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LAND USE/LAND COVER

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    This dataset lists land surface substrate characteristics observed in Rangeland sites across Australia by the TERN Surveillance Monitoring team, using standardised AusPlots methodologies. <br /> Land surface substrate observations are collected at each site as part of the AusPlots <a href="http://linked.data.gov.au/def/ausplots-cv/c5a32483-bf2f-421d-b03d-6d81e1195de2">Point intercept</a> method. At each site, observations on the substrate type (e.g. rock, coarse woody debris, litter) are recorded on transect laid out on the plots see AusPlots <a href="http://linked.data.gov.au/def/ausplots-cv/c5a32483-bf2f-421d-b03d-6d81e1195de2">Point intercept</a> method. These records form the basis for ground cover derivation, see the Ausplots <a href="http://linked.data.gov.au/def/tern-cv/1ae719f6-93f2-494c-822d-2631b1d3e6c3">Ground cover</a> and <a href="http://linked.data.gov.au/def/ausplots-cv/c5a32483-bf2f-421d-b03d-6d81e1195de2">Point intercept</a> methods.<br />

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    Three maps are available: 1) foliage projective cover, 2) forest extent, attributed with the foliage projective cover and 3) accuracy of the extent maps, which also acts as masks of forest and other wooded lands. Each pixel in map 1 estimates the fraction of the ground covered by green foliage. Each pixel in map 2 shows two pieces of information. The first is a classification of whether the vegetation is forest or not. The pixels classified as forest are attributed with the second piece of information: the foliage projective cover. Each pixel in map 3 is a class that provides information on the classification accuracies of the woody extent. These maps are derived from Landsat.

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    This collection contains the data used in the Multi-Criteria Analysis Shell for Spatial Decision Support (MCAS-S) software tool. From the Data menu, explore and download individual supplementary layers, or download the entire datapack. The Multi-Criteria Analysis Shell for Spatial Decision Support (MCAS-S) is a software tool developed by the Australian Bureau of Agricultural and Resource Economics and Sciences that enables multi-criteria analysis (MCA) using spatial data. It is a powerful, easy-to-use and flexible decision-support tool that promotes: - framework for assessing options <br> - common metric for classifying, ranking and weighting of the data <br> - tools to compare, combine and explore spatial data <br> - live-update of alternative scenarios and trade-offs. <br>

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    Two fractional cover decile products, green cover and total cover, are currently produced from the historical timeseries of seasonal fractional cover images. These products compare, at the per-pixel level, the level of cover for the specific season of interest against the long term cover for that same season. For each pixel, all cover values for the relevant seasons within a baseline period (1988-2013) are classified into deciles. The cover value for the pixel in the season of interest is then classified according to the decile in which it falls. This product is based upon the JRSRP Fractional Cover 3.0 algorithm.

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    This product has been superseded and will not be processed from early 2023. Please find the updated version 3 of this product at <a href="https://portal.tern.org.au/metadata/24072"</a>. The seasonal dynamic reference cover method images are created using a modified version of the dynamic reference cover method developed by <a href="https://doi.org/10.1016/j.rse.2012.02.021">Bastin et al (2012) </a>. This approach calculates a minimum ground cover image over all years to identify locations of most persistent ground cover in years with the lowest rainfall, then uses a moving window approach to calculate the difference between the window's central pixel and its surrounding reference pixels. The output is a difference image between the cover amount of a pixel's reference pixels and the actual cover at that pixel for the season being analysed. Negative values indicate pixels which have less cover than the reference pixels. <br> The main differences between this method and the original method are that this method uses seasonal fractional ground cover rather than the preceding ground cover index (GCI) and this method excludes cleared areas and certain landforms (undulating slopes), which are considered unsuitable for use as reference pixels.

