Forest Stocks Research Articles

Large-sized trees and altitude drive aboveground carbon stock in Brazilian Atlantic Cloud Forests: An approach based on carbon hyperdominant taxa.

Research about patterns of aboveground carbon stock (AGC) across different tropical forest types is central to climate change mitigation efforts. However, the aboveground carbon stock (AGC) quantification for Brazilian cloud forest ecosystems along the altitudinal gradient is still scarce. We aimed to evaluate the effects of abiotic and biotic on AGC and the AGC distribution between species and families of tree communities along an altitudinal Brazilian Atlantic cloud forest gradient of the Mantiqueira Mountain Range, Southeastern Brazil. We analyzed the relationships between AGC and biotic (taxonomic and functional diversity based on structural attributes) and abiotic factors (altitude and soil properties) across seventy plots (10×20m) distributed in seven cloud forest sites at different elevations (from 1.100 to 2.330ma.s.l.) using linear mixed models and machine learning approaches. We found significant variations in AGC stock along the altitudinal gradient, which was explained mainly by altitude and large-sized trees. We observed that approximately 5% of the total sampled individuals were responsible for >50% of the AGC stock of the tree community in the different sites. This result demonstrates how carbon-dominant tree species' have a higher relative contribution to the AGC at community level than species richness and abundance. The Myrtaceae was the most species-rich and carbon-dominant family, which holds four of the total hyperdominant species in the study region. This study reveals new and important ecological patterns of AGC stock in Southeastern Brazil's cloud forest tree communities, where large-sized trees and altitude are the main biotic and abiotic factors, respectively. These insights enhance our understanding of AGC stock in these unique forest ecosystems and emphasize the need for targeted conservation strategies that protect dominant species and their habitats.

Anthromes and forest carbon responses to global change

Societal Impact StatementForest ecosystems absorb and store about 25% of global carbon dioxide emissions annually and are increasingly shaped by human land use and management. Climate change interacts with land use and forest dynamics to influence observed carbon stocks and the strength of the land carbon sink. We show that climate change effects on modeled forest land carbon stocks are strongest in tropical wildlands that have limited human influence. Global forest carbon stocks and carbon sink strength may decline as climate change and anthropogenic influences intensify, with wildland tropical forests, especially in Amazonia, likely being especially vulnerable.Summary Human effects on ecosystems date back thousands of years, and anthropogenic biomes—anthromes—broadly incorporate the effects of human population density and land use on ecosystems. Forests are integral to the global carbon cycle, containing large biomass carbon stocks, yet their responses to land use and climate change are uncertain but critical to informing climate change mitigation strategies, ecosystem management, and Earth system modeling. Using an anthromes perspective and the site locations from the Global Forest Carbon (ForC) Database, we compare intensively used, cultured, and wildland forest lands in tropical and extratropical regions. We summarize recent past (1900‐present) patterns of land use intensification, and we use a feedback analysis of Earth system models from the Coupled Model Intercomparison Project Phase 6 to estimate the sensitivity of forest carbon stocks to CO2 and temperature change for different anthromes among regions. Modeled global forest carbon stock responses are positive for CO2 increase but neutral to negative for temperature increase. Across anthromes (intensively used, cultured, and wildland forest areas), modeled forest carbon stock responses of temperate and boreal forests are less variable than those of tropical forests. Tropical wildland forest areas appear especially sensitive to CO2 and temperature change, with the negative temperature response highlighting the potential vulnerability of the globally significant carbon stock in tropical forests. The net effect of anthropogenic activities—including land‐use intensification and environmental change and their interactions with natural forest dynamics—will shape future forest carbon stock changes. These interactive effects will likely be strongest in tropical wildlands.

