Sea-level rise (SLR) is one of the greatest future threats to mangrove forests. Mangroves have kept up with or paced past SLR by maintaining their forest floor elevation relative to sea level through root growth, sedimentation, and peat development. Monitoring surface elevation change (SEC) or accretion rates allows us to understand mangrove response to SLR and prioritizes resilient ecosystems for conservation or vulnerable ecosystems for restoration. We compared three methods to measure SEC and accretion in mangrove forests: ²¹⁰Pb, surface elevation tables (SETs), and a terrestrial light detection and ranging system (compact biomass LiDAR—CBL). Lead-210 accretion rates were not significantly different than SET SEC rates and differences between the two methods (− 2 to 2 mm/year) were within the error of our measurements. Lead-210 only measures accretion in the upper meter of sediment and cannot capture deeper subsurface processes (e.g., subsidence, compaction) that SETs can. The lack of differences suggests the following: (1) surface processes in the active root zone are influencing forest floor elevation more than subsurface processes, (2) subsurface processes were not large enough to effect elevation, or (3) the SETs were not installed deep enough to capture subsurface processes. CBL SEC rates did not differ significantly from SET SEC rates. The larger spatial scale of the CBL scans resulted in significantly different SEC rates from some of the plots. This was due to the CBL measuring areas missed by the SET. The greater number of points measured by CBL (~ 30,000 vs 36) increased precision and lowered standard error. The traditional SET/rSET method is currently 3–10 × cheaper than the ²¹⁰Pb or CBL method, respectively, and can accurately track changes in forest floor elevation. Costs of the use of LiDAR are likely to decrease in the future with the advent of newer and more cost-effective technology.

Mangroves’ ability to store carbon (C) has long been recognized, but little is known about whether planted mangroves can store C as efficiently as naturally established (i.e., intact) stands and in which time frame. Through Bayesian logistic models compiled from 40 years of data and built from 684 planted mangrove stands worldwide, we found that biomass C stock culminated at 71 to 73% to that of intact stands ~20 years after planting. Furthermore, prioritizing mixed-species planting including Rhizophora spp. would maximize C accumulation within the biomass compared to monospecific planting. Despite a 25% increase in the first 5 years following planting, no notable change was observed in the soil C stocks thereafter, which remains at a constant value of 75% to that of intact soil C stock, suggesting that planting effectively prevents further C losses due to land use change. These results have strong implications for mangrove restoration planning and serve as a baseline for future C buildup assessments.

Background and Objectives: In northern Vietnam, nearly 37,100 hectares of mangroves were lost from 1964–1997 due to unsustainable harvest and deforestation for the creation of shrimp aquaculture ponds. To offset these losses, efforts in the late 1990s have resulted in thousands of hectares of mangroves being restored, but few studies to date have examined how effective these efforts are at creating restored mangrove forests that function similarly to the intact mangroves they are intended to replace. Materials and Methods: We quantified and compared soil carbon (C) stocks among restored (mono and mixed species) and intact mangrove forests in the provinces of Quang Ninh, Thai Binh, Nam Dinh and Thanh Hoa in northern Vietnam. A total of 96 soil cores up to a depth of 200 cm were collected every 25 m (25, 50, 75, 100, 125, and 150 m) along 16 linear transects that were 150 m long and perpendicular to the mangrove upland interface (six cores along each transect) at Quang Ninh (four transects), Thai Binh (five), Nam Dinh (four) and Thanh Hoa (three). Five-cm-long soil samples were then collected from the 0–15 cm, 15–30 cm, 30–50 cm, 50–100 cm, and >100 cm depth intervals of each soil core. Results: The study confirmed that the soil C stock of 20–25-year-old restored mangrove forest (217.74 ± 16.82 Mg/ha) was not significantly different from that of intact mangrove forest (300.68 ± 51.61 Mg/ha) (p > 0.05). Soil C stocks of Quang Ninh (323.89 ± 28.43 Mg/ha) were not significantly different from Nam Dinh (249.81 ± 19.09 Mg/ha), but both of those were significantly larger than Thai Binh (201.42 ± 27.65 Mg/ha) and Thanh Hoa (178.98 ± 30.82 Mg/ha) (p < 0.05). Soil C stock differences among provinces could be due to their different geomorphological characteristics and mangrove age. Soil C stocks did not differ among mangroves that were restored with mixed mangrove species (289.75 ± 33.28 Mg/ha), Sonneratia caseolaris (L.) Engl. (255.67 ± 13.11 Mg/ha) or Aegiceras corniculatum (L.) Blanco (278.15 ± 43.86 Mg/ha), but soil C stocks of those mangroves were significantly greater than that of Kandelia obovata Sheue, Liu & Yong (174.04 ± 20.38 Mg/ha) (p < 0.05). Conclusion: There were significant differences in the soil C stocks of mangrove forests among species and provinces in northern Vietnam. The soil C stock of 20–25-year-old restored mangrove forest was not significantly different from that of intact mangrove forest.

Estimating baseline carbon stocks is a key step in designing forest carbon programs. While field inventories are resource-demanding, advances in predictive modeling are now providing globally coterminous datasets of carbon stocks at high spatial resolutions that may meet this data need. However, it remains unknown how well baseline carbon stock estimates derived from model data compare against conventional estimation approaches such as field inventories. Furthermore, it is unclear whether site-level management actions can be designed using predictive model data in place of field measurements. We examined these issues for the case of mangroves, which are among the most carbon dense ecosystems globally and are popular candidates for forest carbon programs. We compared baseline carbon stock estimates derived from predictive model outputs against estimates produced using the Intergovernmental Panel on Climate Change’s (IPCC) three-tier methodological guidelines. We found that the predictive model estimates out-performed the IPCC’s Tier 1 estimation approaches but were significantly different from estimates based on field inventories. Our findings help inform the use of predictive model data for designing mangrove forest policy and management actions.