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    Experimental sites were established in the northern wheat-growing district of western Australia (Lat -29.66°, Long 116.18°) in August 2017, and monitored through to November 2019. We selected five planted old field sites with similar soil types and vegetation composition. Old fields were planted with York gum (Eucalyptus loxophleba Benth.) and dominant shrubs as understorey. At the time of sampling in 2017, vegetation age ranged from 8–13 years and distance from remnant measured 279 m (± 162 m). We established two control and two treatment plots, each measuring 5 m x 5 m, in the interrows of five planted old field sites. Both treatments were randomly assigned to plots within each site. Between August and early November 2017, we measured a total of 30 response variables at each of the control and treatment plots. Response variables included soil physical and chemical properties (bulk density, penetration resistance, soil moisture, nitrogen and carbon pools), microbial biomass, decomposition rate of roiboos and green tea as per the standardized Tea Bag Index (TBI) protocol, herbaceous vegetation cover and richness, and ant abundance and richness, as well as abundance and richness of ant functional groups.

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    The seasonal fractional ground cover product is a spatially explicit raster product that shows the proportion of bare ground, green and non-green ground cover at medium resolution (30 m per-pixel) for each 3-month calendar season. It is derived directly from the seasonal fractional cover product, also produced by Queensland's Remote Sensing Centre. A 3 band (byte) image is produced: band 1 – bare ground fraction (in percent), band 2 - green vegetation fraction (in percent), band 3 – non-green vegetation fraction (in percent). The no data value is 255.</br> The seasonal fractional cover product predicts vegetation cover, but does not distinguish tree and mid-level woody foliage and branch cover from green and dry ground cover. As a result, in areas with even minimal tree cover (>15%), estimates of ground cover become uncertain.</br> With the development of the fractional cover time-series, it has become possible to derive an estimate of ‘persistent green’ based on time-series analysis. The persistent green vegetation product provides an estimate of the vertically-projected green-vegetation fraction where vegetation is deemed to persist over time. These areas are nominally woody vegetation. This separation of the 'persistent green' from the fractional cover product, allows for the adjustment of the underlying spectral signature of the fractional cover image and the creation of a resulting 'true' ground cover estimate for each season. The estimates of cover are restricted to areas of <60% woody vegetation.

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    The seasonal fractional cover product shows representative values for the proportion of bare, green and non-green cover across a season. It is a spatially explicit raster product, which predicts vegetation cover at medium resolution (30 m per-pixel) for each 3-month calendar season. The green and non-green fractions may include a mix of woody and non-woody vegetation. A 3 band (byte) image is produced: band 1 – bare ground fraction (in percent), band 2 - green vegetation fraction (in percent), band 3 – non-green vegetation fraction (in percent). The no data value is 255.

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    The Sentinel-2 seasonal fractional ground cover product shows the proportion of bare ground, green and non-green ground cover and is derived directly from the Sentinel-2 seasonal fractional cover product, also produced by Queensland's Remote Sensing Centre. The seasonal fractional cover product is a spatially explicit raster product, which predicts vegetation cover at medium resolution (10&nbsp;m per-pixel) for each 3-month calendar season. However, the seasonal fractional cover product does not distinguish tree and mid-level woody foliage and branch cover from green and dry ground cover. As a result, in areas with even minimal tree cover (>15%), estimates of ground cover become uncertain. With the development of the fractional cover time-series, it has become possible to derive an estimate of ‘persistent green’ based on time-series analysis. The persistent green vegetation product provides an estimate of the vertically-projected green-vegetation fraction where vegetation is deemed to persist over time. These areas are nominally woody vegetation. This separation of the 'persistent green' from the fractional cover product, allows for the adjustment of the underlying spectral signature of the fractional cover image and the creation of a resulting 'true' ground cover estimate for each season. The estimates of cover are restricted to areas of <60% woody vegetation. Currently, the persistent green product has only been produced at 30&nbsp;m pixel resolution based on Landsat imagery, resulting in this Sentinel-2 seasonal ground cover product having a medium 30&nbsp;m pixel resolution also. This is an experimental product which has not been fully validated. This product is similar to the <a href="https://portal.tern.org.au/metadata/23884 ">Seasonal ground cover - Landsat, JRSRP algorithm Version 3.0, Australia Coverage</a> which is based on a different satellite sensor.