An interlinked dynamic model of timber and carbon stocks in Japan's wooden houses and plantation forests

Carbon absorption in growing trees is an important element of a carbon-neutral society, and the long-term storage of carbon stocks is a crucial sustainability challenge. Previous studies have focused on either live-biomass carbon stocks in plantation forests or anthropogenic carbon stocks in man-made objects. For a comprehensive nature-based climate solution, an analytical framework, dataset, and scenario setup for modeling the interrelationship between timber supply and demand are required. This study developed an interlinked material flow analysis model in which the timber demand for wooden houses is connected with timber supply from managed plantation forestry. We demonstrate the model by quantifying both live-biomass and anthropogenic carbon stocks and their potentials in Japan. We compared multiple scenario-runs of the model for wooden house demands estimated by population change with varying combinations of house types, structures, and lifespans. Our results show that carbon stocks will reach a maximum amount of 1.1 billion t-C by 2050 in a scenario of high demand for wooden detached houses with lifespan extensions. On the other hand, we also found that the aging of plantation forests and their reduced carbon-stocking capacities appear inevitable in any scenario owing to the limited demand for timber. Notably, despite the widely different settings of the various scenarios, our results exhibited narrow variances in future potential carbon storage in Japan. This can be explained by the unique population characteristics and building demographics of Japan. These counterintuitive findings highlight the need for interrelated modeling of the forestry and construction sectors. Our model and its scope are versatile and applicable to other case study areas, estimation periods, and target materials.

Estimation of carbon stock, biomass and CO2 sequestration at mangrove forest in Batu Rusa River and Serata Pasir Padi, Bangka Island

Abstract The aim of this research is to determine the total biomass, carbon stock and CO2 sequestration above ground in mangrove forests in the Baturusa and Pasir Padi River Basins. This research was carried out in October-September 2023-2024, using a non-destructive sampling method based on species-specific allometric equations to find the biomass value of mangrove stands. Data collections were determined using the random purposive sampling method. The result shows that Rhizophora apiculata is the mangrove species that dominates in the Baturusa River, while the Excoecaria agallocha dominates in the Pasir Padi River mangrove forest. The total biomass reserves above ground in the Baturusa River and Pasir Padi River mangrove forests are quite high, namely 195310.4 tonnes/ha, with 188172.2 tonnes/ha for the biomass of the Baturusa River mangrove stands while the mangrove stands in Pasir Padi are 7138.2 tonnes. /Ha. The total above-ground (standing) carbon stock in the Baturusa River mangrove forest is 88440.9 tonnes/ha while in the Pasir Padi River it is 3354.9 tonnes/ha with a total reaching 91795.9 tonnes/ha. The total uptake of CO2 sequestration in mangrove stands in the Baturusa River was 324283.4 tonnes/ha, the Pasir Padi River was 122301.5 tonnes/ha with a total reaching 336585.0 tonnes/ha. Sonneratia alba is a mangrove species that has the highest biomass reserves, carbon stock and CO2 sequestration, namely biomass of 0.701 tonnes/tree, carbon stock of 0.330 tonnes/tree and CO2 uptake of 1.208 tonnes/tree, because it has a fairly large trunk diameter compared to other mangrove species.

Carbon stock estimation of trees and poles in Karendan Forest, North Barito, Central Kalimantan

Abstract Kalimantan is known as one of the biodiversity hotspots. It harbours various endemic, rare, and endangered plant species. This research aimed to inventory plants and estimate the carbon stock of trees and poles in the Karendan forest, Central Kalimantan. The biomass and carbon storage of above-ground biomass (AGB) were measured using non-destructive methods. Around 88 species of tree stands were recorded, consisting of 51 trees and 61 species of poles (young trees). The average estimate of total carbon stock in the Karendan forest was 91.19 tons/ha, while the carbon stock value of trees was equivalent to poles, indicating forest regeneration. We observed forest disturbances caused by fires or selective logging in the past. The Dipterocarpaceae family (meranti) dominantly contributes to carbon biomass, either through its tree species, such as Shorea leprosula (25.07 tons/ha) and S. lamellata (5.05 tons/ha), or its pole species, including S. leprosula (3.39 tons/ha), and S. bracteolata (2.02 tons/ha). This family’s abundance of carbon-contributing trees indicates that the Karendan forest has a relatively high conservation value. The findings of this research are helpful for the management of coal mining companies, considering that this area is within the mining concessions.

