Assessing the risk of carbon dioxide emissions from blue carbon ecosystems

“Blue carbon” ecosystems, which include tidal marshes, mangrove forests, and seagrass meadows, have large stocks of organic carbon (Corg) in their soils. These carbon stocks are vulnerable to decomposition and – if degraded – can be released to the atmosphere in the form of CO2. We present a framework to help assess the relative risk of CO2 emissions from degraded soils, thereby supporting inclusion of soil Corg into blue carbon projects and establishing a means to prioritize management for their carbon values. Assessing the risk of CO2 emissions after various kinds of disturbances can be accomplished through knowledge of both the size of the soil Corg stock at a site and the likelihood that the soil Corg will decompose to CO2

Between a reef and a hard place: capacity to map the next coral reef catastrophe

Increasing sea surface temperature and extreme heat events pose the greatest threat to coral reefs globally, with trends exceeding previous norms. The resultant mass bleaching events, such as those evidenced on the Great Barrier Reef in 2016, 2017, and 2020 have substantial ecological costs in addition to economic and social costs. Advancing remote (nanosatellites, rapid revisit traditional satellites) and in-field (drones) technological capabilities, cloud data processing, and analysis, coupled with existing infrastructure and in-field monitoring programs, have the potential to provide cost-effective and timely information to managers allowing them to better understand changes on reefs and apply effective remediation. Within a risk management framework for monitoring coral bleaching, we present an overview of how remote sensing can be used throughout the whole risk management cycle and highlight the role technological advancement has in earth observations of coral reefs for bleaching events

Remote sensing for cost-effective blue carbon accounting

Blue carbon ecosystems (BCE) include mangrove forests, tidal marshes, and seagrass meadows, all of which are currently under threat, putting their contribution to mitigating climate change at risk. Although certain challenges and trade-offs exist, remote sensing offers a promising avenue for transparent, replicable, and cost-effective accounting of many BCE at unprecedented temporal and spatial scales. The United Nations Framework Convention on Climate Change (UNFCCC) has issued guidelines for developing blue carbon inventories to incorporate into Nationally Determined Contributions (NDCs). Yet, there is little guidance on remote sensing techniques for monitoring, reporting, and verifying blue carbon assets. This review constructs a unified roadmap for applying remote sensing technologies to develop cost-effective carbon inventories for BCE – from local to global scales. We summarise and discuss (1) current standard guidelines for blue carbon inventories; (2) traditional and cutting-edge remote sensing technologies for mapping blue carbon habitats; (3) methods for translating habitat maps into carbon estimates; and (4) a decision tree to assist users in determining the most suitable approach depending on their areas of interest, budget, and required accuracy of blue carbon assessment. We designed this work to support UNFCCC-approved IPCC guidelines with specific recommendations on remote sensing techniques for GHG inventories. Overall, remote sensing technologies are robust and cost-effective tools for monitoring, reporting, and verifying blue carbon assets and projects. Increased appreciation of these techniques can promote a technological shift towards greater policy and industry uptake, enhancing the scalability of blue carbon as a Natural Climate Solution worldwide

Turning the Tide on Mapping Marginal Mangroves with Multi-Dimensional Space–Time Remote Sensing

Mangroves are a globally important ecosystem experiencing significant anthropogenic and climate impacts. Two subtypes of mangrove are particularly vulnerable to climate-induced impacts (1): tidally submerged forests and (2) those that occur in arid and semi-arid regions. These mangroves are either susceptible to sea level rise or occur in conditions close to their physiological limits of temperature and freshwater availability. The spatial extent and impacts on these mangroves are poorly documented, because they have structural and environmental characteristics that affect their ability to be detected with remote sensing models. For example, tidally submerged mangroves occur in areas with large tidal ranges, which limits their visibility at high tide, and arid mangroves have sparse canopy cover and a shorter stature that occur in fringing and narrow stands parallel to the coastline. This study introduced the multi-dimensional space–time randomForest method (MSTRF) that increases the detectability of these mangroves and applies this on the North-west Australian coastline where both mangrove types are prevalent. MSTRF identified an optimal four-year period that produced the most accurate model (Accuracy of 80%, Kappa value 0.61). This model was able to detect an additional 32% (76,048 hectares) of mangroves that were previously undocumented in other datasets. We detected more mangrove cover using this timeseries combination of annual median composite Landsat images derived from scenes across the whole tidal cycle but also over climatic cycles such as EÑSO. The median composite images displayed less spectral differences in mangroves in the intertidal and arid zones compared to individual scenes where water was present during the tidal cycle or where the chlorophyll reflectance was low during hot and dry periods. We found that the MNDWI (Modified Normalised Water Index) and GCVI (Green Chlorophyll Vegetation Index) were the best predictors for deriving the mangrove layer using randomForest

Turning the Tide on Mapping Marginal Mangroves with Multi-Dimensional Space–Time Remote Sensing

