Description: This database describes threatened (declared rare) and priority flora populations for all land tenures, within the State of Western Australia.The database is provided for management purposes. Information topics include taxon identification, location, tenure, habitat,population size, biology (reproductive state, pollination etc) and selected management requirements. Declared rare flora are published in the state government gazette as being rare, threatened or otherwise in need of special protection.
Description: The Threatened and Priority Fauna data layer contains records of conservation significant fauna (native animals); those listed under Part 2 of the Western Australian Biodiversity Conservation Act 2016 as Threatened (critically endangered, endangered or vulnerable), or as Specially Protected (migratory, special conservation interest or otherwise in need of special protection) or as Extinct, or are on the Department of Biodiversity, Conservation and Attractions (DBCA) Priority Fauna List. The data layer is prepared from a combination of records from multiple data sources, including from the DBCA Species and Communities Program’s Threatened and Priority Fauna Database (TFAUNA) and NatureMap. The records may be from surveys, monitoring programs, translocations, opportunistic sightings, evidence/secondary signs, or historical documents. The data can be used to assist with the conservation and management of fauna, especially in relation to proposed land developments or activities likely to impact on fauna and fauna habitat. The data provides an indication of the conservation significant fauna species likely to occur in a specific area of interest. It should be noted that the data does not necessarily represent a comprehensive list of the fauna in the area of interest. Its comprehensiveness is dependent on the amount of surveys carried out within an area. Data users should note that while every effort has been made to prevent errors and omissions in the data, they may be present, and DBCA accepts no responsibility for this. The TFAUNA database is subject to continual updates and amendment, and records are validated as much as practicable prior to entry into the database. Record queries can be sent to fauna.data@dbca.wa.gov.au.
The custodians of this data layer are the Species and Communities Program, DBCA. Access to this dataset is regulated. The degree of access to this information is dependent on how the data will be used and also on the search area size. Detailed locational information for individual species is kept confidential unless direct conservation benefits to the species can be demonstrated. The Species and Communities Program may provide detailed data, on request. Refer to the Data search request information sheet on the Threatened species and communities web page, for further information and contact details for data searches https://www.dpaw.wa.gov.au/plants-and-animals/threatened-species-and-communities.
Refer to the Threatened animals web page for the current list of WA Threatened, Specially Protected, Extinct and Priority Fauna (Threatened and Priority Fauna List), and for the Conservation codes for Western Australian fauna and flora https://www.dpaw.wa.gov.au/plants-and-animals/threatened-species-and-communities/threatened-animals.
License: Creative Commons Non-Commercial (Any)(Retired)
Tags: DPaW, Fauna, Priority, Surveys, Threatened, Wildlife Conservation
Contact: dbca_gis@dbca.wa.gov.au
Description: Locations of DBCA Tower structures, that were/are used as fire lookout towers.Note that towers that have a historical name listed no longer exist and their locations are shown for purely historical purposes. They should not be shown on any map product.
License: Creative Commons Attribution
Tags: Fire Towers
Contact: dbca_gis@dbca.wa.gov.au
Copyright Text: Department of Biodiversity, Conservation and Attractions
Color: [0, 0, 0, 255] Background Color: N/A Outline Color: N/A Vertical Alignment: bottom Horizontal Alignment: center Right to Left: false Angle: 0 XOffset: 0 YOffset: 0 Size: 8 Font Family: Arial Font Style: normal Font Weight: normal Font Decoration: none
Description: The Incident Control Centre Listings dataset represents the fire facilities around the state as nominated by the districts. Each site is either a Regional Control Centre, Fixed Facility Incident Control Centre, Field Incident Control Centre Site, Operations Point or Staging Area. The dataset ideintifies sites for specific purposes should a fire event occur in the area. It is a dynamic dataset that will change over time and be reviewed every year. It is also important to note that an Incident Control Site whether it be regional, fixed or field ICC site can also be used as an Operations Point or Staging Area. An Operations Point can also be a Staging Area but a Staging Area can only be a Staging Area. DEC does not purport that the information contained in this shapefile represents the only sites/all sites that are potentially available to be used as; RCC-Regional Control Centre, FFICC-Fixed Facility Incident Control Centre, FICCS- Field Incident Control Centre Sites, OP- Operations Point or SA-Staging Area. It is however a good guide for managers to determine a management site when required.
License: Creative Commons Attribution
Tags: Fire Incident, Fire, Fixed Facility, Incident Control, Site
Contact: dbca_gis@dbca.wa.gov.au
Copyright Text: Region and Fire Management Services. Department of Biodiversity, Conservation and Attractions
Name: DPIRD Weather Stations and Radar (DPIRD-075)
Display Field: station_name
Type: Feature Layer
Geometry Type: esriGeometryPoint
Description: DPIRD's network of automatic weather stations throughout the state provide timely, relevant and local weather data to assist growers and regional communities make more informed decisions. This data includes air temperature, humidity, rainfall, wind speed and direction, with most stations also measuring incoming solar radiation to calculate evaporation.
The weather stations report to DPIRD’s website every 10 minutes and provide near real time data. Local weather, such as rainfall or frost, can vary widely over short distances. The weather stations are sited to provide good geographical coverage. Their data is sent to the Bureau of Meteorology for use in weather forecasting and climate studies.
Industry uses this data for time critical agribusiness decision-making. These can range from scheduling irrigation of vegetable crops, through to choosing the right conditions for spraying operations in broad scale crops. DPIRD weather data is available to developers of decision support tools or weather applications via our Application Programming Interfaces (APIs). License: Creative Commons Attribution
Tags: climate, weather, weather stations
Contact: gis@dpird.wa.gov.au
Description: The DRA boundaries portrayed in this dataset are based on the published boundaries dipicted on the official statutory pland held in GIS Branch. Creation of the boundaries in this dataset are based on interpretations of the intent of the statutory boundaries in relation to the topographic features contained in the then DEC topgraphic database at the time. Some adjustments have been made to the boundaries in this dataset as spatial positions of topographic features have been adjusted through data maintenance.The gazzetted Disease Risk Area boundaries have been augmneted with areas in the Stirling Range National Park designated as Special Conservation Zones. These area are not officialy gazzetted but are managed as exclusion zones to prevent the spread of dieback.
Description: The consanguineous suites dataset is a wetlands data set that is derived from identifying related wetlands that occur within the same region, within the same setting, and have formed because of similar related factors.
Description: The Contaminated Sites Database holds information on confirmed contaminated sites (those classified ‘contaminated-remediation required’, ‘contaminated - restricted use’ and ‘remediated for restricted use’). Information on all other reported sites is recorded on the Reported Sites Register.
If your search on SLIP produces a nil response, this does not guarantee the selected area is free from contamination. Further investigation is highly recommended. The site may be awaiting classification or may be classified as one of four classification categories where information is not publicly available through Interest Enquiry. All reported sites are recorded on DWER's Reported Sites Register and information can be accessed by submitting a Form 2 to DWER.
For more information or to access a Form 2, contact our office on 1300 762 982 or see the DWER website at https://www.der.wa.gov.au/your-environment/contaminated-sites
Tags: SLIP Future, contaminated, sites, environment, regulation
License: Creative Commons Non-Commercial (Any)
Contact: contaminated.sites@dwer.wa.gov.au
Description: This dataset represents areas recommended for conservation as determined by the Environmental Protection Authority, Western Australia. *** Note: An estimated 15% of areas have differences between 'systems' dataset and publication boundaries. A review is underway and until such time that it is completed, the dataset should be used with caution and with reference to the 1993 Red Book publication. Specific advice should be sought from the Terrestrial Ecosystems Branch for detailed matters. [rod n, july 2009] *** The concept of Proposed Conservation Reserves by the EPA began in the early 1970s and has evolved as areas through a series of publications. The State of Western Australia was divided into 12 broad environmental 'system' areas, each reviewed and assessed for areas of potential conservation reserve. The first series of publications released in the 1970's were the "green books' as they had green covers. A second series of publications were released in the 1980's and known as the 'Red books' (red covers). The last authoritative publication was the 'Red Book Status Report on the Implementation of Conservation Reserves for Western Australia', 1993. Digital versions of the boundaries were established in 1995, until then the Proposed Conservation Reserves existed only in paper map form. System 6 areas were used as the basis for the Bushforever programme (Dept of Planning and Infrastructure), reserving areas for native vegetation preservation in the metropolitan area. Proposed Conservation Reserve or 'Red Book' areas are used within many planning process and environmental assessments. It is important to note that the 'Red Book' initiatives have been adopted and implemented with wide variation by Government agencies. Subsequent management plans, as well as incorporating many of these earlier recommendations, have also revisited and redefined boundaries from those that were originally identified in the "Red Book".
Description: This data set describes the official boundaries of the nine wetland areas initially proposed in February 1990 by the Government of Western Australia for listing as Wetlands of International Importance under the Convention on Wetlands of Importance especially as Waterfowl Habitat, otherwise known as the Ramsar Convention. The nine areas are the Ord River Floodplain, Lakes Argyle & Kununurra, Roebuck Bay, Eighty Mile Beach, Forrestdale & Thomsons Lakes, Peel-Yalgorup System, Toolibin Lake, Vasse-Wonnerup System and Lake Warden System. These nine sites were added to the Ramsar Convention official List of Wetlands of International Importance in June 1990. The February 1990 nomination document was prepared by the Western Australian Department of Conservation & Land Management (DCLM) on behalf of the State Government. The document included a number of A4 maps indicating the approximate boundaries of the nine Ramsar sites. During 1999 and 2000 DCLM has prepared a set of digitised maps of the nine Ramsar sites. These maps which also include basic cadastral and topographic information and area statements form the current data set. They describe the boundaries and areas of the sites more accurately than did the A4 maps and nomination text. These digitised maps are the new official maps for WA existing Ramsar sites. An additional three sites were added to the Ramsar Convention official List of Wetlands of International Importance in February 2001. These sites are Becher Point Wetlands, Lake Gore and Lake Muir-Byenup Lagoon. In late 2006 an additional six sites were proposed. These sites are Ellenbrook, Fortescue Marshes, Lake Ballard, Lake Gregory, Millstream Pools, Spearwood Creek. The Fortescue Marshes boundary was altered in late 2009 in order to more accurately align with the Important Wetlands dataset. Also in late 2009 an additional site, Lake Mcleod, was proposed.
Description: The Geomorphic Wetlands dataset describes the wetlands of the Swan Coastal Plain representing two main aspects, physical classification and environmental evaluation. The data set is updated quarterly if required. Current version - 31 December 2016. If information regarding this data is required please contact DPaW: Principal Coordinator Wetlands: 9219 8714
Description: The former WRC SC region have developed a spreadsheet which aims to document the South Coast natural assets which come under the WRC jurisdiction. There are four main worksheets which cover river systems, wetlands, ground water and surface water. The methodology used to spatially reference the wetlands worksheet of the WRC spreadsheet which contains the data to be used for the purpose of the South Coast Regional Strategy for NRM. Need to display records of the wetlands worksheet spatially was identified. Spatial information regarding the wetlands is available although no data set suited this purpose as the data in the worksheet was gathered from different sources, predominately wetland survey reports by Semeniuk and Ecologica, representing the regionally significant wetlands of the South Coast.