Conversion of tropical forests to water buffalo pastures in lower Amazonia: Carbon losses and social carbon costs

AbstractTropical peat swamp forests provide many important ecosystem services, especially their function as global carbon sinks. These carbon‐rich wetlands are widespread in South America, yet few studies have examined carbon stocks or losses due to land use change. In the lower Amazon, they are being converted to pastures largely utilized by domestic water buffalo (Bubalus bubalis). We quantified carbon stocks in intact peat forests and recently converted pastures (<10 years) at the Lago Piratuba Biosphere Reserve (LPBR) in the lower Amazon of Brazil. The soils of intact forests were typified by shallow organic (peat) horizons at the soil surface. The mean total ecosystem carbon stock (TECS) in intact forests was 354 ± 28 Mg C ha−1. In contrast, the TECS of disturbed sites was significantly lower (p = 0.02) with a mean of 248 ± 17 Mg C ha−1. We estimated greenhouse gas (GHG) emissions from water buffalo (due to enteric fermentation and manure deposition) to be 7.5 Mg CO2e ha−1 year−1. Considering GHG emissions from this land use, the social carbon costs (SCCs) arising from the degradation of coastal Amazon peatlands are as high as US$2742 ha−1 year−1. The SCC of meat produced from this land use is as high as US$100/kg of meat produced, which far exceeds the economic returns from livestock. Based on the estimated numbers of water buffalo for the southern portion of the LPBR and the time since initial disturbance, the annual GHG emissions from this land use are estimated to be 602,846 Mg CO2e year−1 with an SCC as high as US$111,526,524 million year−1. This land use also eliminates opportunity values and services of carbon storage and biodiversity that would be possible from a regenerating biosphere reserve.

Integration of Optical Remote Sensing and Laser Point Cloud for Forest Stock Estimation in Karst Mountainous Areas

Integration of Optical Remote Sensing and Laser Point Cloud for Forest Stock Estimation in Karst Mountainous Areas

Estimation of carbon stocks of tropical mangrove forests along the Carigara Bay in Leyte, Philippines

Mangrove forest ecosystems are known to sequester large quantities of carbon, becoming a significant carbon source when disturbed. This paper presents a quantification in aboveground (standing trees, palm, shrub, standing dead trees, downed wood and litter), belowground (root and soil) and ecosystem carbon stocks in mangrove forests along the Carigara Bay in Leyte, Philippines. The carbon stocks in the different mangrove forest types (fringe and riverine) and zones (landward, middleward, and seaward/along water) were compared. Further, the relationship between environmental factors (eg, interstitial soil salinity, soil water content and soil depth) and ecosystem carbon stocks was examined. The study yielded an ecosystem carbon stock of 558.02±51.13Mg ha-1, partitioned into aboveground and belowground carbon stocks of 251.96±31.08 and 306.06±28.50Mg ha-1, respectively. The ecosystem carbon stocks of the riverine (805.89±80.57Mg ha-1) greatly exceeded that of the fringe mangrove forests (310.15±24.59Mg ha-1). In general, biomass and soil both store a similar proportion of carbon, corresponding to 57% and 43%, respectively. In addition, regression analysis revealed that soil depth was a reasonable predictor of ecosystem carbon stocks, whereby increasing ecosystem carbon stocks were associated with deeper soil deposits. Overall, the study’s results highlight the exceptionally high amount of carbon stored in the mangrove ecosystems, indicating their potential role in climate change mitigation.

Soil phosphorus compared to nitrogen limitation increases the uncertainty of subsoil organic carbon sequestration in Pinus massoniana mixed forests

Limited nitrogen (N) and phosphorus (P) availability will constrain terrestrial carbon sinks in the 21st century. Mixed forests improve the plant community composition and productivity of pure coniferous forests. Nevertheless, it is uncertain whether and to what extent changes in soil N and P dynamics caused by mixed forests can affect forest soil organic carbon (SOC) stocks. The research purpose is to demonstrate and evaluate the effects of soil N and P on SOC stocks in Pinus massoniana mixed forests. Our meta-analysis, which included 616 paired observations, revealed that the coupling of soil N, P, and plant species richness (PSR) contributed 22.6%, 28.8%, and 28.4%, respectively, to SOC stock accumulation in the topsoil (0–20 cm), subsoil (20–100 cm), and whole profile (0–100 cm). The interactions between soil total N and P concentrations (TN:TP ratios) dominated the increase in SOC stocks in the mixed forest topsoil and whole profile, explaining 35.6% and 20.3% of the variation, respectively. Conversely, independent TN and TP concentrations were the primary contributors (explained by 17.5% and 12.3%, respectively) to subsoil SOC stocks increase. The TN and TP concentrations limit SOC stock accumulation in mixed forests for the next 60 years (2025–2085), with the TP concentration and TN:TP ratio having a greater effect in subsoil. Fortunately, the TN and TP limitations on SOC stock increase in mixed forests can be mitigated and balanced by altering soil TN:TP ratio by increasing or decreasing the PSR (PSR thresholds for topsoil, subsoil, and whole profile were 15, 8, and 8, respectively). Overall, plant mixing fails to enhance plant availability of pure forest soil N and P. Accelerated N cycling and increased P reabsorption efficiency are optimal strategies for balancing N and P supply in mixed forests, promoting biomass accumulation, and ensuring SOC stocks increase.

