Search Results (27)

The distribution of forest aboveground biomass density (AGBD) is a key indicator of carbon stock and ecosystem health in the Eastern Himalayas, which represents a global biodiversity hotspot that sustains diverse forest types across an elevation gradient from lowland rainforests to alpine meadows and contributes to the livelihoods of more than 200 distinct indigenous communities. This study aimed to identify the key factors influencing forest AGBD across this region by analyzing the underlying biophysical and anthropogenic drivers through machine learning (random forest). We processed AGBD data from the Global Ecosystem Dynamics Investigation (GEDI) spaceborne LiDAR and applied filtering to retain 30,257 high-quality footprints across ten ecoregions. We then analyzed the relationship between AGBD and 17 climatic, topographic, soil, and anthropogenic variables using random forest regression models. The results revealed significant spatial variability in AGBD (149.6 ± 79.5 Mg ha⁻¹) across the region. State-wise, Sikkim recorded the highest mean AGBD (218 Mg ha⁻¹) and Manipur the lowest (102.8 Mg ha⁻¹). Within individual ecoregions, the Himalayan subtropical pine forests exhibited the highest mean AGBD (245.5 Mg ha⁻¹). Topographic factors, particularly elevation and latitude, were strong determinants of biomass distribution, with AGBD increasing up to elevations of 2000 m before declining. Protected areas (PAs) consistently showed higher AGBD than unprotected forests for all ecoregions, while proximity to urban and agricultural areas resulted in lower AGBD, pointing towards negative anthropogenic impacts. Our full model explained 41% of AGBD variance across the Eastern Himalayas, with better performance in individual ecoregions like the Northeast India-Myanmar pine forests (R² = 0.59). While limited by the absence of regionally explicit stand-level forest structure data (age, stand density, species composition), our results provide valuable evidence for conservation policy development, including expansion of PAs, compensating avoided deforestation and modifications in shifting cultivation. Future research should integrate field measurements with remote sensing and use high-resolution LiDAR with locally derived allometric models to enhance biomass estimation and GEDI data validation. Full article

Saninten (Castanopsis argentea (Blume) A.DC.) is a protected plant that grows in the Mount Gede Pangrango National Park (MGPNP) area in West Java. Its population is limited, and as a valuable biological resource, Castanopsis has traditionally been utilized by indigenous communities, particularly those residing in proximity to the forest. However, the expansion and development of tourism pose a potential threat to the ecosystems of C. argentea and other endemic plant species, as well as to the wildlife that depend on these habitats. Comprehensive data on biodiversity, species composition, forest structure, and carbon stock status are crucial for assessing the potential impact of future tourism development. Our investigation was conducted from November 2023 to March 2024 in a three-hectare utilization zone within the confines of the national park. The findings documented a total of 36 species across 23 distinct plant families, with the families Fagaceae, Moraceae, and Myrtaceae exhibiting the highest levels of dominance. The regeneration of stands at the study site predominantly comprised arboreal species with the most substantial carbon stocks, including C. acuminatissima (Blume) A.DC. (Riung anak), C. argentea (Saninten), and Litsea sp. (Huru). C. argentea supplies several functions within this ecosystem that are interconnected with other components. With aboveground carbon stocks reaching 560.47 tons C/ha, the forest demonstrates high sequestration potential, reinforcing the need to conserve mature stands for both biodiversity and climate benefits. Therefore, in the future, the conservation of C. argentea will benefit the maintenance of the ecosystem’s attractiveness without adversely affecting the social and cultural structures of the local population. Full article

