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 This series includes information relating the version 2.0 products of Spatial BioCondition, which have superseded the version 1.0 products (https://portal.tern.org.au/metadata/TERN/40990eec-5cef-41fe-976b-18286419da0c,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.

High quality passive infrared wildlife cameras were used to acquire information on faunal biodiversity at the Robson Creek site. Two camera traps were deployed at the site between 17-03-2018 and 25-07-2018. The first camera located in proximity to the acoustic sensor SM2/SM4 which is around 100m from the flux tower and at a height of 1.5 meter above ground, on a star picket. The second camera located for a short while near the tower (10 meter) and was attached on a bungy cord tied to a tree, at a height of 0.3 meter above ground.<br><br> The Robson Creek site lies on the Atherton Tablelands in the wet tropical rain forests of Australia at 680-740 m elevation. It is situated in Danbulla National Park within the Wet Tropics World Heritage Area. The Wet Tropics Bioregion of Australia is situated on the north-eastern coast of Queensland, between Cooktown to the north and Townsville to the south. Approximately 40% (7200 km2) of the region is covered by rain forest. Features of the region include very high plant and animal endemism, characteristics of both Gondwanan and Indo-Malaysian forests, and frequent cyclonic disturbance. The site includes core 1 ha plot (100 m x 100 m) which is located within the fetch of the flux tower and is the focal site of recurrent monitoring, and 25 ha vegetation survey plot. The vegetation survey plot has been set up for inclusion in the Smithsonian Tropical Research Institute’s Center for Tropical Forest Science – Forest Global Earth Observatory (CTFS-ForestGEO) global network of forest research plots. <br><br> For additional site information, see https://www.tern.org.au/tern-observatory/tern-ecosystem-processes/robson-creek-rainforest-supersite/ . <br /><br /> Other images collected at the site include time-lapse images taken from 3 phenocams (above canopy). <br /><br /> <iframe allow="autoplay; encrypted-media" allowfullscreen="" frameborder="0" src="https://www.youtube.com/watch?v=WW-cpPMhMz4" title="TERN Robson Creek SuperSite Wildlife 2017" style="height:248px;width:462px;"></iframe> <br />Camera trap results for the TERN FNQ Rainforest SuperSite - Robson Creek, Jan - Feb 2017.

High quality passive infrared wildlife cameras were used to acquire information on faunal biodiversity at the site. Two cameras were deployed from July to Dec 2018 and between March and May 2019. <br /><br /> The Gingin Banksia Woodland SuperSite was established in 2011 and is located in a natural woodland of high species diversity with an overstorey dominated by Banksia species. For additional site information, see https://www.tern.org.au/tern-observatory/tern-ecosystem-processes/gingin-banksia-woodland-supersite/. <br /> Other images collected at the site include digital cover photography, phenocam time-lapse images taken from fixed under and overstorey cameras and ancillary images of flora.

This dataset contains spatial layers describing Forest Canopy Loss and Recovery from 1998-2019 in NSW Regional Forest Agreements (RFA) Areas along the eastern coast. <br> These have been based off the National Greenhouse Gas Inventory (NGGI) National Carbon Accounting System (NCAS) National Forest and Sparse Woody Vegetation Data grids (ABARES, 2020). These base grids are Landsat in origin and have a resolution of 25m. <br> For this dataset product and the processing of metrics, aspects of canopy loss and disturbances in the forest estate were investigated. Measures of canopy loss and recovery are seen as one of the multiple indicators of forest health. This is related to agents or pressures that affect the capacity of native forests and commercial operations to maintain normal ecosystem functions and sustainably provide productive capacity. <br> To attribute disturbances, as a driver of change, a Multiple Lines of Evidence (MLE) approach was used that leveraged available spatial datasets. This allowed for a project-wide disturbance and disturbance context layer to be generated. This information can be interpreted back against forest cover extent change outputs, in particular the differences between individual years, to identify the areas of change and the likely reasons why. Therefore, landscape trends in forest loss can be potentially assigned or at the very least investigated. <br> The time taken, in terms of years, for areas to recover from losses in forest canopy cover extent can has also been determined. This process identifies the time taken for a patch of forest to return to a 20% canopy cover threshold, and other characteristics such as the forest type and likely disturbance or loss event. <br> Forest Canopy Loss and Recovery uses measures of canopy loss and disturbances which can be interpreted back against forest cover extent change outputs, in particular the differences between individual years, to identify the areas of change and the likely reasons why. Therefore, landscape trends in forest canopy loss can be potentially assigned or at the very least investigated. Time taken in years for areas to recover for losses has also been determined, as-well as other characteristics such as forest type and likely disturbance/loss event. <br> Base cover extent grids used are from the NSW RFA Historic Forest Canopy Cover Extent – 1995 to 2019 product. Read more about the project on the Natural Resources Commission website:<br> https://www.nrc.nsw.gov.au/accordion-content-main/fmip-ecosystemhealth-projectfe1<br> This dataset is superseded by 'NSW Forest Monitoring and Improvement Program State-Wide Historic Forest Canopy Loss and Recovery - 1998 to 2020'

