China has committed to achieving peak CO₂ emissions before 2030 and carbon neutrality before 2060; therefore, accelerated efforts are needed to better understand carbon accounting in industry and energy fields as well as terrestrial ecosystems. The carbon sink capacity of plantation forests contributes to the mitigation of climate change. Plantation forests throughout the world are intensively managed, and there is an urgent need to evaluate the effects of such management on long-term carbon dynamics.

We assessed the carbon cycling patterns of ecosystems characterized by three typical plantation species (Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.), oak (Cyclobalanopsis glauca (Thunb.) Oerst.), and pine (Pinus massoniana Lamb.)) in Lishui, southern China, by using an integrated biosphere simulator (IBIS) tuned with localized parameters. Then, we used the state-and-transition simulation model (STSM) to study the effects of active forest management (AFM) on carbon storage by combining forest disturbance history and carbon cycle regimes.

1) The carbon stock of the oak plantation was lower at an early age (<50 years) but higher at an advanced age (>50 years) than that of the Chinese fir and pine plantations. 2) The carbon densities of the pine and Chinese fir plantations peaked at 70 years (223.36 ​Mg·ha^‒1) and 64 years (232.04 ​Mg·ha^‒1), respectively, while the carbon density in the oak plantation continued increasing (>100 years). 3) From 1989 to 2019, the total carbon pools of the three plantation ecosystems followed an upward trend (an annual increase of 0.16–0.22 ​Tg ​C), with the largest proportional increase in the aboveground biomass carbon pool. 4) AFM increased the recovery of carbon storage after 1996 and 2009 in the pine and Chinese fir plantations, respectively, but did not result in higher growth in the oak plantation. 5) The proposed harvest planning is reasonable and conducive to maximizing the carbon sequestration capacity of the forest.

This study provides an example of a carbon cycle coupling model that is potentially suitable for simulating China's plantation forest ecosystems and supporting carbon accounting to monitor peak CO₂ emissions and reach carbon neutrality.

Aboveground biomass (AGB) is a fundamental indicator of forest ecosystem productivity and health and hence plays an essential role in evaluating forest carbon reserves and supporting the development of targeted forest management plans.

Here, we proposed a random forest/co-kriging framework that integrates the strengths of machine learning and geostatistical approaches to improve the mapping accuracies of AGB in northern Guangdong Province of China. We used Landsat time-series observations, Advanced Land Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) data, and National Forest Inventory (NFI) plot measurements, to generate the forest AGB maps at three time points (1992, 2002 and 2010) showing the spatio-temporal dynamics of AGB in the subtropical forests in Guangdong, China.

The proposed model was capable of mapping forest AGB using spectral, textural, topographical variables and the radar backscatter coefficients in an effective and reliable manner. The root mean square error of the plot-level AGB validation was between 15.62 and 53.78 t∙ha^-1, the mean absolute error ranged from 6.54 to 32.32 t∙ha^-1, the bias ranged from − 2.14 to 1.07 t∙ha^-1, and the relative improvement over the random forest algorithm was between 3.8% and 17.7%. The largest coefficient of determination (0.81) and the smallest mean absolute error (6.54 t∙ha^-1) were observed in the 1992 AGB map. The spectral saturation effect was minimized by adding the PALSAR data to the modeling variable set in 2010. By adding elevation as a covariable, the co-kriging outperformed the ordinary kriging method for the prediction of the AGB residuals, because co-kriging resulted in better interpolation results in the valleys and plains of the study area.

Validation of the three AGB maps with an independent dataset indicated that the random forest/co-kriging performed best for AGB prediction, followed by random forest coupled with ordinary kriging (random forest/ordinary kriging), and the random forest model. The proposed random forest/co-kriging framework provides an accurate and reliable method for AGB mapping in subtropical forest regions with complex topography. The resulting AGB maps are suitable for the targeted development of forest management actions to promote carbon sequestration and sustainable forest management in the context of climate change.