Edge effect and species richness modulate biomass stocks and change over time in secondary forest fragments in the subtropical Atlantic Forest

ABSTRACT Background Above-ground biomass (AGB) and its temporal dynamics are key parameters of forest ecosystems, related to their resilience and capacity to fix atmospheric carbon. AGB is related to species richness, composition, forest structure, soils and climate. In human-modified landscapes, fragmentation and human pressure may change the nature of these relationships. Aims Our aim was to quantify how species richness and composition, leaf area index, and edge effect were related to biomass stocks and growth in fragmented sub-tropical secondary forests. Methods We tested the relationship between leaf area index, tree species richness and composition, edge effect and AGB stocks in secondary forest stands in the Atlantic Forest using multiple linear models on data from 104 sites. Results Our results show that biomass stocks were related to species richness, distance to forest edge and the interaction of both (R2 = 0.25; p < 0.05). Biomass change showed positive relationships with pioneer species richness and distance to edge, and negative relationship with the interaction of total species richness and distance to edge (R2 = 0.15; p < 0.05). Conclusion Edge effect can affect AGB dynamics directly and indirectly, by weakening the positive effects of species richness and composition on biomass. AGB loss at some sites suggests that some fragments are under chronic disturbance or may be experiencing delayed mortality due to fragmentation.

Harnessing Biomass and Blue Carbon Potential: Estimating Carbon Stocks in the Vital Wetlands of Eastern Sumatra, Indonesia

Global warming is a critical factor driving climate change, impacting every aspect of life on Earth. The escalating concentration of greenhouse gasses in the atmosphere, the primary contributor to global warming, necessitates immediate action through effective climate mitigation strategies. This study aimed to quantify the biomass and blue carbon stocks in the eastern coastal mangrove forests of North Sumatra and Aceh Provinces in Indonesia, focusing on key sites in Langkat, Deli Serdang, Batu Bara, Tanjung Balai, and Aceh Tamiang Regencies. We measured carbon stock in three carbon pools: biomass (above and below ground), necromass, and soil. By analyzing tree stands using parameters such as tree height and diameter at breast height within circular plots (7 m in radius, 125 m apart), we gathered fundamental data on forest structure, species composition, and above- and below-ground biomass. Additionally, we collected soil samples at various points and depths, measuring the amount of wood, stems, or branches (necromass) that fell to or died on the forest floor. Data were collected in plots along a line transect, comprising three transects and six circular plots each. Sixteen diverse mangrove species were found, demonstrating rich mangrove biodiversity. The mangrove forests in the five regencies exhibited significant carbon storage potential, with estimated average above-ground carbon ranging from 96 to 356 MgC/ha and average below-ground carbon from 28 to 153 MgC/ha. The estimated average deadwood carbon varied between 50 and 91 MgC/ha, while soil carbon ranged from 1200 to 2500 MgC/ha. These findings underscore the significant carbon storage potential of these mangrove forests, highlighting their importance to global carbon cycling and climate change mitigation. This research contributes to a broader understanding of mangroves as vital blue carbon ecosystems, emphasizing the necessity of conservation efforts such as forest restoration and rehabilitation to enhance their role in stabilizing coastal areas and improving global climate resilience.

