Estimates Of Carbon Stocks Research Articles

Use of Pedotransfer Equations (PTF) to Estimate soil Density in the Hydrographic Basins of The Amambai, Iguatemi and Ivinhema Rivers in the State of Mato Grosso do Sul

Theoretical framework: Bulk density (Bd) is a physical property of soil used as an indicator of soil compaction, influencing root growth and density, water movement and retention in the soil, it is an essential characteristic for assessments of carbon stocks or other nutrients in the soil. Objective: This studyevaluates the accuracy and selects pedotransfer functions (PTFs) for Bd estimates for soils in the Amambai, Iguatemi, and Ivinhema River basins in Mato Grosso do Sul - MS. Method: Estimates of Bd values of the samples were made using eleven PTFs-Bd that were calibrated to estimate Bd for Brazilian soils. The accuracy of PTFs-Bd predictions is evaluated based on the lowers root mean square error (RMSE) and the highers coefficient of determination (R²). Results and conclusion: Among the eleven analyzed PTFs-Bd, the most accurate ones were PTFs- Ds 1, 2, 3, and 4 by Huf dos Reis et al. (2024) and the one by Tomasella and Hodnett (1998). PTF-Bd 3, which uses fine and coarse sand and clay as predictors, showed the highest accuracy for the total dataset (88 samples). PTF-Bd 5 obtained better accuracy in estimating Bd in Latossolos Vermelhos, Argissolos Vermelho Amerelos, and Neossolos Quartzarênicos. Research implications: Identifying the best PTFs for estimating Bd for the study area's soils will facilitate other research as carbon stocks estimation and use of hydrophysical PTFs that use Bd as predictor. Originality/Value: PTFs for estimating Bd for soils in the state of MS have not yet been developed, highlighting the need to evaluate and indicate between existing PTFs the more accurate.

Carbon Stock Estimation From Vegetation Biomass Using Spot-7 Imagery

Vegetation absorbs carbon dioxide (CO2 ) emissions during photosynthesis. Covering more areas with trees will increase the CO2 absorption capacity more substantially than other vegetation like bushes, grasses, or rice fields. Trees convert the CO2 captured during photosynthesis into organic carbon to be stored in biomass. Woody trees account for approximately 60% of the total aboveground tree biomass, and trunks, where food reserves produced in photosynthesis are stored, have relatively large biomass compared to other parts of the tree. The biomass of a vegetation stand determines the optimization of air pollutant absorption in urban areas. Yogyakarta City is the center for tourism, education, and cultural activities in Indonesia, which is vulnerable to land-use conversion, a factor of the shrinking green space. This study aimed to estimate carbon stock from vegetation biomass in Yogyakarta City using the remote sensing product SPOT-7 imagery. To calculate the vegetation biomass, the diameter at breast height (DBH) of stands was measured in the field. Then, statistical analyses were performed to determine the correlation and regression between the actual or observed biomass and the Normalized Difference Vegetation Index (NDVI) value derived from the SPOT-7 image. The regression model used was y = 1.4277x – 0.0849. The total biomass produced in Yogyakarta City was estimated at 1,399,487.1 tonnes, which contained 643,764.1 tonnes of carbon stock.

Research on Estimating and Evaluating Subtropical Forest Carbon Stocks by Combining Multi-Payload High-Resolution Satellite Data