Surface mining activities cause severe disruption to ecosystems, resulting in the substantial destruction of surface vegetation, the loss of soil organic carbon stocks, and a decrease in the ecosystem’s ability to sequester carbon. The ecological restoration of mining areas has been found to significantly enhance the carbon storage capacity of ecosystems. This study evaluated ecological restoration strategies in Chongqing’s Tongluo Mountain mining area by integrating GF-6 satellite multispectral data (2 m panchromatic/8 m multispectral resolution) with ground surveys across 45 quadrats to develop a quadratic regression model based on vegetation indices and the field-measured biomass. The methodology quantified carbon storage variations among engineered restoration (ER), natural recovery (NR), and unmanaged sites (CWR) while identifying optimal vegetation configurations for karst ecosystems. The methodology combined the high-spatial-resolution satellite imagery for large-scale vegetation mapping with field-measured biomass calibration to enhance the quantitative accuracy, enabling an efficient carbon storage assessment across heterogeneous landscapes. This hybrid approach overcame the limitations of traditional plot-based methods by providing spatially explicit, cost-effective monitoring solutions for mining ecosystems. The results demonstrate that engineered restoration significantly enhances carbon sequestration, with the aboveground vegetation biomass reaching 5.07 ± 1.05 tC/ha, a value 21% higher than in natural recovery areas (4.18 ± 0.23 tC/ha) and 189% greater than at unmanaged sites (1.75 ± 1.03 tC/ha). In areas subjected to engineered restoration, both the vegetation and soil carbon storage showed an upward trend, with soil carbon sequestration being the primary form, contributing to 81% of the total carbon storage, and with engineered restoration areas exceeding natural recovery and unmanaged zones by 17.6% and 106%, respectively, in terms of their soil carbon density (40.41 ± 9.99 tC/ha). Significant variations in the carbon sequestration capacity were observed across vegetation types. Bamboo forests exhibited the highest carbon density (25.8 tC/ha), followed by tree forests (2.54 ± 0.53 tC/ha), while grasslands showed the lowest values (0.88 ± 0.52 tC/ha). For future restoration initiatives, it is advisable to select suitable vegetation types based on the local dominant species for a comprehensive approach. Full article

It has been well observed that accurate estimation of the aboveground biomass (AGB) of mangrove forests is critical for evaluating ecosystem health, carbon sink capacity, and sustainable development. This study utilizes UAV-LiDAR data and field measurements to develop an AGB inversion model based on 26 feature variables. We employed three machine learning algorithms—random forest (RF), extreme gradient boosting (XGBoost), and support vector machine (SVM)—to estimate mangrove AGB in the Xinyingwan region of Lingao County, Hainan Province, China. The key findings include that: (1) the SVM algorithm demonstrated the highest predictive accuracy, with an R² of 0.8853 and RMSE of 0.4766 kg/m², making it most suitable for this study; (2) the proposed zero-importance feature selection method based on mutual information (MI) outperformed traditional techniques, selecting more effective variables for model development; (3) in the SVM model, variables selected using the zero-importance feature selection method based on MI yielded the best prediction accuracy; and (4) the estimated AGB in the study area ranged from 1.97 to 5.23 kg/m², with an average of 3.83 kg/m². This study not only provides valuable data for mangrove ecosystem conservation and restoration but also offers a scientific basis and technical framework for future biomass estimation and carbon stock assessments. Full article

Forests mitigate greenhouse gas (GHG) emissions by capturing CO₂ and storing it as carbon in various forms, including living biomass, dead wood, soil, and forest litter. Importantly, when trees are harvested, a portion of the above-ground biomass is converted into harvested wood products (HWPs), which can retain carbon for decades. With approximately 7 million hectares of forest (30% of its land area), Romania significantly contributes to the country’s carbon budget through the HWP pool. Using country-specific data from 1961 to 2022 and an IPCC method, we tracked HWP carbon storage and projected future scenarios to evaluate the category’s significance in achieving the 2050 climate target. During this period, the carbon stored in Romanian HWPs more than doubled from 28.20 TgC to 60.76 TgC, with sawnwood products as major contributors. Fluctuations were influenced by domestic policies, market dynamics, and industry changes, notably after the 1990s. Annual carbon inflow dipped to 0.65 TgC in 1994 and peaked at 2.54 TgC in 2013. By analyzing the scenarios, we demonstrated that a moderate growth trajectory in carbon inflow, combined with a focus on producing long-lived wood products, could double carbon stock changes by 2050 to 4.4 TgC—roughly 4% of the country’s current total emissions excluding the LULUCF sector. Additionally, based on sustainable forest management practices in Romania, this approach would significantly enhance the carbon pool and its importance in achieving the country’s climate policies. Full article

