Prioritizing Waterbody Management in the Leichhardt Catchment: using a Landsat TM archive to characterise water permanence and water clarity

[Extract] There are 686 large (>1875m2) waterbodies throughout the Leichhardt catchment. Prioritizing on-ground management for these waterbodies requires a 'whole-of-catchment' assessment of how waterbodies throughout the catchment are behaving over time. This report describes how three different remote sensing products have been combined to describe waterbodies throughout the Leichhardt catchment. An archive of dry season Landsat TM data was used to describe the size, distribution and permanence of individual dry season waterbodies. The same archive was also analysed to characterise the optical water quality dynamics of each waterbody, i.e. which water bodies are always clear in the dry season, and which waterbodies vary between being clear during one dry season and then turbid the next. Daily MODIS data were also used to map the extent and duration of inundation associated with the post Tropical Cyclone Larry flood event

Between the tides: modelling the elevation of Australia’s exposed intertidal zone at continental scale

The intertidal zone represents a critical transition between marine and terrestrial ecosystems, supporting a complex mosaic of highly productive and biologically diverse habitats. However, our understanding of these important coastal environments is limited by a lack of spatially consistent topographic data, which can be extremely challenging and costly to obtain at continental-scale. Satellite remote sensing represents an important resource for monitoring extensive coastal zones. Previous approaches to modelling the elevation of the intertidal zone using earth observation (EO) data have been restricted to small study regions or have relied on manual image interpretation, thus limiting their ability to be applied consistently over large geographic extents. In this study, we present an automated open-source approach to generate satellite-derived elevation data for over 15,387 km2 of intertidal terrain across the entire Australian coastline. Our approach combines global tidal modelling with a 30-year time series archive of spatially and spectrally calibrated Landsat satellite data managed within the Digital Earth Australia (DEA) platform. The resulting National Intertidal Digital Elevation Model (NIDEM) dataset provides an unprecedented three-dimensional representation of Australia's vast exposed intertidal zone at 25 m spatial resolution. We validate our model against LiDAR, RTK GPS and multibeam bathymetry datasets, finding that modelled elevations are highly accurate across sandy beach (±0.41 m RMSE) and tidal flat environments (±0.39 m RMSE). Model performance was least accurate (±2.98 m RMSE) within rocky shores and reefs and other complex coastal environments with extreme and variable tidal regimes. We discuss key challenges associated with modelling intertidal elevation including tidal model performance and biased observations from sun-synchronous satellites, and suggest future directions to improve the accuracy and utility of continental-scale intertidal elevation modelling. Our model can be applied to tidally-influenced coastal environments globally, addressing a key gap between the availability of sub-tidal bathymetry and terrestrial elevation data

A novel approach to modelling mangrove phenology from satellite images: a case study from Northern Australia

Around the world, the effects of changing plant phenology are evident in many ways: from earlier and longer growing seasons to altering the relationships between plants and their natural pollinators. Plant phenology is often monitored using satellite images and parametric methods. Parametric methods assume that ecosystems have unimodal phenologies and that the phenology model is invariant through space and time. In evergreen ecosystems such as mangrove forests, these assumptions may not hold true. Here we present a novel, data-driven approach to extract plant phenology from Landsat imagery using Generalized Additive Models (GAMs). Using GAMs, we created models for six different mangrove forests across Australia. In contrast to parametric methods, GAMs let the data define the shape of the phenological curve, hence showing the unique characteristics of each study site. We found that the Enhanced Vegetation Index (EVI) model is related to leaf production rate (from in situ data), leaf gain and net leaf production (from the published literature). We also found that EVI does not respond immediately to leaf gain in most cases, but has a two- to three-month lag. We also identified the start of season and peak growing season dates at our field site. The former occurs between September and October and the latter May and July. The GAMs allowed us to identify dual phenology events in our study sites, indicated by two instances of high EVI and two instances of low EVI values throughout the year. We contribute to a better understanding of mangrove phenology by presenting a data-driven method that allows us to link physical changes of mangrove forests with satellite imagery. In the future, we will use GAMs to (1) relate phenology to environmental variables (e.g., temperature and rainfall) and (2) predict phenological changes