Forest carbon stock is an important indicator reflecting the structure of forest ecosystems and forest quality, and an important parameter for evaluating the carbon sequestration capacity and carbon balance of forests. It is of great significance to study forest carbon stock in the context of current global climate change. To explore the application ability of multi-loaded, high-resolution satellite data in the estimation of subtropical forest carbon stock, this paper takes Huangfu Mountain National Forest Park in Chuzhou City as the study area, extracts remote sensing features such as spectral features, texture features, backscattering coefficient, and other remote sensing features based on multi-loaded, high-resolution satellite data, and carries out correlation analyses with the carbon stock of different species of trees and different age groups of forests. Regression models for different tree species were established for different data sources, and the optimal modeling factors for multi-species were determined. Then, three algorithms, namely, multiple stepwise regression, random forest, and gradient-enhanced decision tree, were used to estimate carbon stocks of multi-species, and the predictive ability of different estimation models on carbon stocks was analyzed using the coefficient of determination (R2) and the root mean square error (RMSE) as indexes. The following conclusions were drawn: for the feature factors, the texture features of the GF-2 image, the new red edge index of the GF-6 image, the radar intensity coefficient sigma, and radar brightness coefficient beta of the GF-3 image have the best correlation with the carbon stock; for the algorithms, the random forest and gradient-boosting decision tree have the better effect of fitting and predicting the carbon stock of multi-tree species, among which gradient-boosting decision tree has the best effect, with an R2 of 0.902 and an RMSE of 10.261 t/ha. In summary, the combination of GF-2, GF-3, and GF-6 satellite data and gradient-boosting decision tree obtains the most accurate estimation results when estimating forest carbon stocks of complex tree species; multi-load, high-resolution satellite data can be used in the inversion of subtropical forest parameters to estimate the carbon stocks of subtropical forests. The multi-loaded, high-resolution satellite data have great potential for application in the field of subtropical forest parameter inversion.

Estimation of Carbon Stocks of Birch Forests on Abandoned Arable Lands in the Cis-Ural Using Unmanned Aerial Vehicle-Mounted LiDAR Camera

Currently, studies investigating the carbon balance in forest ecosystems are particularly relevant due to the global increase in CO2 content in the atmosphere. Due to natural reforestation over the past 25–30 years, birch (Betula pendula Roth.) forests were extensively grown and established on abandoned agricultural lands in Bashkir Cis-Ural (Republic of Bashkortostan, Russia). The significant positive aspect of reforestation on fallow lands is the carbon sequestration that takes place in the tree phytomass, especially at the growth stage of stand formation. The aim of this article is to test the approach of using a UAV-mounted LiDAR camera to estimate the phytomass and carbon stocks in different-aged birch forests growing on abandoned arable lands in Bashkir Cis-Ural. The methodology was developed using 28 sample plots, where the LiDAR survey was performed using a DJI Matrice 300 RTK UAV. Simultaneously, the stand characteristics and phytomass of stem wood were also estimated, using traditional methods in the field of forest science. The regression equations of phytomass dependence on stand characteristics at different stages of reforestation were constructed using data obtained from LiDAR imagery. It was shown that the above-ground tree biomass could be precisely estimated using the index obtained by multiplying the number of trees and their average height. A comparison of the data obtained using traditional and LiDAR survey methods found that the accuracy of the latter increased in conjunction with stand density. The accuracy of estimation ranged from 0.2 to 6.8% in birch forests aged 20 years and over. To calculate carbon stocks of the above-ground tree stands, the use of regional conversion coefficients is suggested, which could also be applied for the estimation of carbon content in trunk wood and leaves. An equation for the calculation of above-ground biomass carbon stocks of birch forests on abandoned arable lands is proposed.

Organic carbon stocks of Great British saltmarshes

Coastal wetlands, such as saltmarshes, are globally widespread and highly effective at capturing and storing ‘blue carbon’ and have the potential to regulate climate over varying timescales. Yet only Australia and the United States of America have national inventories of organic carbon held within saltmarsh habitats, hindering the development of policies and management strategies to protect and preserve these organic carbon stores. Here we couple a new observational dataset with 4,797 samples from 26 saltmarshes across Great Britain to spatially model organic carbon stored in the soil and the above and belowground biomass of Great British saltmarshes. Using average values derived from the 26 marshes, we deliver first-order estimates of organic carbon stocks across Great Britain’s 448 saltmarshes (451.66 km2). The saltmarshes of Great Britain contain 5.20 ± 0.65 Mt of organic carbon, 93% of which is in the soil. On average, the saltmarshes store 11.55 ± 1.56 kg C m-2 with values ranging between 2.24 kg C m-2 and 40.51 kg C m-2 depending on interlinked factors such as geomorphology, organic carbon source, sediment type (mud vs sand), sediment supply, and relative sea level history. These findings affirm that saltmarshes represent the largest intertidal blue carbon store in Great Britain, yet remain an unaccounted for component of the United Kingdom’s natural carbon stores.

