Geographic Information System (GIS) analyses are an essential part of natural resource management and research. Calculating and summarizing data within intersecting GIS layers is common practice for analysts and researchers. However, the various tools and steps required to complete this process are slow and tedious, requiring many tools iterating over hundreds, or even thousands of datasets. USGS scientists will combine a series of ArcGIS geoprocessing capabilities with custom scripts to create tools that will calculate, summarize, and organize large amounts of data that can span many temporal and spatial scales with minimal user input. The tools work with polygons, lines, points, and rasters to calculate relevant summary data and combine them into a single output table that can be easily incorporated into statistical analyses. These tools are useful for anyone interested in using an automated script to quickly compile summary information within all areas of interest in a GIS dataset

Wetlands in California are protected by several federal and state laws, regulations, and policies. This layer was extracted from the broader vegetation raster from the CA Nature project which was recently enhanced to include a more comprehensive definition of wetland. This wetlands dataset is used as an exclusion as part of the biological planning priorities in the CEC 2023 Land-Use Screens.

This layer is featured in the CEC 2023 Land-Use Screens for Electric System Planning data viewer.

For more information about this layer and its use in electric system planning, please refer to the Land Use Screens Staff Report in the CEC Energy Planning Library.

This data layer references data from a high-resolution tree canopy change-detection layer for Seattle, Washington. Tree canopy change was mapped by using remotely sensed data from two time periods (2016 and 2021). Tree canopy was assigned to three classes: 1) no change, 2) gain, and 3) loss. No change represents tree canopy that remained the same from one time period to the next. Gain represents tree canopy that increased or was newly added, from one time period to the next. Loss represents the tree canopy that was removed from one time period to the next. Mapping was carried out using an approach that integrated automated feature extraction with manual edits. Care was taken to ensure that changes to the tree canopy were due to actual change in the land cover as opposed to differences in the remotely sensed data stemming from lighting conditions or image parallax. Direct comparison was possible because land-cover maps from both time periods were created using object-based image analysis (OBIA) and included similar source datasets (LiDAR-derived surface models, multispectral imagery, and thematic GIS inputs). OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to ensure that the end product is both accurate and cartographically pleasing. No accuracy assessment was conducted, but the dataset was subjected to manual review and correction.University of Vermont Spatial Analysis LaboratoryThis dataset consists of City of Seattle Council District areas as they existed in the first comparison year (2016) which cover the following tree canopy categories:Existing tree canopy percentPossible tree canopy - vegetation percentRelative percent changeAbsolute percent changeFor more information, please see the 2021 Tree Canopy Assessment.

This dataset provides a summary of general water quality conditions, tracks the degree to which a waterbody supports its designated uses, and monitors progress toward the identification and resolution of water quality problems, pollutants, and sources.Service layer is updated as needed but at least once every two years and was last updated August 2022.For more information or to download layer see https://gis.ny.gov/gisdata/inventories/details.cfm?DSID=1117Download the metadata to learn more information about how the data was created and details about the attributes. Use the links within the metadata document to expand the sections of interest.http://gis.ny.gov/gisdata/metadata/nysdec.Waterbody_Inventory_PWL.xmlFor more information please refer to https://www.dec.ny.gov/chemical/36730.html1. The NYSDEC asks to be credited in derived products. 2. Secondary distrubution of the data is not allowed. 3. Any documentation provided is an integral part of the data set. Failure to use the documentation in conjuction with the digital data constitutes a misuse of the data. 4. Although every effort has been made to ensure the accuracy of information, errors may be reflected in data supplied. The user must be aware of data conditions and bear responsibility for the appropriate use of the information with respect to possible errors, orginal map scale, collection methodology, currency of data, and other condition.

Publication Date: May 2025.

