – The PCT’s Keith Formation

• mapSource - The source of the polygon’s PCT attribution. Possible values are:

  • Manual editing
  • Site Survey
  • Spatial Modelling
  • Pre-existing mapping: VIS1849
  • Pre-existing mapping: VIS3863
  • Pre-existing mapping: VIS3913
  • Pre-existing mapping: VIS4184
  • Pre-existing mapping: VIS4778
  • Expert Rules (see note on grassland attribution below)

• PCTIDMod1 - The most likely Plant Community Type identifier as derived from the spatial model.

• PCTIDMod2 - The second most likely Plant Community Type identifier as derived from the spatial model.

• PCTIDMod3 - The third most likely Plant Community Type identifier as derived from the spatial model.

• vegStruct - Vegetation Photo Pattern (VPP) as derived from manual aerial photo interpretation of 50cm ADS40 imagery.

Possible values for vegStruct include direct attribution of some PCT’s where possible in addition to these Vegetation Photo Patterns listed below:

• vegStruct (VPP) Description

  • 0 Non Native
  • 1 Candidate Grasslands
  • 2 Dry Sclerophyll
  • 3 Wet Sclerophyll
  • 5 Floodplain Forest
  • 7 Non Woody Wetlands
  • 8 Grass Open Woodlands
  • 10 Rainforests
  • 11 Riparian Forests
  • 12 Acacia Woodlands
  • 13 Shrublands
  • 15 Mallee
  • 16 Rocky Outcrops
  • 17 Belah
  • 100 Dry Rainforest

• PCTmapAccuracyConfidence - Modelling Confidence for PCTIDMod1 – Note that this reflects the modelling surface (PCTIDMod1) only and may not reflect the confidence of the mapped attribution (PCTID). PCTallocationConfidence can only be accurately applied to the published map surface (PCTID) where mapSource = ‘Spatial Modelling’.

• PCTSiteValidation - Type of field validation used to assess PCT reliability: Possible Values are:

  • Not validated
  • RPD (Rapid)
  • Full floristic validation
  • Unknown

Full details will be provided in the pending Technical Report.

VIS_ID 4893

Abstract

The dataset was derived by the Bioregional Assessment Programme.This dataset is derived from Greater Hunter Native Vegetation Mapping supplied by NSW Office of Water on 19/12/2014. You can find a link to the parent datasets in the Lineage Field in this metadata statement. The History Field in this metadata statement describes how this dataset was derived.

This is essentially the same as the original dataset (see History) except an additional field (Map _Class) has been added to the polygon attribute table where the source vegetation classes have been collapsed into fewer more generic classes to facilitate the production of report map image.

Additionally there is a 50m rasterised version of the re-classified data (HUN_GHM_Veg_50m) derved from the Map_Class field of the polygon features. This was generated to facilitate timely redrawing of the feature layer in the report map MXD.

Dataset History

The GHM geodatabase builds on a wealth of information and previous mapping from the Hunter region. Existing field data, mapping, classification and remote sensing interpretation

were augmented with new survey data to produce the vegetation community classification used in this project. The classification used a series of well documented analyses as well as

expert review to achieve its end-point. This document contains descriptions of the 252 native vegetation communities resulting from the data analysis and classification process.

The GHM geodatabase contains two principal vegetation layers. The GHM Vegetation Type layer and the Canopy Cover (v2) layer (individual tree crowns or clumps of tree crowns). The

GHM also contains field plot localities, associated species information and plot-specific photographs. Data specific to each polygon (e.g. crown cover) and to each native vegetation

community type (e.g. common name, scientific name) are included.

Polygons, the fundamental spatial units, are built from computer-based feature recognition which delineates landscapes patterns. The GHM Vegetation Type map is built by attributing

individual polygons with vegetation type from the GHM floristic classification through a multi-stage process. The process includes visual interpretation of SPOT 5 and ADS40 imagery as

well as species distribution modelling and expert review.

The project included a review of existing mapping and classification and established equivalences between these and the GHM Classification. This was done for two main

reasons; to allow the mapping team to draw the maximum benefit from existing mapping and, to ensure that mapping currently in common use has a recorded and explicit

relationship with the current products. This is important considering that much of this existing mapping is at a very fine scale and may represent sub-classes of the GHM Classification.

This document provides users with a description of 'standard' products and also provides examples of how more advanced use may be made of the GHM.

