High-resolution mapping of losses and gains of Earth’s tidal wetlands

Murray, Nicholas J.; Worthington, Thomas A.; Bunting, Peter; Duce, Stephanie; Hagger, Valerie; Lovelock, Catherine E.; Lucas, Richard; Saunders, Megan I.; Sheaves, Marcus; Spalding, Mark; Waltham, Nathan J.; Lyons, Mitchell

doi:10.1126/science.abm9583

Cited by 231 publications

(194 citation statements)

References 60 publications

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“…Globally it is estimated that saltmarsh habitat is reducing by 1 -2% yr -1 (Duarte et al, 2008) with approximately 25% reduction of the global habitat since 1800 (Bridgham et al, 2006;Mcleod et al, 2011). Yet, recent estimates suggest at a global scale much of the modern habitat loss has been offset by the creation of new saltmarsh (Murray et al, 2022). Nevertheless, significant efforts are still required to preserve these saltmarshes and assure the significant quantities of OC held within their soil is not lost and remineralized, which would further exacerbate global climate change (Schuerch et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Using citizen science to estimate surficial soil Blue Carbon stocks in Great British saltmarshes

Smeaton

Burden²,

Ruranska³

et al. 2022

Front. Mar. Sci.

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A new saltmarsh soil dataset comprising of geochemical and physical property data from 752 soil samples collected through a sampling program supported by citizen scientists has been brought together with existing data to make the first national estimates of the surficial (top 10 cm) soil OC stock for Great British (GB) saltmarshes. To allow the inclusion of secondary data in the soil stock estimate a new bespoke organic matter to organic carbon conversion for GB saltmarsh soil was developed allowing organic matter data measured using loss-on-ignition to be convert to organic carbon content. The total GB surficial soil OC stock is 2.320 ± 0.470 Mt; English saltmarshes hold 1.601 ± 0.426 Mt OC, Scottish saltmarshes hold 0.368 ± 0.091 Mt OC, and Welsh saltmarshes hold 0.351 ± 0.082 Mt OC. The stocks were calculated within a Markov Chain Monte Carlo framework allowing robust uncertainty estimates to be derived for the first time. Spatial mapping tools are available to accompany these stock estimates at individual saltmarsh habitats throughout GB. This data will aid in the protection and management of saltmarshes and represents the first steps towards the inclusion of saltmarsh OC in the national inventory accounting of blue carbon ecosystems.

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Section: Introductionmentioning

confidence: 99%

Using citizen science to estimate surficial soil Blue Carbon stocks in Great British saltmarshes

Smeaton

Burden²,

Ruranska³

et al. 2022

Front. Mar. Sci.

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“…Tidal marshes are among the most vulnerable of the world’s ecosystems. Throughout human civilization, tidal marshes have been reclaimed for agriculture and settlement, and the pace of loss has accelerated in concert with burgeoning coastal populations on all inhabited continents over the past century ( 1 , 2 ). To this pressure has been added the threat of accelerating sea level rise.…”

mentioning

confidence: 99%

Constraints on the adjustment of tidal marshes to accelerating sea level rise

Saintilan

Kovalenko

Guntenspergen

et al. 2022

Science

Self Cite

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Much uncertainty exists about the vulnerability of valuable tidal marsh ecosystems to relative sea level rise. Previous assessments of resilience to sea level rise, to which marshes can adjust by sediment accretion and elevation gain, revealed contrasting results, depending on contemporary or Holocene geological data. By analyzing globally distributed contemporary data, we found that marsh sediment accretion increases in parity with sea level rise, seemingly confirming previously claimed marsh resilience. However, subsidence of the substrate shows a nonlinear increase with accretion. As a result, marsh elevation gain is constrained in relation to sea level rise, and deficits emerge that are consistent with Holocene observations of tidal marsh vulnerability.

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“…(1) The Chinese Academy of Sciences Mangroves (CASM), Jia et al [12]; (2) The Mangrove Forest Distribution Map (MFDM), Chen et al [20]; and (3) The Global Mangrove Map (GMW), Murray et al [56]. These maps were generated using images from 2015, 2015, and 2019 for the CASM, MFDM, and GMW, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…The study by Chen et al was implicit in synthesized quantiles. Murray et al [56] created a global mangrove forest map using random forest classification with 30 m resolution Landsat data. Comparing the map derived from this study with those from the three mangrove forest products, the first advantage of this study is the removal of the S. alterniflora influence.…”

Section: Comparison With Other Mangrove Forests Mapping Productsmentioning

confidence: 99%

An Improved Submerged Mangrove Recognition Index-Based Method for Mapping Mangrove Forests by Removing the Disturbance of Tidal Dynamics and S. alterniflora

Xia

Qin

et al. 2022

Remote Sensing

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Currently, it is a great challenge for remote sensing technology to accurately map mangrove forests owing to periodic inundation. A submerged mangrove recognition index (SMRI) using two high- and low-tide images was recently proposed to remove the influence of tides and identify mangrove forests. However, when the tidal height of the selected low-tide image is not at the lowest tidal level, the corresponding SMRI does not function well, which results in mangrove forests below the low tidal height being undetected. Furthermore, Spartina alterniflora Loisel (S. alterniflora) was introduced to China in 1979 and rapidly spread to become the most serious invasive plant along the Chinese coastline. The current SMRI has failed to distinguish S. alterniflora from submerged mangrove forests because of their similar spectral signatures. In this study, an SMRI-based mangrove forest mapping method was developed using the time series of Sentinel-2 images to mitigate the two aforementioned issues. In the proposed method, quantile synthesis was applied to the time series of Sentinel-2 images to generate a lowest-tide synthetic image for creating SMRI to identify submerged mangrove forests. Unsubmerged mangrove forests were classified using a support vector machine, and a preliminary mangrove forest map was created by merging them. In addition, S. alterniflora was distinguished from the mangrove forests by analyzing their phenological differences. Finally, mangrove forest mapping was performed by masking S. alterniflora. The proposed method was applied to the entire coastline of the Guangxi Province, China. The results showed that it can reliably and accurately identify submerged mangrove forests derived from SMRI by synthesizing low- and high-tide images using quantile synthesis, and the differentiation of S. alterniflora using phenological differences results in more accurate mangrove mapping. This work helps to improve the accuracy of mangrove forest mapping using SMRI and its feasibility for coastal wetland monitoring. It also provides data for sustainable management, ecological protection, and restoration of vegetation in coastal zones.

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High-resolution mapping of losses and gains of Earth’s tidal wetlands

Cited by 231 publications

References 60 publications

Using citizen science to estimate surficial soil Blue Carbon stocks in Great British saltmarshes

Using citizen science to estimate surficial soil Blue Carbon stocks in Great British saltmarshes

Constraints on the adjustment of tidal marshes to accelerating sea level rise

An Improved Submerged Mangrove Recognition Index-Based Method for Mapping Mangrove Forests by Removing the Disturbance of Tidal Dynamics and S. alterniflora

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