This dataset was updated May, 2025.This ownership dataset was generated primarily from CPAD data, which already tracks the majority of ownership information in California. CPAD is utilized without any snapping or clipping to FRA/SRA/LRA. CPAD has some important data gaps, so additional data sources are used to supplement the CPAD data. Currently this includes the most currently available data from BIA, DOD, and FWS. Additional sources may be added in subsequent versions. Decision rules were developed to identify priority layers in areas of overlap.Starting in 2022, the ownership dataset was compiled using a new methodology. Previous versions attempted to match federal ownership boundaries to the FRA footprint, and used a manual process for checking and tracking Federal ownership changes within the FRA, with CPAD ownership information only being used for SRA and LRA lands. The manual portion of that process was proving difficult to maintain, and the new method (described below) was developed in order to decrease the manual workload, and increase accountability by using an automated process by which any final ownership designation could be traced back to a specific dataset.The current process for compiling the data sources includes: Clipping input datasets to the California boundary Filtering the FWS data on the Primary Interest field to exclude lands that are managed by but not owned by FWS (ex: Leases, Easements, etc) Supplementing the BIA Pacific Region Surface Trust lands data with the Western Region portion of the LAR dataset which extends into California. Filtering the BIA data on the Trust Status field to exclude areas that represent mineral rights only. Filtering the CPAD data on the Ownership Level field to exclude areas that are Privately owned (ex: HOAs) In the case of overlap, sources were prioritized as follows: FWS > BIA > CPAD > DOD As an exception to the above, DOD lands on FRA which overlapped with CPAD lands that were incorrectly coded as non-Federal were treated as an override, such that the DOD designation could win out over CPAD.In addition to this ownership dataset, a supplemental _source dataset is available which designates the source that was used to determine the ownership in this dataset. Data Sources: GreenInfo Network's California Protected Areas Database (CPAD2023a). https://www.calands.org/cpad/; https://www.calands.org/wp-content/uploads/2023/06/CPAD-2023a-Database-Manual.pdf US Fish and Wildlife Service FWSInterest dataset (updated December, 2023). https://gis-fws.opendata.arcgis.com/datasets/9c49bd03b8dc4b9188a8c84062792cff_0/explore Department of Defense Military Bases dataset (updated September 2023) https://catalog.data.gov/dataset/military-bases Bureau of Indian Affairs, Pacific Region, Surface Trust and Pacific Region Office (PRO) land boundaries data (2023) via John Mosley John.Mosley@bia.gov Bureau of Indian Affairs, Land Area Representations (LAR) and BIA Regions datasets (updated Oct 2019) https://biamaps.doi.gov/bogs/datadownload.html Data Gaps & Changes:Known gaps include several BOR, ACE and Navy lands which were not included in CPAD nor the DOD MIRTA dataset. Our hope for future versions is to refine the process by pulling in additional data sources to fill in some of those data gaps. Additionally, any feedback received about missing or inaccurate data can be taken back to the appropriate source data where appropriate, so fixes can occur in the source data, instead of just in this dataset.25_1: The CPAD Input dataset was amended to merge large gaps in certain areas of the state known to be erroneous, such as Yosemite National Park, and to eliminate overlaps from the original input. The FWS input dataset was updated in February of 2025, and the DOD input dataset was updated in October of 2024. The BIA input dataset was the same as was used for the previous ownership version.24_1: Input datasets this year included numerous changes since the previous version, particularly the CPAD and DOD inputs. Of particular note was the re-addition of Camp Pendleton to the DOD input dataset, which is reflected in this version of the ownership dataset. We were unable to obtain an updated input for tribral data, so the previous inputs was used for this version.23_1: A few discrepancies were discovered between data changes that occurred in CPAD when compared with parcel data. These issues will be taken to CPAD for clarification for future updates, but for ownership23_1 it reflects the data as it was coded in CPAD at the time. In addition, there was a change in the DOD input data between last year and this year, with the removal of Camp Pendleton. An inquiry was sent for clarification on this change, but for ownership23_1 it reflects the data per the DOD input dataset.22_1 : represents an initial version of ownership with a new methodology which was developed under a short timeframe. A comparison with previous versions of ownership highlighted the some data gaps with the current version. Some of these known gaps include several BOR, ACE and Navy lands which were not included in CPAD nor the DOD MIRTA dataset. Our hope for future versions is to refine the process by pulling in additional data sources to fill in some of those data gaps. In addition, any topological errors (like overlaps or gaps) that exist in the input datasets may thus carry over to the ownership dataset. Ideally, any feedback received about missing or inaccurate data can be taken back to the relevant source data where appropriate, so fixes can occur in the source data, instead of just in this dataset.

