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/fmip-baselines-ecosystem-health-projectfe1<br> This dataset supersedes "NSW Forest Monitoring and Improvement Program RFA Historic Forest Connectivity – 1995 to 2019". https://portal.tern.org.au/metadata/23696.

This geostationary land surface temperature (LST) collection was retrieved using Himawari/AHI observations and calibrated against MODerate-resolution Imaging Spectroradiometer (MODIS) best-quality retrievals for Australia. It was developed under an academic collaboration between the Australian National University (ANU) and the Commonwealth Scientific and Industrial Research Organisation (CSIRO). It has a spatial resolution of 2&nbsp;km and temporal frequency of 10&nbsp;min, and has been periodically updated since its inception in July 2015. This record has a temporal length of 8.5 years (i.e., Jul 2015 - Dec 2023) and the subsequent updates will be published annually.

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/fmip-baselines-ecosystem-health-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/fmip-baselines-ecosystem-health-projectfe1<br> This dataset is superseded by 'NSW Forest Monitoring and Improvement Program State-Wide Historic Forest Connectivity - 1995 to 2020'

This dataset contains spatial layers describing Forest Canopy Extent from 1995-2019 in NSW Regional Forest Agreements (RFA) Areas along the eastern coast. Forest Canopy Extent is the likelihood that a certain area has forest at any given time. Forest Canopy is defined in accordance with the National State of the Forests Report which defines forests as containing as a minimum, a mature or potentially mature stand height exceeding 2 metres, stands dominated by trees usually having a single stem, where the mature or potentially mature stand component comprises 20% canopy coverage using a Crown Projective Cover (CPC) measure. <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> To calculate forest canopy extent, these base grids 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/fmip-baselines-ecosystem-health-projectfe1<br> This dataset is superseded by 'NSW Forest Monitoring and Improvement Program State-Wide Historic Forest Canopy Cover Extent - 1995 to 2020'

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/fmip-baselines-ecosystem-health-projectfe1<br> This dataset is superseded by 'NSW Forest Monitoring and Improvement Program State-Wide Historic Forest Canopy Loss and Recovery - 1998 to 2020'

The spatial layers in this dataset detail forest cover extent over NSW. They have been created for the NSW Natural Resources Commission to detail historic baseline and trends of forest cover extent coverage for NSW for all land tenures, including all RFAs and IFOAs. <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, 2021). These base grids are Landsat in origin and have a resolution of 25m. <br> These base grids 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> These grids cover the years from 1995 to 2020. The year gaps are triennial or biennial data layers from 1995 to 2004. 1996,1997,1999,2001,2003 years missing as these were not assessed in original applied database. From 2004 to 2020 data layers become annualised.<br> Read more about the project on the Natural Resources Commission website:<br> https://www.nrc.nsw.gov.au/fmip-baselines-ecosystem-health-projectfe1<br> This dataset supersedes "NSW Forest Monitoring and Improvement Program RFA Historic Forest Cover Extent – 1995 to 2019". https://portal.tern.org.au/metadata/23696.

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.

This dataset contains Unmanned Aircraft System (UAS) multispectral, pansharpened and long-wave infrared (LWIR) orthomosaics of the Samford Ecological Research Facility (SERF), Queensland University of Technology. SERF is located in the Samford Valley, west of Brisbane, Australia and is the usual place for flight testing and evaluation of new equipment. The QUT's Research Engineering Facility team operated DJI Matrice 300RTK (M300) with latest MicaSense Altum-PT (5-band multispectral sensor, LWIR, panchromatic channels and downlight sensor). The images were geo-referenced using the onboard GNSS in M300 and the D-RTK 2 base station and also georectified with 5 ground control points collected by Emlid Reach RS GNSS receivers. In the processing workflow in Agisoft Metashape, the multispectral orthomosaics were orthorectified and pan-sharpened. Dense point clouds were used to generate multispectral (GSD 3.4 cm/px), panchromatic and multispectral pansharpened (GSD 1.6 cm/pixel) and LWIR (GSD 21 cm/pixel) orthomosaics.