Data

Queensland Southeast Queensland Bioregion Spatial BioCondition, 2019, Version 1.0

Terrestrial Ecosystem Research Network
Pennay, Chris ; Morales, Lucia ; Hardtke, Leonardo
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=http://geonetwork.tern.org.au/geonetwork/srv/eng/catalog.search#/metadata/2c33325c-1dd5-4674-918a-1cd5bfc1a6e3&rft.title=Queensland Southeast Queensland Bioregion Spatial BioCondition, 2019, Version 1.0&rft.identifier=http://geonetwork.tern.org.au/geonetwork/srv/eng/catalog.search#/metadata/2c33325c-1dd5-4674-918a-1cd5bfc1a6e3&rft.publisher=Terrestrial Ecosystem Research Network&rft.description=This is Version 1 of the Southeast Queensland Bioregion Spatial BioCondition dataset. It is superseded by the Version 2 dataset that can be found at: https://doi.org/10.25901/r976-1v85. This is a spatial dataset comprising predictions of vegetation condition for biodiversity for the Southeast Queensland Bioregion. The dataset was created using a gradient boosting decision tree (GBDT) model based on eight vegetation specific remote sensing (RS) datasets and 17,000 training sites of known vegetation community and condition state. Condition score was modelled as a function of the difference in the RS space within homogeneous vegetation communities. The product is intended to represent predicted BioCondition for year 2019 rather than any single date.The Spatial BioCondition 2019 version 1.0 dataset (SBC) was produced by the Queensland Herbarium and Biodiversity Science and the Remote Sensing Sciences business units in the Queensland Department of Environment and Science. The pixel values in SBC dataset represent the predicted condition of vegetation for biodiversity in 2019. The range is 0-100, where lower values indicate poorer condition. No data is represented by a value of 255. No data include: Regional Ecosystems (RE) with insufficient training and reference data to apply the framework Marine, intertidal, native grassland and predominantly unvegetated ecosystems defined in RE preclearing Urban, suburban, commercial, and industrial areas as defined by the Queensland Land Use Mapping Program dataset (https://qldspatial.information.qld.gov.au/catalogue/custom/detail.page?fid=%7B273F1E50-DD95-4772-BD6C-5C1963CAA594%7D). Condition of vegetation for biodiversity may be influenced by agricultural practices, grazing land management, inappropriate fire regimes, urban development, incursion of non-native species, industrial logging, and mining. Queensland has a site-based vegetation condition assessment framework ‘BioCondition’ which assesses the relative capacity of an ecosystem to support the suite of species expected to occur in its relatively undisturbed (reference) state. This is measured using a set of compositional, structural, and functional vegetation attributes which are compared against a reference. The greater the difference from the reference state the worse the condition. The reference state characteristics (the benchmark) are derived from a set of sites in the same vegetation community that are known to be in the best available condition. SBC moves the assessment of vegetation condition for biodiversity from a site-based approach to a predictive modelling approach that can be applied at the regional or state scale. It is based on the premise that the greater the difference (measured as distance in multi-dimensional RS space) from the RS reference, the worse the condition. The model is developed using the remote sensing datasets as predictor variables and training sites with known RE and condition state as the response variable. The resulting model is applied to all vegetated areas to produce predictions of condition. The dataset comprises three bands. Band 2 is the predicted BioCondition score 0-100, with higher values representing better vegetation condition for biodiversity. B1 and B3 show the upper and lower boundary of the 90% prediction interval, that is the likely range in which the true value of the prediction will be.Data CreationThis dataset was created using the Spatial BioCondition modelling workflow. The model uses the following datasets: Sentinel 2 based green and bare fractional cover statistics (2017-2019); Landsat derived fractional cover for the 2019 dry season; Sentinel 2 NDVI derived phenological metrics; Regional ecosystem pre-clearing dataset - version12.2; Selected vegetation field survey data held in departmental databases.The final model output was clipped to the IBRA7 Southeast Queensland bioregion boundary, and the following areas were masked: The pre-clearing extent of: natural grasslands; predominantly unvegetated ecosystems; regional ecosystems extra to the Brigalow Belt and Southeast Queensland bioregions defined by version 12.2 regional ecosystem mapping; Built environments and infrastructure (urban, suburban, commercial and industrial areas) defined by Queensland land use mapping.Masked areas, pixels without predictions, and pixels outside the bioregion are classified as No Data (DN = 255). Band 1 is the 5th percentile of the prediction interval, Band 2 is the predicted BioCondition score and Band 3 is the 95th percentile of the prediction interval.Progress Code: supersededMaintenance and Update Frequency: notPlanned&rft.creator=Pennay, Chris &rft.creator=Morales, Lucia &rft.creator=Hardtke, Leonardo &rft.date=2024&rft.edition=1.0&rft.coverage=Southeast Queensland bioregion.&rft.coverage=northlimit=-26.119; southlimit=-28.42; westlimit=151.25; eastLimit=153.72; projection=EPSG:3577&rft_rights=Creative Commons Attribution 4.0 International Licence http://creativecommons.org/licenses/by/4.0&rft_rights=TERN services are provided on an as-is and as available basis. Users use any TERN services at their discretion and risk. They will be solely responsible for any damage or loss whatsoever that results from such use including use of any data obtained through TERN and any analysis performed using the TERN infrastructure. <br />Web links to and from external, third party websites should not be construed as implying any relationships with and/or endorsement of the external site or its content by TERN. <br /><br />Please advise any work or publications that use this data via the online form at https://www.tern.org.au/research-publications/#reporting&rft_rights=Please cite this dataset as {Author} ({PublicationYear}). {Title}. {Version, as appropriate}. Terrestrial Ecosystem Research Network. Dataset. {Identifier}.&rft_subject=environment&rft_subject=VEGETATION&rft_subject=EARTH SCIENCE&rft_subject=BIOSPHERE&rft_subject=Image Processing&rft_subject=INFORMATION AND COMPUTING SCIENCES&rft_subject=ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING&rft_subject=Spatial statistics&rft_subject=ECOLOGY&rft_subject=BIOLOGICAL SCIENCES&rft_subject=LANDSAT-7&rft_subject=LANDSAT-8&rft_subject=LANDSAT-9&rft_subject=SENTINEL-2A&rft_subject=SENTINEL-2B&rft_subject=MSI&rft_subject=vegetation condition (Unitless)&rft_subject=Unitless&rft_subject=1 meter - < 30 meters&rft_subject=one off&rft_subject=gradient boosting decision tree&rft.type=dataset&rft.language=English Access the data