The Counties clipped layer was created using ArcGIS's Clip (Analysis) Tool to extract the DRCOG County Boundaries that overlay the MHFD District Boundary.

A Groundwater Body (GWB) under the Water Framework Directive (WFD) Art. 2 is defined as a distinct volume of groundwater within an aquifer or aquifers, whereas an aquifer is defined as a geological layer with significant groundwater flow. This definition of a GWB allows a wide scope of interpretations. EU Member States (MS) are under obligation to report the GWBs including the results of the GWB survey periodically according to the schedule of the WFD. Reportnet is used for the submission of GWB data to the EEA by MS and includes spatial data as GIS polygons and GWB characteristics in an XML schema.

The WISE provisional reference GIS WFD Dataset on GWBs combines spatial data consisting of several shape files and certain GWB attributes in a single table submitted by the MS according to Art. 13. The GWBs are divided into horizons, which represent distinct vertical layers of groundwater resources. All GWBs assigned to a certain horizon from one to five are merged into one shape file. GWBs assigned to horizons six or seven are combined in a single further shape file. Another two shape files comprise the GWBs of Reunion Island in the southern hemisphere and the GWBs from Switzerland as a non EU MS, all of which assigned to horizon 1.

The dbf tables of the shape files include the columns “EU_CD_GW” as the GWB identifier and “Horizon” describing the vertical positioning. The polygon identifier “Polygon_ID” was added subsequently, because some GWBs consist of several polygons with identical “EU_CD_GW”even in the same horizon. Some further GWB characteristics are provided with the Microsoft Excel file “GWB_attributes_2012June.xls” including the column “EU_CD_GW”, which serves as a key for joining spatial and attribute data. There is no corresponding spatial data for GWBs in the Microsoft Excel table without an entry in column “EU_CD_GW”. The spatial resolution is given for about a half of the GWBs in the column “Scale” of the xls file, which is varying between the MS from 1 : 10,000 to 1 : 1,000,000 and mostly in the range from 1 : 50,000 to 1 : 250,000. The processing of some of the GWB shape files by GIS routines as clip or intersect in combination with a test polygon resulted in errors. Therefore a correction of erroneous topological features causing routine failures was carried out. However, the GWB layer includes a multitude of in parts very tiny, distinct areas resulting in a highly detailed or fragmented pattern. In certain parts topological inconsistencies appear quite frequently and delineation methodologies are currently varying between the MS in terms of size and three dimensional positioning of GWBs. This version of the dataset has to be considered as a first step towards a consistent GWB picture throughout Europe, but it is not yet of a sufficient quality to support spatial analyses i.e. it is not a fully developed reference GIS dataset. Therefore, the layer is published as a preliminary version and use of this data is subject to certain restrictions outlined in the explanatory notes.

It should be underlined that the methodology used is still under discussion (Working Group C -Groundwater) and is not fully harmonised throughout the EU MS.

For the external publication the whole United Kingdom had to be removed due to licensing restrictions.