Forest above-ground carbon stock (AGCS) is one of the primary ecological evaluation indicators, so it is crucial to estimate the AGCS accurately. In this research, we added the climatic and topographic factors to the estimation process by a remote sensing approach to explore their impact and to achieve more precise estimations. We hope to develop a more accurate estimation method for AGCS based on remote sensing data and climate data. The random forest (RF) method has good robustness and wide applicability. Therefore, we modeled and predicted the AGCS by RF based on sixty field sample plots of Pinus densata pure forests in southwest China and the factors extracted from Landsat 8 OLI images (source I), Sentinel-2A images (source II), and combined Landsat 8 OLI and Sentinel-2A images (source III). We added the topographic and climatic factors to establish the AGCS estimation model and compared the results. The topographic factors contain elevation, slope, and aspect. Climatic factors contain mean annual temperature, annual precipitation, annual potential evapotranspiration, and monthly mean potential evapotranspiration. It was found that the R² and RMSE of the model based on source III were better than the R² and RMSE of the models based on source I and source II. Compared to the models based on source I and source II, the model based on source III improved R² by up to 0.08, reduced RMSE by up to 2.88 t/ha, and improved P by up to 4.29%. Among the models without adding factors, the model based on source III worked the best, with an R² of 0.87, an RMSE of 10.81 t/ha, an rRMSE of 23.19%, and a P of 79.71%. Among the models that added topographic factors, the model based on source III worked best after adding elevation, with an R² of 0.89, an RMSE of 10.01 t/ha, an rRMSE of 21.47%, and a P of 82.17%. Among the models that added climatic factors, the model that added the annual precipitation factor had the best modeling result, with an R² of 0.90, an RMSE of 9.53 t/ha, an rRMSE of 20.59%, and a P of 83.00%. The prediction result exhibited that the AGCS of the Pinus densata forest in 2021 was 9,737,487.52 t. The combination of Landsat 8 OLI and Sentinel-2A could improve the prediction accuracy of the AGCS. The addition of annual precipitation can effectively improve the accuracy of AGCS estimation. Higher resolution of climate data is needed to enhance the modeling in future work. Full article

Sustainable forest bioeconomy (SFB), as a multidimensional approach for establishing mutual benefits between forest ecosystems, the environment, the economy, and humans, is a nature-based solution for a promising future. The study aims to evaluate the potential of carbon stocks (Cstocks) and variability for SFB. It is hypothesized that the decrease in Cstocks is related to an increase in population and agriculture, which caused a decrease in forest area and growing stock and consequently affected SFB. Primary and secondary data were collected from the field, national, and international databases, and analyzed using some statistical and geospatial software packages including IBM SPSS 29.0, CANOCO 5.0, and ArcGIS 10.5. The results revealed that large forest areas were converted to arable lands between 2000 and 2020. Across the forest zones, the aboveground and belowground Cstocks varied significantly, with the aboveground biomass being higher than the belowground biomass. The main drivers of Cstocks were politics and governance (57%), population growth (50%), soil degradation practices (50%), and socio-cultural beliefs (45%). Cstocks had significant negative correlation with population growth, carbon emissions, forest growing stock, forest loss, and the use of forest for biofuel. Evergreen forest zones (rainforest and moist) had more Cstocks than the moist deciduous and swamp/mangrove forests. The study demonstrated that the variability in Cstocks over the last three decades is attributed to an increase in population and agriculture, but Cstocks variability between the forest-vegetation belts could be better explained by differences in trees abundance than population. The study also revealed that the increase in Cstocks contributed to the realization of many SDGs, especially SDG 1, 2, 3, 6, 7, 11, 12, 13, and 15, which in turn support a sustainable forest bioeconomy. Future study is necessary to evaluate Cstocks in individual tree species, biodiversity, and other forest ecosystem services to promote SFB in the country. Full article