Mapping the multi-decadal mangrove dynamics of the Australian coastline

Mangroves globally provide a diverse array of ecosystem services but these are impacted upon by both natural and anthropogenic drivers of change. In Australia, mangroves are protected by law and hence the natural drivers predominate. To determine annual national level changes in mangroves between 1987 and 2016, their extent (by canopy cover type)and dynamics were quantified using dense time-series (nominally every 16 days cloud permitting)of 25 m spatial resolution Landsat sensor data available within Digital Earth Australia (DEA). The potential area that mangroves occupied over this period was established as the union of mangrove maps generated for 1996, 2007–2010 and 2015/16 through the Global Mangrove Watch (GMW). Within this area, the green vegetation fractional cover (GVpc)was retrieved from each available cloud-masked Landsat scene through linear spectral unmixing. The 10th percentile (GVpc10)was then determined for each calendar year by comparing these data in a time-series. The percentage Planimetric Canopy Cover (PCC%)for each Landsat pixel was then estimated using a relationship between GVpc10 and LiDAR-derived PCC% (20%; resolvable at the Landsat resolution)varied from a minima of 10,715 ± 36 km (95% confidence interval)in 1992 to a maxima of 11,388 km ± 38 km (95% CI)in 2010, declining to 11,142 ± 57 km (95% CI)in 2017. In 2010 (maximum extent), the forests were classified as closed canopy (38.8%), open canopy (49.0%)and woodland mangrove (12.2%). The majority of change occurred along the northern Australian coastline and was concentrated in the major gulfs and sounds. The 30 national maps of annual mangrove extent represent a reference dataset, which is publicly available through the Terrestrial Environment Research Network (TERN)landscapes portal. Future efforts are focusing on the routine production of annual mangrove maps beyond 2019 as part of Australia's efforts to monitor the coastal environment

Land Cover Mapping using Digital Earth Australia

This study establishes the use of the Earth Observation Data for Ecosystem Monitoring (EODESM) to generate land cover and change classifications based on the United Nations Food and Agriculture Organisation (FAO) Land Cover Classification System (LCCS) and environmental variables (EVs) available within, or accessible from, Geoscience Australia’s (GA) Digital Earth Australia (DEA). Classifications representing the LCCS Level 3 taxonomy (8 categories representing semi-(natural) and/or cultivated/managed vegetation or natural or artificial bare or water bodies) were generated for two time periods and across four test sites located in the Australian states of QueenslandandNewSouthWales. Thiswasachievedbyprogressivelyandhierarchicallycombining existing time-static layers relating to (a) the extent of artificial surfaces (urban, water) and agriculture and (b) annual summaries of EVs relating to the extent of vegetation (fractional cover) and water (hydroperiod, intertidal area, mangroves) generated through DEA. More detailed classifications that integrated information on, for example, forest structure (based on vegetation cover (%) and height (m); time-static for 2009) and hydroperiod (months), were subsequently produced for each time-step. The overall accuracies of the land cover classifications were dependent upon those reported for the individual input layers, with these ranging from 80% (for cultivated, urban and artificial water) to over95%(forhydroperiodandfractionalcover).Thechangesidentifiedincludemangrovediebackin the southeastern Gulf of Carpentaria and reduced dam water levels and an associated expansion of vegetation in Lake Ross, Burdekin. The extent of detected changes corresponded with those observed using time-series of RapidEye data (2014 to 2016; for the Gulf of Carpentaria) and Google Earth imagery (2009–2016 for Lake Ross). This use case demonstrates the capacity and a conceptual framework to implement EODESM within DEA and provides countries using the Open Data Cube (ODC) environment with the opportunity to routinely generate land cover maps from Landsat or Sentinel-1/2 data, at least annually, using a consistent and internationally recognised taxonomy

ENSO-driven extreme oscillations in mean sea level destabilise critical shoreline mangroves—An emerging threat

Recent ENSO-related, extreme low oscillations in mean sea level, referred to as ‘Taimasa’ in Samoa, have destabilised shoreline mangroves of tropical northern Australia, and possibly elsewhere. In 1982 and 2015, two catastrophic Taimasa each resulted in widespread mass dieback of ~76 km2 of shoreline mangroves along 2,000 km of Australia’s Gulf of Carpentaria. For the 2015 event, we determined that a temporary drop in sea level of ~0.4 metres for up to six months duration caused upper zone shoreline mangroves across the region to die from severe moisture deficit and desiccation. The two dramatic collapse events revealed a previously unrecognised vulnerability of semi-arid tidal wetland habitats to more extreme ENSO influences on sea level. In addition, we also observed a relationship between annual sea level oscillations and mangrove forest productivity where seasonal oscillations in mean sea level were co-incident with regular annual mangrove leaf growth during months of higher sea levels (March-May), and leaf shedding during lower sea levels (September-November). The combination of these periodic fluctuations in sea level defined a mangrove ‘Goldilocks’ zone of seasonal productivity during median-scale oscillations, bracketed by critical threshold events when sea levels became unusually low, or high. On the two occasions reported here when sea levels were extremely low, upper zone mangrove vegetation died en masse in synchrony across northern Australia. Such extreme pulse impacts combined with localised stressors profoundly threaten the longer-term survival of mangrove ecosystems and their benefits, like minimisation of shoreline erosion with rising sea levels. These new insights into such critical influences of climate and sea level on mangrove forests offer further affirmation of the urgency for implementing well-considered mitigation efforts for the protection of shoreline mangroves at risk, especially given predictions of future re-occurrences of extreme events affecting sea levels, combined with on-going pressure of rapidly rising sea levels