In addition to the source polygon data, a 50m rasterised version of the Map Class has been included to facilitate fast re-draw for the large scale report map production.

A comprehensive account of the lineage of this data is give in the accompanying document GHM_v4p0_Geodatabase_Guide_070812.pdf included in this dataset.

Dataset Citation

Bioregional Assessment Programme (2014) Greater Hunter Native Vegetation Mapping with Classification for Mapping. Bioregional Assessment Derived Dataset. Viewed 09 October 2018, http://data.bioregionalassessments.gov.au/dataset/73abc2f6-1b8a-43a0-b458-c67ee4275edc.

Dataset Ancestors

• Derived From Greater Hunter Native Vegetation Mapping

The section of the Menai Straits that were surveyed were divided into three parts.

The hardness/roughness isopleths were combined using CAMRIS GIS to produce a composite map showing 7 types of group (very hard/very rough; hard/rough; moderately hard/moderately rough; moderately soft/moderately smooth; soft/moderately rough; soft/smooth; very soft/very smooth). A map for the whole of the survey area was followed by a map showing the biotope types as observed using the remote video, which are as follows (SA = superabundant; A = abundant; C = common; O = occasional)

Sand/shell: Medium fine sand with shell: very silty. Coarse sand/silt: Coarse sand: very silty. Gravel: Barren Silty cobble: Silt accretion (A); Cerianthus (O-C); Urticina (O-C). Silty cobble/rich: Silt accretion (A); sponges (Haliclona, Halichondria) (C); hydroids (C); Urticina (O-C). Boulder/cobble sponge: Similar to above, but with sponges C-A. Cobble/bounder faunal turf: Hydroids (C); Tubularia (O); Alcyonium(O); Flustra(O). Cobble hydroid turf: Short turf of hydroids (C-A). Cobble Pomatoceros: Pomatoceros and barnacles (A). Bedrock/boulder silt sponge: Similar to boulder/cobble sponge above, but on bedrock/boulder. Boulder Laminaria: Small Laminaria hyperboraea (C); red algal turf (C). Bedrock sponge/Flustra: Bedrock with encrusting sponges (C-A) and Flustra (O-C). Bedrock Mytilus. Small Mytilus (A-SA); Asteria(C-SA); encrusting corralines (O). Bedrock and faunal turf: Hydroids (C); Tubularia(O); Alcyonium(O); encrusting and branching sponges(O-C).

The maps were subsequently transposed into EUNIS in 2006 using expert judgement.

Up-to-date habitat survey of Wales produced by analysis of remote sensing data. The dataset is comprised of contiguous habitat polygons classified into phase 1 categories. It is the product of a large scale remote sensing project aimed at updating the Phase 1 habitat survey carried out in Wales between 1979 and 1999. It was produced in order to have key dataset for Wales which can be used to inform the core work of former Countryside Council for Wales (CCW) and its partners, and map specialist habitats or interpretations of habitats. The maps will be checked through field visits and feedback from CCW staff with local knowledge.

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.

Classification and descriptions of native vegetation types of southeast NSW (including the South Coast and parts of the eastern tablelands), and map of extant distribution of these veg types at 1:100 000 interpretation scale. Based on the South Coast - Illawarra Vegetation Integration (SCIVI) Project, which aimed to integrate many previous vegetation classification and mapping works to produce a single regional classification and map plus information on regional conservation status of vegetation types, to inform the South Coast and Illawarra Regional Strategies. Vegetation classification based on a compilation of ~ 8,500 full-floristic field survey sites from previous studies. Classified vegetation types refered to previous studies. Distribution of veg types was mapped by spatial interpolation (modelling) from classified sites, using a hybrid decision-tree/expert system. Final model was cut to \'extant\' boundaries using a compiled coverage of aerial photograph interpretation (API) of woody and wetland vegetation boundaries. A total of 189 vegetation types were identified, and types related to Endangered Ecological Communities are highlighted. Tozer et al 2006. Native vegetation of southeast NSW: a revised classification and map for the coast and eastern tablelands. ANZNS0359100156 VIS ID 2230

Dataset History

This data and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are represented here as originally supplied.

Data Quality

Lineage: Refer to project report for details. Vegetation classification and mapping based on ~ 8,500 field survey sites compiled from numerous previous surveys by many workers between the 1980s and 2005. Extant boundaries of native vegetation delineated by compilation of new and existing spatial data derived from aerial photo interpretation, augmented in parts by on-screen interpretation from digital orthorectified imagery of 1998 or later.