This dataset contains spatial layers describing Forest Loss and Recovery from 1998-2020 in NSW. For this dataset product and the processing of metrics, aspects of canopy loss and disturbances in the forest estate were investigated. Measures of canopy loss and recovery are seen as one of the multiple indicators of forest health. This is related to agents or pressures that affect the capacity of native forests and commercial operations to maintain normal ecosystem functions and sustainably provide productive capacity. <br> To attribute disturbances, as a driver of change, a Multiple Lines of Evidence (MLE) approach was used that leveraged available spatial datasets. This allowed for a project-wide disturbance and disturbance context layer to be generated. This information can be interpreted back against forest cover extent change outputs, in particular the differences between individual years, to identify the areas of change and the likely reasons why. Therefore, landscape trends in forest loss can be potentially assigned or at the very least investigated. <br> The time taken, in terms of years, for areas to recover from losses in forest cover extent can has also been determined. This process identifies the time taken for a patch of forest to return to a 20% canopy cover threshold, and other characteristics such as the forest type and likely disturbance or loss event.<br> Base cover extent grids used are from the NSW State-wide Historic Forest Cover Extent – 1995 to 2020 product. These have been processed through a series of land use and vegetation type exclusion masking and a through a fuzzy-logic based certainty analysis to reflect a forest cover extent coverage for NSW that is reflective of past and current coverage. <br> Read more about the project on the Natural Resources Commission website:<br> https://www.nrc.nsw.gov.au/accordion-content-main/fmip-ecosystemhealth-projectfe1<br> This dataset supersedes "NSW Forest Monitoring and Improvement Program RFA Historic Forest Loss and Recovery – 1998 to 2019".

This dataset contains spatial layers describing Forest Connectivity from 1995-2019, in NSW Regional Forest Agreements (RFA) Areas along the eastern coast. Forest Connectivity accounts for the general quality of terrestrial habitats supporting biodiversity at each location, the fragmentation of habitat within its neighbourhood and how its position in the landscape contributes to connectivity among the habitats across a region. <br> These have been based off the National Greenhouse Gas Inventory (NGGI) National Carbon Accounting System (NCAS) National Forest and Sparse Woody Vegetation Data grids (ABARES, 2020). These base grids are Landsat in origin and have a resolution of 25m. <br> Forest Connectivity, including canopy cover connectivity and fragmentation is concerned and linked to forest condition. Concepts applied are to be aligned with definitions as found in the NSW Biodiversity Indicator Program (BIP) and the Spatial Links methodology for calculating connectivity.<br> Base cover extent grids used are from the NSW RFA Historic Forest Canopy Cover Extent – 1995 to 2019 product. <br> Read more about the project on the Natural Resources Commission website:<br> https://www.nrc.nsw.gov.au/accordion-content-main/fmip-ecosystemhealth-projectfe1<br> This dataset is superseded by 'NSW Forest Monitoring and Improvement Program State-Wide Historic Forest Connectivity - 1995 to 2020'

