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This data release consists of flux tower measurements of the exchange of energy and mass between the surface and the atmospheric boundary-layer in semi-arid eucalypt woodland using eddy covariance techniques. It been processed using PyFluxPro (v3.3.0) as described in Isaac et al. (2017), <a href="https://doi.org/10.5194/bg-14-2903-2017">https://doi.org/10.5194/bg-14-2903-2017</a>. PyFluxPro takes data recorded at the flux tower and process this data to a final, gap-filled product with Net Ecosystem Exchange (NEE) partitioned into Gross Primary Productivity (GPP) and Ecosystem Respiration (ER). For more information about the processing levels, see <a href="https://github.com/OzFlux/PyFluxPro/wiki">https://github.com/OzFlux/PyFluxPro/wiki</a>. <br /> <br /> The Samford flux station is situated on an improved (<em>Paspalum dilatum</em>) pasture in the humid subtropical climatic region of coastal south-east Queensland. Located only 20km from the centre of Brisbane city, Samford Valley provides an ideal case study to examine the impact of urbanisation and land use change on ecosystem processes. The valley covers an area of some 82km2 and is drained in the southern regions by the Samford creek, which extends some 13km to Samford Village and into the South Pine River. The Samford Valley is historically a rural area experiencing intense urbanisation, with the population increasing almost 50% in the 10 years to 2006 (Morton Bay Regional Council, 2011). Within the Samford valley study region, the Samford Ecological Research Facility (SERF) not only represents a microcosm of current and historical land uses in the valley, but provides a unique opportunity to intensively study various aspects of ecosystem health in a secure, integrated and long term research capacity. Mean annual minimum and maximum temperatures at a nearby Bureau of Meteorology site are 13.1°C and 25.6°C respectively while average rainfall is 1102mm. <br />For additional site information, see https://www.tern.org.au/tern-observatory/tern-ecosystem-processes/samford-peri-urban-supersite/ . <br /><br />
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.
We investigated recovery of soil chemical properties after restoration in semi-arid Western Australia, hypothesising that elevated nutrient concentrations would gradually decline post planting, but available phosphorus (P) concentrations would remain higher than reference conditions. We used a space-for-time substitution approach, comparing 10 planted old field plots with matched fallow cropland and reference woodlands. Sampling on planted old fields and reference woodland plots was stratified into open patches and under tree canopy to account for consistent differences between these areas. Soil samples to 10 cm depth were collected at 20 points across 30 plots. Ten samples were randomly collected and combined from locations beneath trees and a further 10 samples collected in gaps and combined, resulting in one soil sample for beneath tree canopy and another one for gap areas. Sampling occurred in autumn 2017 to capture potentially high concentrations of soil nitrate following the seasonal die-back of exotic annual plants typical of this Mediterranean-climate region. Samples were stored at 4 °C in plastic zip-lock bags until delivery to the CSBP Limited (Bibra Lake, WA) laboratories. Chemical parameters measured were plant available P (Colwell), plant available N (nitrate and ammonium), total N, plant available potassium (Colwell) and plant available sulphur (KCl 40). Lastly, electrical conductivity, pH (H2O, CaCl2), and soil texture were quantified as differences among plots could affect nutrient availability and soil chemistry. Soil available nutrients were also measured using Plant Root Simulator (PRS)TM resin probes (Western Ag Innovations, 2010, https://www.westernag.ca/inn). Probes contain anion or cation exchange membranes within a plastic stake. The membranes act as a sink for collecting nutrients and continuously absorb ions during deployment. Four anion and cation probes were placed vertically in the top 15 cm of soil at each stratification. Probes were left in the ground for three months during the growing season, from August to November 2017. This period was deemed suitable for semi-arid regions to achieve sufficient nutrient uptake but not too long to saturate probes. After removal, probes were cleaned with deionized water and sent to Western Ag Innovations (Canada) for analysis. All soil chemical analyses were conducted under laboratory conditions using standard test procedures. PRS probe nutrients are reported as micrograms/10cm2/time.
This data contains soil physico-chemical characteristics collected at the Warra Tall Eucalypt site in 2012.