Australian forested wetlands under climate change:Collapse or proliferation?

Climatically driven perturbations (e.g. intense drought, fire, sea surface temperature rise) can bring ecosystems that are already stressed by long-term climate change and other anthropogenic impacts to a point of collapse. Recent reviews of the responses of Australian ecosystems to climate change and associated stressors have suggested widespread ecosystem collapse is occurring across multiple biomes. Two commonly cited case studies concern forested wetland ecosystems: mangrove forest dieback in northern Australia (2015-16) and riverine forest dieback in the south-east of the continent (2002-09). We present an alternative interpretation that emphasises the dominant signal of climate change effects, rather than the interdecadal signal of climate variability that drives wetland forest dynamics. For both the south-east Australian riverine forests and mangroves of northern Australia, aerial extent remains greater after dieback than in the early 1990s. We interpret dieback and defoliation in both systems as a dry phase response and provide evidence of a current and near-future climate change trajectory of increased areal extent and cover (i.e. tree colonisation and range infilling). In both case studies, climate change-driven increases in tree cover and extent are occurring at the expense of wetland grasslands and the important ecosystem functions they support

Remote Sensing of Environment: Current status of Landsat program, science, and applications

Formal planning and development of what became the first Landsat satellite commenced over 50 years ago in 1967. Now, having collected earth observation data for well over four decades since the 1972 launch of Landsat- 1, the Landsat program is increasingly complex and vibrant. Critical programmatic elements are ensuring the continuity of high quality measurements for scientific and operational investigations, including ground systems, acquisition planning, data archiving and management, and provision of analysis ready data products. Free and open access to archival and new imagery has resulted in a myriad of innovative applications and novel scientific insights. The planning of future compatible satellites in the Landsat series, which maintain continuity while incorporating technological advancements, has resulted in an increased operational use of Landsat data. Governments and international agencies, among others, can now build an expectation of Landsat data into a given operational data stream. International programs and conventions (e.g., deforestation monitoring, climate change mitigation) are empowered by access to systematically collected and calibrated data with expected future continuity further contributing to the existing multi-decadal record. The increased breadth and depth of Landsat science and applications have accelerated following the launch of Landsat-8, with significant improvements in data quality. Herein, we describe the programmatic developments and institutional context for the Landsat program and the unique ability of Landsat to meet the needs of national and international programs. We then present the key trends in Landsat science that underpin many of the recent scientific and application developments and followup with more detailed thematically organized summaries. The historical context offered by archival imagery combined with new imagery allows for the development of time series algorithms that can produce information on trends and dynamics. Landsat-8 has figured prominently in these recent developments, as has the improved understanding and calibration of historical data. Following the communication of the state of Landsat science, an outlook for future launches and envisioned programmatic developments are presented. Increased linkages between satellite programs are also made possible through an expectation of future mission continuity, such as developing a virtual constellation with Sentinel-2. Successful science and applications developments create a positive feedback loop—justifying and encouraging current and future programmatic support for Landsat

Mapping riparian vegetation functions using remote sensing and terrain analysis

Deposited with permission of the author. © 2005 Leo Lymburner.Land use practices over the last 200 years have dramatically altered the distribution and amount of riparian vegetation throughout many catchments in Australia. This has lead to a number of negative impacts including a decrease in water quality, an increase in sediment transport and a decrease in the quality of terrestrial and aquatic habitats. The task of restoring the functions of riparian zones is an enormous one and requires spatial and temporal prioritisation. An analysis of the existing and historical functions of riparian zones and their spatial distribution is a major aid to this process and will enable efficient use of remediation resources. The approach developed in this thesis combines remote sensing, field measurement and terrain analysis to describe the distribution of five riparian zone functions: sediment trapping, bank stabilization, denitrification, stream shading and large woody debris production throughout a large semi-arid catchment in central Queensland