This data layer references data from a high-resolution tree canopy change-detection layer for Seattle, Washington. Tree canopy change was mapped by using remotely sensed data from two time periods (2016 and 2021). Tree canopy was assigned to three classes: 1) no change, 2) gain, and 3) loss. No change represents tree canopy that remained the same from one time period to the next. Gain represents tree canopy that increased or was newly added, from one time period to the next. Loss represents the tree canopy that was removed from one time period to the next. Mapping was carried out using an approach that integrated automated feature extraction with manual edits. Care was taken to ensure that changes to the tree canopy were due to actual change in the land cover as opposed to differences in the remotely sensed data stemming from lighting conditions or image parallax. Direct comparison was possible because land-cover maps from both time periods were created using object-based image analysis (OBIA) and included similar source datasets (LiDAR-derived surface models, multispectral imagery, and thematic GIS inputs). OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, _location, size, and shape) into the classification process. A series of morphological procedures were employed to ensure that the end product is both accurate and cartographically pleasing. No accuracy assessment was conducted, but the dataset was subjected to manual review and correction.University of Vermont Spatial Analysis LaboratoryThis dataset consists of City of Seattle Council District areas as they existed in the first comparison year (2016) which cover the following tree canopy categories:Existing tree canopy percentPossible tree canopy - vegetation percentRelative percent changeAbsolute percent changeFor more information, please see the 2021 Tree Canopy Assessment.

This dataset collection contains GIS layers for creating the AIMS eReefs visualisation maps (https://ereefs.aims.gov.au/). These datasets are useful for creating A4 printed maps of the Great Barrier Reef and the Coral Sea. It contains the following datasets:
- Countries - Australia plus surrounding countries at 1:10M scale. Crop of Natural Earth Data 1:10 Admin 0 - Countries dataset. Allows filtering out of surrounding countries.
- Cities - 21 Cities along the Queensland coastline.
- Basins - Drainage basins adjacent to the Great Barrier Reef along the eastern Queensland coastline. Derived from Geoscience Australia River Basins 1997 dataset. It is a subset and reprojection.
- Land and Basins - This layer contains both Queensland and PNG land areas, along with the river basins along the eastern Queensland coastline. This is an integrated layer that represents both the background land area and the river basins all in one layer. This layer saves having to map the land area, then overlay the river basins. In this way each polygon only needs to be rendered once. The goal of this layer is to optmise the rendering time of the eReefs base map. This dataset is made up from the Geoscience Australia Australia's River Basins 1997 dataset for the Queensland coastline and the eastern Queensland basins. PNG is copied from Natural Earth Data 10 m countries dataset.
- Rivers - Rivers that drain along the Queensland eastern coast. This is a subset of the Geoscience Australia Geodata Topo 1:5M 2004.
- Reefs - Boundaries of reefs in GBR, Torres Strait and Coral Sea. In the Coral Sea it contains the atoll platform boundaries rather than the individual reefs. This is derived from the GBRMPA GBR features dataset, AIMS Torres Strait features dataset and the AIMS Coral Sea features dataset. These were combined and simplified to a scale of 1:1M. Note that this simplification resulted in multiple neighbouring reefs being grouped together. This dataset is intended for visual rendering of maps.
- Clip regions - Polygons for clipping eReefs data to the GBR. Also contains approximate polygons for Coral Sea, Torres Strait, PNG and New Caledonia. This was created principally for setting the region attribute for the Reefs dataset, but was made available as it is useful for clipping eReefs data to the GBR for plotting purposes.

Methods:
Most of the base map layers are derived from a variety of data sources. The full workflow used to transform these source datasets is documented on GitHub (https://github.com/eatlas/GBR_AIMS_eReefs-basemap).

Limitations of the data:
The datasets in this collection have been cropped and simplified for the purposes of creating low detail printed maps of the GBR. They are not intended for creating a high resolution base map.

Format of the data:
Shapefile and GeoJSON files. The Cities dataset is provided as a CSV file.

Location of the data:
This dataset is filed in the eAtlas enduring data repository at: data\custodian\2018-2024-eReefs\GBR_AIMS_eReefs-basemap

We were required to Georeference topographical maps which had been shared. I digitised a polygon shapefile within the mosaicked image. I used the polygon to clip the raster dataset in which I digitised line, polygon and point features within the clipped raster. The final product was a map of Meru which is shown. We added Kenya counties layer, Kenya schools layer, Kenya health layer and Kenya streets layer to Arcmap. I then clipped my respective county which is Laikipia County,in Kenya. I then clipped the added layers to fit my county so that I could process the required data. I buffered health layer so that it could help me know which schools were within 120 m from the health facilities. Also, i buffered steets to 55m from the schools to know which were closest and their accessibility. This data was to be used by the Ministry of Health to plan for polio vaccination in the county. The finished product was a map as shown below.