This data contains soil description, bulk density and soil moisture characteristics collected at the Calperum Mallee site in 2012.
<br>The aim of this project is to compile land use and management practices and their observed and measured impacts and effects on vegetation condition. The results provide land managers and researchers with a tool for reporting and monitoring spatial and temporal transformations of Australia’s native vegetated landscapes due to changes in land use and management practices. Following are the details about South Brooman State Forest, NSW. </br><br> Pre-European reference-analogue vegetation: The site was originally eucalypt tall open forest, multi-aged open, dry sclerophyll forest. The main overstorey species were spotted gum (<em>Corymbia maculata</em>), <em>Eucalyptus muelleriana</em>, <em>E. paniculata</em>, <em>E. pilularis</em>. The main understorey species were <em>Acacia spp.</em>, <em>Acmena spp.</em> </br><br> Brief chronology of changes in land use and management:<ul style="list-style-type: disc;"> <li>1830: Unmodified</li> <li>1880: Area picked over for high quality sawlogs</li> <li>1945: Area picked over for high quality sawlogs</li> <li>1949: Sawlog harvesting - 85% of area</li> <li>1959: Sawlog harvesting - 85% of area</li> <li>1968: Commercial Thinning - 25% of area</li> <li>1969: Area left to rehabilitate</li> <li>1994: Wildfire - 100% of the area</li> <li>1996: Pole harvesting - 5% of area</li> <li>1998: Sawlog harvesting - 20% of the area</li> <li>1999 and 2003: Hazard reduction</li> <li>1997: Site was burnt (prescribed fire) followed by drought</li> <li>2004-2011: Area left to rehabilitate</li></ul></br>
This dataset contains bird occurrence data collected at the Litchfield Savanna site in 2015.
The dataset consists of composited seasonal surface reflectance images (4 seasons per year) created from the full time series of Sentinel-2 imagery. The imagery has been composited over a season to produce imagery which is representative of that period, using techniques which will reduce contamination by cloud and other problems. This creates a regular time series of reﬂectance values which captures the variability at seasonal time scales. The beneﬁts are a regular time series with minimal missing data or contamination from various sources of noise as well as data reduction. Each season has exactly one value (per band) for each pixel (or is null, i.e., missing), and the value for that season is assumed to be the representative of the whole season. The algorithm is based on the medoid (in reﬂectance space) over the time period (the medoid is a multi-dimensional analogue of the median), which is robust against extreme values. The seasonal surface reflectance is of the 6 TM-like bands (Blue, Green, Red, NIR, SWIR1, SWIR2), all at 10 m resolution. This dataset is intended to be a 10 m equivalent of the Landsat surface reflectance, using only Sentinel-2. The two 20m bands are resampled using cubic convolution. The pixel values are scaled reflectance, as 16-bit integers. To retrieve physical reflectance values, the pixel values should be multiplied by 0.0001.
The Australian Phenology Product is a continental data set that allows the quantitative analysis of Australia’s phenology derived from MODIS Enhanced Vegetation Index (EVI) data using an algorithm designed to accommodate Australian conditions. The product can be used to characterize phenological cycles of greening and browning and quantify the cycles’ inter and intra annual variability from 2003 to 2018 across Australia. Phenological cycles are defined as a period of EVI-measured greening and browning that may occur at any time of the year, extend across the end of a year, skip a year (not occur for one or multiple years) or occur more than once a year. Multiple phenological cycles within a year can occur in the form of double cropping in agricultural areas or be caused by a-seasonal rain events in water limited environments. Based on per-pixel greenness trajectories measured by MODIS EVI, phenological cycle curves were modelled and their key properties in the form of phenological curve metrics were derived including: the first and second minimum point, peak, start and end of cycle; length of cycle, and; the amplitude of the cycle. Integrated EVI under the curve between the start and end of the cycle time of each cycle is calculated as a proxy of productivity.
Destructive sampling of 47 <em>Eucalyptus obliqua</em> trees was carried out in the Warra Tall Eucalypt site to determine a range of biomass measures that can be used to inform allometric equations.