Influence of Soil Texture on Carbon Stocks in Deciduous and Coniferous Forest Biomass in the Forest-Steppe Zone of Oka–Don Plain

Forests play a crucial role in climate change mitigation by acting as a carbon sink. Understanding the influence of soil properties on carbon stocks in forests is essential for developing effective forest management strategies. The aim of the study was to assess the impact of soil texture on carbon stocks in the biomass of deciduous and coniferous tree stands of a forest-steppe ecotone. Soil samples were collected from 55 soil pits, and forest inventory data were obtained from eight permanent sample plots. The results showed that the distribution of mechanical particles in soils, particularly the stocks of silt and clay, significantly influenced the accumulation of carbon in tree stands. The stock of silt and clay was shown to increase with an increase in the diversity of tree species in forests and carbon stocks in forest stands. While soil organic carbon stocks did not exhibit a clear relationship with tree stand carbon stocks, a strong positive correlation (r = 0.802, p &lt; 0.05) was found between the stocks of fine particles in the 2 m root-inhabited soil layer and the carbon stocks in tree biomass. The study provides a classification of forest types based on soil texture, which can facilitate differentiated forest management strategies for enhancing the carbon sequestration potential of forest ecosystems in the forest-steppe zone.

Tree species diversity and spatial distribution of carbon stock in forests under different management regimes in Nepal's Western Terai Arc Landscape

Reliable tree species diversity and carbon stock data are crucial for establishing a baseline and aiding monitoring and management decision-making to ensure ecosystem functions and services, including carbon management. However, such data are not available for many primary forests, especially in developing countries like Nepal. From this study, we generated primary baseline data on tree species diversity, biomass, and carbon stock for forests under different management regimes in one of the critical landscapes of western Nepal. We sampled 94 clusters of concentric circular sample plots placed systematically at the nodes of 3 km by 3 km square grids in biological corridors (managed by community forest user groups) and a national park's buffer zones (managed by buffer zone user groups) to study the tree species composition and estimate the carbon stock. The number of tree species and botanical families reported in different sites did not differ significantly. The basal area was found to be highest in Brahmadev Corridor where tree density per unit area was also highest. Our study sites showed a basal area ranging from 26.42 to 40.65 m2/ha, which is higher than previously reported from similar forests. The total biomass (302.12 to 496 tons/ha) and carbon stock (142 to 233.12 tons/ha), both being highest in Brahmadev Corridor, were within the range of comparable data and national averages. The number of stems in all sites showed the reverse J-shaped pattern, indicating a stable population and good species recruitment. The buffer zones had a large proportion of forest area with the highest carbon stock classes of 300–400 tons/ha. The basal area and diameter at breast height showed a significant positive correlation with carbon stock, indicating that the basal area is a key determinant of carbon stock. Our study provides disaggregated information on tree species diversity, the quality of forests in terms of stem density and size-class distribution, and carbon stock and its distribution in different carbon stock classes in two forest management regimes. In this sense, our study is the first of its kind in providing important insights into forest and carbon management because areas with different carbon stocks need different management prescriptions.

Aboveground Carbon Stock Estimation Based on Backpack LiDAR and UAV Multispectral Imagery at the Forest Sample Plot Scale

Aboveground carbon stocks (AGCs) in forests play an important role in understanding carbon cycle processes. The global forestry sector has been working to find fast and accurate methods to estimate forest AGCs and implement dynamic monitoring. The aim of this study was to explore the effects of backpack LiDAR and UAV multispectral imagery on AGC estimation for two tree species (Larix gmelinii and Betula platyphylla) and to emphasize the accuracy of the models used. We estimated the AGC of Larix gmelinii and B. platyphylla forests using multivariate stepwise linear regression and random forest regression models using backpack LiDAR data and multi-source remote sensing data, respectively, and compared them with measured data. This study revealed that (1) the diameter at breast height (DBH) extracted from backpack LiDAR and vegetation indices (RVI and GNDVI) extracted from UAV multispectral imagery proved to be extremely effective in modeling for estimating AGCs, significantly improving the accuracy of the model. (2) Random forest regression models estimated AGCs with higher precision (Xing’an larch R2 = 0.95, RMSE = 3.99; white birch R2 = 0.96, RMSE = 3.45) than multiple linear regression models (Xing’an larch R2 = 0.92, RMSE = 6.15; white birch R2 = 0.96, RMSE = 3.57). (3) After combining backpack LiDAR and UAV multispectral data, the estimation accuracy of AGCs for both tree species (Xing’an larch R2 = 0.95, white birch R2 = 0.96) improved by 2% compared to using backpack LiDAR alone (Xing’an larch R2 = 0.93, white birch R2 = 0.94).

