This web service contains a selection of remotely sensed raster products used in the Exploring for the Future (EFTF) East Kimberley Groundwater Project. Selected products were derived from LiDAR, Landsat (5, 7, and 8), and Sentinel-2 data. Datasets include: 1) mosaic 5 m digital elevation model (DEM) with shaded relief; 2) vegetation structure stratum and substratum classes; 3) Normalised Difference Vegetation Index (NDVI) 20th, 50th, and 80th percentiles; 4) Tasselled Cap exceedance summaries; 5) Normalised Difference Moisture Index (NDMI) and Normalised Difference Wetness Index (NDWI). Landsat spectral reflectance products can be used to highlight land cover characteristics such as brightness, greenness and wetness, and vegetation condition; Sentinel-2 datasets help to detect vegetation moisture stress or waterlogging; LiDAR datasets providing a five meter DEM and vegetation structure stratum classes for detailed analysis of vegetation and relief.

Background: This is a sub-product of DEA Surface Reflectance (Sentinel-2A MSI) - Geoscience Australia Sentinel-2A MSI Analysis Ready Data Collection 3. See the parent product for more information. Reflectance data at top of atmosphere (TOA) collected by Sentinel-2A MSI sensors can be affected by atmospheric conditions, sun position, sensor view angle, surface slope and surface aspect. Surfaces with varying terrain can introduce inconsistencies to optical satellite images through irradiance and bidirectional reflectance distribution function (BRDF) effects. For example, slopes facing the sun appear brighter compared with those facing away from the sun. Likewise, many surfaces on Earth are anisotropic in nature, so the signal picked up by a satellite sensor may differ depending on the sensor’s position. These need to be reduced or removed to ensure the data is consistent and can be compared over time. What this product offers: This product takes Sentinel-2A MSI imagery captured over the Australian continent and corrects the inconsistencies across the land and coastal fringe. It achieves this using Nadir corrected Bi-directional reflectance distribution function Adjusted Reflectance (NBAR). In addition, this product applies terrain illumination correction to correct for varying terrain. The resolution is a 10/20/60 m grid based on the the ESA level 1C archive. Applications: - The development of derivative products to monitor land, inland waterways and coastal features, such as: - urban growth - coastal habitats - mining activities - agricultural activity (e.g. pastoral, irrigated cropping, rain-fed cropping) - water extent - The development of refined information products, such as: - areal units of detected surface water - areal units of deforestation - yield predictions of agricultural parcels - Compliance surveys - Emergency management

This web service contains a selection of remotely sensed raster products used in the Exploring for the Future (EFTF) East Kimberley Groundwater Project. Selected products were derived from LiDAR, Landsat (5, 7, and 8), and Sentinel-2 data. Datasets include: 1) mosaic 5 m digital elevation model (DEM) with shaded relief; 2) vegetation structure stratum and substratum classes; 3) Normalised Difference Vegetation Index (NDVI) 20th, 50th, and 80th percentiles; 4) Tasselled Cap exceedance summaries; 5) Normalised Difference Moisture Index (NDMI) and Normalised Difference Wetness Index (NDWI). Landsat spectral reflectance products can be used to highlight land cover characteristics such as brightness, greenness and wetness, and vegetation condition; Sentinel-2 datasets help to detect vegetation moisture stress or waterlogging; LiDAR datasets providing a five meter DEM and vegetation structure stratum classes for detailed analysis of vegetation and relief.

This compilation data release is a selection of remotely sensed imagery used in the Exploring for the Future (EFTF) East Kimberley Groundwater Project. Datasets include: • Mosaic 5 m digital elevation model (DEM) with shaded relief • Normalised Difference Vegetation Index (NDVI) percentiles • Tasselled Cap exceedance summaries • Normalised Difference Moisture Index (NDMI) • Normalised Difference Wetness Index (NDWI) The 5m spatial resolution digital elevation model with associated shaded relief image were derived from the East Kimberley 2017 LiDAR survey (Geoscience Australia, 2019b). The Normalised Difference Vegetation Index (NDVI) percentiles include 20th, 50th, and 80th for dry seasons (April to October) 1987 to 2018 and were derived from the Landsat 5,7 and 8 data stored in Digital Earth Australia (see Geoscience Australia, 2019a). Tasselled Cap Exceedance Summary include brightness, greenness and wetness as a composite image and were also derived from the Landsat data. These surface reflectance products can be used to highlight vegetation characteristics such as wetness and greenness, and land cover. The Normalised Difference Moisture Index (NDMI) and Normalised Difference Water Index (NDWI) were derived from the Sentinel-2 satellite imagery. These datasets have been classified and visually enhanced to detect vegetation moisture stress or water-logging and show distribution of moisture. For example, positive NDWI values indicate waterlogged areas while waterbodies typically correspond with values greater than 0.2. Waterlogged areas also correspond to NDMI values of 0.2 to 0.4. Geoscience Australia, 2019a. Earth Observation Archive. Geoscience Australia, Canberra. http://dx.doi.org/10.4225/25/57D9DCA3910CD Geoscience Australia, 2019b. Kimberley East - LiDAR data. Geoscience Australia, Canberra. C7FDA017-80B2-4F98-8147-4D3E4DF595A2 https://pid.geoscience.gov.au/dataset/ga/129985

The Bonaparte and Browse Basins 3D seismic derived bathymetry compilation (20220002C) was produced by the University of Western Australia, Norwegian Geotechnical Institute and UniLasalle in collaboration with Geoscience Australia through the AusSeabed initiative. The compilation integrates 127 bathymetry grids derived from available and workable 3D seismic datasets into a 30 m resolution 32-bit geotiff. A detailed workflow is described in: Lebrec, U., Paumard, V., O'Leary, M. J., and Lang, S. C., 2021, Towards a regional high-resolution bathymetry of the North West Shelf of Australia based on Sentinel-2 satellite images, 3D seismic surveys, and historical datasets: Earth System Science Data, v. 13, no. 11, p. 5191-5212 https://doi.org/10.5194/essd-13-5191-2021, 2021. This dataset is not to be used for navigational purposes.

