<p>This data set consists of a shapefile/kml of mangrove extent and dominant species for Kakadu National Park mangroves generated from true colour aerial photographs acquired in 1991.</p> <p>From true color 1991 orthomosaics of Field Island and the Wildman, West, and South Alligator Rivers, mangroves were mapped by first applying a fine scale spectral difference segmentation within eCognition to all three visible bands (blue, green, and red). A maximum likelihood (ML) algorithm within the environment for visualizing images (ENVI) software was then used to classify all segments using training areas associated with mangroves, but also water, mudflats, sandflats, and coastal woodlands. These were identified through visual interpretation of the imagery. Segmentation was necessary as 1) the diversity of structures and shadows within and between tree crowns limited the application of pixel-based classification procedures and 2) the color balance between the different photographs comprising the orthomosaics varied. All segments were examined individually and methodically to determine whether they should be reallocated to a non-mangrove class (e.g., mudflats) or confirmed as mangroves. Open woodlands dominated by Eucalyptus species could also be visually identified within the aerial photography (AP) orthoimages, although their discrimination was assisted by only considering areas where the underlying LiDAR DTM (Digital Terrain Model) exceeded 10 m, assuming this excludes tidally inundated sections.</p>

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.

This dataset comprises spatially and temporally dynamic estimates of the monthly latent heat flux (λE) and sensible heat flux (H) for all of Australia. The available energy (A, being net radiation [Rn] less the gound heat flux [G]) can be obtained by adding the λE and H datasets provided. Energy variables have been provided as hydrological equivalent units of depth, normalised to daily rates (mm/d). TERN OzFlux Surface Energy Balance (SEB) data were used to scale MODIS-based covariates of surface temperature less air temperature (Ts – Ta) and Rn using a Spatial and Temporal General Linear Model (ST-GLM) to third order. The ST-GLM SEB model was implemented across all of Australia on a 0.005° spatial grid (~ 500 m) on a monthly timestep from March 2000 through June 2023. Coefficients of the model were determined from the OzFlux network of eddy covariance flux tower data. Three flux tower sites were used to independently validate the accuracy of the model, being Calperum, SA, Howard Springs, NT, and Tumbarumba, NSW. The mean absolute difference (MAD) for λE, H and A was estimated as: 0.37, 0.39 and 0.34&nbsp;mm/d, respectively. The relative errors determined by the MAD percentage (MADP) for λE, H, and A were estimated to be: 16%, 26%, and 9%, respectively. This dataset represents a new pathway for operational regional- to global-scale estimation of dynamic SEB variables.

This is Version 1 of the Brigalow Belt Bioregion Spatial BioCondition dataset. It is superseded by the Version 2 dataset that can be found at: https://doi.org/10.25901/rnqz-cn10.<br><br> 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 eight vegetation specific remote sensing (RS) datasets and 17,000 training sites of known vegetation community and condition state. Condition score was modelled as a function of the difference in the RS space within homogeneous vegetation communities. The product is intended to represent predicted BioCondition for year 2019 rather than any single date.

This dataset contains Unmanned Aircraft System (UAS) multispectral, pansharpened and long-wave infrared (LWIR) orthomosaics of the Samford Ecological Research Facility (SERF), Queensland University of Technology. SERF is located in the Samford Valley, west of Brisbane, Australia and is the usual place for flight testing and evaluation of new equipment. The QUT's Research Engineering Facility team operated DJI Matrice 300RTK (M300) with latest MicaSense Altum-PT (5-band multispectral sensor, LWIR, panchromatic channels and downlight sensor). The images were geo-referenced using the onboard GNSS in M300 and the D-RTK 2 base station and also georectified with 5 ground control points collected by Emlid Reach RS GNSS receivers. In the processing workflow in Agisoft Metashape, the multispectral orthomosaics were orthorectified and pan-sharpened. Dense point clouds were used to generate multispectral (GSD 3.4 cm/px), panchromatic and multispectral pansharpened (GSD 1.6 cm/pixel) and LWIR (GSD 21 cm/pixel) orthomosaics.

<br>Hermitage Research Station (28&deg; 12’ S, 152&deg; 06’ E) situated near Warwick, is the site of a 33 year study of carbon cycling, storage and emissions in a southern Queensland winter cereal system. Mean annual temperature at the site is 17.5&deg;C and mean annual rainfall is 685&nbsp;mm. The soil is a Vertosol containing 65% clay, 24% silt, and 11% sand. Treatments at the trial included stubble burnt (SB), stubble retained (SR), conventional tillage (CT), no tillage (NT), nitrogen fertiliser added (NF) and no nitrogen fertiliser added (N0). It has provided guidance to farmers on optimising nitrogen use efficiency through fine tuning rates to meet crop need, e.g. delivering nitrogen when it is needed by the crop possibly using split applications and coated fertilisers with slower nutrient release profiles. Sourcing nitrogen from pulse crop and pasture was also studied as an option for meeting nitrogen needs with lower emissions and reduced cost.</br>

The datasets in this series comprise predictions of biocondition for Queensland's Bioregions. The datasets are created using a gradient boosting decision tree (GBDT) model based on eight vegetation specific remote sensing (RS) datasets and 17,000 training sites of known vegetation community and condition state. Condition score was modelled as of the difference in the RS space within homogeneous vegetation communities. The product is intended to represent predicted BioCondition for year 2019 rather than any single date.

<br>The Brigalow Catchment Study (BCS) in the brigalow (<em>Acacia harpophylla</em>) bioregion of central Queensland, commenced in 1965 with a pre-clearing calibration phase of 17 years to define the hydrology of 3 adjoining catchments (12-17&nbsp;ha). Following clearing of 2 catchments in 1982, 3 land uses, brigalow forest, cropping, and grazed pasture, were established and monitored for water balance, resource condition and productivity. This trial has provided data and scientific understanding on the interaction of climate, soils, water, land use and management for resource condition across the three major land uses. Soil samples from the trial site have been used in calibration of the Roth C model for use in estimating Australia’s national greenhouse gas inventory.</br>

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.

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 eight vegetation specific remote sensing (RS) datasets and 17,000 training sites of known vegetation community and condition state. Condition score was modelled as a function of the difference in the RS space within homogeneous vegetation communities. The product is intended to represent predicted BioCondition for year 2019 rather than any single date.