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.

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 />

This is a spatial dataset comprising predictions of vegetation condition for biodiversity for the Southeast Queensland bioregion. The dataset was created using a gradient boosting decision tree (GBDT) model based on 10 vegetation-specific remote sensing datasets and 7,938 training sites of known vegetation community and condition state across Southeast Queensland, Brigalow Belt and Central Queensland Coast bioregions. Condition score was modelled as a function of distance in the remote sensing (RS) space within homogeneous vegetation communities. The product is intended to represent predicted BioCondition for 2021 rather than any singe date.

This is a spatial dataset comprising predictions of vegetation condition for biodiversity for the Central Queensland Coast bioregion. The dataset was created using a gradient boosting decision tree (GBDT) model based on 10 vegetation-specific remote sensing datasets and 7,938 training sites of known vegetation community and condition state across Southeast Queensland, Brigalow Belt and Central Queensland Coast bioregions. Condition score was modelled as a function of distance in the remote sensing (RS) space within homogeneous vegetation communities. The product is intended to represent predicted BioCondition for 2021 rather than any singe date.

This is a spatial dataset comprising predictions of vegetation condition for biodiversity for the Brigalow Belt bioregion. The dataset was created using a gradient boosting decision tree (GBDT) model based on 10 vegetation-specific remote sensing datasets and 7,938 training sites of known vegetation community and condition state across Southeast Queensland, Brigalow Belt and Central Queensland Coast bioregions. Condition score was modelled as a function of distance in the remote sensing (RS) space within homogeneous vegetation communities. The product is intended to represent predicted BioCondition for 2021 rather than any singe date.

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.

<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>

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.

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.

This dataset list landform characteristics (i.e. visible features of a land area) observed in Rangeland sites across Australia by the TERN Surveillance Monitoring team, using standardised AusPlots methodologies. <br /> Landform observations are recorded at each site as part of the AusPlots Plots and Physical Descriptions method. Observations on the landform elements and their patterns are recorded as part of this protocol.<br />