Recognizing initial degradation levels is essential to planning effective measures to restore tropical forest ecosystems. However, measuring indicators of forest degradation is labour-intensive, time-consuming, and expensive. This study explored the use of canopy-height models and orthophotos, derived from drone-captured RGB images, above sites at various stages of degradation in northern Thailand to quantify variables related to initial degradation levels and subsequent restoration progression. Stocking density (R² = 0.71) and relative cover of forest canopy (R² = 0.83), ground vegetation (R² = 0.71) and exposed soil + rock (R² = 0.56) correlated highly with the corresponding ground-survey data. However, mean tree height (R² = 0.31) and above-ground carbon density (R² = 0.45) were not well correlated. Differences in correlation strength appeared to be site-specific and related to tree size distribution, canopy openness, and soil exposure. We concluded that drone-based quantification of forest-degradation indicator variables is not yet accurate enough to replace conventional ground surveys when planning forest restoration projects. However, the development of better geo-referencing in parallel with AI systems may improve the accuracy and cost-effectiveness of drone-based techniques in the near future. Full article

Aboveground wood carbon (AWC) stocks in forest ecosystems are mediated by biotic and abiotic variables. Understanding the internal regulatory mechanisms of forests is important for future forest management and global climate change mitigation. However, how these factors affect AWC in subtropical mixed forests remains poorly understood. Using a database from the National Forest Inventory (NFI) from China, we observed the effects of climate variables (temperature and precipitation), stand structure indices (stand density and DBH coefficient of variation and diversity), stand diversity indices (taxonomic diversity, functional diversity, and phylogenetic diversity), and stand functional indices on coniferous mixed forests (CMF), coniferous–broadleaf mixed forests (CBMF), and broadleaf mixed forests (BMF). Meanwhile, we examined the AWC based on a linear mixed model and a structural equation model for each mixed forest. We found that both stand structure and stand diversity can affect the AWC through their indirect effects on the stand function, aligning with the niche complementarity effect. Stand age is an important factor affecting AWC because it interacts with stand structure and stand diversity. Our study highlights that AWC is dependent on the regulation of stand age and structure, which can be crucial for boosting high carbon stocks in subtropical forests. Full article

Eucalyptus plantations play an important role in the timber supply and global warming mitigation around the world. Forest age is a critical factor for evaluating and modeling forest structure (e.g., diameter at breast height (DBH), height (H), aboveground carbon stocks (ACS)) and their dynamics. Recently, the spatial distribution of forest age at different scales based on time series remote sensing data has been widely investigated. However, it is unclear whether such data can effectively support the simulation and assessment of forest structure, especially in fast-growing plantation forests. In this study, the physiological principles in predicting growth (3-PG) model was firstly optimized and calibrated using survey and UAV lidar data at the sample plot (SP) scale, and was then applied at the forest sub-compartment (FSC) scale by designing different simulation scenarios driven by different forest age data sources and adjustments. The sensitivity of the simulated forest structure parameters to forest age was assessed at the SP and FSC levels. The results show that both the survey forest age data and the remote-sensing-derived forest age data could accurately estimate the DBH, H, and ACS of eucalyptus plantations with the coefficients of determination (R²) ranging from 0.87 to 0.94, and the relative root mean square error (RRMSE) below 20% at SP level. At the FSC level, the simulation results based on remotely sensed forest age data are significantly better than FSC forest age data from surveys by forestry bureaus, with R² of ACS 0.7, RMSE 9.12 Mg/ha, and RRMSE 28.24%. The results of the sensitivity analysis show that the DBH, H, and ACS show different degrees of variation under different adjusted forest ages at SP and FSC level. The maximum difference in ACS is 82.91% at the SP scale if the forest age decreases 12 months and 41.23% at the FSC scale if the forest age increases 12 months. This study provides an important reference for future studies using forest age data obtained by remote sensing to drive the forest carbon model in a large spatial scale. Full article

The East-African lowland coastal forest (LCF) is one of Africa’s centres of species endemism, representing an important biodiversity hotspot. However, deforestation and forest degradation due to the high demand for fuelwood has reduced forest cover and diversity, with unknown consequences for associated terrestrial carbon stocks in this LCF system. Our study assessed spatio-temporal land use and land cover changes (LULC) in 1998, 2008, 2018 in the LCF ecosystem, Tanzania. In addition, we conducted a forest inventory survey and calculated associated carbon storage for this LCF ecosystem. Using methods of land use change evaluation plug-in in QGIS based on historical land use data, we modelled carbon stock trends post-2018 in associated LULC for the future 30 years. We found that agriculture and grassland combined increased substantially by 21.5% between the year 1998 and 2018 while forest cover declined by 29%. Furthermore, forest above-ground live biomass carbon (AGC) was 2.4 times higher in forest than in the bushland, 5.8 times in the agriculture with scattered settlement and 14.8 times higher than in the grassland. The estimated average soil organic carbon (SOC) was 76.03 ± 6.26 t/ha across the entire study area. Our study helps to identify land use impacts on ecosystem services, supporting decision-makers in future land-use planning. Full article

