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    This dataset contains UAV multispectral imagery collected as part of a field trial to test the Uncrewed Aerial System to be used for the TERN Drone project. The UAS platform is DJI Matrice 300 RTK with 2 sensors: Zenmuse P1 (35 mm) RGB mapping camera and Micasense RedEdge-MX Dual (10-band multispectral sensor). P1 imagery were geo-referenced using the onboard GNSS in M300 and the D-RTK 2 Mobile Station. P1 Camera positions were post-processed using <a href="https://www.ga.gov.au/scientific-topics/positioning-navigation/geodesy/auspos">AUSPOS</a>. Flight conducted between 10:26 am and 10:47 am AEDT at flying height 80 m, forward and side overlaps for Zenmuse P1 set to 80%. MicaSense RedEdge-MX Dual triggered using timer mode (every second). <br><br> Micasense multispectral sensor positions were interpolated using P1, following which a standard workflow was followed in Agisoft Metashape to generate this orthomosaic (resolution 5 cm). Reflectance calibration was performed using captures of the MicaSense Calibration Panel taken before the flight. The orthomosaic raster has the relative reflectance (no unit) for the 10 bands (Coastal Blue, Blue, Green 531, Green, Red 650, Red, RedEdge 705, RedEdge, RedEdge 740, NIR). The cloud optimised (COG) GeoTIFF was created using rio command line interface. The coordinate reference system of the COG is EPSG 7855 - GDA2020 MGA Zone 55. <br><br> In the raw data RedEdge-MX image file suffixes correspond to bands like so - 1: Blue, 2: Green, 3: Red, 4: NIR, 5: Red Edge, 6: Coastal Blue, 7: Green 531, 8: Red 650, 9: RedEdge 705, 10: RedEdge 740. However, in the processed Orthomoasic GeoTIFF, the bands 1-10 are ordered as per the Central Wavelength (Coastal Blue, Blue, Green 531, Green, Red 650, Red, RedEdge 705, RedEdge, RedEdge 740, NIR).

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    This dataset contains UAV multispectral imagery collected as part of a field trial to test the Uncrewed Aerial System to be used for the TERN Drone project. The UAS platform is DJI Matrice 300 RTK with 2 sensors: Zenmuse P1 (35 mm) RGB mapping camera and Micasense RedEdge-MX (5-band multispectral sensor). P1 imagery were geo-referenced using the onboard GNSS in M300 and the D-RTK 2 Mobile Station. P1 Camera positions were post-processed using <a href="https://www.ga.gov.au/scientific-topics/positioning-navigation/geodesy/auspos">AUSPOS</a>. The flights took place between 14:58 and 03:08 at a height of 80m with a flying speed set to 5 m/s. Forward and side overlaps of photographs were set to 80%. <br><br> Micasense multispectral sensor positions were interpolated using P1, following which a standard workflow was followed in Agisoft Metashape to generate this orthomosaic (resolution 5 cm). Reflectance calibration was performed using captures of the MicaSense Calibration Panel taken before the flight. The orthomosaic raster has the relative reflectance (no unit) for the 5 bands (B, G, R, RedEdge, NIR). This cloud optimised (COG) GeoTIFF was created using rio command line interface. The coordinate reference system of the COG is EPSG 7855 - GDA2020 MGA Zone 55. <br><br> In the raw data RedEdge-MX image file suffixes correspond to bands like so - 1: Blue, 2: Green, 3: Red, 4: NIR, 5: Red Edge. However, in the processed Orthomoasic GeoTIFF, the bands are ordered in the wavelength order (Blue, Green, Red, Red Edge, NIR).

