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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:  Site identifiers (ID and Site Name);  Plot Establishment Dates;  Tree identifiers and descriptors (ID, Species, Status, Growth Stage, Crown Class);  Tree measurements (Diameter, Point of Measurement, Height, Location);  Comments and ancillary information; and  List of Metagenomic Sample Identifiers.
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 These are estimated using the unpublished CSP model (v2) for both live and senesced mass in units t/ha.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.
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.
Vertical plant profiles for the Australian continent were derived through integration of ICESat GLAS waveforms with ALOS PALSAR and Landsat data products. Co-registered Landsat Foliage Projected Cover (FPC) and ALOS PALSAR L-band HH and HV mosaics were segmented to generate objects with similar radar backscatter and cover characteristics. Within these, height, cover, age class and L-band backscatter characteristics were summarised based on the ICESat and Landsat time-series and ALOS PALSAR datasets.
The ecocloud Platform provides unprecedented access to datasets from hundreds of publishers across Australia in a single interface, including key ecoscience publishers such as ALA, TERN and IMOS. It then connects this data with common analysis tools like RStudio, Jupyter Notebooks and Virtual Desktops running tools like Kepler, KNIME, QGIS, Biodiverse, marcoecoDesktop, Panoply, Jupyter lab, RStudio and file sharing applications Dropbox and ownCloud. Curated data is also available through discipline-specific workflows like the BCCVL and Biodiverse, all of which also connect users to Australia’s national cloud computing infrastructure. ecocloud also includes an innovative training and skills development program (ecoEd) to help drive a skilled workforce of students, researchers, government practitioners and industry professionals working across the domain.
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.
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.