Estimation of Carbon Stocks in Soils of Forest Ecosystems as a Basis for Monitoring Climatically Active Substances

The eluvozems and soddy eluvozems on two-layer deposits dominating in the soil cover of the Zvenigorod biostation of Moscow State University contain, on average, 65–83 t/ha of organic carbon in the litter and a meter-deep layer of mineral strata. Carbon stocks are minimal in the soddy eluvozem of the complex spruce forest (59–68 t/ha), which is characterized by a lighter granulometric composition, and reach 76–92 t/ha in the soils of the birch–spruce and complex pine–spruce forests. At the same time, 3.3–5.8 t C/ha or 4–9% of the total organic carbon reserves are concentrated in the litter, and 64–69% in the upper mineral layer (0–20 cm). Different levels and profile distribution of organic carbon reserves in soils are determined by lithological and granulometric features and the nature of vegetation. The contribution of water-extractable organic carbon compounds to their total content in the upper mineral horizons of soils does not exceed 1.3–1.8%, microbial carbon, 1.7–2.4%. In acidic light loamy soils, the enrichment in calcium and potassium, the cation exchange capacity, the content of exchangeable bases, and the degree of saturation can serve as indicators of the content and reserves of organic carbon at the ecosystem level. The relationship with the content of finely dispersed fractions and oxalate-extractable Al and Fe is manifested to a lesser extent due to the similar origin and properties of soils. The variability of organic carbon stocks in soils is determined to the greatest extent by its content, the influence of which decreases with depth. Accounting for spatial heterogeneity, field measurements of the density and proportion of fine earth, and correct analytical determinations are essential components of the assessment of carbon stocks in soils of forest ecosystems as part of a national monitoring system for carbon pools and greenhouse gas fluxes under development.

Estimation of Carbon Stocks in Soils of Forest Ecosystems as a Basis for Monitoring the Climatically Active Substances

—The eluvozems and soddy eluvozems on two-layered deposits dominating in the soil cover of the Zvenigorod Biostation of Moscow State University, contain, on average, 65–83 t/ha of organic carbon in the organic layer and the upper meter of mineral strata. Carbon stock is minimal (59–68 t/ha) in the coarser-textured soddy eluvozem of the spruce forest and reaches 76–92 t/ha in soils of birch–spruce and pine–spruce forests. Organic layers store 3.3–5.8 t C/ha or 4–9% of the total soil organic carbon stock; the upper mineral layer (0–20 cm) stores 64–69%. Different levels and profile distribution of organic carbon in soils are determined by lithological and textural features of the soil profiles and by the nature of vegetation. The contribution of water-extractable organic carbon to the total organic carbon content in the upper mineral horizons does not exceed 1.3–1.8%; the contribution of microbial carbon is 1.7–2.4%. In acidic loamy soils, the enrichment in calcium and potassium, the cation exchange capacity, the content of exchangeable bases, and the degree of base saturation can serve as indicators of the content and stocks of organic carbon at the ecosystem level. The relationship with the content of clay fractions and oxalate-extractable Al and Fe is manifested to a lesser extent due to the similar origin and properties of soils. The variability of organic carbon stocks in soils is largely determined by its content, the influence of which decreases with depth. Accounting for spatial heterogeneity, field measurements of the soil bulk density and proportion of fine earth, and correct analytical determinations are essential components of the assessment of carbon stocks in soils of forest ecosystems as a part of the national monitoring system for carbon pools and greenhouse gas fluxes.

Estimation of carbon stock at the mangrove forest of Sangkulirang District, East Kutai Regency, East Kalimantan Province

Mangrove forests provide environmental, economic, and social benefits such as fish breeding, lumber production, water purification, recreation, education, and carbon sequestration. Mangroves are important organic carbon sinks, and there is a lot of interest in using them to reduce greenhouse gas emissions. Future climate change and deforestation would have a negative impact on organic carbon storage potential, hence worldwide estimates of future change remain a major research subject. The carbon stock varies by location. The purpose of this study was to estimate the carbon stock of mangroves in Sangkulirang District, East Kutai, East Borneo. Mangrove sediment organic carbon, above-ground biomass, under-ground biomass, and necromass were the parameters studied. Carbon stock was conducted by surveying the mangrove species, diameter, and height on a plot of 20x20m for tree, 10x10m for pole, 5x5 m for sapling, and 2x2 m for seedling. The biomass and carbon stock estimation using an allometric equation. Necromass carbon was conducted by taking a necromas in 1x1m plot and weight them for wet and dry weight. The mangrove carbon estimation method of Indonesian National Standard SNI_7724-2011 was applied in this study. According to the research, the total average carbon in mangroves in Sangkulirang District is 1386,87 Mg C ha-1.

