Poster Paper submission detailing the progress, benefits and vision of the Unlocking the Landsat Archive Project

GA through the Unlocking the Landsat Archive (ULA) Project is funded by the Department of Industry, Innovation, Science, Research and Tertiary Education (DIISRTE) to process and make available well calibrated Landsat imagery for the period 2000-2010. Having now processed this data to produce well calibrated optical surface reflectance data products at the National-Scale we are now able to promote these products through various means. This promotional animation shows the time-series "stacked" mosaics for each month of the year.

<b>This record was retired 15/09/2022 with approval from S.Oliver as it has been superseded by eCat 146091 DEA Water Observations Statistics (Landsat)</b> In previous versions of WOfS, the basic water classifications, statistical summaries and confidence products were contained within one product with several datasets. As of version 2.1.5, WOfS is split into three products: Water Observation Feature Layers (WO_25_2.1.5), Summary Statistics (WO-STATS_25_2.1.5), and Filtered Summary Statistics (WO-FILT-STATS_25_2.1.5). This product is Water Observations from Space - Filtered Statistics (WO-FILT-STATS), consisting of a Confidence layer that compares the WO-STATS water summary to other national water datasets, and the Filtered Water Summary which uses the Confidence to mask areas of the WO-STATS water summary where Confidence is low. The Filtered Water Summary provides the long term understanding of the recurrence of water in the landscape, with much of the noise due to misclassification filtered out. WO-FILT-STATS consists of the following datasets: Confidence: the degree of agreement between water shown in the Water Summary and other national datasets. The Confidence layer provides understanding of whether the water shown in the Water Summary agrees with where water should exist in the landscape, such as due to sloping land or whether water has been detected in a location by other means. Filtered Water Summary: A simplified version of the Water Summary, showing the frequency of water observations where the Confidence is above a cutoff level. This layer gives a noise-reduced view of surface water across Australia. Even though confidence filtering is applied to the Filtered Water Summary, some cloud and shadow, and sensor noise does persist.

This project commenced in November 2012 and is intended to provide satellite data and related scientific services to support the Murray-Darling Basin Authority's monitoring of how the condition of riparian vegetation responds to changing river run-off and wetland inundation levels. Under this project, Geoscience Australia started to build a satellite data processing infrastructure; named the 'datacube', as a proof of concept for expected on-going time series analysis applications including historical flood and bathymetry mapping. The work incorporates an automated processing chain for Landsat satellite images from Geoscience Australia's extensive archive, into customised high level intermediate products, including automated ortho-rectification, atmospheric correction, cloud-removal, and mosaicking, and finally into statistics on the spectral and derivative indices (that is, vegetation condition indices or various types) for the summer periods of December-March, each year for the period 2000-2013. These vegetation indices and associate statistics are then used, by the Murray-Darling Basin Authority and its collaborators, as inputs to a mathematical model of vegetation types and their respective conditions within the Murray-Darling Basin.

<b>This record was retired 29/03/2022 with approval from S.Oliver as it has been superseded by eCat 132310 GA Landsat 7 ETM+ Analysis Ready Data Collection 3</b> Surface Reflectance (SR) is a suite of Earth Observation (EO) products from GA. The SR product suite provides standardised optical surface reflectance datasets using robust physical models to correct for variations in image radiance values due to atmospheric properties, and sun and sensor geometry. The resulting stack of surface reflectance grids are consistent over space and time which is instrumental in identifying and quantifying environmental change. SR is based on radiance data from the Landsat TM/ETM+ and OLI sensors.

<b>This record was retired 29/03/2022 with approval from S.Oliver as it has been superseded by eCat 130853 GA Landsat 5 TM Analysis Ready Data Collection 3</b> Surface Reflectance (SR) is a suite of Earth Observation (EO) products from GA. The SR product suite provides standardised optical surface reflectance datasets using robust physical models to correct for variations in image radiance values due to atmospheric properties, and sun and sensor geometry. The resulting stack of surface reflectance grids are consistent over space and time which is instrumental in identifying and quantifying environmental change. SR is based on radiance data from the Landsat TM/ETM+ and OLI sensors.

