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    The Advanced Very High Resolution Radiometer (AVHRR) and AVHRR/3 sensors have been carried on the US NOAA polar orbiting satellites since the 1980s. These data have been acquired via direct reception from the satellite by reception stations located in Australia. CSIRO has stitched together data received by different stations and agencies to compile a national high quality data set. The daylight satellite overpasses have been extracted from this data set for each day in the 30 year period commencing 1 April 1992. The data have been geolocated, and calibrated to produce imagery channels of solar reflectance and brightness temperature using community published methods. Satellite view and sun illumination angles for each pixel are provided, together with a preliminary cloud mask based on the CLAVR algorithm. The spatial resolution is ~1 km and temporal coverage is daily. The reprojected data set (in EPSG:4326, lon-lat) is available via the CSIRO EASI hub.

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    Tree demographic, tree biomass and shrub count data for two Ausplots adjacent to Credo Flux tower (Salmon Gum, SG100E and Gimlet, Gim100W). Floristic survey data and 1000 points of cover. Tree demographics was measured using a tape at 130cm for diameter and 2 different laser height finders. These gave consistently different measures and both are presented. Plot biomass was calculated from allometric regression published by Jonson and Freudenberger (2011). All shrubs with mature heights of over 0.5m were measured in ten, 10m wide by 100m transects to ensure all shrubs in the one hectare plots were counted. Floristic survey was undertaken and 1000 point intercepts recorded along 10 lines (5 north-south, 5 east-west with one point per meter) for SG100W according to Ausplots methodology (Foulkes et al., 2011)

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    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 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.

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    This terrestrial LiDAR dataset captures detailed vegetation structural information at the Calperum Mallee SuperSite on Calperum Station near Renmark, South Australia. The purpose of this data is to enhance understanding of vegetation dynamics and ecosystem function in the region. The dataset is part of a broader collection of Terrestrial LiDAR data acquired from all TERN SuperSites, aimed at achieving a standardised and highly detailed capture of 3D vegetation structure across Australia.

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    This terrestrial LiDAR dataset captures detailed vegetation structural information at the Alice Mulga SuperSite on Pine Hill Cattle Station in the Northern Territory, Australia. The purpose of this data is to enhance understanding of vegetation dynamics and ecosystem function in the region. The dataset is part of a broader collection of Terrestrial LiDAR data acquired from all TERN SuperSites, aimed at achieving a standardized and highly detailed capture of 3D vegetation structure across Australia.

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    This terrestrial LiDAR dataset captures detailed vegetation structural information at the Tumbarumba Wet Eucalypt site in the Bago State Forest, New South Wales, Australia. The purpose of this data is to enhance understanding of vegetation dynamics and ecosystem function in the region. The dataset is part of a broader collection of Terrestrial LiDAR data acquired from all TERN SuperSites, aimed at achieving a standardized and highly detailed capture of 3D vegetation structure across Australia.

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    This terrestrial LiDAR dataset captures detailed vegetation structural information at the Litchfield Savanna SuperSite in NT, Australia. The purpose of this data is to enhance understanding of vegetation dynamics and ecosystem function in the region. The dataset is part of a broader collection of Terrestrial LiDAR data acquired from all TERN SuperSites, aimed at achieving a standardized and highly detailed capture of 3D vegetation structure across Australia.

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    This terrestrial LiDAR dataset captures detailed vegetation structural information at the Cumblerland Plain Woodland SuperSite in Western Sydney, Australia. The purpose of this data is to enhance understanding of vegetation dynamics and ecosystem function in the region. The dataset is part of a broader collection of Terrestrial LiDAR data acquired from all TERN SuperSites, aimed at achieving a standardized and highly detailed capture of 3D vegetation structure across Australia.

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    This terrestrial LiDAR dataset captures detailed vegetation structural information at the Warra Tall Eucalypt SuperSite in southern Tasmania, Australia. The purpose of this data is to enhance understanding of vegetation dynamics and ecosystem function in the region. The dataset is part of a broader collection of Terrestrial LiDAR data acquired from all TERN SuperSites, aimed at achieving a standardized and highly detailed capture of 3D vegetation structure across Australia.

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    This terrestrial LiDAR dataset captures detailed vegetation structural information at the Robson Creek Tropical Rainforest SuperSite within Danbulla National Park, North Queensland, Australia. The purpose of this data is to enhance understanding of vegetation dynamics and ecosystem function in the region. The dataset is part of a broader collection of Terrestrial LiDAR data acquired from all TERN SuperSites, aimed at achieving a standardized and highly detailed capture of 3D vegetation structure across Australia.