Correlation of Tree Health Productivity Indicators with Tree Carbon Stock Estimation (Case Study of Lampung Mangrove Center)

Currently, the Lampung Mangrove Center (LMC) forest is experiencing a decline in tree health productivity. This condition is caused by human activities and seawater abrasion, which has an impact on increasing greenhouse gases (GHG) and estimating carbon stocks. The aim of this study was to determine the correlation between tree health productivity and tree carbon stock estimation. The stages of the research carried out were: data collection on tree health productivity using the Forest Health Monitoring (FHM) technique; calculation of tree carbon stock estimation using the Allometric formula based on tree species; and correlation analysis using the Rank-Spearman method. Based on the results of the study, the value of productivity based on volume and the estimated value of carbon stock for Avicennia sp. of 0.38 m3 and 136.14 tons, and Rhizophora sp. of 0.21 m3 and 37.18 tons The correlation coefficient values obtained are: 0.704 (Avicennia sp.) and 0.843 (Rhizophora sp.). Thus, there is a strong and positive correlation between tree health productivity and tree carbon estimates. Therefore, it is necessary to pay attention to aspects of tree protection to maintain the function of mangroves as CO2 absorbers.

Accuracy of the Copernicus High-Resolution Layer Forest Type (HRL FTY) assessed with domestic NFI sampling plots in Poland

Abstract Over the past years, several remote sensing maps of land cover have been produced, but they still exhibit certain differences compared to the real land use that reduce their value for climate and carbon cycle modelling as well as for national estimates of forest carbon stocks and their change. This paper outlines a straightforward framework for evaluating map accuracy and estimating uncertainty in land cover area, specifically for forest-related land cover maps in Poland for the year 2018. The study compares stratified field-based data from the National Forest Inventory (NFI) with remote sensing data on forest variables, at the pixel level, in order to identify suitable methods for accuracy and area uncertainty estimation. Additionally, the paper introduces and presents a variety of accuracy metrics applicable to assess overall uncertainties in GHG inventories. The results indicate that the High-Resolution Layer Forest Type (HRL FTY) product (part of the broader Copernicus Land Monitoring Service [CLMS] portfolio), assessed using NFI field-based information, achieved an overall accuracy (OA) of 69.2%. This metric varies among particular nature protection forms, with the highest observed ones in Natura 2000 sites of 70.45%. The primary source of map errors was associated with distinguishing between broad-leaved and coniferous forest areas. Improving future maps necessitates more precise differentiation between species to better support national forest monitoring systems for the purpose of greenhouse gas (GHG) inventories where information on the spatial distribution and variability of forests sources, biodiversity assessment, threat prevention, estimation of carbon content is becoming an important part of the associated reporting system.

Modelling above-ground biomass estimation using combination of vegetation height and forest canopy density at secondary peat swamp forest of Kalimantan

Rapid estimation of carbon stock, particularly carbon from Above-Ground Biomass (AGB) in a high-potential carbon forest, is required to assess potential carbon emission projects. Satellite imagery has been widely used as the main data source to rapidly estimate AGB. Canopy Height (CH) data from NASA’s Global Ecosystem Dynamics Investigation (GEDI) and Forest Canopy Density (FCD) data from satellite Landsat 8 imagery are widely used to estimate AGB. An allometric model with multiple linear regression equations has been used to estimate the AGB value using both parameters of the FCD value and the CH value. 43 values of calculated AGB by non-destructive field survey located in a peat swamp forest area use as a training dataset to develop an allometric model. The CH and FCD indices performed several tests, and the test result met the requirements for developing allometric equations. The coefficient correlation (r) of CH and AGB from 43 sample plots is 0.546, indicating a moderate correlation. The coefficient correlation (r) for FCD and AGB is 0.267 which indicates a weak correlation. The validation test was carried out to assess AGB from the developed allometric model and AGB of field survey with a non-destructive method. The validation result of the allometric model has the value of coefficient correlation (r), RMSE, and R2, respectively 0.569; 22.235; and 0.324. In terms of allometric model validation, another forest with typical conditions, particularly basal area, the model’s AGB result, and the validated area have RMSE 46.506 and r value 0.664.