Name: Geomorphic Wetlands, Augusta to Walpole (DBCA-017)
Display Field: waw_class
Type: Feature Layer
Geometry Type: esriGeometryPolygon
Description: This set of geomorphic wetland mapping data was created for the Waters and Rivers Commission as part of the 1997 wetlands study: Wetland mapping classification between Augusta and Walpole, (V C Semeniuk). The Augusta to Walpole regionwas assessed and mapped using aerial assessment and some field work. All mapped wetlands were classified by physical characteristics. The result of the study areas has revealed detailed wetland mapping and other new information on wetlandgrouping.Mapping and classification of wetland between Augusta and Walpole show how extensive and interconnected the Region's wetland resources are.
Description: The Geomorphic Wetlands Cervantes Eneabba Stage 1 dataset displays the location, boundary and geomorphic classification of wetlands. The majority of wetlands in this dataset have been classified into types according to the geomorphic wetland classification system (Semeniuk & Semeniuk 1995 and unpublished report to the Department of Environment and Conservation (DEC 2007; VCSRG 2006). This classification system defines wetlands based on their landform and water permanence. Channel type wetlands (rivers, creeks, troughs and wadis), beaches, wetlands on offshore islands, subterranean and artificial wetlands were not included in the scope of the project. The Geomorphic Wetlands Cervantes Eneabba Stage 1 dataset is within the Shires of Dandaragan, Coorow & Carnamah on the Midwest coast of Western Australia. It is partially on the Swan Coastal Plain and partially on the Geraldton Sandplains, in the vicinity of Cervantes, Eneabba and Badgingarra town sites. The project area is approximately 360,000 ha (excludes oceanic area). The original mapping was conducted by V & C Semeniuk Research Group (VCSRG) in 2006 using information sources such as hard copy 1:25,000 stereoscopic aerial photographs and topographic maps. Wetland boundaries were traced by superimposing transparent sheets (overlays) on top of 1:50,000 orthophotos. A limited amount of wetlands were visited in the field by VCSRG to groundtruth desktop outputs. The wetlands depicted on these overlays were then digitised by Department of Water (DoW) to produce a draft dataset in 2006 (DoW 2006). The original mapping project was funded by the Natural Heritage Trust. In 2010 the draft dataset was reviewed by DEC and DoW to ensure it accurately reflected information depicted on the VCSRG overlays. The resulting product was a revised dataset named the Geomorphic Wetlands Cervantes Eneabba Stage 1 dataset (DEC 2010). DEC Wetlands Section managed the review project and the development of the final dataset with funding from DoW through the National Water Commission's Groundwater Action Plan Fund. The original project evaluated (assessed the values) of wetlands in the project area, however, the evaluation methodology and data was not included in the review and it is not an attribute of this dataset. A total of 458 wetlands are mapped in the dataset comprising of 16,594 ha of mapped wetland extent (approximately 4.6% of total project area). The number of wetlands mapped, by type, plus the relevant percentage is as follows: dampland 131 (28.6%); sumpland 112 (24.4%); playa 83 (18.1%); palusplain 31 (6.7%); Paluslope 29 (6.3%); barlkarra 26 (5.6); not classified 32 (5.8%); floodplain 12 (2.6%); and lake 2 (0.4%).
Description: The Geomorphic Wetlands Darkan Duranillin dataset displays the location, boundary and geomorphic classification of wetlands in the Darkan Duranillin area. Wetlands are classified according to their host landform and hydroperiod. Evaluation of conservation significance is not part of t1his dataset.
Description: The Geomorphic Wetlands Cervantes Coolimba Coastal Stage 2 dataset displays the location, boundary and geomorphic classification and Stage 2 management categories of wetlands. The dataset covers a coastal area within the Shires of Dandaragan, Coorow & Carnamah on the Midwest coast of Western Australia. It is partially on the Swan Coastal Plain and partially on the Geraldton Sandplains, in the vicinity of Cervantes, Jurien Bay, Greenhead and Leeman town sites. The project area is approximately 100,000 ha (excludes oceanic area).There are three components to the project:1.Wetland desktop mapping with limited on-ground confirmation (identification of wetlands, boundary delineation of wetlands and classification of wetland using the geomorphic wetlands classification system)2.Wetland desktop evaluation (Stage 2 wetland management categories)3.Methodology for deriving a wetland management category for environmental impact assessment using desktop and on-ground information (Stage 3 wetland management categories)Wetlands in this dataset have been classified into types according to the geomorphic wetland classification system (Semeniuk 1987, Semeniuk & Semeniuk 1995). In addition to the self-emergent wetlands, springs, estuary-peripheral and estuary type wetlands. The geomorphic classification system defines wetlands based on their landform and water permanence.Beaches, wetlands on offshore islands, subterranean and artificial wetlands were not included in the scope of the project.Wetlands have been assessed for their conservation significance and assigned a Stage 2 management category (Conservation, Rehabilitation Potential and Multiple Use) using desktop evaluation techniques and set criteria.Detailed methodology and results are described in the wetland mapping report: Wetland identification, delineation and classification: Results for the Geomorphic Wetlands Cervantes Coolimba Coastal Stage 2 dataset (Shanahan/DEC 2012) and the applied evaluation report: Wetland evaluation: Stage 2 results for the Geomorphic Wetlands Cervantes Coolimba Coastal Stage 2 dataset (Shanahan/DEC 2012)A total of 315 wetlands are mapped in the dataset comprising of 21,280 ha of mapped wetland extent (approximately 21% of total project area). The number of wetlands mapped, by type, plus the relevant percentage is as follows:Lake 2 (9.82%)Sumpland 113 (20.6%)Playa 54 (8.27%)Dampland 29 (6.29%)Floodplain 8 (0.21%)Barlkarra 18 (19.76%)Palusplain 35 (25.34%)Paluslope 11 (2.67%)River 3 (0.86%)Creek 12 (1.34%)Wadi 17 (1.16%)Estuary 1 (0.01%)Estuary-peripheral 3 (0.7%)Self-emergent wetland 2 (2.53%)Spring 7 (1.25%)73% of wetlands in the project area are assigned a Stage 2 management category of Conservation, 0.5% are Rehabilitation Potential and 25% are Multiple Use.Significant wetlands identified are as follow:• Salt lakes from Jurien Bay to Coolimba including Leeman Lagoon and Eatha Claypan• Springs connected to salt lakes including Eatha Spring, Diamond of the Desert Spring and Thetis Claypan springs. • Large sumpland to the east of Leeman Lagoon (UFI269; Shanahan Sumpland)• Other springs and self-emergent wetlands including Roman Forte wetland, Little Three Springs and South Little Three Springs• Hill River and Hill River Estuary• Lake Logue / Indoon system• Cockleshell Gully• Lake Thetis
Name: Geomorphic Wetlands Cervantes South (DBCA-013)
Display Field: wgc_classifica
Type: Feature Layer
Geometry Type: esriGeometryPolygon
Description: This project was managed by DEC Wetlands Section and funded by the Department of Water through the National Water Commission's Groundwater Action Plan Fund. The Geomorphic Wetlands Cervantes South dataset displays the location, boundary and geomorphic classification of wetlands. Wetlands in this dataset have been classified into types according to the geomorphic wetland classification system (Semeniuk & Semeniuk 1995 and unpublished report to the Department of Environment and Conservation (DEC 2007; VCSRG 2006a)). This classification system defines wetlands based on their landform and water permanence. Beaches and offshore islands and subterranean and artificial wetlands were not included in the scope of the project. Detailed methodology and results are described in the report Wetland mapping and classification Cervantes south (ENV 2010). A project summary is provided in the report Executive Summary: Cervantes South wetland mapping and classification project (DEC 2010). Both are available on DEC website. The Geomorphic Wetlands Cervantes South dataset project area is located on the Swan Coastal Plain. It is in the vicinity of Cervantes and Cataby, Shire of Dandaragan within the Midwest region of Western Australia. The project area is approximately 100,000ha and is based on the land area encompassed by eight 1:25,000 map sheets. Wetland extent was identified and geomorphic types identified and classified using a range of information sources including Landsat, orthophotos, hard copy stereoscopic aerial photographs, topography, soil types, remnant vegetation and hydrography. Approximately 4% of the total number of wetlands (8% of wetland area) was visited in the field to groundtruth desktop outputs and to provide positional accuracy and attribute accuracy data. A total of 770 wetlands were mapped in the project area and comprised approximately 20,221ha of mapped wetland extent (20% of total project area). The wetland types mapped (and the relative extent) were Palusplains (52%), Floodplains (27%), Damplands (11%), Creeks (3%), Sumplands (3%), Barlkarra (2%), Lakes (1%) and Rivers (0.3%). References: Department of Environment and Conservation, 2007. Framework for mapping, classification and evaluation of wetlands in Western Australia, Department of Environment and Conservation. Department of Environment and Conservation, 2010. Project Summary: Cervantes South wetland mapping and classification project, Department of Environment and Conservation. ENV Australia Pty Ltd, 2010. Wetland mapping and classification Cervantes south, prepared for the Department of Environment and Conservation, Department of Environment and Conservation, Western Australia. Semeniuk, C A & Semeniuk, V., 1995. "A Geomorphic Approach to Global Classification for Inland Wetlands", Vegetatio 118:103-124 V & C Semeniuk Research Group, 2006. Wetlands mapping, classification and evaluation - southwest region. Unpublished Report to the Department of Environment and Conservation. Perth
Description: The pre-European vegetation mapping of Western Australia dataset is an output of a joint project between DAFWA and DEC. It maps original natural vegetation presumed to have existed prior to European settlement in Western Australia. Descriptions of each of the vegetation types can be found in the accompanying memoir.
Published as Beard, J. S., Beeston, G.R., Harvey, J.M., Hopkins, A. J. M. and Shepherd, D. P. 2013. The vegetation of Western Australia at the 1:3,000,000 scale. Explanatory memoir. Second edition. Conservation Science Western Australia 9: 1-152.
The major sources of data in this database are the published and unpublished mapping of J.S. Beard at 1:250,000 scale. There are c 30,000 polygons covering 160 1:250,000 map sheets. Data on the original vegetation of all of Western Australia, with the exception of three map sheets in the south-west corner, were captured from J S Beard’s original working drawings, where these were available, or from published maps, all at the scale of 1:250,000. For the three map sheets in the south-west corner, a new data set was compiled in a form consistent with Beard’s approach, from existing data (A.J.M. Hopkins, unpublished).