Abstract

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

This dataset is a GIS layer of Victorian groundwater-surface water interaction data clipped to the Gippsland region.

Groundwater generally interacts with surface water through various processes and pathways. The development of ground water often has impacts on surface streams and visa versa. The data results from an assessment of the SW-GW interaction across the study area. The data describes GW and SW interaction in four broad classes : neutral/losing, gaining, variable and unclassified. The Australian Hydrological Geospatial Fabric Surface Hydrology Catchments dataset was used as the base layer for considering surface water as an asset. This dataset provided mapped major rivers for Victoria. Numerous investigations into the interaction between groundwater and surface water have occurred across Victoria. Where this information was available in a format suitable for use in a GIS, it was collated to inform a state wide dataset of groundwater and surface water interaction. The resulting product attributes the stream base layer (described above) with the following classification of groundwater and surface water interaction: Gaining//Variable//Neutral/Losing//Unclassified

Dataset History

GIS data based on DSE, 2012. Victorian groundwater-surface water interaction spatial data. Compiled for the Secure Allocation, Future Entitlements (SAFE) Project by the Department of Sustainability and Environment, Victoria.

The method used to generate the dataset of groundwater and surface water interaction in Victoria can be summarised as: (1) Where a hydrogeochemistry study by Monash University was available to provide an indication of groundwater and surface water interaction it was adopted with high reliability; (2) Where a Murray-Darling Basin sustainable yields groundwater and surface water interaction study was available, it was adopted with medium reliability; (3) Where Melbourne Water Corporation (MWC) had indicated river reaches characterised by remnant pools of water in prolonged drought, this was considered to be indicative of gaining river conditions, with medium reliability; (4) Where a baseflow indices study was available, it was indicative of a gaining river reach and assigned medium reliability; (5) Where no other information existed and the river was classified as "unmodified", it was assumed to be gaining if it was perennial and neutral/losing if it was not perennial. These river reaches were assigned a low reliability; (6) Where no other information existed and the river was classified as "modified", an indication of groundwater-surface water interaction could not occur, given that the modification of flows could be responsible for the perenniality of the river reach, as opposed to being indicative of losing or gaining groundwater; (7) A reliability dataset was also generated that indicates the assumed reliability of the classification, which was directly correlated to the reliability of the input data.

Dataset Citation

Bioregional Assessment Programme (XXXX) Gippsland region clip of Victorian groundwater-surface water interaction. Bioregional Assessment Derived Dataset. Viewed 05 October 2018, http://data.bioregionalassessments.gov.au/dataset/e889b765-9822-4d0a-aef6-99071b0ec7ad.

Dataset Ancestors

• Derived From Groundwater and Surface Water Interaction Victoria

Tree canopy was derived from high-resolution remotely sensed data -- 2018 NAIP and 2019 LiDAR. Object-based image analysis techniques (OBIA) were employed to extract potential tree canopy and trees using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:2000 and all observable errors were corrected.