This web service contains a selection of remotely sensed raster products used in the Exploring for the Future (EFTF) East Kimberley Groundwater Project. Selected products were derived from LiDAR, Landsat (5, 7, and 8), and Sentinel-2 data. Datasets include: 1) mosaic 5 m digital elevation model (DEM) with shaded relief; 2) vegetation structure stratum and substratum classes; 3) Normalised Difference Vegetation Index (NDVI) 20th, 50th, and 80th percentiles; 4) Tasselled Cap exceedance summaries; 5) Normalised Difference Moisture Index (NDMI) and Normalised Difference Wetness Index (NDWI). Landsat spectral reflectance products can be used to highlight land cover characteristics such as brightness, greenness and wetness, and vegetation condition; Sentinel-2 datasets help to detect vegetation moisture stress or waterlogging; LiDAR datasets providing a five meter DEM and vegetation structure stratum classes for detailed analysis of vegetation and relief.

This resource contains a bathymetry compilation prepared by the University of Western Australia for the North West Shelf of Australia, between the Cape Range and the Dampier Peninsula. The compilation includes, by decreasing resolution: - Publicly available MBES datasets, made available by Geoscience Australia by December 2019. - Satellite derived bathymetry produced using 1000+ images acquired between January 2017 and December 2019. - Seismic derived bathymetry extracted from 100+ surveys acquired between 1981 and 2015. - SRTM topography, reprocessed by Galant et al, 2011: https://pid.geoscience.gov.au/dataset/ga/72759 - 2009 Australian Bathymetry and Topography grid: https://pid.geoscience.gov.au/dataset/ga/67703 The Seismic and Satellite derived bathymetry grids are also available as individual layers. The vertical and spatial accuracy of the datasets have been thoroughly assessed using high-resolution datasets including publicly available MBES and LADS surveys. The assessment indicates that the seismic derived bathymetry has a depth accuracy better than 1 m + 2% of the absolute water depths while the satellites derived bathymetry has a depth accuracy better than 1 m + 5% of the absolute water depths. A detailed methodology is provided in: Lebrec et al, 2021. Towards a regional high-resolution bathymetry of the North West Shelf of Australia based on Sentinel-2 satellite images, 3D seismic surveys and historical datasets. (in prep.) This dataset is published with the permission of the CEO, Geoscience Australia. AUTHOR’S NOTICE: This dataset should not be used, under any circumstances, for navigation. When used, the dataset should be referenced as follow: Lebrec, U., Paumard, V., O'Leary, M. J., and Lang, S. C.: Towards a regional high-resolution bathymetry of the North West Shelf of Australia based on Sentinel-2 satellite images, 3D seismic surveys and historical datasets, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2021-128, in review, 2021.

The Barest Earth Sentinel-2 Map Index web map service depicts the 1 to 250 000 maps sheet tile frames that have been used to generate individual tile downloads of the Barest Earth Sentinel-2 product. This web service is designed to be used in conjunction with the Barest Earth Sentinel-2 web service to provide users with direct links for imagery download.

The Barest Earth Sentinel-2 Map Index web map service depicts the 1 to 250 000 maps sheet tile frames that have been used to generate individual tile downloads of the Barest Earth Sentinel-2 product. This web service is designed to be used in conjunction with the Barest Earth Sentinel-2 web service to provide users with direct links for imagery download.

Accurate information about the extent, frequency and duration of forest inundation is required to inform ecological, biophysical and hydrological models and enables floodplain managers to quantify the efficacy of flood mitigation/modification activities. Open water classifiers derived from optical remote sensing typically underestimate or fail to detect floodplain forest inundation. This paper presents a new method for detecting forest inundation dynamics using freely available Landsat and Sentinel 2 data, referred to as short-wave infrared mapping under vegetation. The method uses a dynamic threshold that accounts for the additional shortwave infrared reflectance caused by the presence of tree canopies over floodwater. The method is demonstrated at five Ramsar listed River Red Gum floodplain forest wetlands in southeastern Australia. Accuracy assessment based on independent drone imagery from a wide range of vegetated wetlands showed an absolute accuracy of 67%–70% and a fuzzy accuracy of 81%–83%. We found the method is conservative, and underestimates inundation (16%–18%) but very rarely misclassifies dry pixels as inundated (0.3%–0.6%). When compared to river gauge data, the method shows similar trends to an open water classifier (i.e., the area of inundated vegetation increases with increasing river height). The method is conservative compared to lidar-based floodplain inundation models but can be applied wherever cloud-free scenes of Landsat or Sentinel 2 have been acquired, thereby enabling floodplain managers with the ability to quantify changes in inundation dynamics in places/time-periods where lidar is unavailable. <b>Citation:</b> Lymburner, L., Ticehurst, C., Adame, M. F., Sengupta, A., & Kavehei, E. (2024). Seeing the floods through the trees: Using adaptive shortwave infrared thresholds to map inundation under wooded wetlands. <i>Hydrological Processes</i>, 38(6), e15174. https://doi.org/10.1002/hyp.15174