Description: A data set containing vegetation extent polygons from the mapping of remnant vegetation in Western Australia. This was originally compiled as part of the vegetation theme of the National Land and Water Resource Audit (NLWRA). The data for the intensive land-use zone (ILZ) in south-western Australia was originally derived from 1995 LandSat TM satellite imagery and has been corrected using digital aerial photography (orthophotos) acquired 1996 to 2006. The extensive land-use zone (ELZ) is presumed to carry vegetation cover except for the major irrigated areas at Kununurra and Carnarvon. These have been removed from extent mapping for the ELZ. The dataset has been progressively updated by the Department post-NLWRA with assistance the Department of Conservation and Land Management. This data is supplied under Creative Commons Non-Commercial (Any) licensing. If you wish to use this data for commercial purposes please contact the data custodian for alternative licencing.
Tags: CAR, Harvested, SLIP Future, biology, conservation, ecology, environment, native, pir, plants, protected, remnant, statistics, vegetation
Contact: gis@dpird.wa.gov.au
Description: he data contained within the Wheatbelt Wetlands Stage 1 mapping layer covers wetlands within most of the Wheatbelt region of south-west Western Australia, as well as in a small area of the Rangelands to the east and the Darling Scarp to the west. The data was captured from 1990 and 2000 satellite images at a scale of 1:100 000 with 25m pixel resolution.
Description: The Swan Canning Riverpark was established under the Swan and Canning Rivers Management Act 2006 (the Act) and is 72.1km2 of public land and adjoining river reserve including the waterways and adjacent Crown land reserves of the Swan, Canning, Helena and Southern rivers. Private property is not included in the Riverpark. Department of Biodiversity, Conservation and Attractions is responsible for the waterways and has joint responsibility for the Riverpark shoreline in conjunction with the local or state government land manager under which the land is vested.
Copyright Text: Canning, DPaW, Estuary, River, Riverpark, Swan, Swan River Trust
Description: This data set describes Threatened Ecological Community (TEC) and Priority Ecological Community (PEC) Sites (Buffered) in WA. Threatened Ecological Communities (TEC) have been endorsed by the minister as threatened while Priority Ecological Communities (PEC) are those which have not yet been endorsed. Threatened Ecological Communities are described as either: "Presumed Totally Destroyed", "Critically Endangered", "Endangered" and "Vulnerable" while Priority Ecological Communities are described as being "Priority 1-5". Other Ecological Communities can be described as being: "Lower Risk" or "Not evaluated". Communities are based on various life-forms including plants, invertebrates and micro-organisms.
Description: Using CSIRO’s Urban Monitor high resolution digital photography, vegetation height strata of endemic and exotic species has been calculated and reported as an area for each height strata of 0 – 3 m, 3 – 8 m, 8 – 15 m and 15+ m. The area of grass covered areas falling into the 0 – 50 cm range has also been calculated and recorded in square metres. Vegetation coverage greater than 3 metres in height has been deemed tree canopy. The canopies have been aggregated and reported as total canopy coverage in square metres. Urban Forest Mesh Blocks have been published for the following years: 2009, 2014, 2016, 2018.
Parcels to be analysed were sourced from the 2016 Integrated Land Information Database (ILID) and supplied to CSIRO by the Department of Planning, Lands and Heritage. The results were assembled into Urban Forest features where the Urban Monitor coverage was complete.
Land parcels were assigned locational data (2016 ABS meshblocks, suburbs, local government authority (LGA) and planning sub-region) based on the parcel centroid. They were then attributed with the following land use categories:
• Street Block: residential, commercial, industrial, hospital/medical, educational, and some agricultural and transport land uses
• Parks: public parks, open space, private recreation grounds and State Forest
• Roads: roads including road reserves
• Other Infrastructure: rail, airports and utilities infrastructure
• Other: land uses in transition that have not progressed sufficiently to be Street Blocks or do not conform to urban form
• Rural: primary production land that does not fall in categories above
• Water: ocean and other waterways, including reservoirs
The vegetation height strata areas and total canopy coverage values were calculated for each land parcel. The statistics were then aggregated by the field MB_MonitorCategory, a concatenation of the fields MB_CODE16 and MonitorCategory. Canopy coverage percentages and ranges were calculated based on the sum of the area of parcels within each MB_MonitoryCategory. As parcels with sufficient Urban Monitor coverage for Urban Forest analysis may vary between years making comparison difficult, a field called MBPercentage was added which shows the percentage of the total MB_MonitorCategory area (MBArea) covered by parcels with Urban Forest values for that year. Corresponding MBPercentage values for all published years were also added to inform users.
NOTE: As mesh block attributes were assigned based on parcel centroid, aggregated mesh block boundaries based on the parcels may not match ABS mesh block boundaries, and MBAreas will not match ABS mesh block areas. If further aggregation is required, contact DPLH to discuss data provision.
License: Creative Commons Non-Commercial (Any)
Tags: forest, grass, tree, urban canopy
Contact: spatialdata@dplh.wa.gov.au
Description: Using CSIRO’s Urban Monitor high resolution digital photography, vegetation height strata of endemic and exotic species has been calculated and reported as an area for each height strata of 0 – 3 m, 3 – 8 m, 8 – 15 m and 15+ m. The area of grass covered areas falling into the 0 – 50 cm range has also been calculated and recorded in square metres. Vegetation coverage greater than 3 metres in height has been deemed tree canopy. The canopies have been aggregated and reported as total canopy coverage in square metres. Urban Forest Mesh Blocks have been published for the following years: 2009, 2014, 2016, 2018.
Parcels to be analysed were sourced from the 2016 Integrated Land Information Database (ILID) and supplied to CSIRO by the Department of Planning, Lands and Heritage. The results were assembled into Urban Forest features where the Urban Monitor coverage was complete.
Land parcels were assigned locational data (2016 ABS meshblocks, suburbs, local government authority (LGA) and planning sub-region) based on the parcel centroid. They were then attributed with the following land use categories:
• Street Block: residential, commercial, industrial, hospital/medical, educational, and some agricultural and transport land uses
• Parks: public parks, open space, private recreation grounds and State Forest
• Roads: roads including road reserves
• Other Infrastructure: rail, airports and utilities infrastructure
• Other: land uses in transition that have not progressed sufficiently to be Street Blocks or do not conform to urban form
• Rural: primary production land that does not fall in categories above
• Water: ocean and other waterways, including reservoirs
The vegetation height strata areas and total canopy coverage values were calculated for each land parcel. The statistics were then aggregated by the field MB_MonitorCategory, a concatenation of the fields MB_CODE16 and MonitorCategory. Canopy coverage percentages and ranges were calculated based on the sum of the area of parcels within each MB_MonitoryCategory. As parcels with sufficient Urban Monitor coverage for Urban Forest analysis may vary between years making comparison difficult, a field called MBPercentage was added which shows the percentage of the total MB_MonitorCategory area (MBArea) covered by parcels with Urban Forest values for that year. Corresponding MBPercentage values for all published years were also added to inform users.
NOTE: As mesh block attributes were assigned based on parcel centroid, aggregated mesh block boundaries based on the parcels may not match ABS mesh block boundaries, and MBAreas will not match ABS mesh block areas. If further aggregation is required, contact DPLH to discuss data provision.
License: Creative Commons Non-Commercial (Any)
Tags: forest, grass, tree, urban canopy
Contact: spatialdata@dplh.wa.gov.au
Description: Using CSIRO’s Urban Monitor high resolution digital photography, vegetation height strata of endemic and exotic species has been calculated and reported as an area for each height strata of 0 – 3 m, 3 – 8 m, 8 – 15 m and 15+ m. The area of grass covered areas falling into the 0 – 50 cm range has also been calculated and recorded in square metres. Vegetation coverage greater than 3 metres in height has been deemed tree canopy. The canopies have been aggregated and reported as total canopy coverage in square metres. Urban Forest Mesh Blocks have been published for the following years: 2009, 2014, 2016, 2018.
Parcels to be analysed were sourced from the 2016 Integrated Land Information Database (ILID) and supplied to CSIRO by the Department of Planning, Lands and Heritage. The results were assembled into Urban Forest features where the Urban Monitor coverage was complete.
Land parcels were assigned locational data (2016 ABS meshblocks, suburbs, local government authority (LGA) and planning sub-region) based on the parcel centroid. They were then attributed with the following land use categories:
• Street Block: residential, commercial, industrial, hospital/medical, educational, and some agricultural and transport land uses
• Parks: public parks, open space, private recreation grounds and State Forest
• Roads: roads including road reserves
• Other Infrastructure: rail, airports and utilities infrastructure
• Other: land uses in transition that have not progressed sufficiently to be Street Blocks or do not conform to urban form
• Rural: primary production land that does not fall in categories above
• Water: ocean and other waterways, including reservoirs
The vegetation height strata areas and total canopy coverage values were calculated for each land parcel. The statistics were then aggregated by the field MB_MonitorCategory, a concatenation of the fields MB_CODE16 and MonitorCategory. Canopy coverage percentages and ranges were calculated based on the sum of the area of parcels within each MB_MonitoryCategory. As parcels with sufficient Urban Monitor coverage for Urban Forest analysis may vary between years making comparison difficult, a field called MBPercentage was added which shows the percentage of the total MB_MonitorCategory area (MBArea) covered by parcels with Urban Forest values for that year. Corresponding MBPercentage values for all published years were also added to inform users.
NOTE: As mesh block attributes were assigned based on parcel centroid, aggregated mesh block boundaries based on the parcels may not match ABS mesh block boundaries, and MBAreas will not match ABS mesh block areas. If further aggregation is required, contact DPLH to discuss data provision.
License: Creative Commons Non-Commercial (Any)
Tags: forest, grass, tree, urban canopy
Contact: spatialdata@dplh.wa.gov.au
Description: Using CSIRO’s Urban Monitor high resolution digital photography, vegetation height strata of endemic and exotic species has been calculated and reported as an area for each height strata of 0 – 3 m, 3 – 8 m, 8 – 15 m and 15+ m. The area of grass covered areas falling into the 0 – 50 cm range has also been calculated and recorded in square metres. Vegetation coverage greater than 3 metres in height has been deemed tree canopy. The canopies have been aggregated and reported as total canopy coverage in square metres. Urban Forest Mesh Blocks have been published for the following years: 2009, 2014, 2016, 2018.
Parcels to be analysed were sourced from the 2016 Integrated Land Information Database (ILID) and supplied to CSIRO by the Department of Planning, Lands and Heritage. The results were assembled into Urban Forest features where the Urban Monitor coverage was complete.