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    This dataset contains UAV RGB imagery collected as part of a field trial to test the Uncrewed Aerial System to be used for the TERN Drone project. The UAS platform is DJI Matrice 300 RTK with 2 sensors: Zenmuse P1 (35 mm) RGB mapping camera and Micasense RedEdge-MX Dual (10-band multispectral sensor). P1 imagery were georeferenced using the onboard GNSS in M300 and the D-RTK 2 Mobile Station. Camera positions were post-processed using <a href="https://www.ga.gov.au/scientific-topics/positioning-navigation/geodesy/auspos">AUSPOS</a>. Flight conducted between 10:26 am and 10:47 am AEDT at flying height 80 m, forward and side overlap set to 80%. <br><br> RGB orthomosaic (resolution: 1 cm. CRS: EPSG 7855 - GDA2020 MGA Zone 55) generated using Agisoft Metashape Professional, and a cloud optimised GeoTIFF was created using rio command line interface.

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    This dataset contains UAV RGB imagery collected as part of a field trial to test the Uncrewed Aerial System to be used for the TERN Drone project. The UAS platform is DJI Matrice 300 RTK with 2 sensors: Zenmuse P1 (35 mm) RGB mapping camera and Micasense RedEdge-MX (5-band multispectral sensor). P1 imagery were georeferenced using the onboard GNSS in M300 and the D-RTK 2 Mobile Station. Camera positions were post-processed using <a href="https://www.ga.gov.au/scientific-topics/positioning-navigation/geodesy/auspos">AUSPOS</a>. The flight took place between 14:00 and 14:08 at a height of 80m with a flying speed set to 5 m/s. Forward and side overlaps of photographs were set to 80%. <br><br> Agisoft Metashape was used to generate this RGB orthomosaic (resolution 1 cm). This cloud optimised GeoTIFF was created using rio command line interface. The coordinate reference system of the orthomosaic is EPSG 7855 - GDA2020 MGA Zone 55.

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    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.

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    The Soil Moisture Integration and Prediction System (SMIPS) produces national extent daily estimates of volumetric soil moisture at a resolution of approximately 1km or 0.01 decimal degrees. SMIPS also generates an index of between 0-1 which approximates how full the 90cm metre soil moisture store is at a particular location and time. The SMIPS model itself consists of two linked soil moisture stores, a shallow quick responding 10cm upper store and a deeper, slower responding 80cm store. SMIPS is parameterised using physical properties from the <a href ='https://www.clw.csiro.au/aclep/soilandlandscapegrid/'>Soil and Landscape Grid of Australia </a>and takes a data model fusion approach for model forcing. Version 1.0 of the SMIPS model uses precipitation and potential evapotranspiration data from the Bureau of Meteorology’s <a href="http://www.bom.gov.au/water/landscape/assets/static/publications/AWRALv6_Model_Description_Report.pdf">AWRA Model</a>. In addition to version 1.0 of the model, an experimental version of the model is available for user testing. This version of the model uses precipitation data supplied by an experimental CSIRO daily rainfall surface generated using spatial data from the NASA Global Precipitation Mission as a base and enhanced using rainfall observations from the Bureau of Meteorology (BoM) rainfall gauge network, and various landscape covariates, processed using a machine learning approach. <br> To help increase model accuracy, the internal SMIPS model states are adjusted or ‘bumped’ by daily observational data from the European Space Agency’s Soil Moisture and Ocean Salinity (SMOS) satellite mission.

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    <p> AusPlots is a collection of ecological data and samples gathered from a network of plots and transects across Australia by the TERN Surveillance Monitoring team, using standardised methodologies. </p> <p>The AusPlots collection provides the ecological infrastructure to: </p> <ul><li>quantify the richness and cover of plant species (including weeds); </li><li>quantify the diversity and abundance of soil biodiversity; </li><li>assess the state, spatial heterogeneity and structural complexity of vegetation, including life-stage; </li><li>record vegetation and soil parameters that assist with the validation of remotely sensed ecological products;</li><li>analyse vegetation structure and change based on a series of photo reference images; </li><li>better estimate soil carbon and nutrient stocks; </li><li>conduct taxonomic validation studies based on collected plant voucher specimens; </li><li>conduct DNA barcoding and population genetic profiling based on collected tissue samples. </li></ul> <p> Overall this information will progress understanding of ecosystem processes, structure and function, and more generally progress understanding of the response to disturbance and longer-term environmental change of rangeland ecosystems, which underpins sustainable management practice.</p>.