Dr Chris Owers

Mangroves are encroaching into salt marshes throughout the world as a result of environmental change. Previous studies suggest mangroves trap sediment more efficiently than adjacent salt marshes, providing mangroves greater capacity to adapt to sea level rise; this may occur by displacing salt marshes. However, sediment transport in adjacent marsh-mangrove systems and its role in mangrove encroachment upon salt marsh remain poorly understood. Here we directly test the hypothesis that mangroves reduce the ability of adjacent marsh to adjust to sea level rise by measuring sediment transport across salt marsh platforms, with and without 6 m of fringing mangroves at the tidal creek edge. We find that salt marshes and mangroves have equivalent sediment trapping efficiencies along the wetland edge. Suspended sediment concentrations, mass accumulation rates, and long-term accretion rates are not lower in salt marshes landward of mangroves than salt marshes without fringing mangroves. Therefore, our work suggests that a relatively narrow zone of mangroves does not impact salt marsh accretion, and activities that limit mangrove encroachment into salt marsh, such as removal of seedlings, will not improve the capacity of salt marsh to trap sediments.

Earth Observation (EO) has been recognised as a key data source for supporting the United Nations Sustainable Development Goals (SDGs). Advances in data availability and analytical capabilities have provided a wide range of users access to global coverage analysis-ready data (ARD). However, ARD does not provide the information required by national agencies tasked with coordinating the implementation of SDGs. Reliable, standardised, scalable mapping of land cover and its change over time and space facilitates informed decision making, providing cohesive methods for target setting and reporting of SDGs. The aim of this study was to implement a global framework for classifying land cover. The Food and Agriculture Organisation¿s Land Cover Classification System (FAO LCCS) provides a global land cover taxonomy suitable to comprehensively support SDG target setting and reporting. We present a fully implemented FAO LCCS optimised for EO data; Living Earth, an open-source software package that can be readily applied using existing national EO infrastructure and satellite data. We resolve several semantic challenges of LCCS for consistent EO implementation, including modifications to environmental descriptors, inter-dependency within the modular-hierarchical framework, and increased flexibility associated with limited data availability. To ensure easy adoption of Living Earth for SDG reporting, we identified key environmental descriptors to provide resource allocation recommendations for generating routinely retrieved input parameters. Living Earth provides an optimal platform for global adoption of EO4SDGs ensuring a transparent methodology that allows monitoring to be standardised for all countries.

Carbon mitigation services provided by coastal wetlands are not spatially homogeneous, nevertheless are commonly described on the basis of vegetation distribution within the intertidal zone. Distribution of mangrove and saltmarsh varies in response to frequency of tidal inundation, resulting in environmental gradients in edaphic factors that influence vegetation structure, and subsequently affect sedimentary carbon additions by vegetation and carbon losses by decomposition. Current sampling approaches and reporting do not adequately account for variability of carbon storage within a wetland, and assessments need to capture spatial variation associated with carbon storage to improve estimates of potential carbon mitigation services by natural ecosystems. This study quantifies the variation in near-surface carbon storage (i.e. upper 30 cm) across an intertidal gradient using a stratified sampling approach that recognises vegetation structure. Vegetation distribution and structure, as well as sedimentary controls on carbon content, explained variation in carbon storage. Saltmarsh near-surface carbon storage varied considerably between structural form. This was less evident for mangrove structural forms (i.e. tall, shrub, dwarf), which may be due to mangrove roots extending to depths beyond 30 cm. Sedimentary characteristics correlated with carbon content, demonstrating considerable influence on near-surface carbon storage within a wetland. The principal finding of this study was that variation within a wetland corresponds to the variation between sites. Stable carbon isotopes offer a means to identify previous vegetation contributions to sediment, associated with an earlier stage of wetland development, likely reflecting previous environmental conditions. A stratified sampling approach that recognises vegetation structure provides the capacity to account for variability of carbon within a wetland that is inadequately described by current sampling protocols.

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 Queensland and New South Wales. This was achieved by progressively and hierarchically combining 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 over 95% (for hydroperiod and fractional cover). The changes identified include mangrove dieback in 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.