Estimation of Aboveground Carbon Stock in The Pertamina Hulu Rokan (PHR) Forest in Pinggir District, Bengkalis Regency, Riau Province

Extreme climate change affects human life. One of the causes of climate change is the increase of CO2 gas in the air. Forests can absorb CO2 from the air and store it in the form of tree biomass through the process of photosynthesis. One of the forests that can absorb and store carbon is Pertamina Hulu Rokan (PHR) forest, therefore it is necessary to conduct research on estimating the carbon content of PHR Forest area. The methods to be used in this research are non-destructive sampling and destructive sampling methods. The method with harvesting is only for seedlings, undergrowth and litter, while for saplings, poles, trees and necromass with the method without harvesting. Aboveground carbon storage in PHR forest was found to be 95.37 tonnes/ha. The largest carbon stocks were from trees (68.96 tonnes/ha), poles (13.06 tonnes/ha), necromas (5.70 tonnes/ha), litter (3.72 tonnes/ha), saplings (3.54 tonnes/ha) and the smallest carbon stocks were seedlings and understorey (0.39 tonnes/ha).

Estimation of aboveground biomass and carbon stocks of Quercus ilex L. saplings using UAV-derived RGB imagery

Key messageCrown area, sapling height, and biovolume extracted from UAV-acquired RGB images provided accurate estimates of aboveground biomass and carbon stocks in a 5-year-old holm oak (Quercus ilex L.) plantation. Our models regressing UAV-derived sapling variables against ground-based measurements exhibited high R2 values (0.78–0.89), thereby reflecting that RGB data can be used as an effective tool for measuring young individuals.ContextThe monitoring of tree sapling performance from the early stages of reforestation is of particular importance in the context of the global efforts to restore forests. Yet, most models to estimate carbon sequestration are developed for adult trees. Thus, the few models specifically developed for young trees rely on ground-based field sampling of tree growth parameters, which is time-consuming and difficult to implement at large spatial scales.AimsOur objectives were as follows: (1) to study the potential of UAV-based RGB imagery to detect and extract sapling variables (e.g., crown area, height, and biovolume) by comparing ground-based sapling measurements with UAV-derived data and (2) to compare the accuracy of the data estimated from RGB imagery with existing traditional field-based allometric equations.MethodsWe used a 5-year-old holm oak (Quercus ilex L. subsp. ballota (Desf.) Samp.) plantation (N = 617 plants), and their crown area, height, and biovolume were estimated from RGB imagery. Subsequently, the plants were harvested and the UAV-derived data were compared with field-measured sapling height and aboveground biomass values. Carbon content in leaves and stems was measured in a subsample of the saplings to estimate carbon stocks.ResultsThe models fitted with UAV-derived variables displayed high performance, with R2 values from 0.78 to 0.89 for height, leaf and stem biomass, total aboveground biomass, and carbon stocks. Moreover, aboveground biomass outputs calculated with field height and UAV-derived height using allometric equations exhibited R2 values from 0.65 to 0.68.ConclusionsGiven the affordable cost of RGB cameras and the versatility of drones, we suggest that UAV-based models may be a cost-effective method to estimate the biomass and carbon stocks of young plantations. However, further studies conducting drone flights in different conditions are needed to make this approach more scalable.

Blue Carbon in Mangroves of the Arid Zones of San Ignacio and El Dátil Lagoons, El Vizcaino Biosphere Reserve, Baja California, Mexico

