Data

Queensland Spatial BioCondition Data Collection

Terrestrial Ecosystem Research Network
Verrall, Brodie ; Hardtke, Leonardo ; Pennay, Chris
Viewed: [[ro.stat.viewed]] Cited: [[ro.stat.cited]] Accessed: [[ro.stat.accessed]]
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/e79c49ae-ee0c-4352-b65b-76d6e76784c4&rft.title=Queensland Spatial BioCondition Data Collection&rft.identifier=http://geonetwork.tern.org.au/geonetwork/srv/eng/catalog.search#/metadata/e79c49ae-ee0c-4352-b65b-76d6e76784c4&rft.publisher=Terrestrial Ecosystem Research Network&rft.description=This is a series comprises of vegetation condition predictions for biodiversity for the bioregions of Queensland. The datasets were created using a gradient boosting decision tree (GBDT) model based on 10 vegetation-specific remote sensing (RS) datasets and 7,938 training sites of known vegetation community and condition state across Southeast Queensland, Brigalow Belt and Central Queensland Coast bioregions. Condition score was modelled as a function of distance in the remote sensing (RS) space within homogeneous vegetation communities. The product is intended to represent predicted BioCondition for 2021 rather than any singe date. This series includes information relating the version 2.0 products of Spatial BioCondition, which have superseded the version 1.0 products (https://portal.tern.org.au/metadata/TERN/40990eec-5cef-41fe-976b-18286419da0c, https://portal.tern.org.au/metadata/TERN/2c33325c-1dd5-4674-918a-1cd5bfc1a6e3). Spatial BioCondition is not suitable for the measurement of changes in condition over time, and direct comparisons of predictions between versions 1.0 and 2.0 are not advised.The Spatial BioCondition 2021 version 2.0 dataset (SBC) was produced by the Queensland Herbarium and Biodiversity Science and the Remote Sensing Sciences business units in the Queensland Department of Environment, Science and Innovation. The pixel values in SBC dataset represent the predicted condition of vegetation for biodiversity in 2021. The range is 0-100, where lower values indicate poorer condition. No Data is represented by a value of 255. No Data include areas where: a. Regional Ecosystems with insufficient training and reference data to apply the framework b. Marine, intertidal, native grassland, sedgeland, forbland and predominantly unvegetated ecosystems defined in RE preclearing c. Urban, suburban, commercial, and industrial areas including intensive use lakes, estuaries, canals, dams and reservoirs as defined by Queensland Land Use Mapping dataset (https://qldspatial.information.qld.gov.au/catalogue/custom/detail.page?fid={BE30CE16-B1B9-48B1-BF21-DBE70597FA93}) Condition of vegetation for biodiversity may be influenced by agricultural practices, grazing land management, fire regimes and wildfire, urban development, incursion of invasive 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 suite 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 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 remote sensing space) from the 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 with sufficient training and reference data 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. Bands 1 and 3 show the lower and upper boundary of the 90% prediction interval, respectively. This prediction interval provides a 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 (2019-2021); Landsat-derived fractional cover for the 2021 dry season; Sentinel 2 NDVI-derived phenological metrics; Landsat- and GEDI-derived canopy height (2019); Regional ecosystem pre-clearing dataset - version 13.0; and selected vegetation field survey data held in departmental databasesThe final model output was clipped to the Brigalow Belt, Central Queensland Coast and Southeast Queensland biogeographic bioregion boundaries, and the following areas were masked: the pre-clearing extent of natural grasslands, sedgelands and forblands; predominantly unvegetated ecosystems; and regional ecosystems extra to the Brigalow Belt, Central Queensland Coast and Southeast Queensland bioregions defined by version 13.0 regional ecosystem mapping; built environments and infrastructure (urban, suburban, commercial and industrial areas including intensive use lakes, estuaries, canals, dams and reservoirs) defined by Queensland Land Use Mapping dataset (https://qldspatial.information.qld.gov.au/catalogue/custom/detail.page?fid={BE30CE16-B1B9-48B1-BF21-DBE70597FA93})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 Spatial BioCondition score and Band 3 is the 95th percentile of the prediction intervalProgress Code: completedMaintenance and Update Frequency: notPlanned&rft.creator=Verrall, Brodie &rft.creator=Hardtke, Leonardo &rft.creator=Pennay, Chris &rft.date=2023&rft.edition=2.0&rft.coverage=Queensland&rft.coverage=northlimit=-9.067217; southlimit=-29.177898; westlimit=137.994642; eastLimit=153.552945; 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=vegetation condition (Unitless)&rft_subject=Unitless&rft_subject=1 meter - < 30 meters&rft_subject=Annual&rft_subject=biocondition&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 a series comprises of vegetation condition predictions for biodiversity for the bioregions of Queensland. The datasets were created using a gradient boosting decision tree (GBDT) model based on 10 vegetation-specific remote sensing (RS) datasets and 7,938 training sites of known vegetation community and condition state across Southeast Queensland, Brigalow Belt and Central Queensland Coast bioregions. Condition score was modelled as a function of distance in the remote sensing (RS) space within homogeneous vegetation communities. The product is intended to represent predicted BioCondition for 2021 rather than any singe date. This series includes information relating the version 2.0 products of Spatial BioCondition, which have superseded the version 1.0 products (https://portal.tern.org.au/metadata/TERN/40990eec-5cef-41fe-976b-18286419da0c, https://portal.tern.org.au/metadata/TERN/2c33325c-1dd5-4674-918a-1cd5bfc1a6e3). Spatial BioCondition is not suitable for the measurement of changes in condition over time, and direct comparisons of predictions between versions 1.0 and 2.0 are not advised.

