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1 meter - < 30 meters

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    <p>This data set consists of .tif files of true colour orthomosaics for expansive areas of mangroves in Kakadu National Park in Australia's Northern Territory.</p> <p>The orthomosaics were generated from 68 stereo pairs of true colour aerial photographs acquired in 1991 in the lower reaches of the East Alligator, West Alligator, South Alligator and Wildman Rivers and Field Island, Kakadu National Park, Northern Australia (Mitchell et al., 2007). The photographs were taken at a flying height of 13,000 ft (3,960 m) using a Wild CR10, a standard photogrammetric camera with a frame size of 230 x 230 mm. The focal length was 152 mm. The photographs were scanned by Airesearch (Darwin) with a photogrammetric scanner to generate digital images with a pixel resolution between 12 and 15 mm. The orthomosaics have a spatial resolution of 1 m, cover an area of approximately 742 km<sup>2</sup> and a coastal distance of 86 km. </p> <p>These orthomosaics were co-registered using ground control points identified from 1:100,000 digital topographic maps with a Universal Transverse Mercator (UTM), and subsequently co-registered to LiDAR data acquired over the same region in 2011.</p>

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    This dataset list soil characteristics observed in Rangeland sites across Australia by the TERN Surveillance Monitoring team, using standardised AusPlots methodologies. <br /> Soil observations are recorded at each site as part of the AusPlots Soil and Landscapes method. Observations on the soil surface conditions are also recorded as part of the AusPlots Plot description method.<br />

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    <p>This data set consists of a shapefile/kml of mangrove extent and dominant species for Kakadu National Park mangroves generated from true colour aerial photographs acquired in 1991.</p> <p>From true color 1991 orthomosaics of Field Island and the Wildman, West, and South Alligator Rivers, mangroves were mapped by first applying a fine scale spectral difference segmentation within eCognition to all three visible bands (blue, green, and red). A maximum likelihood (ML) algorithm within the environment for visualizing images (ENVI) software was then used to classify all segments using training areas associated with mangroves, but also water, mudflats, sandflats, and coastal woodlands. These were identified through visual interpretation of the imagery. Segmentation was necessary as 1) the diversity of structures and shadows within and between tree crowns limited the application of pixel-based classification procedures and 2) the color balance between the different photographs comprising the orthomosaics varied. All segments were examined individually and methodically to determine whether they should be reallocated to a non-mangrove class (e.g., mudflats) or confirmed as mangroves. Open woodlands dominated by Eucalyptus species could also be visually identified within the aerial photography (AP) orthoimages, although their discrimination was assisted by only considering areas where the underlying LiDAR DTM (Digital Terrain Model) exceeded 10 m, assuming this excludes tidally inundated sections.</p>

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    The datasets in this series comprise predictions of biocondition for Queensland's Bioregions. The datasets are 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 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.

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    This dataset contains predictions of the aboveground biomass density (AGBD) for Australia for 2020. Data were generated by the Global Ecosystem Dynamics Investigation (GEDI) NASA mission, which used a full-waveform LIDAR attached to the International Space Station to provide the first global, high-resolution observations of forest vertical structure. Data include both Level 4A (~25&nbsp;m footprints) and Gridded Level 4B (1&nbsp;km x 1&nbsp;km) Version 2. The Australian portion of the data was extracted from the original global datasets <a href="https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=2056">GEDI L4A Footprint Level Aboveground Biomass Density</a> and <a href="https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=2299">GEDI L4B Gridded Aboveground Biomass Density</a>.

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    Foliage Projective Cover (FPC) is the percentage of ground area occupied by the vertical projection of foliage. The Remote Sensing Centre FPC mapping is based on regression models applied to dry season (May to October) Landsat-5 TM, Landsat-7 ETM+ and Landsat-8 OLI imagery for the period 1988-2014. An annual woody spectral index image is created for each year using a multiple regression model trained from field data collected mostly over the period 1996-1999. A robust regression of the time series of the annual woody spectral index is then performed. The estimated foliage projective cover is the prediction at the date of the selected dry season image for 2014. Where this deviates significantly from the woody spectral index for that date, further tests are undertaken before this estimate is accepted. In some cases, the final estimate is the woody spectral index value rather than the robust regression prediction. The product is further masked to remove areas classified as non-woody.

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    <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>

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    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'

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    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'

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    <p>Digital Hemispherical Photography (DHP) upward-looking images are collected three times per year to capture vegetation and crown cover at the Gingin Banksia Woodland SuperSite. These images are used to estimate Leaf area index (LAI). </p> <p> 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/. </p><p> Other images collected at the site include digital cover photography (DCP), photopoints, phenocam time-lapse images taken from fixed under and overstorey cameras and ancillary images of fauna and flora. </p>