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    The seasonal dynamic reference cover method images are created using a modified version of the dynamic reference cover method developed by <a href="https://doi.org/10.1016/j.rse.2012.02.021">Bastin et al (2012) </a>. This approach calculates a minimum ground cover image over all years to identify locations of most persistent ground cover in years with the lowest rainfall, then uses a moving window approach to calculate the difference between the window's central pixel and its surrounding reference pixels. The output is a difference image between the cover amount of a pixel's reference pixels and the actual cover at that pixel for the season being analysed. Negative values indicate pixels which have less cover than the reference pixels. The main differences between this method and the original method are that this method uses seasonal fractional ground cover rather than the preceding ground cover index (GCI) and this method excludes cleared areas and certain landforms (undulating slopes), which are considered unsuitable for use as reference pixels. This product is based upon the JRSRP Fractional Cover 3.0 algorithm.

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    The dataset consists of composited seasonal surface reflectance images (4 seasons per year) created from the full time series of Sentinel-2 imagery. The imagery has been composited over a season to produce imagery which is representative of that period, using techniques which will reduce contamination by cloud and other problems. This creates a regular time series of reflectance values which captures the variability at seasonal time scales. The benefits are a regular time series with minimal missing data or contamination from various sources of noise as well as data reduction. Each season has exactly one value (per band) for each pixel (or is null, i.e., missing), and the value for that season is assumed to be the representative of the whole season. The algorithm is based on the medoid (in reflectance space) over the time period (the medoid is a multi-dimensional analogue of the median), which is robust against extreme values. The seasonal surface reflectance is of the 6 TM-like bands (Blue, Green, Red, NIR, SWIR1, SWIR2), all at 10 m resolution. This dataset is intended to be a 10 m equivalent of the Landsat surface reflectance, using only Sentinel-2. The two 20m bands are resampled using cubic convolution. The pixel values are scaled reflectance, as 16-bit integers. To retrieve physical reflectance values, the pixel values should be multiplied by 0.0001.

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    This dataset consists of images of fauna, flora, fungi or general scenery or events captured at the site on an ad-hoc basis and may provide the researcher with information regarding the species that occupy, frequent or traverse this site.<br /> <br /> The Mitchell Grass Rangeland SuperSite is located at Rosebank Station approximately 11 km south-east of Longreach in Queensland. The site is arid tussock grassland with a variety of grass species including <em>Astrebla lappacea</em> and <em>Astrebla squarrosa</em> over black vertosol soil that supports sheep and beef cattle grazing. Traditional owners at this site are the Iningai people. For additional site information, see https://www.tern.org.au/tern-observatory/tern-ecosystem-processes/mitchell-grass-rangeland-supersite/ . <br /><br /> Panoramic images and photopoints are also collected at the site.

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    This dataset consists of images of fauna, flora, fungi or general scenery or events captured at the site on an ad-hoc basis and may provide the researcher with information regarding the species that occupy, frequent or traverse this site.<br /> <br /> The Karawatha Peri-Urban SuperSite was established in 2007 and decommissioned in 2018. The site was located in Eucalypt forest at Karawatha Forest. For additional site information, see https://deims.org/f15bc7aa-ab4a-443b-a935-dbad3e7101f4 . <br /> <br /> Other images collected at the site include photopoints and digital cover photography.

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    This dataset consists of images of fauna, flora, fungi or general scenery or events captured at the site on an ad-hoc basis and may provide the researcher with information regarding the species that occupy, frequent or traverse this site.<br /> <br /> The Litchfield Savanna SuperSite was established in 2013 in Litchfield National Park. Site selection was influenced by the history of long-term monitoring work undertaken in this area by the Darwin Centre for Bushfire Research (formerly Bushfires NT). The core 1ha plot is dominated by <em>Eucalyptus miniata</em>. The site is representative of the dominant ecosystem type across northern Australia: frequently burnt tropical savanna in high rainfall areas. For additional site information, see https://www.tern.org.au/tern-observatory/tern-ecosystem-processes/litchfield-savanna-supersite/ . <br /> Phenocam images and photopoints are also collected at the site.