Lineage

The Spatial BioCondition 2021 version 2.0 dataset (SBC) was produced by the Queensland Herbarium and Biodiversity Science and the Remote Sensing Sciences business units in the Queensland Department of Environment, Science and Innovation. The pixel values in SBC dataset represent the predicted condition of vegetation for biodiversity in 2021. The range is 0-100, where lower values indicate poorer condition. No Data is represented by a value of 255. No Data include areas where: a. Regional Ecosystems with insufficient training and reference data to apply the framework b. Marine, intertidal, native grassland, sedgeland, forbland and predominantly unvegetated ecosystems defined in RE preclearing c. Urban, suburban, commercial, and industrial areas including intensive use lakes, estuaries, canals, dams and reservoirs as defined by Queensland Land Use Mapping dataset (https://qldspatial.information.qld.gov.au/catalogue/custom/detail.page?fid={BE30CE16-B1B9-48B1-BF21-DBE70597FA93}) Condition of vegetation for biodiversity may be influenced by agricultural practices, grazing land management, fire regimes and wildfire, urban development, incursion of invasive 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 suite 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 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 remote sensing space) from the 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 with sufficient training and reference data 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. Bands 1 and 3 show the lower and upper boundary of the 90% prediction interval, respectively. This prediction interval provides a 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 (2019-2021); Landsat-derived fractional cover for the 2021 dry season; Sentinel 2 NDVI-derived phenological metrics; Landsat- and GEDI-derived canopy height (2019); Regional ecosystem pre-clearing dataset - version 13.0; and selected vegetation field survey data held in departmental databases
The final model output was clipped to the Brigalow Belt, Central Queensland Coast and Southeast Queensland biogeographic bioregion boundaries, and the following areas were masked: the pre-clearing extent of natural grasslands, sedgelands and forblands; predominantly unvegetated ecosystems; and regional ecosystems extra to the Brigalow Belt, Central Queensland Coast and Southeast Queensland bioregions defined by version 13.0 regional ecosystem mapping; built environments and infrastructure (urban, suburban, commercial and industrial areas including intensive use lakes, estuaries, canals, dams and reservoirs) defined by Queensland Land Use Mapping dataset (https://qldspatial.information.qld.gov.au/catalogue/custom/detail.page?fid={BE30CE16-B1B9-48B1-BF21-DBE70597FA93})
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 Spatial BioCondition score and Band 3 is the 95th percentile of the prediction interval

Progress Code: completed
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, as 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 of a bioregion. 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. Following version 1.0, this version predicts the condition of vegetation for biodiversity in 2021 for the Brigalow Belt, Central Queensland Coast and Southeast Queensland bioregion.
Data Quality Information

Data Quality Assessment Scope
local : dataset
Predicted Spatial BioCondition scores are validated against 270 independent field observations of BioCondition that are scored against Regional Ecosystem specific benchmarks. Stratified randomised sampling was used to ensure geographic spread of field sites as well as evenly distributed validation of predicted Spatial BioCondition condition scores.

Data Quality Assessment Result
local : Quality Result
Based on 270 independent field observations collected between 2022 and 2024, predicted Spatial BioCondition scores had a Mean Absolute Error (MAE) of 13.4 and an R squared of 0.70. 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.

Created: 2024-09-13

Issued: 2023-06-30

Modified: 2024-10-24

Data time period: 2021-01-01 to 2021-12-31

This dataset is part of a larger collection

153.55295,-9.06722 153.55295,-29.1779 137.99464,-29.1779 137.99464,-9.06722 153.55295,-9.06722

145.7737935,-19.1225575

text: Queensland