From 1 - 7 / 7
  • Categories  

    The Australian cosmic-ray soil moisture monitoring network was first established in 2010 to provide Australian and global researchers with spatially distributed intermediate scale soil moisture observations. A cosmic-ray sensor (CRS) provides continuous estimates of soil moisture over an area of approximately 30 hectares by measuring naturally generated fast neutrons (energy 10–1000 eV) that are produced by cosmic rays passing through the Earth’s atmosphere. The neutron intensity above the land surface is inversely correlated with soil moisture as it responds to the hydrogen contained in the soil and to a lesser degree to plant and soil carbon compounds. The cosmic-ray technique is also passive, non-contact, and is largely insensitive to bulk density, surface roughness, the physical state of water, and soil texture. The scale of CRS measurements fills the void between point scale sensor measurements and large scale satellite observations. The depth of measurements varies with the moisture content of the soil but is typically between 10-30 cm. The depth of observations is reported as ‘effective depth’. The CosmOz network is expanding as new sensors are added over time. The initial network was funded by CSIRO Land and Water but more recently TERN has funded work to maintain the network add new sensors and deliver data more efficiently. The standard CRS installation includes; a cosmic-ray neutron tube, a rain gauge (2m high), temperature and humidity sensors, and an atmospheric pressure sensor. Measures of all parameters are reported at an hourly interval. Each CRS requires an in-field calibration across the footprint of measurements to convert neutron counts to soil moisture content. The calibration includes collection of soil samples for bulk density, lattice water content and soil organic carbon. The extent of the network and available data can be seen at the CosmOz network web page: https://cosmoz.csiro.au/ The calibration and correction procedures used by the network are described by Hawdon et al. 2014.

  • Categories  

    Dynamically downscaled high-resolution (~10 km spatial resolution) climate change projection data for Queensland. Downscaling was completed using CSIRO Conformal Cubic Atmospheric Model (CCAM) for two RCPs (RCP4.5 and RCP8.5) from 11 CMIP5 global coarse resolution models for period 1980-2099. The Queensland Future Climate Dashboard (www.longpaddock.qld.gov.au/qld-future-climate/ ) provides easy access to climate projection for Queensland. The dashboard allows users to explore, visualize and download the latest high-resolution climate modelling data for specific regions, catchments, disaster areas, local government areas and grid squares. Underlying data is provided via TERN for easy access for each of 11 downscaled models. The Queensland Future Climate Dataset provides high resolution data for over 30 different metrics grouped in six climate themes: (i) Mean Climate; (ii) Heatwaves; (iii) Extreme Temperature Indices; (iv) Extreme Precipitation Indices; (v) Droughts; and (vi) Floods. In addition selected variables at daily and monthly intervals are also available.

  • Categories    

    1. Restoration of degraded landscapes has become increasingly important for conservation of species and their habitats owing to habitat destruction and rapid environmental change. An increasing focus for restoration activity are old-fields as agricultural land abandonment has expanded in the developed world. Studies examining outcomes of ecological restoration predominantly focus on vegetation structure and plant diversity, and sometimes vertebrate fauna. Fewer studies have systematically investigated effects of restoration efforts on soil chemical and biophysical condition or ground-dwelling invertebrates and there is limited synthesis of these data. 2. This dataset comprised data for a global meta-analysis of published studies to assess the effects on soil properties and invertebrates of restoring land that was previously used for agriculture. Studies were included if the site had been either cropped or grazed, restoration was either active (planting) or passive (abandonment, fencing) and if adequate data on soil chemical or physical properties or invertebrate assemblages were reported for restored, control (cropped/grazed) or reference sites. 3. The dataset includes 42 studies, published between 1994 and 2019 that met the inclusion criteria, covering 16 countries across all continents. More studies assessed passive restoration approaches than active planting, and native species were more commonly planted than exotic species.

  • Categories  

    This dataset is modelled national pasture productivity. It describes the dynamics in grassland/pasture Gross Primary Production (GPP), Net Primary Production (NPP) and Carbon mass. GPP indicates total rate of carbon fixed through photosynthesis, in units gC/m2/day. It is the GPP of grasses only and so describes the production of grasslands and pastures. GPP is estimated separately for C3 and for C3 grasses using the Diffuse model (Donohue et al. 2014, see publication links). NPP is the net rate of carbon fixed through photosynthesis (GPP minus plant respiration) for grasses, in units of gC/m2/day. Grass carbon mass is the above-ground mass of grasslands and pastures, estimated using the CSP model. These are estimated using the unpublished CSP model (v2) for both live and senesced mass in units t/ha. Biomass is typically approximated as double the carbon mass. Inputs include MODIS MOD13Q1, minimum and maximum air temperature, elevation data and rainfall as described in the lineage section.

  • Categories  

    Vegetation Fractional Cover represents the exposed proportion of Photosynthetic Vegetation (PV), Non-Photosynthetic Vegetation (NPV) and Bare Soil (BS) within each pixel. The sum of the three fractions is 100% (+/- 3%) and shown in Red/Green/Blue colors. In forested canopies the photosynthetic or non-photosynthetic portions of trees may obscure those of the grass layer and/or bare soil. This product is derived from the MODIS Nadir BRDF-Adjusted Reflectance product (MCD43A4) collection 6 and has 500 meters spatial resolution. A suite of derivative products are also produced including monthly fractional cover, total vegetation cover (PV+NPV), and anomaly of total cover against the time series. Monthly: The monthly product is aggregated from the 8-day composites using the medoid method. Anomaly: represents the difference between total vegetation cover (PV+NPV) in a given month and the mean total vegetation cover for that month in all years available, expressed in units of cover. For example, if the mean vegetation cover in January (2001-current year) was 40% and the vegetation cover for the pixel in January 2018 was 30%, the anomaly for the pixel in Jan 2018 would be -10%. Decile: represents the ranking (in ten value intervals) for the total vegetation cover in a given month in relation to the vegetation cover in that month for all years in the time-series. MODIS fractional cover has been validated for Australia.

  • Categories  

    The dataset comprises data from the first survey of ~24,000 large trees (>10 cm diameter at breast height; DBH) within 48 1 ha forest monitoring plots established across Australia between 2011 and 2015. Data includes: [1] Site identifiers (ID and Site Name); [2] Plot Establishment Dates; [3] Tree identifiers and descriptors (ID, Species, Status, Growth Stage, Crown Class); [4] Tree measurements (Diameter, Point of Measurement, Height, Location); [5] Comments and ancillary information; and [6] List of Metagenomic Sample Identifiers.

  • Categories  

    This website provides access to data used in the Multi-Criteria Analysis Shell for Spatial Decision Support (MCAS-S) software tool. From the Data menu, explore and download individual supplementary layers, or download the entire datapack. The Multi-Criteria Analysis Shell for Spatial Decision Support (MCAS-S) is a software tool developed by the Australian Bureau of Agricultural and Resource Economics and Sciences that enables multi-criteria analysis (MCA) using spatial data. It is a powerful, easy-to-use and flexible decision-support tool that promotes: - framework for assessing options - common metric for classifying, ranking and weighting of the data - tools to compare, combine and explore spatial data - live-update of alternative scenarios and trade-offs.