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TERN services are provided on an "as-is" and "as available" basis. Users use any TERN services at their discretion and risk. They will be solely responsible for any damage or loss whatsoever that results from such use including use of any data obtained through TERN and any analysis performed using the TERN infrastructure.
Web links to and from external, third party websites should not be construed as implying any relationships with and/or endorsement of the external site or its content by TERN.

Please advise any work or publications that use this data via the online form at https://www.tern.org.au/research-publications/#reporting

Please cite this dataset as {Author} ({PublicationYear}). {Title}. {Version, as appropriate}. Terrestrial Ecosystem Research Network. Dataset. {Identifier}.

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Brief description

This is Version 1 of the Southeast Queensland Bioregion Spatial BioCondition dataset. It is superseded by the Version 2 dataset that can be found at: https://doi.org/10.25901/r976-1v85.

This is a spatial dataset comprising predictions of vegetation condition for biodiversity for the Southeast Queensland Bioregion. The dataset was created using a gradient boosting decision tree (GBDT) model based on eight vegetation specific remote sensing (RS) datasets and 17,000 training sites of known vegetation community and condition state. Condition score was modelled as a function of the difference in the RS space within homogeneous vegetation communities. The product is intended to represent predicted BioCondition for year 2019 rather than any single date.

Lineage


The Spatial BioCondition 2019 version 1.0 dataset (SBC) was produced by the Queensland Herbarium and Biodiversity Science and the Remote Sensing Sciences business units in the Queensland Department of Environment and Science.

