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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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    This dataset lists land surface characteristics observed in Rangeland sites across Australia by the TERN Ecosystem Surveillance team, using standardised AusPlots methodologies. <br /> Land surface observations are collected at each site as part of the AusPlots <a href="http://linked.data.gov.au/def/ausplots-cv/74615bb8-9cc5-4a63-868b-3258108ffcb4">Plot description</a> method. At each site, observations on ground cover, lithology, erosion (state, extent, and human accelerated), surface drainage, microrelief, aspect and angle are recorded as part of 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/74615bb8-9cc5-4a63-868b-3258108ffcb4">Plot description</a> methods.<br />

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    The forest fuel survey dataset comprises site-level summary data from the well-designed fuel load surveys across 48 AusPlots Forests- 1-ha monitoring plots across Australia. Data presented here includes data on the surface, near-surface, and elevated fuel loads for each of the Forest Ausplots. It includes iButton data on 1) temperature and humidity, 2) data on litterfall and 3) decomposition rates. We also provide additional information on soil nutrient data, species composition of the understorey and midstorey, and panorama photos from the plot centre. This dataset is the second version of the <i> AusPlots Forest Fuel Survey site-level data summary, 2014 - 2015. Version 1.0.0. Terrestrial Ecosystem Research Network.</i> (dataset). <em>https://doi.org/10.25901/efnh-sk06</em>

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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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    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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    The seasonal fractional cover product shows representative values for the proportion of bare, green and non-green cover, created from a time series of Sentinel 2 imagery. It is a spatially explicit raster product, which predicts vegetation cover at medium resolution (10 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. This model was originally developed for Landsat imagery, but has been adapted for us with Sentinel-2 imagery to produce a 10 m resolution equivalent product.

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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 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.

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    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.