Estimating Carbon Stock in Unmanaged Forests Using Field Data and Remote Sensing

Unmanaged forest ecosystems play a critical role in addressing the ongoing climate and biodiversity crises. As there is no commercial interest in monitoring the health and development of such inaccessible habitats, low-cost assessment approaches are needed. We used a method combining RGB imagery acquired using an Unmanned Aerial Vehicle (UAV), Sentinel-2 data, and field surveys to determine the carbon stock of an unmanaged forest in the UNESCO World Heritage Site wilderness area Dürrenstein-Lassingtal in Austria. The entry-level consumer drone (DJI Mavic Mini) and freely available Sentinel-2 multispectral datasets were used for the evaluation. We merged the Sentinel-2 derived vegetation index NDVI with aerial photogrammetry data and used an orthomosaic and a Digital Surface Model (DSM) to map the extent of woodland in the study area. The Random Forest (RF) machine learning (ML) algorithm was used to classify land cover. Based on the acquired field data, the average carbon stock per hectare of forest was determined to be 371.423 ± 51.106 t of CO2 and applied to the ML-generated class Forest. An overall accuracy of 80.8% with a Cohen’s kappa value of 0.74 was achieved for the land cover classification, while the carbon stock of the living above-ground biomass (AGB) was estimated with an accuracy within 5.9% of field measurements. The proposed approach demonstrated that the combination of low-cost remote sensing data and field work can predict above-ground biomass with high accuracy. The results and the estimation error distribution highlight the importance of accurate field data.

Stand structure and Brazilian pine as key determinants of carbon stock in a subtropical Atlantic forest.

Understanding the drivers of variations in carbon stocks is essential for developing the effective management strategies that contribute to mitigating climate change. Although a positive relationship between biodiversity and the aboveground carbon (AGC) has been widely reported for various Brazilian forest types, representing a win-win scenario for climate change mitigation, this association has not been commonly found in Brazilian subtropical forests. Therefore, in the present study, we aimed to evaluate the effects of Araucaria angustifolia, stand structure and species diversity in shaping AGC stocks in Brazilian subtropical mixed forest. We hypothesized that the effects on the AGC of stand structure and diversity would be mediated by A. angustifolia. We also evaluated the expectation of higher carbon stocks in protected forest as a result of their positive correlation with biodiversity conservation. We found that stand structure, followed by A. angustifolia, played the most important role in shaping the AGC stock. Our hypothesis was partially confirmed, the indirect effects of A. angustifolia on stand structure being found to have shaped the AGC. Similarly, our expectation was partially supported, with the higher AGC in the protected area being related not to diversity, but rather to the presence of larger trees, denser stands, and a greater abundance of A. angustifolia. Although the win-win strategy between diversity conservation and carbon storage is not a peculiarity of Araucaria forests, we highlight the potential of these forests as a nature-based climate solution, maintaining high levels of carbon storage in harmony with the provision of keystone socio-economic resources.

The Effect of Transition to Close-to-Nature Forestry on Growing Stock, Wood Increment and Harvest Possibilities of Forests in Slovakia

The aim of the study is to quantify the impacts of a possible transition to close-to-nature forestry in Slovakia and to compare the expected development of the total volume production, growing stock, merchantable wood increment and harvesting possibilities of forests in Slovakia with current conventional management using the FCarbon forest-growth model and available data from the Information System of Forest Management. The subject of the study was all forest stands available for wood supply (FAWS). The simulations were run in annual iterations using tree input data aggregated over 10-year-wide age classes. The calculation of wood increments was based on available growth models. In the business-as-usual (BAU) scenario, stock losses were based on the actual intensity of wood harvesting in the reference period 2013–2022. In the scenario of the transition to close-to-nature forest management, the losses were specifically modified from the usual harvesting regime at the beginning, to the target harvesting mode in selective forest at the end of the simulated period. With the modelling method used, a gradual increase in forest stocks occurred in both evaluated scenarios in the monitored period, namely by 10% in the case of BAU and by 23% in the case of close-to-nature forest management until 2050. In absolute mining volume, CTNF is by 5–10% lower than BAU management, with the difference gradually decreasing. The results show that the introduction of close-to-nature forest management will temporarily reduce the supply of wood to the market, but this reduction will not be significant and will be compensated by a higher total volume production, and thus also by increased carbon storage in forests.