As various political initiatives have set goals to reach net-zero emissions by the mid-21st century, forests will play an important role as a carbon sink for sequestering unavoidable emissions. Forest management can take two approaches by either decreasing harvest and enlarging the forest carbon stock or increasing harvest to increase carbon uptake and create harvested wood products (HWPs). Currently, these two management options seem at odds with seemingly conflicting policy directives being written. We used the BEKLIFUH model to assess six management scenarios based on carbon offset potential taking into consideration forest carbon, HWPs and the material and energetic substitution effects. The results show that while conservation leads to a higher above-ground carbon pool, including HWPs, material and energetic substitution leads to more overall carbon offsets for management scenarios with more timber harvesting. With compromise being possible by selectively conserving old growth forests with a high biodiversity value. In conclusion, if the forest sector decouples GHG reporting from forest management and includes all the secondary effects of timber harvest, this new approach can lead to a different cost–benefit analysis for the choice between harvest vs. conservation. This could result in a paradigm shift to a future where biodiversity and carbon neutrality can coexist. Full article

The sub-tropical broadleaved forests in Pakistan are the main constituents of the ecosystem services playing a vital role in the global carbon cycle. Monotheca buxifolia (Falc.) A. DC. is an important constituent of these forests, encompassing a variety of ecological and commercial uses. To our best knowledge, no quantitative studies have been conducted in these forests across the landscape to establish a baseline for future monitoring. We investigated the forest structural attributes, growing stock characteristics and total biomass carbon stock and established relationships among them in the phytocoenosis of Monotheca forests along an altitudinal gradient in Pakistan to expand an eco-systemic model for assessment of the originally-implemented conservation strategies. A floristic survey recorded 4986 individuals of 27 species in overstory and 59 species in the understory stratum. Species richness (ANOVA; F = 3.239; p = 0.045) and Simpson’s diversity (ANOVA; F = 2.802; p = 0.043) differed significantly in three altitudinal zones, with a maximum value for lower elevations, followed by middle and higher elevations. Based on the importance values, Acacia modesta and Olea ferruginea are strong companions of M. buxifolia at lower and higher altitudes, whereas forests at mid elevation represent pure crop of M. buxifolia (IVI = ≥85.85%). A similar pattern in stem density, volume and Basal area were also recorded. The carbon stock in trees stratum (51.81 T ha⁻¹) and understory vegetation (0.148 T ha⁻¹) contributes high values in the lower elevation forests. In contrast, soil carbon had maximum values at higher elevation (36.21 T ha⁻¹) and minimum at lower elevation (16.69 T ha⁻¹) zones. Aboveground biomass carbon stock (AGB BMC) of woody trees, understory vegetation and soil organic carbon (SOC) were estimated higher (77.72 T ha⁻¹) at higher and lower (68.65 T ha⁻¹) elevations. Likewise, the AGB BMC exhibited a significant (p < 0.05) negative correlation with elevation and positive correlation with soil carbon. We concluded that lower elevation forests are more diverse and floristically rich in comparison to higher altitudinal forests. Similarly, the biomass carbon of Monotheca forests were recorded maximum at low altitudes followed by high and middle ranges, respectively. Full article