Land parcels were assigned locational data (2016 ABS meshblocks, suburbs, local government authority (LGA) and planning sub-region) based on the parcel centroid. They were then attributed with the following land use categories:
• Street Block: residential, commercial, industrial, hospital/medical, educational, and some agricultural and transport land uses
• Parks: public parks, open space, private recreation grounds and State Forest
• Roads: roads including road reserves
• Other Infrastructure: rail, airports and utilities infrastructure
• Other: land uses in transition that have not progressed sufficiently to be Street Blocks or do not conform to urban form
• Rural: primary production land that does not fall in categories above
• Water: ocean and other waterways, including reservoirs
The vegetation height strata areas and total canopy coverage values were calculated for each land parcel. The statistics were then aggregated by the field MB_MonitorCategory, a concatenation of the fields MB_CODE16 and MonitorCategory. Canopy coverage percentages and ranges were calculated based on the sum of the area of parcels within each MB_MonitoryCategory. As parcels with sufficient Urban Monitor coverage for Urban Forest analysis may vary between years making comparison difficult, a field called MBPercentage was added which shows the percentage of the total MB_MonitorCategory area (MBArea) covered by parcels with Urban Forest values for that year. Corresponding MBPercentage values for all published years were also added to inform users.
NOTE: As mesh block attributes were assigned based on parcel centroid, aggregated mesh block boundaries based on the parcels may not match ABS mesh block boundaries, and MBAreas will not match ABS mesh block areas. If further aggregation is required, contact DPLH to discuss data provision.
License: Creative Commons Non-Commercial (Any)
Tags: forest, grass, tree, urban canopy
Contact: spatialdata@dplh.wa.gov.au
Description: Using CSIRO’s Urban Monitor high resolution digital photography, vegetation height strata of endemic and exotic species has been calculated and reported as an area for each height strata of 0 – 3 m, 3 – 8 m, 8 – 15 m and 15+ m. The area of grass covered areas falling into the 0 – 50 cm range has also been calculated and recorded in square metres. Vegetation coverage greater than 3 metres in height has been deemed tree canopy. The canopies have been aggregated and reported as total canopy coverage in square metres. Urban Forest Mesh Blocks have been published for the following years: 2009, 2014, 2016, 2018, 2020.
Parcels to be analysed were sourced from the 2016 Integrated Land Information Database (ILID) and supplied to CSIRO by the Department of Planning, Lands and Heritage. The results were assembled into Urban Forest features where the Urban Monitor coverage was complete.
Land parcels were assigned locational data (2016 ABS meshblocks, suburbs, local government authority (LGA) and planning sub-region) based on the parcel centroid. They were then attributed with the following land use categories: • Street Block: residential, commercial, industrial, hospital/medical, educational, and some agricultural and transport land uses • Parks: public parks, open space, private recreation grounds and State Forest • Roads: roads including road reserves • Other Infrastructure: rail, airports and utilities infrastructure • Other: land uses in transition that have not progressed sufficiently to be Street Blocks or do not conform to urban form • Rural: primary production land that does not fall in categories above • Water: ocean and other waterways, including reservoirs
The vegetation height strata areas and total canopy coverage values were calculated for each land parcel. The statistics were then aggregated by the field MB_MonitorCategory, a concatenation of the fields MB_CODE16 and MonitorCategory. Canopy coverage percentages and ranges were calculated based on the sum of the area of parcels within each MB_MonitoryCategory. As parcels with sufficient Urban Monitor coverage for Urban Forest analysis may vary between years making comparison difficult, a field called MBPercentage was added which shows the percentage of the total MB_MonitorCategory area (MBArea) covered by parcels with Urban Forest values for that year. Corresponding MBPercentage values for all published years were also added to inform users.
NOTE: As mesh block attributes were assigned based on parcel centroid, aggregated mesh block boundaries based on the parcels may not match ABS mesh block boundaries, and MBAreas will not match ABS mesh block areas.
Name: Geomorphic Wetlands South West - Unreviewed (DBCA-040)
Display Field: wgs_site_id
Type: Feature Layer
Geometry Type: esriGeometryPolygon
Description: The Geomorphic Wetlands, South West dataset describes the physical classification and environmental evaluation of the wetlands of the area to the east of Margaret River, south of the Geomorphic Wetlands Swan Coastal Plaindataset, and north of the Geomorphic Wetlands Augusta to Walpole dataset.Areas defined as wetland have a physical classification based on land and water attributes (Semeniuk & Semeniuk 1995), e.g. lake, palusplain. Areas also have an environmental evaluation of the ecological and cultural/scientific attributes of the wetland at the time of assessment, e.g. Conservation, Resource Enhancement and Multiple Use category. The dataset displays the location, boundary, geomorphic classification (wetland type) and management category of wetlands.Wetlands were classified according to the prevailing hydrological conditions at the time. This classification may require to be re-examined if hydrological conditions are altered by irreversible anthropological effects or by cyclic climatic variability.Wetlands were evaluated according to available resources and data, and site visits where possible. This evaluation may require to be re-examined if further information is obtained through future investigation.
Copyright Text: Environmental Management Branch. Department of Biodiversity, Conservation and Attractions
Description: The proposed tenure and purpose for land vested in the Conservation Commission within the area covered by the Forest Management Plan 2014-2023 (FMP).
Copyright Text: Environmental Management Branch. Department of Biodiversity, Conservation and Attractions
Name: Forest Management Plan (FMP) Boundary: 2024 - 2033 (DBCA-080)
Display Field: DESCRIPT
Type: Feature Layer
Geometry Type: esriGeometryPolygon
Description: The FMP is a plan developed every 10 years to manage all conservation reserves, state forest and other lands in the SW of WA that are vested in the Conservation Commission of WA.This layer shows the extent of the management plan area - it comprises a total area of over 2.5 million Ha. The aim of the plan, based on the principles of ecologically sustainable forest management (ESFM), is to maintain the economic and social values derived from the use of natural areas ensuring that impacts on biodiversity are minimal and precautionary in nature.
Description: This boundary defines the South West Forest Region of the State used in the Regional Forest Agreement. The Comprehensive Regional Assessments undertaken by the State and Commonwealth used this boundary to assess forest management, environmental, heritage, economic and social targets to negotiate the Regional Forest Agreement
Copyright Text: GIS Branch, Department of Biodiversity, Conservation and Attractions
Description: The Proposed Walpole Wilderness Area is currently unmapped in regards to geomorphological classification within wetlands, with little information collected regarding organic rich soils. As these areas are particularly vulnerable to fire events, it is foreseen that classification and identification of wetland areas and the associated organic rich soils, will allow the increased protection of these areas in the event of prescribed burning or wildfire suppression events. Taking current datasets, and combining these with photographic interpretation, it is hoped that an efficient and accurate dataset can be created. This dataset will clearly delineate between uplands, inundated and waterlogged areas, and swamps and basins, with particular reference made to topographical position and period of inundation within the classification system. Field sampling and site verification will allow the accuracy of the proposed classification and identification procedures to be determined, and the subsequent classification of wetland areas and organic rich soils.After thorough investigation of available datasets such as soils, vegetation and geomorphology in conjunction with photo interpretation and satellite data classification, it is evident that organic rich soils cannot be mapped remotely. Without additional resources the project was not able achieved its stated aim. The resultant dataset is a map of possibe peat sites with structural vegetative characteristics that can be used for future on-ground validation. Further field visits and site sampling are required to provide a key to better understand the differentiation of wetland types.Photo interpretation has been extremely useful in isolating flat systems from surrounding upland areas and provided a preliminary classification of wetlands. Satellite data then provided a key to hydroperiod but the subsequent creation of a classification key will be required to enable the determination of wetland class, and isolate the organic rich soils. This will lead to the creation of a robust and effective dataset which can be used not only in conjunction with the management of the Proposed Walpole Wilderness Area, but which also can be projected into other parts of the Region and be used for specific management roles such as fire management and suppression.
License: Creative Commons Non-Commercial (Any)
Tags: Peat Wetlands, Walpole Wilderness, Wetlands
Contact: dbca_gis@dbca.wa.gov.au
Copyright Text: Environmental Management Branch. Department of Biodiversity, Conservation and Attractions
Name: Geomorphic Wetlands Leeuwin Naturaliste Ridge and Donnybrook to Nannup - Unreviewed (DBCA-043)
Display Field: wgl_boundary_comments
Type: Feature Layer
Geometry Type: esriGeometryPolygon
Description: The Geomorphic Wetlands Leeuwin Naturaliste Ridge and Donnybrook to Nannup areas dataset displays the location, boundary and geomorphic classification of wetlands in the above areas. Wetlands in this dataset have been classified into types according to the geomorphic wetland classification system (Semeniuk & Semeniuk 1995 and unpublished report to the Department of Environment and Conservation (DEC; VCSRG 2006a)) This classification systems defines wetlands based on their landform and water permanence.Wetlands were classified according to the prevailing hydrological conditions at the time. This classification may require to be re-examined if hydrological conditions are altered by irreversible anthropological effects or by cyclic climatic variability.There are wetlands that have already been altered by anthropological impacts, for example dam construction. where boundaries may be less accurate. These wetlands are identified as having an approximate boundary. A separate layer describes the portion of boundary that is approximate.
Copyright Text: Environmental Management Branch. Department of Biodiversity, Conservation and Attractions
Name: Geomorphic Wetlands Manjimup to Northcliffe - Unreviewed (DBCA-044)
Display Field: wgm_wetland_code
Type: Feature Layer
Geometry Type: esriGeometryPolygon
Description: The Geomorphic Wetlands Manjimup to Northcliffe dataset displays the location, boundary and geomorphic classification of wetlands in the above area. Wetlands in this dataset have been classified into types according to the geomorphic wetland classification system (Semeniuk & Semeniuk 1995 and unpublished report to the Department of Environment and Conservation (DEC; VCSRG 2006a)) This classification systems defines wetlands based on their landform and water permanence.There are wetlands that have already been altered anthropological impacts, for example dam construction where boundaries may be less accurate. These wetlands are identified as having an approximate boundary. A separate layer describes the portion of boundary that is approximate.
Copyright Text: Environmental Management Branch. Department of Biodiversity, Conservation and Attractions.
Name: Directory of Important Wetlands in Australia - Western Australia (DBCA-045)
Display Field: diw_name
Type: Feature Layer
Geometry Type: esriGeometryPolygon
Description: This is a polygon coverage representing the Western Australian wetlands cited in the "A Directory of Important Wetlands in Australia" Third Edition (EA, 2001), plus various additions for wetlands listed after 2001. The full dataset is collated by the Australian Government Department of the Environment from various datasets supplied by the relevant State agencies. A subset of the WA Important Wetlands are listed as RAMSAR wetlands.
Copyright Text: Environmental Management Branch. Department of Biodiversity, Conservation and Attractions.
Description: Department of Biodiversity, Conservation and Attractions (DBCA) - Vegetation Complexes - SCP250k
The dataset shows pre-1750 distribution of vegetation complexes characteristic of various combinations of landforms, soils and rainfall along the Swan Coastal Plain south of Lancelin. For the majority of this area, the vegetation complexes are those defined by Heddle et al. (1980) at the scale of 1:250,000 and include some minor attribution corrections undertaken in 2015.