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    This data package comprises fire severity scores from Kakadu in 2014. A total of 220 permanent monitoring plots (40&nbsp;m x &nbsp;20 m) were established across three parks (Kakadu, Litchfield and Nitmiluk) in 1994-1995 to monitor biotic change. Of these, 132 plots are located in Kakadu. These sample a variety of landform and vegetation type/habitat conditions. A substantial proportion of plots were positioned deliberately at sites likely to reveal environmental dynamics, especially at ecotones and in patches of fire-sensitive vegetation. For example stands of <i>Callitris</i>, sandstone heaths. As well, many plots are located at, or in the near vicinity of, intensively managed sites such as camp-grounds and other tourist destinations. A synopsis of related data packages which have been collected as part of the Three Park Savanna Fire-effects Plot Network’s full program is provided at <a href="http://www.ltern.org.au/index.php/ltern-plot-networks/three-parks-savanna ">LTERN</a>

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    <p>The dataset comprises well-designed survey data from the first fuel load survey across 192 transects within the 48 AusPlots Forests, 1-ha monitoring plots across Australia. Data includes: [1] Site identifiers (ID and Site Name) and site- or transect- specific notes from the fuel survey campaign; [2] Transect survey dates; [3] Transect photograph numbers and attributes (Bearing, Slope and Aspect); [4] Fuel measurements (Grass and Litter height; Duff depth; Fine Woody fuel counts and Coarse Woody fuel counts and diameter; Projective cover for biomass components (Grass, Litter, Herbs, Vines and Shrubs), and Mass of biomass components (Grass, Litter, Herbs and Vines)); [5] Moisture content for biomass components (Grass, Litter, Herbs and Vines).</p> Descriptions of the data and coding protocols used in the database are explained in (a) the database itself; (b) the explanatory file attached to this dataset and (c) the Ausplots Forest Monitoring Network Manual. The protocols and coding used in this module are drawn directly from international forest fuel survey protocols and are consistent with other Australian forest fuel inventory methodologies.

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    In 1963, the Glen Canyon Dam, in Hite Utah was completed, creating the Lake Powell reservoir along the Colorado River. The water levels of Lake Powell peaked in 1983 and have declined since, releasing over-pressure on the underlying sediment. This release in over-pressure created mud volcanoes, structures along the shoreline made of cavities that allow fluid and gases to rise to the surface and escape. Green house gases including methane are released from these structures, and to better understand how development of natural wetlands can result in unintended increased levels of greenhouse gas emissions, we asked 1) how much of each gas is generated or and whether the amount of each gas is changing through time and 2) how are these gases forming in the subsurface? We first measured the amounts of carbon dioxide (CO2), methane (CH4), and air (N) in volcano gas samples collected in 2014, 2015, and 2016. We found that from 2014 through 2016, methane levels from these volcanoes fluctuated significantly. In 2016, we looked at the amounts of carbon and hydrogen isotopes in the methane, which told us the gas is generated from microorganisms feeding on organic matter and is released during water-level fluctuations. We looked at mud volcanoes only located along the Lake Powell marina delta in Hite, Utah. The data spans geological structures restricted to one marina delta.

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    The monthly blended ground cover product is a spatially explicit raster product that shows the proportion of bare ground, green and non-green ground cover at medium resolution (30&nbsp;m per-pixel) for each calendar month. It is derived directly from both the Landsat-based fractional cover product and the Sentinel-2-based fractional cover product by Queensland's Remote Sensing Centre. A 3 band (byte) image is produced: band 1 - bare ground fraction (in percent), band 2 - green vegetation fraction (in percent), band 3 - non-green vegetation fraction (in percent). The no data value is 255. This product is derived from the <a href="https://portal.tern.org.au/metadata/TERN/8d3c8b36-b4f1-420f-a3f4-824ab70fb367 ">Monthly blended fractional cover - Landsat and Sentinel-2, JRSRP algorithm Version 3.0, Queensland coverage</a>

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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 https://portal.tern.org.au/metadata/24071. Long term temporal statistic products derived from the seasonal ground cover product for each fraction. Statistics include: 5th percentile minimum, mean, median, 95th percentile maximum, standard deviation and observation count. There is one raster image for each season and each bare and green fraction for the full time series of imagery available. Min/max (5th and 95th percentile) products are also made for each fraction using all seasonal ground cover images available.

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    The dataset provides information on soil chemistry from a 10 year chronosequence sample of restoration in southern Australia. The parameters include: A) Physical properties- Soil moisture (%), Gravel (%) - ( >2.0 mm), Soil Texture, i.e.Course Sand (%) (200-2000 µm), Fine Sand (%) - (20-200 µm), Sand (%), Silt (%) (2-20 µm), Clay (%) (<2 µm), and B) Chemical properties- such as, Ammonium Nitrogen (mg/Kg), Nitrate Nitrogen (mg/Kg), Phosphorus Colwell (mg/Kg), Potassium Colwell (mg/Kg), Sulphur (mg/Kg), Organic Carbon (%), Conductivity (dS/m), pH (CaCl2), pH (H2O), DTPA Copper (mg/Kg), DTPA Iron (mg/Kg), DTPA Manganese (mg/Kg), DTPA Zinc (mg/Kg), Exc. Aluminium (meq/100g), Exc. Calcium (meq/100g), Exc. Magnesium (meq/100g), Exc. Potassium (meq/100g), Exc. Sodium (meq/100g) and Boron Hot CaCl2 (mg/Kg). This data would have application for land managers. The soil chemistry data is also related to the eDNA OTU table published on "https://doi.org/10.4227/05/5878480a91885", titled "Revegetation rewilds the soil bacterial microbiome of an old field. Part 1: OTU raw data matrix", and as such it would have an appeal to researchers undertaking a meta-analysis on eDNA and restoration outcomes.