Short Communication: Carbon stock of mangrove forest in Pantai Sederhana, Bekasi District, West Java, Indonesia

Abstract. Erniasari I, Hernawan E, Mulyaningrum. 2024. Short Communication: Carbon stock of mangrove forest in Pantai Sederhana, Bekasi District, West Java, Indonesia. Biodiversitas 25: 2974-2980. Mangrove forests play an important role in the carbon cycle because this forest can store greater amounts of carbon than other ecosystems including tropical rainforests. Mangrove forests in Bekasi District, West Java, Indonesia have enormous potential to absorb carbon in this industrial area. In this research, we estimate the aboveground carbon (AGC), belowground carbon (BGC), and soil organic carbon (SOC) of mangrove forest in Pantai Sederhana, Muaragembong Subdistrict, Bekasi District. Carbon estimation of AGC and BGC was carried out using the allometric method, while SOC at four depth intervals (0-15, 15-30, 30-50, and 50-100 cm) was estimated using the LOI (Loss of Ignition) method. There were four mangrove species in Pantai Sederhana, namely Avicennia marina, Avicennia alba, Rhizophora apiculata, and Rhizophora mucronata. The total carbon stock was 329.54 Mg.C.ha-1, which was composed by aboveground carbon (114.51 Mg.C.ha-1), belowground carbon (61.46 Mg.C.ha-1) and soil carbon (153.56 Mg.C.ha-1). The largest carbon pool was contributed from soils with 47% of the total carbon stock. The result of this study can be used as the initial information of the importance of the mangrove ecosystem in Bekasi District.

Prediction of land use/land cover and environmental estimation of carbon stocks in the Atlantic forest: A study in the state of Sergipe, Brazil

The forest ecosystems of the Atlantic Forest biome in Brazil are experiencing intense deforestation, contributing to climate change. Valuing the ecosystem service of CO2 sequestration can help in environmental policies to reduce deforestation and promote ecological restoration in this region. In this study, we sought to understand the dynamics of land use and land cover in the Atlantic Forest region of the state of Sergipe between 1985 and 2021 and to simulate land use and land cover for 2031 and 2051, assuming the current business as usual (BAU) trend was used as a reference scenario. The results showed that the agriculture-pasture mosaic class obtained the greatest percentage (5.27 %) and net (around 514.00 km²) increase among the land use and cover classes evaluated in the study area between 1985 and 2021. In the BAU scenario, total emissions reach 1,137,942.07 Mg CO2 in 2031 and 692,815.16 Mg CO2 in 2051, imposing an opportunity cost for society of US$ 7.94 million and US$ 4.35 million, respectively.

Advancing Carbon Stock Estimation and 3D Tree Modeling: Harnessing the Potential of Low-Cost Backpack LiDAR Technology

Abstract The maintenance of carbon stocks in the forest is fundamental to the global carbon cycle and is essential for informing climate change policy. Traditional methods for measuring carbon stocks have often been labor-intensive and expensive, involving measurements that include DBH, and tree height. However, LiDAR technology provides an efficient alternative for swiftly assessing forest vegetation structure. In this study, a low-cost backpack equipped with LiDAR technology was utilized to create three-dimensional tree models, enabling precise estimations of stem volume and carbon stock. By collecting data on DBH and vertical plant structure, the researchers could produce highly accurate three-dimensional models. The results indicated that the low-cost backpack LiDAR yielded DBH estimates very close to manual measurements, characterized by a linear regression equation of y = 1.0106x - 0.2911 and a coefficient of determination R2 = 0.99822. The results confirmed the high accuracy of DBH measurement using LiDAR, with RMSE in a value of 0.53 cm. This study unequivocally establishes that low-cost backpack LiDAR is an effective tool for measuring biomass and carbon stocks in forests, offering superior monitoring capabilities compared to conventional methods.