Mangroves are a globally important ecosystem experiencing significant anthropogenic and climate impacts. Two subtypes of mangrove are particularly vulnerable to climate-induced impacts (1): tidally submerged forests and (2) those that occur in arid and semi-arid regions. These mangroves are either susceptible to sea level rise or occur in conditions close to their physiological limits of temperature and freshwater availability. The spatial extent and impacts on these mangroves are poorly documented, because they have structural and environmental characteristics that affect their ability to be detected with remote sensing models. For example, tidally submerged mangroves occur in areas with large tidal ranges, which limits their visibility at high tide, and arid mangroves have sparse canopy cover and a shorter stature that occur in fringing and narrow stands parallel to the coastline. This study introduced the multi-dimensional space–time randomForest method (MSTRF) that increases the detectability of these mangroves and applies this on the North-west Australian coastline where both mangrove types are prevalent. MSTRF identified an optimal four-year period that produced the most accurate model (Accuracy of 80%, Kappa value 0.61). This model was able to detect an additional 32% (76,048 hectares) of mangroves that were previously undocumented in other datasets. We detected more mangrove cover using this timeseries combination of annual median composite Landsat images derived from scenes across the whole tidal cycle but also over climatic cycles such as EÑSO. The median composite images displayed less spectral differences in mangroves in the intertidal and arid zones compared to individual scenes where water was present during the tidal cycle or where the chlorophyll reflectance was low during hot and dry periods. We found that the MNDWI (Modified Normalised Water Index) and GCVI (Green Chlorophyll Vegetation Index) were the best predictors for deriving the mangrove layer using randomForest

Is climate change shifting the poleward limit of mangroves?

Ecological (poleward) regime shifts are a predicted response to climate change and have been well documented in terrestrial and more recently ocean species. Coastal zones are amongst the most susceptible ecosystems to the impacts of climate change, yet studies particularly focused on mangroves are lacking. Recent studies have highlighted the critical ecosystem services mangroves provide, yet there is a lack of data on temporal global population response. This study tests the notion that mangroves are migrating poleward at their biogeographical limits across the globe in line with climate change. A coupled systematic approach utilising literature and land surface and air temperature data was used to determine and validate the global poleward extent of the mangrove population. Our findings indicate that whilst temperature (land and air) have both increased across the analysed time periods, the data we located showed that mangroves were not consistently extending their latitudinal range across the globe. Mangroves, unlike other marine and terrestrial taxa, do not appear to be experiencing a poleward range expansion despite warming occurring at the present distributional limits. Understanding failure for mangroves to realise the global expansion facilitated by climate warming may require a focus on local constraints, including local anthropogenic pressures and impacts, oceanographic, hydrological, and topographical conditions

Impact of mooring activities on carbon stocks in seagrass meadows [dataset]

The database compiles published data (in Serrano et al. 2016, Scientific Reports, in press) on biogeochemical characteristics (density, organic carbon, calcium carbonate, stable carbon isotopes and sediment grain size) of sediments underneath seagrass meadows and adjacent un-vegetated patches after mooring disturbances in Rottnest Island (Perth, Western Australia). The dataset compiles data on biogeochemical sediment characteristics for a total of 16 cores, 50 cm-long (4 cores from seagrass meadows and 4 cores from adjacent bare sediments at Thompson Bay, and 4 cores from seagrass meadows and 4 cores from adjacent bare sediments at Stark Bay). Enquiries about the dataset may be sent to Oscar Serrano [email protected]

Response to Gallagher (2022)—the Australian Tidal Restoration for Blue Carbon method 2022—conservative, robust, and practical

The Blue Carbon Accounting Model (BlueCAM) is a tool for tidal restoration projects established under the Tidal Restoration for Blue Carbon method (2022) of the Australian voluntary carbon market. The commentary of Gallagher discussed that BlueCAM did not subtract allochthonous carbon, which is carbon in wetland soils from external sources, either terrestrial or marine sources, from estimated net abatement. This approach was used because all organic carbon preserved in a restored wetland soil, irrespective of its source, is deposited because the wetland was restored, and thus all carbon preserved above the baseline is “additional.” As restoration projects develop, further characterization of different soil carbon fractions as suggested by Gallagher may improve BlueCAM. BlueCAM is transparent, conservative, and importantly is feasible for implementation, as well as being consistent with the Intergovernmental Panel for Climate Change guidelines for National Greenhouse Gas Inventories and the Australian legislative offsets integrity standards

An Australian blue carbon method to estimate climate change mitigation benefits of coastal wetland restoration

Restoration of coastal wetlands has the potential to deliver both climate change mitigation, called blue carbon, and adaptation benefits to coastal communities, as well as supporting biodiversity and providing additional ecosystem services. Valuing carbon sequestration may incentivize restoration projects; however, it requires development of rigorous methods for quantifying blue carbon sequestered during coastal wetland restoration. We describe the development of a blue carbon accounting model (BlueCAM) used within the Tidal Restoration of Blue Carbon Ecosystems Methodology Determination 2022 of the Emissions Reduction Fund (ERF), which is Australia\u27s voluntary carbon market scheme. The new BlueCAM uses Australian data to estimate abatement from carbon and greenhouse gas sources and sinks arising from coastal wetland restoration (via tidal restoration) and aligns with the Intergovernmental Panel for Climate Change guidelines for national greenhouse gas inventories. BlueCAM includes carbon sequestered in soils and biomass and avoided emissions from alternative land uses. A conservative modeled approach was used to provide estimates of abatement (as opposed to on-ground measurements); and in doing so, this will reduce the costs associated with monitoring and verification for ERF projects and may increase participation in blue carbon projects by Australian landholders. BlueCAM encompasses multiple climate regions and plant communities and therefore may be useful to others outside Australia seeking to value blue carbon benefits from coastal wetland restoration