Coastal forested wetlands provide important ecosystem services along the southeastern region of the United States, but are threatened by anthropogenic and natural disturbances. Here, we examined the species composition, mortality, aboveground biomass, and carbon content of vegetation and soils in natural pine forests of the lower coastal plain in eastern North Carolina, USA. We compared a forest clearly in decline (termed “ghost forest”) adjacent to a roadside canal that had been installed as drainage for a road next to an adjacent forest subject to “natural” hydrology, unaltered by human modification (termed “healthy forest”). We also assessed how soil organic carbon (SOC) accumulation changed over time using ¹⁴C radiocarbon dating of wood sampled at different depths within the peat profile. Our results showed that the ghost forest had a higher tree density at 687 trees ha⁻¹, and was dominated by swamp bays (Persea palustric), compared to the healthy forest, which had 265 trees ha⁻¹ dominated by pond pine (Pinus serotina Michx). Overstory tree mortality of the ghost forest was nearly ten times greater than the healthy forest (p < 0.05), which actually contributed to higher total aboveground biomass (55.9 ± 12.6 Mg C ha⁻¹ vs. 27.9 ± 8.7 Mg ha⁻¹ in healthy forest), as the dead standing tree biomass (snags) added to that of an encroaching woody shrub layer during ecosystem transition. Therefore, the total aboveground C content of the ghost forest, 33.98 ± 14.8 Mg C ha⁻¹, was higher than the healthy forest, 24.7 ± 5.2 Mg C ha⁻¹ (p < 0.05). The total SOC stock down to a 2.3 m depth in the ghost forest was 824.1 ± 46.2 Mg C ha⁻¹, while that of the healthy forest was 749.0 ± 170.5 Mg C ha⁻¹ (p > 0.05). Carbon dating of organic sediments indicated that, as the sample age approaches modern times (surface layer year 2015), the organic soil accumulation rate (1.11 to 1.13 mm year⁻¹) is unable to keep pace with the estimated rate of recent sea level rise (2.1 to 2.4 mm year⁻¹), suggesting a causative relationship with the ecosystem transition occurring at the site. Increasing hydrologic stress over recent decades appears to have been a major driver of ecosystem transition, that is, ghost forest formation and woody shrub encroachment, as indicated by the far higher overstory tree mortality adjacent to the drainage ditch, which allows the inland propagation of hydrologic/salinity forcing due to SLR and extreme storms. Our study documents C accumulation in a coastal wetland over the past two millennia, which is now threatened due to the recent increase in the rate of SLR exceeding the natural peat accumulation rate, causing an ecosystem transition with unknown consequences for the stored C; however, much of it will eventually be returned to the atmosphere. More studies are needed to determine the causes and consequences of coastal ecosystem transition to inform the modeling of future coastal wetland responses to environmental change and the estimation of regional terrestrial C stocks and flux. Full article

Tropical forest management has both positive and negative effects on climate change, and quantifying these effects is important both to avoid or minimize negative impacts and to reward net positive effects. This study contributes to this effort by estimating the aboveground volume and carbon present in commercial tree species in a managed forest in the forest harvest stage in Brazil’s state of Acre. A total of 12,794 trees of commercial species were measured. Trees were categorized and quantified as: “harvested trees” (“harvest or cut”), which were felled in the harvest stage, and “remaining trees” (“future cutting,” “trees in permanent protection areas or APPs,” “seed trees,” “rare trees” and “trees protected by law”) that remained standing in the forest post-harvest. Aboveground volume and carbon stocks of the 81 commercial species (diameter at breast height [DBH] ≥ 10 cm) totaled 79.19 m³ ha⁻¹ and 21.54 MgC ha⁻¹, respectively. The category “harvested trees” represents 44.48% and “remaining trees” 55.49% of the aboveground volume stocks. In the managed area, the category “harvested trees” is felled; this is composed of the commercial bole that is removed (19.25 m³ ha⁻¹ and 5.32 MgC ha⁻¹) and the stump and crown that remain in the forest as decomposing organic material (15.97 m³ ha⁻¹ and 4.41 MgC ha⁻¹). We can infer that the 21.54 MgC ha⁻¹ carbon stock of standing commercial trees (DBH ≥ 10 cm) represents 13.20% of the total aboveground carbon in the managed area. The commercial boles removed directly from the forest represent 3.26% of the total aboveground carbon, and the stumps and crowns of the harvested trees represent the loss of an additional 2.70%. For sustainability of the management system in terms of carbon balance, growth in the 35-year management cycle must be sufficient to replace not only these amounts (0.27 MgC ha⁻¹ year⁻¹) but also losses to collateral damage and to additional logging-related effects from increased vulnerability to forest fires. Financial viability of future management cycles will depend on replenishment of commercial trees of harvestable size (DBH ≥ 50 cm). Full article