Scope: dataset

Completeness: Spatial completeness: final map of extant native vegetation boundaries relies on compilation of API of extant native vegetation. API standards vary across the study area. Smaller patches of woody vegetation and areas of non-woody non-wetland vegetation (eg. primary and secondary/derived grasslands) are not mapped as extant native vegetation. Classification completeness: Classification based on ~8,500 full-floristic field samples compiled from numerous previous surveys. This is the most comprehensive classification of the native vegetation of this region to date, however every classification can be improved by further sampling. Report gives the number of field samples classified as each veg type (=map unit) - this gives a general indication of how comprehensive the description of each unit is, and the likely reliability of modelling for that vegetation type. Verification completeness: No verification has been undertaken across the full study area, as all available site data was used to maximise power of model. Verification / statements of accuracy will be possible in future.

Logical consistency: Distribution of veg types was mapped by spatial interpolation (modelling) from ~ 8,500 classified field survey sites, using a hybrid decision-tree/expert system to explore relationships between veg types and environmental variables including substrate, topography and climate. Final map is based on an explicit set of rules defining the environmental space occupied by each vegetation type. See report for discussion of the modelling process and its limitations.

Positional accuracy: Spatial accuracy of modelled boundaries between vegetation types not tested, as no independent classified site data were available on completion of project. Accuracy of extant vegetation boundaries varies across the study area due to compilation of large number of previous coverages. Generally estimated to be 20-50m.

Attribute accuracy: Refer to project report for details. Accuracy of modelled vegetation types not tested as no independent classified site data were available following modelling. Accuracy of extant native vegetation boundaries varies across the study area according to standards of compiled API coverages: northern part (Sydney south to Araluen/Batemans Bay) delineated remnants andge;1ha, southern end andge;~2ha, small central area (Narooma/Cobargo) has minimum polygon size of 10ha.

Dataset Citation

NSW Department of Environment, Climate Change and Water (2010) Southeast NSW Native Vegetation Classification and Mapping - SCIVI VIS_ID 2230 20030101. Bioregional Assessment Source Dataset. Viewed 18 June 2018, http://data.bioregionalassessments.gov.au/dataset/0f1aeb33-1b49-4839-88fa-8b635cf9d3ab.

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.

A spatial dataset of soil and landform classification in Gippsland. The map units are broad `packages' of land - divided primarily on the basis of soil type, landform pattern and geology. It contains soil and land information at a scale of 1:100 000 for all land in the region.

The dataset has been derived from a combination of past studies and has been collated primarily by Ian Sargeant and Mark Imhof from 1994 to 2013. Data from older surveys have also been included in this consolidated dataset. Mapping in east and northern Gippsland regions is restricted to freehold lands. Webpages on Victorian Resources Online provide a description of each of the map units and indicate source studies used to define the map unit.

In June 2013 a dominant soil type was assigned to each unit (by David Rees, Mark Imhof and Ian Sargeant) to facilitate the creation of a digital soil map of Victoria. Australian Soil Classification (Order and SubOrder) have been included in the dataset's attribute table. At the map scale of this dataset soil-landform units are not homogeneous. For each defined soil-landform unit, the number and proportion of landforms and soil types will vary. Representative sites and their associated profile properties are recorded on the Victorian Resources Online website (http://vro.depi.vic.gov.au/dpi/vro/wgregn.nsf/pages/wg_soil_detailed). Importantly it should be noted that soil attributes (for example texture, sodicity, pH) are expected to vary between acquired soil sites. As the variability of soil attributes within a map unit is difficult to predict, it is important to note that representative soils should be used as a guide only.

Purpose

Showing soil types and extent within the Gippsland region.

Dataset History

Data Set Source:

Remote Sensed (Radiometrics, DEM), Expert Interviews, Soil site data, Field work, earlier land studies

Collection Method:

Field work, API, and derived with other datasets

Processing Steps:

Survey of existing soil and land unit mapping data from earlier studies.

New field work and observations to collection soil, land and land use information.

Combining old and new data with radiometrics and DEM in GIS.

Additional Metadata: The detail available in the current datasets is good for their mapping scale but is not sufficient to provide landscape analysis at finer scales and should not therefore be used to plan land use strategies at more detailed scales (1:25 000 and larger) unless additional soil and land survey is captured to enhance map line work and subdivide the map units.

