Mangrove reforestation provides greater blue carbon benefit than afforestation for mitigating global climate change.

Significant efforts have been invested to restore mangrove forests worldwide through reforestation and afforestation. However, blue carbon benefit has not been compared between these two silvicultural pathways at the global scale. Here, we integrated results from direct field measurements of over 370 restoration sites around the world to show that mangrove reforestation (reestablishing mangroves where they previously colonized) had a greater carbon storage potential per hectare than afforestation (establishing mangroves where not previously mangrove). Greater carbon accumulation was mainly attributed to favorable intertidal positioning, higher nitrogen availability, and lower salinity at most reforestation sites. Reforestation of all physically feasible areas in the deforested mangrove regions of the world could promote the uptake of 671.5-688.8 Tg CO2-eq globally over a 40-year period, 60% more than afforesting the same global area on tidal flats (more marginal sites). Along with avoiding conflicts of habitat conversion, mangrove reforestation should be given priority when designing nature-based solutions for mitigating global climate change.

Relationships between above- and below-ground carbon stocks in mangrove forests facilitate better estimation of total mangrove blue carbon.

BACKGROUND: Although great efforts have been made to quantify mangrove carbon stocks, accurate estimations of below-ground carbon stocks remain unreliable. In this study, we examined the distribution patterns of mangrove carbon stocks in China and other countries using our own field survey data and datasets from published literature. Based on these data, we investigated the possible relationships between above-ground carbon stock (AGC) and below-ground carbon stock (BGC) for mangrove forests, aiming to provide a scientific basis for estimation of total mangrove carbon stocks. RESULTS: The average above-ground carbon stock in each region was sizeable (ranging from 12.0 to 150.2 Mg/ha), but average below-ground carbon stock was dominant (ranging from 46.6 to 388.6 Mg/ha), accounting for 69-91% of total carbon stock at the sites studied in China. Significant positive relationships were found between above-ground and below-ground mangrove carbon stocks, with the best fitting equation as BGC = 1.58 * AGC + 81.06 (Mg/ha, R2 = 0.62, p < 0.01, n = 122) for China. Such linear relationships vary for mangrove forests of different types and locations, from different geographical regions in China to other countries worldwide. CONCLUSION: The positive relationship we found between above- and below-ground carbon stocks of mangrove forests in China and worldwide can facilitate more accurate assessments of mangrove blue carbon stocks at regional or global scales using modern techniques including remote sensing.

[Inversion of aboveground biomass of Pinus tabuliformis plantations based on GF-2 data]. / 基于GF-2的油松人工林地上生物量反演.

Pinus tabuliformis is an important afforestation species in the Loess Plateau. Quick and accurate estimation of aboveground biomass (AGB) of P. tabuliformis plantations plays an important role in monitoring regional forest resources. Here, we used multi-spectral remote sensing data of domestic satellite GF-2 and the field data to estimate the aboveground biomass of P. tabuliformis plantations in Shibao forest farm of Huanglong Mountain in Shaanxi Province. We calculated eight texture features and five vegetation indices, and then built models based four texture windows (3×3, 5×5, 7×7, 9×9) by using five regression methods including normal regression, stepwise regression, ridge regression, Lasso regression and principal component regression. We used the leave-one-out cross validation (LOOCV) to test the estimation accuracy of each model. We found serious multi-collinearity relationships between the extracted remote sensing factors. Most of the remote sensing factors had significant correlations with aboveground biomass of P. tabuliformis plantations. GF-2 data could achieve higher accuracy in the inversion of aboveground biomass of P. tabuliformis plantations in the Shibao forest farm. The best estimation result was the principal component regression model using 9×9 texture window, and the worst one was the normal regression model using 3×3 texture window. Inversion of aboveground biomass of P. tabuliformis plantation using domestic high-resolution satellite imagery could provide a scientific basis for forestry biomass monitoring, resource management, and sustainable management in the forestry departments of northwest China.