<b>This record was retired 29/03/2022 with approval from S.Oliver as it has been superseded by eCat 146091 Geoscience Australia Landsat Water Observation Statistics Collection 3</b> WOfS-STATS (WO_STATS_2.1.5) is a set of statistical summaries of the water observations contained in WOfS (WO_2.1.5). The layers available are: the count of clear observations;the count of wet observations;the percentage of wet observations over time. This product is Water Observations from Space - Statistics (WO-STATS), a set of statistical summaries of the WOfS product that combines the many years of WOfS observations into summary products that help the understanding of surface water across Australia. WO-STATS consists of the following datasets: Clear Count: how many times an area could be clearly seen (ie. not affected by clouds, shadows or other satellite observation problems), Wet Count: how many times water was detected inobservations that were clear, Water Summary: what percentage of clear observations were detected as wet (ie. the ration of wet to clear as a percentage) As no confidence filtering is applied to this product, it is affected by noise where misclassifications have occurred in the WOfS water classifications, and hence can be difficult to interpret on its own. The confidence layer and filtered summary are contained in the WO-Fil-STATS product, which provide a noise-reduced view of the water summary. WO-STATS is available in multiple forms, depending on the length of time over which the statistics are calculated. At present the following are available: WO-STATS: statistics calculated from the full depth of time series (1986 to present) WO-STATS-ANNUAL: statistics calculated from each calendar year (1986 to present) WO-STATS-NOV-MAR: statistics calculated yearly from November to March (1986 to present) WO-STATS-APR-OCT: statistics calculated yearly from April to October (1986 to present)

<b>This record was retired 01/04/2022 with approval from M.Wilson as it has been superseded by eCat 146091 Geoscience Australia Landsat Water Observation Statistics Collection 3</b> WOfS is a gridded dataset indicating areas where surface water has been observed using the Geoscience Australia (GA) Earth observation satellite data holdings. The WOfS product version 1.5 includes observations taken between 1987 to November 2014 from the Landsat 5 and 7 satellites. WOfS version 1.5 includes observations from 1987 to March 2014. Future versions of the product will extend the temporal range and diversify the data sources. WOfS covers all of mainland Australia and Tasmania but excludes off-shore Territories.

This is a proof of concept web service displaying trial samples of historic flood mapping from satellite. Over the next 2 years this service will be developed into a nationwide portal displaying flooding across Australia as observed by satellite since 1987. The service shows a summary of water observed by the Landsat-5 and MODIS satellites across Australia for periods between 2000 and 2012. The first layer set displays national observed water from MODIS fvrom 2000 to 2012, as derived by Geoscience Australia using an automated flood mapping algorithm. The colouring of the display represents the frequency of observed water in a 500 x 500m grid. The higher the number, the more often water was observed by the satellites over the period. This means that floods have low values, while lakes, dams and other permanent water bodies have high values. The three additional layer sets are study areas demonstrating the water observed in each study area by the Landsat-5 satellite, as derived by Geoscience Australia using an automated flood mapping algorithm. The study areas and the observation periods are: Study Area 1, Condamine River system between Condamine and Chinchilla, Qld, observed between 2006 and 2011 Study Area 2, North-west Victorian rivers between Shepparton and Kerang, observed between 2006 and 2011 Study Area 3, Northern Qld rivers, near Normanton, observed between 2003 and 2011 Each Study Area layer set includes a water summary displaying the frequency of observed water in 25 x 25m grids, plus individual flood extents for specific dates where flooding was observed. Similar to the national, MODIS summary, the higher the value, the more often water was observed by the satellites over the period. Limitations of the Information The automated flood mapping algorithm can confuse cloud shadows and snow with flood water, so some areas shown as water may be incorrect. This is a proof of concept dataset and has not been validated.

<b>This record was retired 29/03/2022 with approval from S.Oliver as it has been superseded by eCat 145498 Geoscience Australia Landsat Fractional Cover Collection 3</b> The Fractional Cover (FC) algorithm was developed by the Joint Remote Sensing Research Program and is described in described in Scarth et al. (2010). It has been implemented by Geoscience Australia for every observation from Landsat Thematic Mapper (Landsat 5), Enhanced Thematic Mapper (Landsat 7) and Operational Land Imager (Landsat 8) acquired since 1987. It is calculated from surface reflectance (SR-N_25_2.0.0). FC_25 provides a 25m scale fractional cover representation of the proportions of green or photosynthetic vegetation, non-photosynthetic vegetation, and bare surface cover across the Australian continent. The fractions are retrieved by inverting multiple linear regression estimates and using synthetic endmembers in a constrained non-negative least squares unmixing model. For further information please see the articles below describing the method implemented which are free to read: - Scarth, P, Roder, A and Schmidt, M 2010, 'Tracking grazing pressure and climate interaction - the role of Landsat fractional cover in time series analysis', Proceedings of the 15th Australasian Remote Sensing and Photogrammetry Conference, Schmidt, M, Denham, R and Scarth, P 2010, 'Fractional ground cover monitoring of pastures and agricultural areas in Queensland', Proceedings of the 15th Australasian Remote Sensing and Photogrammetry Conference A summary of the algorithm developed by the Joint Remote Sensing Centre is also available from the AusCover website: http://data.auscover.org.au/xwiki/bin/view/Product+pages/Landsat+Fractional+Cover Fractional cover data can be used to identify large scale patterns and trends and inform evidence based decision making and policy on topics including wind and water erosion risk, soil carbon dynamics, land management practices and rangeland condition. This information could enable policy agencies, natural and agricultural land resource managers, and scientists to monitor land conditions over large areas over long time frames.