It should be noted that soil attributes (for example texture, sodicity, pH) are expected to vary between acquired soil sites. As the variability of soil attributes within a map unit is difficult to predict, it is important to note that representative soils should be used as a guide only.

http://vro.depi.vic.gov.au/dpi/vro/wgregn.nsf/pages/wg_soil_detailed

Dataset Citation

Victorian Department of Environment and Primary Industries (2014) Land units of the Gippsland region of Victoria. Bioregional Assessment Source Dataset. Viewed 05 October 2018, http://data.bioregionalassessments.gov.au/dataset/5c7f4d52-8e46-4bda-a5c8-70124aaad67b.

This version (v1.0) is released for interim review for 3 months from release date. A subsequent official release will be published after review and subsequent alterations.

Please note, Central West / Lachlan Regional Native Vegetation Map (NSW Formation and Class) Version 1.0. VIS_ID 4214 web service and zipped dataset will be archived and will no longer be available on line after 31st March 2025.

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 download package includes a merged and simplified version of the full dataset.

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

MAP SHEET DATA: (CWLpct_v1p0.gdb\PCTv1) provided as 100k map sheets comprising detailed vegetation line-work with the following attributions per polygon:

FIELDS: !CWLpct_v1! - Plant Community Type (PCT) Codes !CommName! - Plant Community Type Common Names !KeithClass! - The mapped PCT's associated Keith Class !KeithForm! - The maplped PCT's assocated Keith Formation !Cover! - Percent woody cover per polygon (0 - 1.0) derived from the NSW Woody Vegetation Extent 2011 dataset. !EnvPCT1! - Most likely modelled PCT !EnvPCT2! - Second most likely modelled PCT !EnvPCT3! - Third most likely modelled PCT !confcat! - Categorical modelling confidence levels of EnvPCT1 (High > 60%, Medium 30%-60%, Low < 30%, None). None = non-modelling attribution (either manual expert API or pre-existing mapping) !cwlscv1up! - Expert Manual API code of structural class. Look up table below. !hectares! - Size of polygon in hectares

While much of the aerial photo interpretation employed was undertaken at around 1:8000, PCT attribution is generally at a much coarser scale. We recommend that the highest resolution appropriate for this product be 1:15000.

VALIDATION SUMMARY: Pending Technical Report.

ACCOMPANYING DATASETS:

KEITH CLASS QUICK-VIEW DISSOLVE: CWLpct_v1p0.gdb\KCv1\CWL_KC This is a dissolved (internal attribute polygon boundaries removed) and CWL-wide merged version of the Keith Class as found in the complete linework sheets of 100K Map sheet Data.

CWl BOUNDARY: CWLpct_v1p0.gdb\Boundaries\CWL_Boundary: A polygon boundary of the Central-West Lachlan v1 Regional Vegetation Map

CWL 100K SHEET BOUNDARIES: CWLpct_v1p0.gdb\Boundaries\CWL_100k Polygon boundaries of the 100k sheet extents within the CWL v1 extent.

SURVEY SITES: CWLpct_v1p0.gdb\Surveys\CWL_Sites_PCT_20150528 Point locations of on-ground rapid or full floristic vegetation surveys. Pertinent Fields: !SURVEYID! - code identifying for which project the sites were commissioned. !SITENUMBER! - Unique site code identifier. !PCT_1_0528! - Attributed PCT Code classification !PCT_1_0528! - Alternative PCT Code classification

LOOK UP TABLE: PCT Code, Name, Keith class & Keith Formation look-up: CWLpct_v1p0.gdb\CWL_LUT

MXD: CWLpct_v1p0.mxd

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

VIS_ID 4214

The NSW Office of Environment and Heritage (OEH) is producing a new map of the State’s native vegetation. This seamless map of NSW’s native vegetation types will enable government, industry and the community to better understand the composition and the relative significance of the native vegetation in their local area.

The State Vegetation Type Map (SVTM) (http://www.environment.nsw.gov.au/vegetation/state-vegetation-type-map.htm) is constructed from the best available imagery, site survey records, and environmental information.

Existing vegetation mapping has been integrated in some locations. Each vegetation survey record is being assigned to a Plant Community Type (PCT) and this is used to create a model of the distribution of each type. Their place in the landscape is then attributed based on the visual interpretation of vegetation photo pattern. The SVTM is designed to be dynamically improved and upgraded as new local information becomes available.