The Heddle mapping was restricted to the System 6 area and thus the vegetation complex mapping did not extend to the far southern section of Swan Coastal Plain. This southern section was subsequently captured by Webb et al. (2016) at a scale of 1:250,000. The 2016 mapping also consolidated the vegetation complex boundaries along the Whicher and Darling scarp interface using complexes defined by Mattiske & Havel (1990), soil landscape phases (DAFWA 2007) and information in Hagan et al. (2011).
See additional metadata for information on the location of summary descriptions of each complex and references. License: Creative Commons Non-Commercial (Any)
Tags: Complexes, DBCA, Heddel, Swan-Coastal-Plain, Vegetation
Contact: dbca_gis@dbca.wa.gov.au
Name: Vegetation Complexes - South West forest region of Western Australia (DBCA-047)
Display Field: vsw_complex_name
Type: Feature Layer
Geometry Type: esriGeometryPolygon
Description: The dataset is a comprehensive coverage of pre-1750 distribution of vegetation complexes of the south west forest region of Western Australia. This 1:50,000 mapping was undertaken by Mattiske and Havel (1998) as part of the biodiversity assessment for the comprehensive regional assessment for the south west forest region. The outputs from this project were used as inputs to the assessments of national estate, ecologically sustainable forest management, endangered species and the integration of all these values in the 1999 Regional Forest Agreement (RFA). This dataset covers the full extent mapped by Mattiske and Havel (1998) not just the area within the RFA boundary. In March 2015 the dataset was reviewed to correct known and documented minor attributing errors and additional fields were incorporated including a unique numerical identifier (SWFor_ID). See further details under lineage. Webb et al. (2016) reviewed the 1:50,000 mapping of the Whicher Scarp and changes were made to ensure the complexes were a continuation of those defined by Mattiske and Havel (1998) and the extent of the landform correlated to that as defined by soil-landscape mapping (DAFWA 2007). In addition the review consolidated the boundaries along the Whicher and Darling Scarp interface with the Swan Coastal Plain. All Swan Coastal Plain complexes were removed and incorporated into the 2016 mapping of the “Swan Coastal Plain Vegetation Complexes” (Webb et al. 2016). License: dbca_gis@dbca.wa.gov.au
Tags: Complexes, DBCA, Forest, Havel, Mattiske, South-west, Vegetation
Contact: Creative Commons Non-Commercial (Any)
Name: Clearing Instruments Proposals (Areas Applied to Clear) (DWER-075)
Display Field: id
Type: Feature Layer
Geometry Type: esriGeometryPolygon
Description: Clearing Instruments Proposals (areas applied to be cleared under a clearing permit) for Western Australia are derived from authorisations and other instruments relating to Clearing of Native Vegetation under the Environmental Protection Act 1986. Data is captured by the Department of Environment Regulation and other agencies under delegated authority. Data captured represents the land clearing request submitted by an applicant, the location where clearing may be conducted and areas where conditions may apply. Data is captured to the accuracy required by the request. The request can be captured directly from within the Clearing Permit System application (freeform shape) using image based maps as a background for simple requests or by importing a shape file when the request is complex. License: Creative Commons Non-Commercial (Any)
Tags: Clearing Permits, Environment
Contact: gisadmin@dwer.wa.gov.au, nvp@dwer.wa.gov.au
Name: Clearing Instruments Activities (Areas Approved to Clear) (DWER-076)
Display Field: id
Type: Feature Layer
Geometry Type: esriGeometryPolygon
Description: Clearing Instruments Activities (areas approved to be cleared under a clearing permit) for Western Australia are derived from authorisations and other instruments relating to Clearing of Native Vegetation under the Environmental Protection Act 1986. Data is captured by the Department of Water and Environmental Regulation and other agencies under delegated authority. Data captured represents the location of land clearing where clearing may be conducted. Data is captured to the accuracy required by the request. The request can be captured directly from within the Clearing Permit System application (freeform shape) using image based maps as a background for simple requests or by importing a shape file when the request is complex.
License: Creative Commons Non-Commercial (Any)
Tags: Clearing Permits, Environment
Contact: gisadmin@dwer.wa.gov.au
Name: Clearing Instruments Conditions (Areas Subject to Conditions) (DWER-077)
Display Field: id
Type: Feature Layer
Geometry Type: esriGeometryPolygon
Description: Clearing Instruments Conditions (areas subject to conditions under a clearing permit) for Western Australia are derived from authorisations and other instruments relating to Clearing of Native Vegetation under the Environmental Protection Act 1986. Data is captured by the Department of Water and Environmental Regulation and other agencies under delegated authority. Data captured represents the land clearing areas where conditions may apply. Data is captured to the accuracy required by the request. The request can be captured directly from within the Clearing Permit System application (freeform shape) using image based maps as a background for simple requests or by importing a shape file when the request is complex.
License: Creative Commons Non-Commercial (Any)
Tags: Clearing Permits, Environment
Contact: gisadmin@dwer.wa.gov.au
Description: The DBCA Burn Options Program dataset depicts prescribed burns of the Department of Biodiversity, Conservation and Attractions that are planned to take place over the next financial year. These are indicative only and Fire Management Services Branch should be contacted (DBCA_FIRE@dbca.wa.gov.au) for the most current information. Last update July 2022. Previously called DBCA Annual Indicative Burn Program (DBCA-007) and prior to that identified as DPAW-002.
Copyright Text: Regional and Fire Management Services, Department of Biodiversity, Conservation and Attractions
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Description: The Parks and Wildlife, Three Year Indicative Burn Program dataset, depicts prescribed burns that are planned to take place over the next 3 financial years. These are indicative only and FMSB should be contacted for the most current information.
Copyright Text: Regional and Fire Management Services, Department of Biodiversity, Conservation and Attractions
Name: Prescribed Burn - Land Management Zones (DBCA-059)
Display Field: lmz_zone
Type: Feature Layer
Geometry Type: esriGeometryPolygon
Description: Three Land Management Zones (LMZs) have been defined within Parks and Wildlife managed land at specified distances from the edge of the populated area. (see Reported Populated Areas data). The depth of these zones is defined by fire behaviour characteristics relevant to the zone purpose. The 200,000 ha prescribed burning target is allocated between the three zones in proportion to the amount of Parks and Wildlife managed land (including UCL) within each zone. Land Management Zone-A • LMZ-A is nearest to populated areas, including Parks and Wildlife managed lands within populated areas. • The objective of management in this zone is to reduce the likelihood of ember attack on populated areas in the event of a bushfire, by maintaining fuel in a condition that will not give rise to extreme fire behaviour. • The depth of LMZ-A is the spotting distance of the average fuels in LMZ-B; meaning that spot fires generated by a fire in LMZ-B will not reach the populated fringe. Land Management Zone-B • The next concentric buffer from the populated interface is LMZ-B. • The objective of management in this zone is to maintain an area within which a high intensity fire run could be arrested before it reaches LMZ-B. • The depth of LMZ-B is equal to the distance travelled by an ‘average’ bushfire in four hours, a period of time considered equivalent to the hottest period of an average day during the fire season. Land Management Zone-C • The third concentric buffer from the populated interface is LMZ-C, comprising the remainder of Parks and Wildlife managed land in the SWBRZ. • The objective of management in this area is to reduce the likelihood of the occurrence of large, intense fires in the landscape.
Lecense: Creative Commons Attribution
Tags: Land Management Zones, Presecribed Burns
Contact: dbca_gis@dbca.wa.gov.au
Copyright Text: Regional and Fire Management Services, Department of Biodiversity, Conservation and Attractions
Description: The Environmental Offsets Register lists all environmental offsets required under Part IV and Part V of the Environmental Protection Act since 1 July 2011. It provides spatial data on environmental offsets conditioned for the projects in Western Australia through the Environmental Protection Act 1986 approvals processes.
License: Creative Commons Non-Commercial (Any)
Tags: Offsets Register, Clearing Permits, Environmental Offsets
Contact: gisadmin@dwer.wa.gov.au
Description: The Environmental Offsets Register lists all environmental offsets required under Part IV and Part V of the Environmental Protection Act since 1 July 2011. It provides spatial data on the locations of environmental offsets projects in Western Australia through the Environmental Protection Act 1986 approvals processes.
License: Creative Commons Non-Commercial (Any)
Tags: Offsets Register, Clearing Permits, Environmental Offsets Projects
Contact: gisadmin@dwer.wa.gov.au
Description: Wild Rivers are rivers that are largely unchanged natural systems, where biological and hydrological processes continue without significant disturbance. They occur in a variety of landscapes, and may be permanent, seasonal or dry watercourses that flow or only flow occasionally, (Water and Rivers Commission, 1999). Wild Rivers are considered to be of high value because of their rarity, biodiversity or habitats, water quality and scientific value as benchmarks of natural catchment conditions. They remain generally undisturbed due to their isolation, rugged topography or land tenure. Through a project with the Australian Heritage Commission, the Department of Water and Environmental Regulation (then Water and Rivers Commission) originally identified 49 wild rivers catchments in Western Australia. New development and continuing pressures, such as grazing, tourism/recreation, and invasion by non endemic species, have the potential to degrade wild rivers over time. Since initial identification, the Upper Yule River has been DWERngraded, due to development in the catchment. Western Australia currently has 48 wild rivers, of which 37 are located in the Kimberley and Pilbara regions. See Water Note 37: Wild rivers in Western Australia (www.water.wa.gov.au/PublicationStore/83725.pdf). Wild river catchments classified as Priority 1 (P1) or Priority 2 (P2) are both considered to be of high value and are generally managed the same way, because there are nominal differences between the classifications. P1 wild rivers are those with no or minor impact from clearing, altering the landscape, loss of vegetation due to grazing, road or track construction, introduced exotic animals, plants or plant diseases, increased fire frequency, unnatural erosion and sedimentation or alterations to waterway and riparian ecosystem. P2 wild rivers are those with some but not extensive impacts from the activities or processes listed above. Since their initial identification, the Department of Water and Environmental Regulation has not undertaken a programme to monitor the condition of wild rivers catchments.
The Department of Water and Environmental Regulation advises that activities in wild rivers catchments should be managed so they do not adversely impact on values, including water quantity and quality. Conservation guidelines for the management of wild rivers: Part C - A Code for the Management of Wild Rivers (Australian Heritage Commission 1998, www.environment.gov.au/node/20154) provides management principles. License: Other (Not open)
Tags: WATER Rivers, WATER Catchment, WATER Wetlands, WATER Surface, WATER Quality, WATER Hydrology, ECOLOGY Habitat, HERITAGE Natural, LAND Geography, VEGETATION Natural
Contact: spatialsupport@dwer.wa.gov.au
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Name: HIR Carbon Sequestration Projects (DPLH-072)
Display Field: project_name
Type: Feature Layer
Geometry Type: esriGeometryPolygon
Description: The carbon sequestration using the human-induced regeneration (HIR) methodology project data set shows pastoral leases where HIR projects are located, the boundaries of those project areas and other related information.