The pixel values in SBC dataset represent the predicted condition of vegetation for biodiversity in 2019. The range is 0-100, where lower values indicate poorer condition. No data is represented by a value of 255. No data include:
  • Regional Ecosystems (RE) with insufficient training and reference data to apply the framework
  • Marine, intertidal, native grassland and predominantly unvegetated ecosystems defined in RE preclearing
  • Urban, suburban, commercial, and industrial areas as defined by the Queensland Land Use Mapping Program dataset (https://qldspatial.information.qld.gov.au/catalogue/custom/detail.page?fid=%7B273F1E50-DD95-4772-BD6C-5C1963CAA594%7D).
Condition of vegetation for biodiversity may be influenced by agricultural practices, grazing land management, inappropriate fire regimes, urban development, incursion of non-native species, industrial logging, and mining. Queensland has a site-based vegetation condition assessment framework ‘BioCondition’ which assesses the relative capacity of an ecosystem to support the suite of species expected to occur in its relatively undisturbed (reference) state. This is measured using a set of compositional, structural, and functional vegetation attributes which are compared against a reference. The greater the difference from the reference state the worse the condition. The reference state characteristics (the benchmark) are derived from a set of sites in the same vegetation community that are known to be in the best available condition. SBC moves the assessment of vegetation condition for biodiversity from a site-based approach to a predictive modelling approach that can be applied at the regional or state scale. It is based on the premise that the greater the difference (measured as distance in multi-dimensional RS space) from the RS reference, the worse the condition. The model is developed using the remote sensing datasets as predictor variables and training sites with known RE and condition state as the response variable. The resulting model is applied to all vegetated areas to produce predictions of condition.

The dataset comprises three bands. Band 2 is the predicted BioCondition score 0-100, with higher values representing better vegetation condition for biodiversity. B1 and B3 show the upper and lower boundary of the 90% prediction interval, that is the likely range in which the true value of the prediction will be.

Data Creation
This dataset was created using the Spatial BioCondition modelling workflow. The model uses the following datasets: Sentinel 2 based green and bare fractional cover statistics (2017-2019); Landsat derived fractional cover for the 2019 dry season; Sentinel 2 NDVI derived phenological metrics; Regional ecosystem pre-clearing dataset - version12.2; Selected vegetation field survey data held in departmental databases.
The final model output was clipped to the IBRA7 Southeast Queensland bioregion boundary, and the following areas were masked: The pre-clearing extent of: natural grasslands; predominantly unvegetated ecosystems; regional ecosystems extra to the Brigalow Belt and Southeast Queensland bioregions defined by version 12.2 regional ecosystem mapping; Built environments and infrastructure (urban, suburban, commercial and industrial areas) defined by Queensland land use mapping.
Masked areas, pixels without predictions, and pixels outside the bioregion are classified as No Data (DN = 255). Band 1 is the 5th percentile of the prediction interval, Band 2 is the predicted BioCondition score and Band 3 is the 95th percentile of the prediction interval.

Progress Code: superseded
Maintenance and Update Frequency: notPlanned

Notes

Credit
We at TERN acknowledge the Traditional Owners and Custodians throughout Australia, New Zealand and all nations. We honour their profound connections to land, water, biodiversity and culture and pay our respects to their Elders past, present and emerging.
Purpose
Spatial BioCondition (SBC) is a mapping framework that aligns with Queensland’s Regional Ecosystem (RE) and BioCondition frameworks, it integrates site-based vegetation condition assessment methods and remote sensing (RS) to provide predictions of the condition of vegetation for biodiversity across most terrestrial ecosystems in Queensland. There is an increasing requirement for new vegetation information to support current and emergent drivers in natural resource management. The SBC framework has been developed to support reforms to the Queensland Vegetation Management Act 1999 that aim to provide more holistic reporting on vegetation extent and condition in Queensland. This initial version provides predictions of the condition of vegetation for biodiversity in 2019 for the southeast Queensland bioregion.
Data Quality Information

Data Quality Assessment Scope
local : dataset
The model Mean Absolute Error (MAE) for predicted BioCondition scores is 15.0. This RMSE is based on 231 independent field observations collected during 2022. The estimated rate of error for predicted BioCondition scores is lowest at high and low scores (<40 and >60) and has higher estimated error for mid-range scores.

Positional Accuracy Report
uri : https://sentinel.esa.int/documents/247904/685211/Sentinel-2_L1C_Data_Quality_Report

Data Quality Assessment Result
local : Quality Result
The data set was generated from Sentinel-2 imagery. In July 2018 ESA reported a geometric accuracy of 12 m (95% confidence).

Created: 2023-06-20

Issued: 2024-09-26

Modified: 2024-09-26

Data time period: 2019-01-01

This dataset is part of a larger collection

Click to explore relationships graph

153.72,-26.119 153.72,-28.42 151.25,-28.42 151.25,-26.119 153.72,-26.119

152.485,-27.2695

text: Southeast Queensland bioregion.