The Western NSW PCT DRAFT v0.1 data-set provides a draft map of Plant Community Types over western NSW and it is released for expert feedback. This feedback will contribute to an updated v1.0 release. VIS_ID 4492

The Queensland pest distribution survey series describes broad-scale distribution of invasive plants and animals across Queensland. Pest species include restricted and prohibited (but present) invasive species defined in the Biosecurity Act (2014), as well as several unlisted but emerging pests. The complete list of species mapped during each survey is available here: https://www.publications.qld.gov.au/dataset/invasive-plant-animal-map/resource/dbf1789a-7736-491c-9c9b-443cfbfae298 The survey grid is based on the Australian 100k topographic map series across all of Queensland, and each grid cell size is approx. 0.167 x 0.167 degree (10mins). Pest presence or absence is mapped to a vector grid. If the pest is present, an indication of density and distribution is also recorded. The information is collected via a series of workshops across Queensland, where pest 'experts' participate in the survey, including representatives from Local Government, NRM bodies and other agencies involved in pest management. The dataset is subsequently reviewed by pest management ‘experts’ where available, prior to publication. 'Absent' cells have been removed to reduce dataset size. The dataset includes surveys conducted in 2006, 2007, 2008, 2009, 2011-12, 2013-14, 2018 and 2022-23. From 2012 onwards, the surveys were also informed by data contributed by participants and from data repositories such as Atlas of Living Australia, Feral Scan and Queensland Herbarium. The 2022-23 survey included a density / distribution value of 'extirpated', to indicate where the pest was historically present but has been removed or locally eradicated. Extirpated cells include records where the pest has not naturalised.2006 survey includes 44 invasive plants, 8 invasive animals and 1 invasive ant species, most of these are declared Class 1, 2 or 3 pest species as defined in the Land Protection (Pest and Stock Route Management) Act 2002, as well as 4 undeclared species. 2007 survey includes 31 invasive plants and 5 invasive animals all of these are declared Class 1, 2 or 3 pest species as defined in the Land Protection (Pest and Stock Route Management) Act 2002.2008 survey includes 58 invasive plants and 12 invasive animals, 19 are declared Class 1, 26 are declared Class 2, and 7 are declared Class 3 as defined in the Land Protection (Pest and Stock Route Management) Act 2002, as well as 18 undeclared species.2009 survey includes 65 invasive plants and 1 invasive animal, 17 are declared Class 1, 23 are declared Class 2, and 10 are declared Class 3 as defined in the Land Protection (Pest and Stock Route Management) Act 2002, as well as 16 undeclared species.2011-12 survey (referred to as 2012 survey) includes 53 invasive plants and 8 invasive animals, 11 are declared Class 1, 24 are declared Class 2, and 11 are declared Class 3 as defined in the Land Protection (Pest and Stock Route Management) Act 2002, as well as 15 undeclared species.2013-14 survey (referred to as 2014 survey) includes 50 invasive plants, 14 invasive animals and 1 invasive ant species, 25 are declared Class 1, 28 are declared Class 2, and 4 are declared Class 3 as defined in the Land Protection (Pest and Stock Route Management) Act 2002, as well as 8 undeclared species.2018 survey includes 17 invasive plants, 2 invasive animals and 1 invasive ant, 18 are listed as Restricted species under the Biosecurity Act 2014, whilst 2 species are not listed.2022-23 survey (referred to as 2023 survey) includes 59 invasive plants and 4 deer species that were collated via the mapping workshops, whilst the remaining 39 species were mapped using internal datasets, cross-referenced against expert advice from Biosecurity Queensland operational personnel. 79 species are listed as Restricted species and 23 are listed as Prohibited species under the Biosecurity Act 2014.

Uganda: Level of satisfaction with MAP features after mock use (N = 36b'*').

This national digital GIS product produced by the British Geological Survey indicates the susceptibility of corroded underground ferrous (iron) assets (e.g. pipes) to failure, as a result of ground instability. It is largely derived from the digital geological map and expert knowledge. The GIS dataset contains eight fields. The first field is a summary map that gives an overview of where corroded ferrous assets may fail. The other seven fields indicate the properties of the ground with respect to corrosivity and hazards associated with soluble rocks, landslides, compressible ground, collapsible ground, swelling clays and running sands. The data is useful to asset managers in water companies, local authorities and utility companies who would like to understand where underground ferrous assets are susceptible to failure as a result of ground conditions.