License: Creative Commons Non-Commercial (Any)
Tags: HIR, carbon farming, farming, pastoral lease
Contact: spatialdata@dplh.wa.gov.au
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Description: Swan Coastal Plain Remnant Vegetation in 2000. Part of a suite of polygon datasets that best represents remnants of original pre-1750 vegetation on the Swan Coastal Plain (DBCA-046) as at 2000, 2005, 2010, 2015 and 2020. The polygons represent interpreted areas of vegetation using current and historical digital aerial photography (1953-present) sourced from Landgate, Digital globe and Google. Simplified condition of the vegetation has been attributed.
Description: Swan Coastal Plain Remnant Vegetation in 2005. Part of a suite of polygon datasets that best represents remnants of original pre-1750 vegetation on the Swan Coastal Plain (DBCA-046) as at 2000, 2005, 2010, 2015 and 2020. The polygons represent interpreted areas of vegetation using current and historical digital aerial photography (1953-present) sourced from Landgate, Digital globe and Google. Simplified condition of the vegetation has been attributed.
Description: Swan Coastal Plain Remnant Vegetation in 2010. Part of a suite of polygon datasets that best represents remnants of original pre-1750 vegetation on the Swan Coastal Plain (DBCA-046) as at 2000, 2005, 2010, 2015 and 2020. The polygons represent interpreted areas of vegetation using current and historical digital aerial photography (1953-present) sourced from Landgate, Digital globe and Google. Simplified condition of the vegetation has been attributed.
Description: Swan Coastal Plain Remnant Vegetation in 2015. Part of a suite of polygon datasets that best represents remnants of original pre-1750 vegetation on the Swan Coastal Plain (DBCA-046) as at 2000, 2005, 2010, 2015 and 2020. The polygons represent interpreted areas of vegetation using current and historical digital aerial photography (1953-present) sourced from Landgate, Digital globe and Google. Simplified condition of the vegetation has been attributed.
Description: Swan Coastal Plain Remnant Vegetation in 2020. Part of a suite of polygon datasets that best represents remnants of original pre-1750 vegetation on the Swan Coastal Plain (DBCA-046) as at 2000, 2005, 2010, 2015 and 2020. The polygons represent interpreted areas of vegetation using current and historical digital aerial photography (1953-present) sourced from Landgate, Digital globe and Google. Simplified condition of the vegetation has been attributed.
Name: Juvenile Period in Slow-Maturing Plants (Projected Change) - SW of WA (DBCA-072)
Display Field: jp_change
Type: Feature Layer
Geometry Type: esriGeometryPolygon
Description: By quantifying the length of time after fire for obligate-seeding plant species to become reproductively mature (the juvenile period), the risk of population decline under specific fire intervals can be delineated to inform local fire and conservation management. In this project, juvenile period data for serotinous obligate-seeder taxa across south-west Australia were collated from several studies. Linear models were then developed to estimate juvenile period based on measures of environmental productivity. These models were then spatially projected to the classic and drier Mediterranean agro-climatic class areas (Hutchinson et al. 2005) within south-west Australia. Full details of the modelling can be found in Gosper et al. (2022). Data are spatial projections of modelled juvenile period based on two metrics: (a) the number of years until 50% of individuals in the population have flowered, and (b) two times (2×) the number of years until 50% of individuals in the population have flowered. Spatial projections of juvenile period under recent conditions and future climate scenarios (2050 and 2090) were produced and are outlined below.JP - recent– Juvenile period as years until 50% of individuals in the population have flowered under recent conditions (30-year period centred on 1990) based on a model featuring the environmental variables mean annual precipitation, annual mean minimum temperature and gross primary productivity. (Fig 5 a in Gosper et al. 2022)JP 2×- recent–Juvenile period as 2×years until 50% of individuals in the population have floweredunder recent conditions (30-year period centred on 1990) based on a model featuring the environmental variables mean annual precipitation, annual mean minimum temperature and gross primary productivity. (Fig 5 a – 2×legend)JP - 2050 RCP 4.5– Juvenile period as years until 50% of individuals in the population have flowered under future conditions (30-year period centred on 2050) with the RCP 4.5 emissions scenario based on a model featuring annual precipitation. (Fig 5 b)JP - 2×2050 RCP 4.5- Juvenile period as 2×years until 50% of individuals in the population have flowered under future conditions (30-year period centred on 2050) with the RCP 4.5 emissions scenario based on a model featuring annual precipitation. (Fig 5 b –2×legend)JP – 2090 RCP 4.5– Juvenile period as years until 50% of individuals in the population have flowered under future conditions (30-year period centred on 2090) with the RCP 4.5 emissions scenario based on a model featuring annual precipitation. (Fig 5 e)JP – 2× 2090 RCP 4.5 –Juvenile period as 2×years until 50% of individuals in the population have flowered under future conditions (30-year period centred on2090) with the RCP 4.5 emissions scenario based on a model featuring annual precipitation. (Fig 5 e –2×legend)JP change– Projected change (in years) in juvenile period between recent conditions (Product 1) and 2050 under RCP 4.5 (Product 3). Juvenile period metric is years to 50% of individuals in the population having flowered. (Fig 5 f)JP – 2090 RCP 8.5- Juvenile period as years until 50% of individuals in the population have floweredunder future conditions (30-year period centred on2090) with the RCP 8.5 emissions scenario based on a model featuring annual precipitation. (Fig. S1 in Supplementary Material to Gosper et al. 2022)JP – 2× 2090 RCP 8.5–Juvenile period as 2×years until 50% of individuals in the population have floweredunder future conditions (30-year period centred on2090) with the RCP 8.5 emissions scenario based on a model featuring annual precipitation. (Fig. S1 – 2×legend)
Name: Juvenile Period in Slow-Maturing Plants (Recent) - SW of WA (DBCA-073)
Display Field:
Type: Group Layer
Geometry Type: null
Description: By quantifying the length of time after fire for obligate-seeding plant species to become reproductively mature (the juvenile period), the risk of population decline under specific fire intervals can be delineated to inform local fire and conservation management. In this project, juvenile period data for serotinous obligate-seeder taxa across south-west Australia were collated from several studies. Linear models were then developed to estimate juvenile period based on measures of environmental productivity. These models were then spatially projected to the classic and drier Mediterranean agro-climatic class areas (Hutchinson et al. 2005) within south-west Australia. Full details of the modelling can be found in Gosper et al. (2022). Data are spatial projections of modelled juvenile period based on two metrics: (a) the number of years until 50% of individuals in the population have flowered, and (b) two times (2×) the number of years until 50% of individuals in the population have flowered. Spatial projections of juvenile period under recent conditions and future climate scenarios (2050 and 2090) were produced and are outlined below.JP - recent– Juvenile period as years until 50% of individuals in the population have flowered under recent conditions (30-year period centred on 1990) based on a model featuring the environmental variables mean annual precipitation, annual mean minimum temperature and gross primary productivity. (Fig 5 a in Gosper et al. 2022)JP 2×- recent–Juvenile period as 2×years until 50% of individuals in the population have floweredunder recent conditions (30-year period centred on 1990) based on a model featuring the environmental variables mean annual precipitation, annual mean minimum temperature and gross primary productivity. (Fig 5 a – 2×legend)JP - 2050 RCP 4.5– Juvenile period as years until 50% of individuals in the population have flowered under future conditions (30-year period centred on 2050) with the RCP 4.5 emissions scenario based on a model featuring annual precipitation. (Fig 5 b)JP - 2×2050 RCP 4.5- Juvenile period as 2×years until 50% of individuals in the population have flowered under future conditions (30-year period centred on 2050) with the RCP 4.5 emissions scenario based on a model featuring annual precipitation. (Fig 5 b –2×legend)JP – 2090 RCP 4.5– Juvenile period as years until 50% of individuals in the population have flowered under future conditions (30-year period centred on 2090) with the RCP 4.5 emissions scenario based on a model featuring annual precipitation. (Fig 5 e)JP – 2× 2090 RCP 4.5 –Juvenile period as 2×years until 50% of individuals in the population have flowered under future conditions (30-year period centred on2090) with the RCP 4.5 emissions scenario based on a model featuring annual precipitation. (Fig 5 e –2×legend)JP change– Projected change (in years) in juvenile period between recent conditions (Product 1) and 2050 under RCP 4.5 (Product 3). Juvenile period metric is years to 50% of individuals in the population having flowered. (Fig 5 f)JP – 2090 RCP 8.5- Juvenile period as years until 50% of individuals in the population have floweredunder future conditions (30-year period centred on2090) with the RCP 8.5 emissions scenario based on a model featuring annual precipitation. (Fig. S1 in Supplementary Material to Gosper et al. 2022)JP – 2× 2090 RCP 8.5–Juvenile period as 2×years until 50% of individuals in the population have floweredunder future conditions (30-year period centred on2090) with the RCP 8.5 emissions scenario based on a model featuring annual precipitation. (Fig. S1 – 2×legend)
Description: By quantifying the length of time after fire for obligate-seeding plant species to become reproductively mature (the juvenile period), the risk of population decline under specific fire intervals can be delineated to inform local fire and conservation management. In this project, juvenile period data for serotinous obligate-seeder taxa across south-west Australia were collated from several studies. Linear models were then developed to estimate juvenile period based on measures of environmental productivity. These models were then spatially projected to the classic and drier Mediterranean agro-climatic class areas (Hutchinson et al. 2005) within south-west Australia. Full details of the modelling can be found in Gosper et al. (2022). Data are spatial projections of modelled juvenile period based on two metrics: (a) the number of years until 50% of individuals in the population have flowered, and (b) two times (2×) the number of years until 50% of individuals in the population have flowered. Spatial projections of juvenile period under recent conditions and future climate scenarios (2050 and 2090) were produced and are outlined below.JP - recent– Juvenile period as years until 50% of individuals in the population have flowered under recent conditions (30-year period centred on 1990) based on a model featuring the environmental variables mean annual precipitation, annual mean minimum temperature and gross primary productivity. (Fig 5 a in Gosper et al. 2022)JP 2×- recent–Juvenile period as 2×years until 50% of individuals in the population have floweredunder recent conditions (30-year period centred on 1990) based on a model featuring the environmental variables mean annual precipitation, annual mean minimum temperature and gross primary productivity. (Fig 5 a – 2×legend)JP - 2050 RCP 4.5– Juvenile period as years until 50% of individuals in the population have flowered under future conditions (30-year period centred on 2050) with the RCP 4.5 emissions scenario based on a model featuring annual precipitation. (Fig 5 b)JP - 2×2050 RCP 4.5- Juvenile period as 2×years until 50% of individuals in the population have flowered under future conditions (30-year period centred on 2050) with the RCP 4.5 emissions scenario based on a model featuring annual precipitation. (Fig 5 b –2×legend)JP – 2090 RCP 4.5– Juvenile period as years until 50% of individuals in the population have flowered under future conditions (30-year period centred on 2090) with the RCP 4.5 emissions scenario based on a model featuring annual precipitation. (Fig 5 e)JP – 2× 2090 RCP 4.5 –Juvenile period as 2×years until 50% of individuals in the population have flowered under future conditions (30-year period centred on2090) with the RCP 4.5 emissions scenario based on a model featuring annual precipitation. (Fig 5 e –2×legend)JP change– Projected change (in years) in juvenile period between recent conditions (Product 1) and 2050 under RCP 4.5 (Product 3). Juvenile period metric is years to 50% of individuals in the population having flowered. (Fig 5 f)JP – 2090 RCP 8.5- Juvenile period as years until 50% of individuals in the population have floweredunder future conditions (30-year period centred on2090) with the RCP 8.5 emissions scenario based on a model featuring annual precipitation. (Fig. S1 in Supplementary Material to Gosper et al. 2022)JP – 2× 2090 RCP 8.5–Juvenile period as 2×years until 50% of individuals in the population have floweredunder future conditions (30-year period centred on2090) with the RCP 8.5 emissions scenario based on a model featuring annual precipitation. (Fig. S1 – 2×legend)