<br>The carbon balance of NSW forests project is part of the NSW Forest Monitoring and Improvement Program. The Mullion Group was engaged to develop a spatial time-series dataset of forest carbon history for the state of NSW at ~25&nbsp;m resolution. The project used FLINTpro software to integrate historical environmental and land management data to model carbon stock and fluxes. This dataset details the annual total forest carbon stock which is the sum of aboveground, belowground and dead organic matter carbon stocks. Aboveground biomass refers to the amount of carbon stored within aboveground forest components (pools) which includes leaves, branches, bark and stems. Belowground biomass refers to the amount of carbon stored within belowground forest components (pools) which includes coarse and fine roots. Dead organic matter refers to the amount of carbon stored within dead forest components (pools) which includes leaf litter, branch litter, bark litter, stem litter, and dead roots. Harvested wood products in use refers to the amount of carbon stored in wood products (excluding wood products in landfill). Harvested wood products in use are included in carbon stock and flux results, but excluded from spatial outputs. Carbon stored within soil is not included within any of these datasets.</br> <br>This dataset supersedes <a href="https://portal.tern.org.au/metadata/TERN/57abd38f-eb38-4868-a2ea-072aec1b9176">NSW Forest Carbon Stock - Aboveground, Belowground and Dead Organic Matter Carbon Mass 1990-2020</a></br>

<p> This data set provides the photosynthetic pathways for 4832 species recorded across plots surveyed by Australia’s Terrestrial Ecosystem Research Network (TERN) between 2011 and May 2022 (inclusive). TERN survey plots are 1&nbsp;ha (100 x 100&nbsp;m) permanently established sites located in a homogeneous area of terrestrial vegetation. At each plot, TERN survey teams record vegetation composition and structural characteristics and collect a range of plant samples using a point-intercept method. Species were assigned a photosynthetic pathway using literature and carbon stable isotope analysis of bulk tissue collected by TERN at the survey plots. </p><p>The data set is comprised of one data table that contains a list of each species and its photosynthetic pathway, and one metadata file which provides a data descriptor that defines data values and a list of all the peer-reviewed sources used to create this data set. </p> Version 1 (2020) included the photosynthetic pathways of 2428 species recorded across TERN plots surveyed between 2011 and 2017 (inclusive) and was originally published in 2020. Key updates in version 2 (2024) include an expanded species list and updated taxonomy were applicable </p>

The spatial layers in this dataset detail forest connectivity over NSW. Forest Connectivity accounts for the general quality of terrestrial habitats supporting biodiversity at each location, the fragmentation of habitat within its neighbourhood and how its position in the landscape contributes to connectivity among the habitats across a region. <br> Forest canopy cover connectivity and fragmentation is concerned and linked to forest condition. Concepts applied are to be aligned with definitions as found in the Biodiversity Indicator Program (BIP) and the Spatial Links methodology for calculating connectivity. <br> Base cover extent grids used are from the NSW Forest Monitoring and Improvement Program Statewide Historic Forest Cover Extent – 1995 to 2020 product. These have been processed through a series of land use and vegetation type exclusion masking and a through a fuzzy-logic based certainty analysis to reflect a forest cover extent coverage for NSW that is reflective of past and current coverage.<br> Read more about the project on the Natural Resources Commission website:<br> https://www.nrc.nsw.gov.au/accordion-content-main/fmip-ecosystemhealth-projectfe1<br> This dataset supersedes "NSW Forest Monitoring and Improvement Program RFA Historic Forest Connectivity – 1995 to 2019". https://portal.tern.org.au/metadata/TERN/fef2d61b-7c5e-42be-88c1-849a3fc6a70a.

Vertical plant profiles for the Australian continent were derived through integration of ICESat GLAS waveforms with ALOS PALSAR and Landsat data products. Co-registered Landsat Foliage Projected Cover (FPC) and ALOS PALSAR L-band HH and HV mosaics were segmented to generate objects with similar radar backscatter and cover characteristics. Within these, height, cover, age class and L-band backscatter characteristics were summarised based on the ICESat and Landsat time-series and ALOS PALSAR datasets.