article · Environmental Research Letters
This research addresses the need for detailed spatial data on mangrove soil carbon stocks to support climate change mitigation. Existing global estimates lack the fine-scale variability required for local decision-making. A large georeferenced database of mangrove soil carbon measurements was compiled, and a novel machine-learning model was developed to map carbon density at a 30-metre resolution. The model, which incorporated a prior estimate from SoilGrids and used variables like suspended sediment load and Landsat imagery, explained 63% of the variability in soil organic carbon density. The global estimate for the year 2000 was 6.4 Pg C in the top metre of soil. Furthermore, the study estimated a loss of 30-122 Tg C due to mangrove habitat destruction between 2000 and 2015, with over 75% of this loss occurring in Indonesia, Malaysia, and Myanmar.
Mangrove forests are crucial for storing carbon, which helps combat climate change. This research provides a highly detailed global map of where this carbon is stored, helping conservationists and policymakers make better, more localised decisions about protecting and restoring these vital ecosystems to enhance carbon sequestration efforts.
This research provides a high-resolution global map of mangrove soil carbon, a critical data product for environmental planning. It can inform policymakers, conservation organisations, and land managers on where to prioritise mangrove protection and restoration efforts for climate change mitigation. This applied research offers a valuable tool for more effective land management and carbon sequestration strategies.
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With the growing recognition that effective action on climate change will require a combination of emissions reductions and carbon sequestration, protecting, enhancing and restoring natural carbon sinks have become political priorities. Mangrove forests are considered some of the most carbon-dense ecosystems in the world with most of the carbon stored in the soil. In order for mangrove forests to be included in climate mitigation efforts, knowledge of the spatial distribution of mangrove soil carbon stocks are critical. Current global estimates do not capture enough of the finer scale variability that would be required to inform local decisions on siting protection and restoration projects. To close this knowledge gap, we have compiled a large georeferenced database of mangrove soil carbon measurements and developed a novel machine-learning based statistical model of the distribution of carbon density using spatially comprehensive data at a 30 m resolution. This model, which included a prior estimate of soil carbon from the global SoilGrids 250 m model, was able to capture 63% of the vertical and horizontal variability in soil organic carbon density (RMSE of 10.9 kg m -3 ). Of the local variables, total suspended sediment load and Landsat imagery were the most important variable explaining soil carbon density. Projecting this model across the global mangrove forest distribution for the year 2000 yielded an estimate of 6.4 Pg C for the top meter of soil with an 86-729 Mg C ha -1 range across all pixels. By utilizing remotely-sensed mangrove forest cover change data, loss of soil carbon due to mangrove habitat loss between 2000 and 2015 was 30-122 Tg C with >75% of this loss attributable to Indonesia, Malaysia and Myanmar. The resulting map products
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DOI: 10.1088/1748-9326/aabe1c
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