This dataset contains maps of woody vegetation extent and woody foliage projective cover (FPC) for New South Wales at 5 metre resolution. <br /><br /> Woody vegetation is a key feature of our landscape and an integral part of our society. We value it because it contributes to the economy, protects the land, provides us with recreation, and gives refuge to the unique and diverse range of fauna that we regard so highly. Yet it poses a significant threat to us in times of fire and storm. So information about trees is vital for a range of business, property planning, monitoring, risk assessment, and conservation activities. <br /><br /> The datasets are: <br /> Woody vegetation extent. A presence/absence map showing areas of trees and shrubs, taller than two metres, that are visible at the resolution of the imagery used in the analysis. This shows the location, extent, and density of foliage cover for stands of woody vegetation, enabling identification of small features such as trees in paddocks and scattered woodlands through to the largest expanses of forest in the State. Woody extent products contain 'bcu' in the file name.<br /><br /> Woody foliage projective cover (FPC). FPC is a measure of the proportion of the ground area covered by foliage (or photosynthetic tissue) held in a vertical plane and is a measure of canopy density. Woody FPC products contain 'bcv' in the file name. <br /><br /> Both mosaics and tiles are available, along with a shape file that identifies the location of the tiles.

<p>This dataset provides accurate, high-resolution (30 m) / high-frequency (monthly) / continuous (no gaps due to cloud) actual evapotranspiration (AET) for Australia using the CMRSET algorithm. The CMRSET algorithm uses reflective remotely sensed indices to estimate AET from potential evapotranspiration (PET; calculated using daily gridded meteorological data generated by the Bureau of Meteorology). Blending high-resolution / low-frequency AET estimates (e.g., Landsat and Sentinel-2) with low-resolution / high-frequency AET estimates (e.g., MODIS and VIIRS) results in AET data that are high-resolution / high-frequency / continuous (no gaps due to cloud) and accurate. These are all ideal characteristics when calculating the water balance for a wetland, paddock, river reach, irrigation area, landscape or catchment. </p><p> Accurate AET information is important for irrigation, food security and environmental management. Like many other parts of the world, water availability in Australia is limited and AET is the largest consumptive component of the water balance. In Australia 70% of available water is used for crop and pasture irrigation and better monitoring will support improved water use efficiency in this sector, with any water savings available as environmental flows. Additionally, ground-water dependent ecosystems (GDE) occupy a small area yet are "biodiversity hotspots", and knowing their water needs allows for enhanced management of these critical areas in the landscape. Having high-resolution, frequent and accurate AET estimates for all of Australia means this AET data source can be used to model the water balance for any catchment / groundwater system in Australia. </p><p> Details of the CMRSET algorithm and its independent validation are provided in Guerschman, J.P., McVicar, T.R., Vleeshouwer, J., Van Niel, T.G., Peña-Arancibia, J.L. and Chen, Y. (2022) Estimating actual evapotranspiration at field-to-continent scales by calibrating the CMRSET algorithm with MODIS, VIIRS, Landsat and Sentinel-2 data. Journal of Hydrology. 605, 127318, doi:10.1016/j.jhydrol.2021.127318</p> <p> <i>We strongly recommend users to use the TERN CMRSET AET V2.2</i>. Details of the TERN CMRSET AET V2.2 data product generation are provided in McVicar, T.R., Vleeshouwer, J., Van Niel, T.G., Guerschman, J.P., Peña-Arancibia, J.L. and Stenson, M.P. (2022) Generating a multi-decade gap-free high-resolution monthly actual evapotranspiration dataset for Australia using Landsat, MODIS and VIIRS data in the Google Earth Engine platform: Development and use cases. Journal of Hydrology (In Preparation).

We selected nine study sites, each incorporating three vegetation states: (a) fallow cropland, representing the restoration starting point, (b) planted old field (actively restored site), and (c) reference York gum (E. loxophleba) woodland. Plant species richness and cover All annual and perennial plant species were recorded in spring 2017 within each plot and identified to genus and species level where possible. Nomenclatures follow the Western Australian Herbarium (2017). A point intercept method previously demonstrated to provide objective and repeatable measures of cover (Godínez-Alvarez, Herrick, Mattocks, Toledo & Van Zee 2009; Prober, Standish & Wiehl 2011) was used to quantify cover of individual plant species, total vegetation cover and substrate types (i.e., bare ground, litter cover, plant cover). Ground cover, individual species, and canopy cover intercepting at every 2 m along four parallel, evenly spaced 50 m transects across each plot were recorded using a vertically placed dowel (8 mm wide, 2 m tall), resulting in 100 intercepting points per plot. For planted old fields, transects were placed parallel to planting rows, with two centred on rows and two centred between rows. This approximately represented the relative abundance of planted rows and non-planted inter-rows. If a species was recorded in the plot but did not intercept the dowel on any transect it was assigned 0.5 points. This method provided a measure of relative abundance (percentage cover) of plant species across the plot. To calculate species richness and cover across different life history and growth forms, species were classified into the following groups: total, native trees, native shrubs, native non – planted shrubs, native grasses, native perennial forbs, native annual forbs, exotic grasses and exotic annual forbs using the Western Australian Herbarium (2017) classification. Woody debris and leaf litter surveys Leaf-litter dry mass was estimated by collecting leaf-litter from five randomly placed 25 cm x 25 cm quadrats along two 50 m transects across each plot. Litter was stored in paper bags for transportation and then oven dried for 36 hours at 60 °C. The dried litter was weighed to 3 decimal points. Cover of fine and coarse woody debris and litter depth was estimated at every meter along two 20 m transects for each plot. Woody debris was classified by diameter. Length, max and min diameter was measured for all logs with a diameter greater than 10 cm.

The qualities of these data include: (i) sound experimental design to detect a change between confounding factors, (ii) large sample size, (iii) microchipped animals, (iv) validated heamatological processing on the wild Australian lizard Tiliqua rugosa involving a collaboration between wildlife ecologists and veterinary scientists. Its reuse potential may involve a comparative analysis of body size, haematological parameters with other long-lived, medium-sized lizards, ectoparasite studies (Aponomma hydrosauri, Amblyomma libatum) for different host populations, and background justification for ecotoxicological (pesticide) studies in farmland. Using a using a multivariate, one-way nested Type I PERMANCOVA (analysis of covariance) design, body size, blood samples and ectoparasite presence was collected on a total of 119 animals from two different populations in southern Australia. One population was from an intensively managed cropping environment and one was from an adjacent a less intensively managed grazing environment. This study took place in extensive rangelands and the fragmented landscapes of the South Australian Murray Mallee cereal cropland in southern Australia. Adult and juvenile T. rugosa were captured for sampling at one rangeland (baseline) site and three severely modified (severe) landscape-scaled sites (LS1, LS2, LS3) over a large area (68 km × 84 km or 571,200 ha) across the croplands. Two animal sampling designs were used to collect data on physiological health (Design 1: Baseline vs Severe and Design 2 - Severe only). Data collected: Record No., Animal No., Treatment, Habitat Type, Landscape No., Connectivity Class, Age Class, Linear Body Size Index (LBSI), Heterophil (H) Field of View, Heterophil per microlitre, Total White Blood Cell Count, Absolute Heterophil Count, % Heterophil Count, Absolute Lymphocyte (L) Count, % Lymphocytes, H:L Ratio (Absolute), H:L Ratio (%), Absolute Monocytes, % Monocytes , Absolute Other Granulocytes , % Other Granulocytes, % Polychromasia, Snout-Vent Length (mm), Total No. Ectoparasites per Animal.