Wood density can best predict carbon stock in the forest aboveground biomass following restoration in a post open limestone mining in a tropical region.

Introduction: Reforestation has been widely considered to best solve this problem, but this requires an accurate estimation of carbon stocks in the forest aboveground biomass (AGB) at a large scale. AGB models based on traits and remote sensing indices (moisture vegetation index (MVI)) are the two good methods for this purpose. But limited studies have developed them to estimate carbon stock in AGB during restoration of degraded mining areas.

Methods: Here, we have successfully addressed this challenge as we have developed trait-based and MVI-based AGB models to estimate carbon stock in the AGB after performing reforestation in a 0.2 km degraded tropical mining area in Hainan Island in China. During this reforestation, seven non-native fast-growing tree species were planted, which has successfully recovered soil processes (including soil microorganisms, nematodes and chemical and physical properties).

Results And Discussions: By using these two models to evaluate carbon stock in AGB, we have found that an average of 78.18 Mg C hm could be accumulated by our reforestation exercise. Moreover, wood density could predict AGB for this restored tropical mining site, and indicated that strategies of planting fast-growing species leads to fast-growing strategies (indicated by wood density) which in turn determined the largely accumulated carbon stocks in the AGB during restoration. This restoration technology (multiple-planting of several non-native fast-growing tree species) and the two accurate and effective AGB models (trait-based and MVI-based AGB models) developed by us could be applied to 1) restore other degraded tropical mining area in China, and 2) estimate carbon stock in forest AGB after performing restoration.