Above-ground biomass represents a small yet significant contributor to carbon storage in coastal wetlands. Despite this, above-ground biomass is often poorly quantified, particularly in areas where vegetation structure is complex. Traditional methods for providing accurate estimates involve harvesting vegetation to develop mangrove allometric equations and quantify saltmarsh biomass in quadrats. However broad scale application of these methods may not capture structural variability in vegetation resulting in a loss of detail and estimates with considerable uncertainty. Terrestrial laser scanning (TLS) collects high resolution three-dimensional point clouds capable of providing detailed structural morphology of vegetation. This study demonstrates that TLS is a suitable non-destructive method for estimating biomass of structurally complex coastal wetland vegetation. We compare volumetric models, 3-D surface reconstruction and rasterised volume, and point cloud elevation histogram modelling techniques to estimate biomass. Our results show that current volumetric modelling approaches for estimating TLS-derived biomass are comparable to traditional mangrove allometrics and saltmarsh harvesting. However, volumetric modelling approaches oversimplify vegetation structure by under-utilising the large amount of structural information provided by the point cloud. The point cloud elevation histogram model presented in this study, as an alternative to volumetric modelling, utilises all of the information within the point cloud, as opposed to sub-sampling based on specific criteria. This method is simple but highly effective for both mangrove (r2 = 0.95) and saltmarsh (r2 > 0.92) vegetation. Our results provide evidence that application of TLS in coastal wetlands is an effective non-destructive method to accurately quantify biomass for structurally complex vegetation.

Carbon mitigation services provided by coastal wetlands are not spatially homogeneous. The scale of assessment at which above-ground biomass is measured will directly influence carbon storage estimates. Greater confidence in estimates is obtained with approaches that describe more variation. There is a need to improve accuracy while optimising assessment effort efficiency. Accurate quantification of carbon storage is dependent upon accurate assessment of biomass, carbon content and the extent of vegetation for which carbon storage is being assessed. This study demonstrates that vegetation structure influences above-ground biomass of mangrove and saltmarsh, resulting in considerable variability in biomass estimates and associated carbon storage of temperate coastal wetlands in southeast Australia. For mangrove, variability in above-ground biomass (Mg ha-1 ± SE) was best described by measuring height, stem diameter, crown area and vegetation density, whereby tall mangrove (3¿8 m in height; 71.50 ± 12.53 Mg ha-1) had higher biomass than both shrub (1.3¿3 m in height; 53.06 ± 6.94 Mg ha-1) and dwarf mangrove (<1.3 m in height; 10.68 ± 1.77 Mg ha-1). Saltmarsh above-ground biomass was best described by height, species and vegetation density, which demonstrated significant differences between rush saltmarsh (15.97 ± 2.35 Mg ha-1) and herbs, grasses and sedges saltmarsh (7.51 ± 0.91 Mg ha-1). The effect of this variation was compounded by carbon content (% C), which varied markedly between vegetation structural form and species (30.9¿49.8% C). Maintaining accuracy when assessing carbon storage requires mapping units that correspond to the scale of biomass assessments. Results from this study suggest that recognition of variation in biomass and carbon content of mangrove and saltmarsh vegetation structure will enhance the accuracy of estimates of carbon storage, and provide the confidence necessary for carbon storage inventories.

Coastal wetland vegetation is complex in form and function. Accurately mapping the spatial variation of vegetation complexity within these ecosystems is important for identifying areas of high conservation value that provide essential ecosystem services. In this study we delineate wetland vegetation, particularly mangrove and saltmarsh, to a vegetative morphological level that identifies spatial complexity in vegetation structure. This was achieved by integrating light detection and ranging (Lidar) and aerial imagery with an object-based approach. The results demonstrate that this is an effective methodology to identify vegetation complexity, with all study sites having greater than 90% classification accuracy. These high classification accuracies were underpinned by the use of Lidar data that provide detailed structural information about vegetation that is not captured with aerial imagery. This research highlights the importance of identifying spatial variability in vegetation structure when considering the value of coastal ecosystems and the services they provide.

Quantifying carbon storage in coastal wetland environments is important for identifying areas of high carbon sequestration value that could be targeted for conservation. This study combines remote sensing and sediment analysis to identify spatial variation in soil carbon storage for Currambene Creek, New South Wales, Australia to establish whether vegetation structure influences soil carbon storage in the upper 30 cm. Wetland vegetation was delineated to capture structural complexity within vegetation communities using Light detection and ranging (Lidar) point cloud data and aerial imagery with an object-based image analysis approach. Sediment cores were collected and analysed for soil carbon content to quantify below-ground carbon storage across the site. The total soil carbon storage in the upper 30 cm for the wetland (59.6 ha) was estimated to be 3933 ± 444 Mg C. Tall mangrove were found to have the highest total carbon storage (1420 ± 198 Mg C), however are particularly sensitive to changes in sea-level as they are positioned lowest in the intertidal frame. Conservation efforts targeted at protecting areas of high carbon sequestration, such as the tall mangrove, will lead to a greater contribution to carbon mitigation efforts.