The National Wetlands Inventory (NWI) dataset was downloaded from the US Fish and Wildlife Service website at https://www.fws.gov/wetlands/Data/State-Downloads.html on 05/04/2021. Metadata derives from original NWI database and updated to reflect the date of download. According to website, this layer was updated last October 1st, 2020. This is a clip of all wetlands features within the Westchester County boundary minus all riverines, Long Island Sound, and the Hudson River.This data set represents the extent, approximate location and type of wetlands and deepwater habitats in the United States and its Territories. These data delineate the areal extent of wetlands and surface waters as defined by Cowardin et al. (1979). The National Wetlands Inventory - Version 2, Surface Waters and Wetlands Inventory was derived by retaining the wetland and deepwater polygons that compose the NWI digital wetlands spatial data layer and reintroducing any linear wetland or surface water features that were orphaned from the original NWI hard copy maps by converting them to narrow polygonal features. Additionally, the data are supplemented with hydrography data, buffered to become polygonal features, as a secondary source for any single-line stream features not mapped by the NWI and to complete segmented connections. Wetland mapping conducted in WA, OR, CA, NV and ID after 2012 and most other projects mapped after 2015 were mapped to include all surface water features and are not derived data. The linear hydrography dataset used to derive Version 2 was the U.S. Geological Survey's National Hydrography Dataset (NHD). Specific information on the NHD version used to derive Version 2 and where Version 2 was mapped can be found in the 'comments' field of the Wetlands_Project_Metadata feature class. Certain wetland habitats are excluded from the National mapping program because of the limitations of aerial imagery as the primary data source used to detect wetlands. These habitats include seagrasses or submerged aquatic vegetation that are found in the intertidal and subtidal zones of estuaries and near shore coastal waters. Some deepwater reef communities (coral or tuberficid worm reefs) have also been excluded from the inventory. These habitats, because of their depth, go undetected by aerial imagery. By policy, the Service also excludes certain types of "farmed wetlands" as may be defined by the Food Security Act or that do not coincide with the Cowardin et al. definition. Contact the Service's Regional Wetland Coordinator for additional information on what types of farmed wetlands are included on wetland maps. This dataset should be used in conjunction with the Wetlands_Project_Metadata layer, which contains project specific wetlands mapping procedures and information on dates, scales and emulsion of imagery used to map the wetlands within specific project boundaries.

This data set was processed by BORIS staff from the original vector data of species, crown closure, cutting class, and site classification/subtype into raster files. The original polygon data were received from Linnet Graphics, the distributor of data for MNR. In the case of the species layer, the percentages of species composition were removed. This reduced the amount of information contained in the species layer of the gridded product, but it was necessary in order to make the gridded product easier to use. The original maps were produced from 1:15,840-scale aerial photography collected in 1988.

This category of planning priorities in the CEC 2023 Land-Use Screens provides an estimate of terrestrial landscape condition based on the extent to which human impacts such as agriculture, urban development, natural resource extraction, and invasive species have disrupted the landscape across the State of California. It is based on the open-source logic modeling framework Environmental Evaluation Modeling System (EEMS) developed by Conservation Biology Institute (CBI). This multicriteria evaluation model result, last updated in 2016 and resolved at 1-kilometer square, spans values ranging from -1 to 1. The higher end of the spectrum indicates areas that are relatively intact based on the more than 30 input variables, and values in the lower end of the spectrum indicate where these human impacts to disturb the landscape and ecological function are relatively high.¹

In the adapted version of the CBI Terrestrial Landscape Intactness given here, the dataset is partitioned into high and low categories based on the mean. Values of the dataset that lie above 0.3 are considered highly intact and are used as an exclusion. Values of the dataset that are less than or equal to 0.3 are allowed to remain in consideration for resource potential. Applying the partition at the mean allows for lands that are relatively more intact than disturbed to be considered for resource potential. The high category of landscape intactness given by this dataset is used as an exclusion in both the Core and SB 100 Terrestrial Climate Resilience Study screens.

This layer is featured in the CEC 2023 Land-Use Screens for Electric System Planning data viewer.

More information about this layer and its use in electric system planning is available in the Land Use Screens Staff Report in the CEC Energy Planning Library.

[1] Degagne, R., J. Brice, M. Gough, T. Sheehan, and J. Strittholt. 2016. “Terrestrial Landscape Intactness 1 kilometer, California.” Conservation Biology Institute.https://databasin.org/datasets/e3ee00e8d94a4de58082fdbc91248a65/

Last updated on 06/17/2022

Overview

The national fire history perimeter data layer of conglomerated Agency Authoratative perimeters was developed in support of the WFDSS application and wildfire decision support for the 2021 fire season. The layer encompasses the final fire perimeter datasets of the USDA Forest Service, US Department of Interior Bureau of Land Management, Bureau of Indian Affairs, Fish and Wildlife Service, and National Park Service, the Alaska Interagency Fire Center, CalFire, and WFIGS History. Perimeters are included thru the 2021 fire season. Requirements for fire perimeter inclusion, such as minimum acreage requirements, are set by the contributing agencies.

