Numerous studies claim that rewetting interventions reduce CO₂ and increase CH₄ fluxes. To verify the claim, we conducted a systematic review and meta-analysis of the effects of rewetting on CO₂ and CH₄ fluxes and dissolved organic carbon (DOC). We identified 28 primary articles eligible for meta-analysis, from which we calculated 48 effect sizes for CO₂ emissions, 67 effect sizes for CH₄ emissions, and 5 effect sizes for DOC. We found that rewetting significantly decreased CO₂ fluxes, with temperate zones showing the highest Hedges’ g effect size (−0.798 ± 0.229), followed by tropical (−0.338 ± 0.269) and boreal (−0.209 ± 0.372) zones. Meanwhile, rewetting increased CH₄ fluxes, with the highest Hedges’ g effect size shown in temperate zones (1.108 ± 0.144), followed by boreal (0.805 ± 0.183) and tropical (0.096 ± 0.284) zones. In addition, based on yearly monitoring after rewetting, the CH₄ emissions effect size increased significantly over the first 4 years (r² = 0.853). Overall, the rewetting intervention reduced CO₂ emissions by −1.43 ± 0.35 Mg CO₂–C ha⁻¹ year⁻¹, increased CH₄ emissions by 0.033 ± 0.003 Mg CH₄–C ha⁻¹ year⁻¹, and had no significant impact on DOC. To improve the precision and reduce the bias of rewetting effect size quantification, it is recommended to conduct more experimental studies with extended monitoring periods using larger sample sizes and apply the before-after control-impact study design, especially in boreal and tropical climate zones.

Degraded and drained peat swamp forests (PSFs) are major sources of carbon emissions in the forestry sector. Rewetting interventions aim to reduce carbon loss and to enhance the carbon stock. However, studies of rewetting interventions in tropical PSFs are still limited. This study examined the effect of rewetting interventions on carbon dynamics at a rewetted site and an undrained site. We measured aboveground carbon (AGC), belowground carbon (BGC), litterfall, heterotrophic components of soil respiration (R_h), methane emissions (CH₄), and dissolved organic carbon (DOC) concentration at both sites. We found that the total carbon stock at the rewetted site was slightly lower than at the undrained site (1886.73 ± 87.69 and 2106.23 ± 214.33 Mg C ha⁻¹, respectively). The soil organic carbon (SOC) was 1685 ± 61 Mg C ha⁻¹ and 1912 ± 190 Mg C ha⁻¹ at the rewetted and undrained sites, respectively, and the carbon from litterfall was 4.68 ± 0.30 and 3.92 ± 0.34 Mg C ha⁻¹ year⁻¹, respectively. The annual average R_h was 4.06 ± 0.02 Mg C ha⁻¹ year⁻¹ at the rewetted site and was 3.96 ± 0.16 Mg C ha⁻¹ year⁻¹ at the undrained site. In contrast, the annual average CH₄ emissions were −0.0015 ± 0.00 Mg C ha⁻¹ year⁻¹ at the rewetted site and 0.056 ± 0.000 Mg C ha⁻¹ year⁻¹ at the undrained site. In the rewetted condition, carbon from litter may become stable over a longer period. Consequently, carbon loss and gain mainly depend on the magnitude of peat decomposition (R_h) and CH₄ emissions.

Peat swamp forests play a critical role in maintaining the ecological integrity of tropical peatlands. The conservation and restoration efforts on these ecosystems have gained considerable attention considering their vulnerability. Accurate spatial mapping of aboveground biomass (AGB) is important to support such measures, and it can be accurately implemented using airborne LiDAR. However, the high operational cost of LiDAR surveys typically restricts their spatial coverage. This study estimated AGB of peat swamp forests by combining two remote sensing datasets, i.e., Planet NICFI imagery (2023–2024) and partially covered airborne LiDAR (10.56% of the total area), in the Katingan–Mentaya peat swamp forest, Central Kalimantan, Indonesia. Two workflows were proposed and compared. The first, DL-PowerReg, estimated canopy height model (CHM) using a U-Net deep learning model trained on Planet imagery, followed by AGB mapping through power regression. The second, StepwiseReg-DL, derived LiDAR-based AGB through stepwise regression of LiDAR metrics, then upscaled it to the full study area using U-Net with Planet imagery as input. DL-PowerReg (MAE = 52.24 t/ha) outperformed StepwiseReg-DL (MAE = 62.08 t/ha) and additionally produced an intermediate CHM map, providing complementary information on forest structure. The two approaches estimated total AGB storage in the study area at 47.75 Mt (mean = 239.91 t/ha) and 40.07 Mt (mean = 201.30 t/ha), respectively. This study demonstrates a methodological framework for leveraging spatially incomplete LiDAR data in high-resolution wall-to-wall forest biomass mapping.