Description: By quantifying the length of time after fire for obligate-seeding plant species to become reproductively mature (the juvenile period), the risk of population decline under specific fire intervals can be delineated to inform local fire and conservation management. In this project, juvenile period data for serotinous obligate-seeder taxa across south-west Australia were collated from several studies. Linear models were then developed to estimate juvenile period based on measures of environmental productivity. These models were then spatially projected to the classic and drier Mediterranean agro-climatic class areas (Hutchinson et al. 2005) within south-west Australia. Full details of the modelling can be found in Gosper et al. (2022). Data are spatial projections of modelled juvenile period based on two metrics: (a) the number of years until 50% of individuals in the population have flowered, and (b) two times (2×) the number of years until 50% of individuals in the population have flowered. Spatial projections of juvenile period under recent conditions and future climate scenarios (2050 and 2090) were produced and are outlined below.JP - recent– Juvenile period as years until 50% of individuals in the population have flowered under recent conditions (30-year period centred on 1990) based on a model featuring the environmental variables mean annual precipitation, annual mean minimum temperature and gross primary productivity. (Fig 5 a in Gosper et al. 2022)JP 2×- recent–Juvenile period as 2×years until 50% of individuals in the population have floweredunder recent conditions (30-year period centred on 1990) based on a model featuring the environmental variables mean annual precipitation, annual mean minimum temperature and gross primary productivity. (Fig 5 a – 2×legend)JP - 2050 RCP 4.5– Juvenile period as years until 50% of individuals in the population have flowered under future conditions (30-year period centred on 2050) with the RCP 4.5 emissions scenario based on a model featuring annual precipitation. (Fig 5 b)JP - 2×2050 RCP 4.5- Juvenile period as 2×years until 50% of individuals in the population have flowered under future conditions (30-year period centred on 2050) with the RCP 4.5 emissions scenario based on a model featuring annual precipitation. (Fig 5 b –2×legend)JP – 2090 RCP 4.5– Juvenile period as years until 50% of individuals in the population have flowered under future conditions (30-year period centred on 2090) with the RCP 4.5 emissions scenario based on a model featuring annual precipitation. (Fig 5 e)JP – 2× 2090 RCP 4.5 –Juvenile period as 2×years until 50% of individuals in the population have flowered under future conditions (30-year period centred on2090) with the RCP 4.5 emissions scenario based on a model featuring annual precipitation. (Fig 5 e –2×legend)JP change– Projected change (in years) in juvenile period between recent conditions (Product 1) and 2050 under RCP 4.5 (Product 3). Juvenile period metric is years to 50% of individuals in the population having flowered. (Fig 5 f)JP – 2090 RCP 8.5- Juvenile period as years until 50% of individuals in the population have floweredunder future conditions (30-year period centred on2090) with the RCP 8.5 emissions scenario based on a model featuring annual precipitation. (Fig. S1 in Supplementary Material to Gosper et al. 2022)JP – 2× 2090 RCP 8.5–Juvenile period as 2×years until 50% of individuals in the population have floweredunder future conditions (30-year period centred on2090) with the RCP 8.5 emissions scenario based on a model featuring annual precipitation. (Fig. S1 – 2×legend)
Name: Juvenile Period in Slow-Maturing Plants (2050 RCP 4.5) - SW of WA (DBCA-074)
Display Field:
Type: Group Layer
Geometry Type: null
Description: By quantifying the length of time after fire for obligate-seeding plant species to become reproductively mature (the juvenile period), the risk of population decline under specific fire intervals can be delineated to inform local fire and conservation management. In this project, juvenile period data for serotinous obligate-seeder taxa across south-west Australia were collated from several studies. Linear models were then developed to estimate juvenile period based on measures of environmental productivity. These models were then spatially projected to the classic and drier Mediterranean agro-climatic class areas (Hutchinson et al. 2005) within south-west Australia.
JP - 2050 RCP 4.5 – Juvenile period as years until 50% of individuals in the population have flowered under future conditions (30-year period centred on 2050) with the RCP 4.5 emissions scenario based on a model featuring annual precipitation. (Fig 5 b) JP - 2× 2050 RCP 4.5 - Juvenile period as 2× years until 50% of individuals in the population have flowered under future conditions (30-year period centred on 2050) with the RCP 4.5 emissions scenario based on a model featuring annual precipitation. (Fig 5 b –2× legend). Please see full metadata in 'Resources' section below.
Name: Juvenile Period in Slow-Maturing Plants (2090 RCP 4.5) - SW of WA (DBCA-075)
Display Field:
Type: Group Layer
Geometry Type: null
Description: By quantifying the length of time after fire for obligate-seeding plant species to become reproductively mature (the juvenile period), the risk of population decline under specific fire intervals can be delineated to inform local fire and conservation management. In this project, juvenile period data for serotinous obligate-seeder taxa across south-west Australia were collated from several studies. Linear models were then developed to estimate juvenile period based on measures of environmental productivity. These models were then spatially projected to the classic and drier Mediterranean agro-climatic class areas (Hutchinson et al. 2005) within south-west Australia.
JP – 2090 RCP 4.5 – Juvenile period as years until 50% of individuals in the population have flowered under future conditions (30-year period centred on 2090) with the RCP 4.5 emissions scenario based on a model featuring annual precipitation. (Fig 5 e) JP – 2× 2090 RCP 4.5 –Juvenile period as 2× years until 50% of individuals in the population have flowered under future conditions (30-year period centred on 2090) with the RCP 4.5 emissions scenario based on a model featuring annual precipitation. (Fig 5 e –2× legend) Please see full metadata in 'Resources' section below
Description: By quantifying the length of time after fire for obligate-seeding plant species to become reproductively mature (the juvenile period), the risk of population decline under specific fire intervals can be delineated to inform local fire and conservation management. In this project, juvenile period data for serotinous obligate-seeder taxa across south-west Australia were collated from several studies. Linear models were then developed to estimate juvenile period based on measures of environmental productivity. These models were then spatially projected to the classic and drier Mediterranean agro-climatic class areas (Hutchinson et al. 2005) within south-west Australia. Full details of the modelling can be found in Gosper et al. (2022). Data are spatial projections of modelled juvenile period based on two metrics: (a) the number of years until 50% of individuals in the population have flowered, and (b) two times (2×) the number of years until 50% of individuals in the population have flowered. Spatial projections of juvenile period under recent conditions and future climate scenarios (2050 and 2090) were produced and are outlined below.JP - recent– Juvenile period as years until 50% of individuals in the population have flowered under recent conditions (30-year period centred on 1990) based on a model featuring the environmental variables mean annual precipitation, annual mean minimum temperature and gross primary productivity. (Fig 5 a in Gosper et al. 2022)JP 2×- recent–Juvenile period as 2×years until 50% of individuals in the population have floweredunder recent conditions (30-year period centred on 1990) based on a model featuring the environmental variables mean annual precipitation, annual mean minimum temperature and gross primary productivity. (Fig 5 a – 2×legend)JP - 2050 RCP 4.5– Juvenile period as years until 50% of individuals in the population have flowered under future conditions (30-year period centred on 2050) with the RCP 4.5 emissions scenario based on a model featuring annual precipitation. (Fig 5 b)JP - 2×2050 RCP 4.5- Juvenile period as 2×years until 50% of individuals in the population have flowered under future conditions (30-year period centred on 2050) with the RCP 4.5 emissions scenario based on a model featuring annual precipitation. (Fig 5 b –2×legend)JP – 2090 RCP 4.5– Juvenile period as years until 50% of individuals in the population have flowered under future conditions (30-year period centred on 2090) with the RCP 4.5 emissions scenario based on a model featuring annual precipitation. (Fig 5 e)JP – 2× 2090 RCP 4.5 –Juvenile period as 2×years until 50% of individuals in the population have flowered under future conditions (30-year period centred on2090) with the RCP 4.5 emissions scenario based on a model featuring annual precipitation. (Fig 5 e –2×legend)JP change– Projected change (in years) in juvenile period between recent conditions (Product 1) and 2050 under RCP 4.5 (Product 3). Juvenile period metric is years to 50% of individuals in the population having flowered. (Fig 5 f)JP – 2090 RCP 8.5- Juvenile period as years until 50% of individuals in the population have floweredunder future conditions (30-year period centred on2090) with the RCP 8.5 emissions scenario based on a model featuring annual precipitation. (Fig. S1 in Supplementary Material to Gosper et al. 2022)JP – 2× 2090 RCP 8.5–Juvenile period as 2×years until 50% of individuals in the population have floweredunder future conditions (30-year period centred on2090) with the RCP 8.5 emissions scenario based on a model featuring annual precipitation. (Fig. S1 – 2×legend)
Description: By quantifying the length of time after fire for obligate-seeding plant species to become reproductively mature (the juvenile period), the risk of population decline under specific fire intervals can be delineated to inform local fire and conservation management. In this project, juvenile period data for serotinous obligate-seeder taxa across south-west Australia were collated from several studies. Linear models were then developed to estimate juvenile period based on measures of environmental productivity. These models were then spatially projected to the classic and drier Mediterranean agro-climatic class areas (Hutchinson et al. 2005) within south-west Australia. Full details of the modelling can be found in Gosper et al. (2022). Data are spatial projections of modelled juvenile period based on two metrics: (a) the number of years until 50% of individuals in the population have flowered, and (b) two times (2×) the number of years until 50% of individuals in the population have flowered. Spatial projections of juvenile period under recent conditions and future climate scenarios (2050 and 2090) were produced and are outlined below.JP - recent– Juvenile period as years until 50% of individuals in the population have flowered under recent conditions (30-year period centred on 1990) based on a model featuring the environmental variables mean annual precipitation, annual mean minimum temperature and gross primary productivity. (Fig 5 a in Gosper et al. 2022)JP 2×- recent–Juvenile period as 2×years until 50% of individuals in the population have floweredunder recent conditions (30-year period centred on 1990) based on a model featuring the environmental variables mean annual precipitation, annual mean minimum temperature and gross primary productivity. (Fig 5 a – 2×legend)JP - 2050 RCP 4.5– Juvenile period as years until 50% of individuals in the population have flowered under future conditions (30-year period centred on 2050) with the RCP 4.5 emissions scenario based on a model featuring annual precipitation. (Fig 5 b)JP - 2×2050 RCP 4.5- Juvenile period as 2×years until 50% of individuals in the population have flowered under future conditions (30-year period centred on 2050) with the RCP 4.5 emissions scenario based on a model featuring annual precipitation. (Fig 5 b –2×legend)JP – 2090 RCP 4.5– Juvenile period as years until 50% of individuals in the population have flowered under future conditions (30-year period centred on 2090) with the RCP 4.5 emissions scenario based on a model featuring annual precipitation. (Fig 5 e)JP – 2× 2090 RCP 4.5 –Juvenile period as 2×years until 50% of individuals in the population have flowered under future conditions (30-year period centred on2090) with the RCP 4.5 emissions scenario based on a model featuring annual precipitation. (Fig 5 e –2×legend)JP change– Projected change (in years) in juvenile period between recent conditions (Product 1) and 2050 under RCP 4.5 (Product 3). Juvenile period metric is years to 50% of individuals in the population having flowered. (Fig 5 f)JP – 2090 RCP 8.5- Juvenile period as years until 50% of individuals in the population have floweredunder future conditions (30-year period centred on2090) with the RCP 8.5 emissions scenario based on a model featuring annual precipitation. (Fig. S1 in Supplementary Material to Gosper et al. 2022)JP – 2× 2090 RCP 8.5–Juvenile period as 2×years until 50% of individuals in the population have floweredunder future conditions (30-year period centred on2090) with the RCP 8.5 emissions scenario based on a model featuring annual precipitation. (Fig. S1 – 2×legend)
Name: Juvenile Period in Slow-Maturing Plants (2090 RCP 8.5) - SW of WA (DBCA-076)
Display Field:
Type: Group Layer
Geometry Type: null
Description: By quantifying the length of time after fire for obligate-seeding plant species to become reproductively mature (the juvenile period), the risk of population decline under specific fire intervals can be delineated to inform local fire and conservation management. In this project, juvenile period data for serotinous obligate-seeder taxa across south-west Australia were collated from several studies. Linear models were then developed to estimate juvenile period based on measures of environmental productivity. These models were then spatially projected to the classic and drier Mediterranean agro-climatic class areas (Hutchinson et al. 2005) within south-west Australia.