WFIGS, NPS and CALFIRE data now include Prescribed Burns.

Data Input

Several data sources were used in the development of this layer:

• Alaska fire history
• USDA FS Regional Fire History Data
• BLM Fire Planning and Fuels
• National Park Service - Includes Prescribed Burns
• Fish and Wildlife Service
• Bureau of Indian Affairs
• CalFire FRAS - Includes Prescribed Burns
• WFIGS - BLM & BIA and other S&L

Data Limitations

Fire perimeter data are often collected at the local level, and fire management agencies have differing guidelines for submitting fire perimeter data. Often data are collected by agencies only once annually. If you do not see your fire perimeters in this layer, they were not present in the sources used to create the layer at the time the data were submitted. A companion service for perimeters entered into the WFDSS application is also available, if a perimeter is found in the WFDSS service that is missing in this Agency Authoratative service or a perimeter is missing in both services, please contact the appropriate agency Fire GIS Contact listed in the table below.

Abstract

The dataset was derived by the Bioregional Assessment Programme from multiple source datasets.

The source datasets are identified in the Lineage field in this metadata statement.

The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

This dataset contains rasters of the 5th, 50th and 95th percentile of drawdown in the model layers as well as grids of the probability of exceeding 0.2, 2 and 5 m drawdown as estimated with the GAL AEM model. The original rasters have been modified slightly so as to produce maps. These maps are an essential part of the visualisation of groundwater model results in Peeters et al. 2017). The process involved in creating these is described in the history.

Peeters L, Ransley T, Turnadge C, Kellett J, Harris-Pascal C, Kilgour P and Evans T (2016) Groundwater numerical modelling for the Galilee subregion. Product 2.6.2 for the Galilee subregion from the Lake Eyre Basin Bioregional Assessment. Department of the Environment, Bureau of Meteorology, CSIRO and Geoscience Australia, Australia.

Dataset History

The Galilee drawndown rasters were derived from GAL_quantile_interpolation_v2 (c7c4f7d0-ed25-475b-abcc-de7a1cbca3c3) using the ArcGIS platform for the purposes of creating maps.

The rasters were created by converting the original ascii grids to tiffs and then clipping the tiffs to the appropriate extents.

Both the Clematis and BCB rasters were clipped using datasets from the GAL_Model_Aquifer_Extents (5afbf7f1-1ee0-444b-9f77-dbad8d8de95b). The Clematis rasters were clipped using Clematis_warang_top_extent and the BCB rasters were clipped using Bandanna_top_extent.

Dataset Citation

Bioregional Assessment Programme (2016) Galilee Drawdown Rasters. Bioregional Assessment Derived Dataset. Viewed 12 December 2018, http://data.bioregionalassessments.gov.au/dataset/fa841a4b-810a-4768-950c-b6e35532cb4c.

Dataset Ancestors

• Derived From Surface Geology of Australia, 1:2 500 000 scale, 2012 edition

• Derived From Queensland Geological Digital Data - Detailed state extent, regional. November 2012

• Derived From Galilee model HRV receptors gdb

• Derived From Queensland petroleum exploration data - QPED

• Derived From Galilee groundwater numerical modelling AEM models

• Derived From Galilee drawdown grids

• Derived From Three-dimensional visualisation of the Great Artesian Basin - GABWRA

• Derived From QLD Department of Natural Resources and Mines Groundwater Database Extract 20142808

• Derived From Galilee Hydrological Response Variable (HRV) model

• Derived From Geoscience Australia GEODATA TOPO series - 1:1 Million to 1:10 Million scale

• Derived From Galilee Groundwater Model, Hydrogeological Formation Extents v01

• Derived From GAL Aquifer Formation Extents v01

• Derived From GAL Aquifer Formation Extents v02

• Derived From Phanerozoic OZ SEEBASE v2 GIS