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.

As it is the main role to maintain hydrological function, peatland has been a limelight since drainage construction for agriculture evolved. Drainage construction will decrease water table depth (WTD) and result in CO₂ emission release to the atmosphere. Regardless of human intervention, WTD fluctuations can be affected by seasonal climate and climate variability, foremost El Niño Southern Oscillation (ENSO). This study aims to determine the correlation between rainfall in Katingan and ENSO index, analyze the pattern of WTD fluctuation of open area and forest area in 2015 (during very strong El Niño) and 2016 (during weak La Niña), calculate the WTD trendline slope during the dry season, and rainfall and WTD correlation. The result showed that open area has a sharper slope of decreasing or increasing WTD when entering the dry, compared to the forest area. Also, it is found that very strong El Niño in 2015 generated a pattern of more extreme decreasing WTD during the dry season than weak La Niña in 2016.

Restoration using rainforest species in Australia and elsewhere has been limited to a small number of widely known species, mainly pioneer or early successional species, Using the presumed successional status as a guideline for species selection in reforestation should be taken with a caveat since a species’ capacity to adjust to light gradients is not easily predicted. This study examined the photosynthetic and growth responses of four Australian subtropical rainforest species in the context of using late successional species in restoration programs. Since the selected species [Sloanea australis ((Benth.) F. Muell.), Cinnamomum oliveri (F. M. Bailey),Caldcluvia paniculosa ((F. Muell.) Hoogland) and Geissois benthamiana (F. Muell.)] are considered late-successional species, this study also discussed the possibility of separating these species according to their acclimation level towards light gradients. Seedlings of four species were grown under four light treatments using neutral density shade cloth (5, 33, 64 and 80% irradiance) during summer November 2014 to February 2015. All species demonstrated a narrow range of photosynthetic acclimation to different light levels, experienced photoinhibition and photodamage in 80% irradiance and allocated more biomass to leaves in 5% irradiance, supporting their classification as late successional species. Cinnamomum oliveri was the only species able to utilize higher irradiance, with a higher light saturated rate of photosynthesis than the other species. Canonical analysis of principal coordinates revealed that the degree of plasticity of each species in response to contrasting irradiance levels varied. This analysis separated the species into three light tolerance classes: obligate shade-adapted species (S. australis and G. benthamiana), high light-adapted species (C. paniculosa) and the generalist (C. oliveri). Overall, this study suggests that the four species can be planted and will grow well under 33–64% irradiance since either lower or higher irradiance inhibits growth, and additionally that C. paniculosa and C. oliveri can be possibly planted in early phase of restoration planting with other early-successional species.

The 2015’s massive forest fires have left large areas of burned peatlands that need to be restored, demanding substantial number of resources. To understand natural regeneration on burned peatland and how planting might accelerate its recovery process, we measured recruitment on burned peatland with different fire frequency. Three transects were established each consisting of five 20x20 m2plots developed at a 30 m interval. Our finding suggests that the absence of remnant trees due to frequent or severe fires does not always impede the emergence of new recruitments, although diversity of forest regrowth is likely to be affected by its proximity to forest remnant. The floristic composition also showed a domination of pioneer species, giving evidence that forest recovery is initiated. We conclude that to support the recovery process through planting activity, the successional stage of the designated sites should be determined first. During the early phase of succession, stand initiation should be addressed first by the colonization of rapidly recruiting species to ensure continuous regeneration. Therefore, the common planting method on large areas with mixed climax-high valuable trees is unbeneficial unless the restoration sites have reached the later stages of succession.

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.