JP – 2090 RCP 8.5 - Juvenile period as years until 50% of individuals in the population have flowered under future conditions (30-year period centred on 2090) with the RCP 8.5 emissions scenario based on a model featuring annual precipitation. (Fig. S1 in Supplementary Material to Gosper et al. 2022) JP – 2× 2090 RCP 8.5 –Juvenile period as 2× years until 50% of individuals in the population have flowered under future conditions (30-year period centred on 2090) with the RCP 8.5 emissions scenario based on a model featuring annual precipitation. (Fig. S1 – 2× legend) Please see full metadata in 'Resources' section below
Description: By quantifying the length of time after fire for obligate-seeding plant species to become reproductively mature (the juvenile period), the risk of population decline under specific fire intervals can be delineated to inform local fire and conservation management. In this project, juvenile period data for serotinous obligate-seeder taxa across south-west Australia were collated from several studies. Linear models were then developed to estimate juvenile period based on measures of environmental productivity. These models were then spatially projected to the classic and drier Mediterranean agro-climatic class areas (Hutchinson et al. 2005) within south-west Australia. Full details of the modelling can be found in Gosper et al. (2022). Data are spatial projections of modelled juvenile period based on two metrics: (a) the number of years until 50% of individuals in the population have flowered, and (b) two times (2×) the number of years until 50% of individuals in the population have flowered. Spatial projections of juvenile period under recent conditions and future climate scenarios (2050 and 2090) were produced and are outlined below.JP - recent– Juvenile period as years until 50% of individuals in the population have flowered under recent conditions (30-year period centred on 1990) based on a model featuring the environmental variables mean annual precipitation, annual mean minimum temperature and gross primary productivity. (Fig 5 a in Gosper et al. 2022)JP 2×- recent–Juvenile period as 2×years until 50% of individuals in the population have floweredunder recent conditions (30-year period centred on 1990) based on a model featuring the environmental variables mean annual precipitation, annual mean minimum temperature and gross primary productivity. (Fig 5 a – 2×legend)JP - 2050 RCP 4.5– Juvenile period as years until 50% of individuals in the population have flowered under future conditions (30-year period centred on 2050) with the RCP 4.5 emissions scenario based on a model featuring annual precipitation. (Fig 5 b)JP - 2×2050 RCP 4.5- Juvenile period as 2×years until 50% of individuals in the population have flowered under future conditions (30-year period centred on 2050) with the RCP 4.5 emissions scenario based on a model featuring annual precipitation. (Fig 5 b –2×legend)JP – 2090 RCP 4.5– Juvenile period as years until 50% of individuals in the population have flowered under future conditions (30-year period centred on 2090) with the RCP 4.5 emissions scenario based on a model featuring annual precipitation. (Fig 5 e)JP – 2× 2090 RCP 4.5 –Juvenile period as 2×years until 50% of individuals in the population have flowered under future conditions (30-year period centred on2090) with the RCP 4.5 emissions scenario based on a model featuring annual precipitation. (Fig 5 e –2×legend)JP change– Projected change (in years) in juvenile period between recent conditions (Product 1) and 2050 under RCP 4.5 (Product 3). Juvenile period metric is years to 50% of individuals in the population having flowered. (Fig 5 f)JP – 2090 RCP 8.5- Juvenile period as years until 50% of individuals in the population have floweredunder future conditions (30-year period centred on2090) with the RCP 8.5 emissions scenario based on a model featuring annual precipitation. (Fig. S1 in Supplementary Material to Gosper et al. 2022)JP – 2× 2090 RCP 8.5–Juvenile period as 2×years until 50% of individuals in the population have floweredunder future conditions (30-year period centred on2090) with the RCP 8.5 emissions scenario based on a model featuring annual precipitation. (Fig. S1 – 2×legend)
Description: By quantifying the length of time after fire for obligate-seeding plant species to become reproductively mature (the juvenile period), the risk of population decline under specific fire intervals can be delineated to inform local fire and conservation management. In this project, juvenile period data for serotinous obligate-seeder taxa across south-west Australia were collated from several studies. Linear models were then developed to estimate juvenile period based on measures of environmental productivity. These models were then spatially projected to the classic and drier Mediterranean agro-climatic class areas (Hutchinson et al. 2005) within south-west Australia. Full details of the modelling can be found in Gosper et al. (2022). Data are spatial projections of modelled juvenile period based on two metrics: (a) the number of years until 50% of individuals in the population have flowered, and (b) two times (2×) the number of years until 50% of individuals in the population have flowered. Spatial projections of juvenile period under recent conditions and future climate scenarios (2050 and 2090) were produced and are outlined below.JP - recent– Juvenile period as years until 50% of individuals in the population have flowered under recent conditions (30-year period centred on 1990) based on a model featuring the environmental variables mean annual precipitation, annual mean minimum temperature and gross primary productivity. (Fig 5 a in Gosper et al. 2022)JP 2×- recent–Juvenile period as 2×years until 50% of individuals in the population have floweredunder recent conditions (30-year period centred on 1990) based on a model featuring the environmental variables mean annual precipitation, annual mean minimum temperature and gross primary productivity. (Fig 5 a – 2×legend)JP - 2050 RCP 4.5– Juvenile period as years until 50% of individuals in the population have flowered under future conditions (30-year period centred on 2050) with the RCP 4.5 emissions scenario based on a model featuring annual precipitation. (Fig 5 b)JP - 2×2050 RCP 4.5- Juvenile period as 2×years until 50% of individuals in the population have flowered under future conditions (30-year period centred on 2050) with the RCP 4.5 emissions scenario based on a model featuring annual precipitation. (Fig 5 b –2×legend)JP – 2090 RCP 4.5– Juvenile period as years until 50% of individuals in the population have flowered under future conditions (30-year period centred on 2090) with the RCP 4.5 emissions scenario based on a model featuring annual precipitation. (Fig 5 e)JP – 2× 2090 RCP 4.5 –Juvenile period as 2×years until 50% of individuals in the population have flowered under future conditions (30-year period centred on2090) with the RCP 4.5 emissions scenario based on a model featuring annual precipitation. (Fig 5 e –2×legend)JP change– Projected change (in years) in juvenile period between recent conditions (Product 1) and 2050 under RCP 4.5 (Product 3). Juvenile period metric is years to 50% of individuals in the population having flowered. (Fig 5 f)JP – 2090 RCP 8.5- Juvenile period as years until 50% of individuals in the population have floweredunder future conditions (30-year period centred on2090) with the RCP 8.5 emissions scenario based on a model featuring annual precipitation. (Fig. S1 in Supplementary Material to Gosper et al. 2022)JP – 2× 2090 RCP 8.5–Juvenile period as 2×years until 50% of individuals in the population have floweredunder future conditions (30-year period centred on2090) with the RCP 8.5 emissions scenario based on a model featuring annual precipitation. (Fig. S1 – 2×legend)
Description: A key role of the EPA is to provide Government with advice on the environmental acceptability of statutory planning schemes and their amendments. This dataset is a spatial representation of Local Government Authority planning schemes (and their amendments) referred to the EPA under Part IV of the Environmental Protection Act 1986. Part IV of the act provides the legislative framework for the assessment of environmental impacts.
The boundaries in this dataset serve as the administrative record of where proposals are spatially assessed against other environmental data of interest. Note that:
a) not all proposals contain a spatial component; if not, no boundary is recorded
b) the accuracy of boundaries is dependent on the quality of data provided by the proponent.
The spatial boundaries in the dataset are provided by proponents (LGAs), with additional attributes completed by DWER Spatial Services.
Name: EPA Referred Significant Proposals (DWER-120)
Display Field: folder
Type: Feature Layer
Geometry Type: esriGeometryPolygon
Description: A key role of the EPA is to provide Government with advice on the environmental acceptability of proposed development. Development proposals include proposals for mining, industry, as well as infrastructure such as ports, railways, and pipelines.
This dataset is a spatial representation of significant development proposals referred to the EPA under Part IV of the Environmental Protection Act 1986. Part IV of the act provides the legislative framework for the assessment of environmental impacts. The boundaries in this dataset serve as the administrative record of where proposals are spatially assessed against other environmental data of interest. Note that:
a) not all proposals contain a spatial component; if not, no boundary is recorded
b) the accuracy of boundaries is dependent on the quality of data provided by the proponent.
The spatial boundaries in the dataset are provided by proponents, with additional attributes completed by DWER Spatial Services.