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30 meters - < 100 meters

76 record(s)
 
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    The Central Appalachian region, USA, contains several high elevation-endemic woodland salamanders (genus Plethodon), which are thought to be particularly vulnerable to climate change due to their restricted distributions and low vagility. In West Virginia, there is a strong management focus on protection and recovery of the federally threatened Cheat Mountain salamander (Plethodon nettingi; CMS). To support this focus, there is a need for improved understanding of CMS occurrence-habitat relationships and spatially explicit projections of fine-scale contemporary and potential future habitat quality to inform management actions. In addition, there is concern among resource managers that climate change may increase habitat quality at high elevations for CMS competitors, particularly the eastern red-backed salamander (Plethodon cinereus; RBS), potentially resulting in increased competition pressure for CMS. To address these knowledge gaps, we created ecological niche models for CMS and RBS using the Random Forest classification algorithm and used the estimated occurrence-habitat relationships to assess ecological niche overlap between the species and project fine-scale contemporary and potential future habitat availability and quality. We estimated that the ecological niches of CMS and RBS were 80.5% similar, and habitat projections indicated the species would exhibit opposite responses to climate change in our region. For CMS, we estimated that amount of high-quality habitat will be reduced by mid-century and potentially lost by end-of-century, but that moderate and low-quality habitat will persist. For RBS, we estimated that amount of high-quality habitat will increase through end-of-century, and that high elevations will become more suitable for the species, indicating that competition pressure for CMS is likely to increase. This study improves understanding of important habitat characteristics for CMS and RBS, and our spatially explicit projections can assist natural resource managers with habitat protection actions, species monitoring efforts, and climate change adaptation strategies.

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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 Point intercept 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. These records form the basis for ground cover derivation, see the AusPlots Ground cover and Point intercept methods below.<br />

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    Terrestrial laser scans were acquired in native Eucalypt Open Forest (dry sclerophyll Box-Ironbark forest) in Victoria, Australia. Two plots (RUSH06 and RUSH07) with a 40 m radius were established in Rushworth forest and partially harvested in May 2012 to acquire accurate estimates of above-ground biomass. The main tree species in these plots were Eucalyptus leucoxylon, Eucalyptus microcarpa and Eucalyptus tricarpa. Single trees were extracted from the TLS data and quantitative structure models were used to estimate the tree volume directly from the point cloud data. Above-ground biomass (AGB) was inferred from the derived volumes and basic wood density information, and compared with estimates of above-ground biomass derived from allometric equations and destructive sampling. See <a href="https://doi.org/10.1111/2041-210X.12301">Calders et al. (2014)</a> and <a href="http://www.vcccar.org.au/publication/final-report/comprehensive-carbon-assessment-program">Murphy et al. (2014)</a> for further information.

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    The climate adjusted linear seasonal persistent green trend is derived from analysis of the linear seasonal persistent green trend, adjusted for rainfall. The current version is based on the 1987-2014 period. <br> Seasonal persistent green cover is derived from seasonal cover using a weighted smooth spline fitting routine. This weights a smooth line to the minimum values of the seasonal green cover. This smooth minimum is designed to represent the slower changing green component, ideally consisting of perennial vegetation including over-storey, mid-storey and persistent ground cover. The seasonal persistent green is then summarised using simple linear regression, and the slope of the fitted line is captured in the linear seasonal persistent green product. This product is further processed to produce a climate-adjusted version.

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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/24070. 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 to 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.

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    The seasonal dynamic reference cover method product compares the current ground cover level of each pixel to a reference pixel based on the historical timeseries and is available for Queensland from 1987 to present. It is 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.<br> This product is based upon the JRSRP Fractional Cover 3.0 algorithm.

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    An estimate of persistent green cover per season across Australia from 1989 to the present season, minus 2 years. This is intended to estimate the portion of vegetation that does not completely senesce within a year, which primarily consists of woody vegetation (trees and shrubs), although there are exceptions where non-woody cover remains green all year round. It is derived by fitting a multi-iteration minimum weighted smoothing spline through the green fraction of the seasonal fractional cover (dp1) time series. A single band image is produced: persistent green vegetation cover (in percent). The no data value is 255.

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    For some time, Remote Sensing Sciences, has produced Foliage Projective Cover (FPC) using a model applied to Landsat surface reflectance imagery, calibrated by field observations. An updated model was developed which relates field measurements of FPC to 2-year time series of Normalized Difference Vegetation Index (NDVI) computed from Landsat seasonal surface reflectance composites. The model is intended to be applied to Landsat and Sentinel-2 satellite imagery, given their similar spectral characteristics. However, due to insufficient field data coincident with the Sentinel-2 satellite program, the model was fitted on Landsat imagery using a significantly expanded, national set of field data than was used for the previous Landsat FPC model fitting. The FPC model relates the field measured green fraction of mid- and over-storey foliage cover to the minimum value of NDVI calculated from 2-years of Landsat seasonal surface reflectance composites. NDVI is a standard vegetation index used in remote sensing which is highly correlated with vegetation photosynthesis. The model is then applied to analogous Sentinel-2 seasonal surface reflectance composites to produce an FPC image at Sentinel-2 spatial resolution (i.e. 10&nbsp;m) using the radiometric relationships established between Sentinel-2 and Landsat in Flood (2017). This is intended to represent the FPC for that 2-year period rather than any single date, hence the date range in the dataset file name. The dataset is generally expected to provide a reasonable estimate of the range of FPC values for any given stand of woody vegetation, but it is expected there will be over- and under-estimation of absolute FPC values for any specific location (i.e. pixel) due to a range of factors. The FPC model is sensitive to fluctuations in vegetation greenness, leading to anomalies such as high FPC on irrigated pastures or locations with very green herbaceous or grass understoreys. A given pixel in the FPC image, represents the predicted FPC in the season with the least green/driest vegetation cover over the 2-year period assumed to be that with the least influence of seasonally variable herbaceous vegetation and grasses on the more seasonally stable woody FPC estimates. The two-year period was used partly because it represents a period relative to tree growth but was also constrained due to the limited availability of imagery in the early Sentinel-2 time series. The FPC dataset is constrained by the woody vegetation extent dataset for the FPC year.

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    This data set contains information on Electrical Conductivity and pH from bore water from two plots, Blackbutt and Salmongum the Great Western Woodland Site.

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