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.

The development and refinement of methods for estimating organic carbon accumulation in biomass and soils during mangrove restoration and rehabilitation can encourage uptake of restoration projects for their ecosystem services, including those of climate change mitigation, or blue carbon. To support the development of a blue carbon method for Australia under the Emission Reduction Fund scheme we investigated; (1) whether carbon accumulation data from natural mangroves could be used to estimate carbon accumulation during restoration; (2) modeling mangrove biomass accumulation; and (3) how modeled carbon accumulation could be achieved over heterogeneous sites. First, we assessed carbon accumulation in soil and biomass pools from the global literature, finding that estimating carbon accumulation using data from natural mangroves provided similar estimates as those for restored or rehabilitated mangroves. We assessed mangrove biomass accumulation from global chronosequence studies, which we used to develop regional models for estimating biomass accumulation with restoration in Australia using values from local natural mangroves. Estimating biomass carbon accumulation using site-based means of stand biomass provided similar estimates as values estimated through use of regional means values stratified by elevation; and reduced overestimates of biomass carbon accumulation that were based on regional mean values. Modeling soil carbon accumulation over environmentally heterogeneous project sites can apply a similar approach, stratifying over variation in site elevation. Our analysis provides a strategy for modeling blue carbon pools for an Australian blue carbon method that accommodates regional differences and is based on data from natural mangroves.Efforts to restore mangroves are increasing to recover global mangrove cover, which has been reduced by 30-50% over the last century (Friess et al. 2019). Recovering global mangrove extent increases the resilience of coastlines because mangroves provide ecosystem functions and services for communities that include coastal protection, enhancement of water quality, support of biodiversity, fisheries, supply of forest and nonforest products and carbon sequestration, or blue carbon

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 underutilising 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 (r 2 = 0.95) and saltmarsh (r 2 > 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.