This dataset contains data on the use of nest boxes by yellow-tailed black-cockatoos on Kangaroo Island (binary response variable; 1 = used in a breeding season, 0 = not used in a breeding season) for nest boxes monitored between 2015-2017 and again in 2020-2023 and the year of monitoring. In addition to this, data was sourced via spatial analysis and remote sensing on the distance to mature Pinus radiata plantation, distance to banksia dominated habitat, and amount of native vegetation within a 10km radius to approximate human disturbance and fragmentation. The name of nest boxes is included, and each nest's spatial location can be identified by this name on a separate Kangaroo Island Landscape Board spatial dataset. The spatial location of nests has been withheld as they identify important breeding sites for endangered bird species.

This dataset contains global dryland literature abstracts from over the last 75 years (8218 articles) to identify areas in arid ecology that are well studied and topics that are emerging.

The physical drivers of ecosystem formation – macroclimate, lithology and landform – along with vegetation structural formations are key determinants of current ecosystem type. Each combination of these ecosystem drivers – each ‘ecological facet’ – provides a unique set of opportunities and challenges for life. <br> Management and conservation should seek to understand and take in to account these drivers of ecosystem formation. By understanding the unique combinations of these drivers management strategies can plan for their full range of variation, and conservation efforts can ensure that unique ecosystems are not lost. Unfortunately, there is currently no Australia-wide standardized map of ecological facets at management-appropriate scales. <br> By understanding the magnitude and distribution of unique combinations of these drivers, management strategies can plan for their full range of variation, and conservation efforts can ensure that unique ecosystems are not lost. Additionally, by improving our understanding of the past and present conditions that have given rise to current ecological facets this dataset could facilitate future predictive environmental modelling. Finally, this data could assisting biodiversity conservation, climate change impact studies and mitigation, ecosystem services assessment, and development planning <br> Further information about the dataset can be found at <a href="https://ternaus.atlassian.net/wiki/spaces/TERNSup/pages/2276130817/GEOSS+Ecosystem+Map">GEOSS Ecosystem Map,TERN Knowledge Base </a> .

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. <br></br> The pixel values are scaled reflectance, as 16-bit integers. To retrieve physical reflectance values, the pixel values should be multiplied by 0.0001.

<br>Tropical rainforests play a powerful role in mediating the global climate through the exchange and storage of carbon and water. Climate change is expected to generate higher atmospheric water demand in many areas, potentially increasing the rate of evaporation. In this study, we show that higher evaporative demand may in fact lead to lower fluxes of water from tropical rainforests and a reduced capacity of these forests to store carbon.</br> The record contains meteorological and forest inventory data in addition to data on soil water potential, sapflow measurements and tree hydraulic vulnerability measures from Robson Creek and Cow Bay study sites in Far North Queensland. The measurements occurred over a period of two years form 2019 to 2020.

This dataset contains LiDAR data collected at TERN’s Calperum Mallee SuperSite during a field trial of an Uncrewed Aircraft System (UAS), undertaken to evaluate the use of drone-based LiDAR for the TERN Drone project across current and future TERN sites (AusPlots, SuperSites, and Cal/Val sites). Standardised TERN Ecosystem Surveillance Drone Data Collection and Data Processing protocols are used to collect drone imagery and to generate orthomosaics. The aim of drone surveys is to capture the vegetation structure. The standardised data collection and data processing protocols developed in 2022 are based on the DJI Matrice 300 (M300) RTK drone platform. Lidar sensor DJI Zenmuse L1 is used with DJI Matrice 300 (M300) RTK platform to capture RGB colourised 3D point clouds. The data is georeferenced using the onboard GNSS in M300 and the D-RTK 2 base station. DJI Terra software was used to generate 3D point clouds from the raw lidar data. The protocols include flight planning and data collection guidelines for a 100 x 100 m TERN plot, and the processing workflow used on DJI Terra. Mission-specific metadata for each plot is provided in the imagery/metadata folder (please refer to the imagery collection). The Drone Data Collection and Lidar Processing protocols can be found at <em> https://www.tern.org.au/field-survey-apps-and-protocols/ </em>.

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).

This dataset contains bird occurrence data collected at the Alice Mulga site in 2015.

This dataset contains bird occurrence data collected at the Karawatha Peri-urban site in 2007, 2012 and 2015

This dataset contains RGB and multispectral imagery collected at TERN’s Calperum Mallee SuperSite during a field trial of an Uncrewed Aircraft System (UAS), conducted to assess the use of drone-based imagery for the TERN Drone project across existing and future TERN sites (AusPlots, SuperSites, and Cal/Val sites). Standardised TERN Ecosystem Surveillance Drone Data Collection and Data Processing protocols are used to collect drone imagery and to generate orthomosaics. The protocols developed in 2022 are based on the DJI Matrice 300 (M300) RTK drone platform. DJI Zenmuse P1 and MicaSense RedEdge-MX/Dual sensors are used with M300 to capture RGB and multispectral imagery simultaneously. The data is georeferenced using the DJI D-RTK2 base station and onboard GNSS RTK. In the processing workflow, the multispectral image positions (captured with navigation-grade accuracy) are interpolated using image timestamp and RGB image coordinates. Dense point clouds and the fine-resolution RGB smoothed surface were used to generate co-registered RGB (1 cm/pixel) and multispectral (5 cm/pixel) orthomosaics. Mission-specific metadata for each plot is provided in the imagery/metadata folder. The Drone Data Collection and RGB and Multispectral Imagery Processing protocols can be found at <em> https://www.tern.org.au/field-survey-apps-and-protocols/ </em>.