Estimation of carbon (C) stocks revealed a very high carbon sequestration potential of mangroves, which play a major role in the global C cycle. The C stored in the biomass of live trees can be estimated from above- and below-ground measurements, i.e., tree diameter and height, leaf litter, root biomass, necromass, and soil. The allocation of biomass and C in the scrub mangrove forest is influenced by various factors, including low structural development. The objective of this study was to estimate the carbon stock (in relation to biomass) and storage in the soil of the San Ignacio and El Dátil lagoons in an arid region of the north Pacific. Above-ground biomass (AGB) was estimated based on mangrove structure and leaf litter; below-ground biomass (BGB) was measured by extracting root cores (45 cm depth) and soil (1.2 m depth). Biomass values were converted to carbon with allometric equations. We found an inverse relationship between BGB content (roots) and above-ground structural development, with a mean total biomass (AGB + BGB) of 101.7 MgC ha−1. Below-ground carbon content (roots, necromass, and soil) was 2.8 times higher than above-ground carbon content (trees and litter). Control sites (devoid of vegetation) adjacent to the mangrove have recorded low carbon stocks of 7.3 MgC ha−1, which supports the recommendations for conserving and restoring degraded areas. The present study contributes valuable information on carbon related to mangrove biomass and stored in the soil of arid mangrove areas of northwestern Mexico.

Biomass production, carbon stock and sequestration potential of prominent agroforestry systems in north-western Himalaya, India

Agroforestry is an age-old practise of integrating farming with practises, preferably on the same unit of land on sustainable basis. There is tremendous scope for Agroforestry because India has achieved self-sufficiency in food production. Now its attention is becoming more focused on the ecological problems and shortage of fuel, fodder and other outputs as well as unemployment. So, agroforestry land use systems offer a better option by serving as carbon sinks and contributing in attaining food safety, subsistence income, and habitat amelioration. The current study was conducted in this context in the south Kashmir area of India with the objective of recognising the capability of various agroforestry systems for biomass estimation and carbon sequestration assessment for mitigating climate variation. The present study entitled “Biomass production, carbon stock and sequestration potential of prominent agroforestry systems in North-western Himalaya, India” was carried out, during 2018–2019. The study site is located between 33° 72’N and 75° 14’E at an altitude ranging from 2,900 to 5,900 M (amsl). Multistage random sampling technique was used for selection of sampled tehsils, blocks, villages and farmers in Anantnag district. The first stage was the random sampling of six tehsils namely Anantnag, Shangus, Kokernag, Dooru, Sallar, and Larnu and blocks per tehsil namely, viz.; Anantnag, Shangus, Sagam, Shahabad, Pahalgam and Larnu. Then four villages within each block like Anantnag (Ruhoo, Dialgam, Wanihama and Donipawa), Shangus (Shangus, Nowgam, Kutheir and Andoora), Sagam (Hiller, Khalhar, Sagam and Bidder), Shahabad (Larkipora, Shankerpora, Nathipora and Bragam), Pahalgam (Yanner, Charigam, Movra and Wahdan) and Larnu (Leesu, Dandipora, Bidhard and Larnu). Ten farmers within each village were selected, thus making a total of 240 farmers, which accounts 25% sampling intensity. Four agroforestry systems including horti-agriculture, agrisilviculture, (boundary planting), agri-horti-silviculture and horti-silvipasture were chosen for the study as the experimental configuration. Maximum biomass was found in horti-silvipastoral contributing 66.55 t ha−1 followed by agri-horti-silviculture as 50.18 t ha−1, horti-agriculture as 36.22 t ha−1 and agri-silviculture as 34.87 t ha−1. The maximum mean biomass of fruit trees (30.54 t ha−1), forest trees (25.19 t ha−1), grass (7.18 t ha−1) during the first cut and 3.64 t ha−1 during the second cut, was calculated in horti-silvipasture system. The overall biomass output of diverse agroforestry systems goes along with the sequence: horti-silvipasture > agri-horti-silviculture > horti-agriculture > boundary plantation, respectively. As a result, estimates of carbon stocks and carbon sequestration for all systems showed the same trend as values for biomass since they are both directly related to the capacity for biomass generation of various plant components. By providing for rural livelihoods and serving as an efficient carbon sink through carbon sequestration, these agroforestry systems in the Himalayas can significantly increase total production and productivity. The results of the current study could have a big impact on how alternative agroforestry systems and tree combinations are chosen for efficient carbon budget management in delicate Himalayan ecosystems.

Distribution of biomass and carbon content in estimation of carbon density for typical forests

Accurate measurements of parameters related to carbon stock are crucial for evaluating the potential of regional forest ecosystems to sequester carbon and implement enhanced carbon sequestration. This study analysed the differences in biomass and carbon content (CC) among fourteen forest types and assessed the capacity of forest ecosystems to sequester carbon, using karst typical forest ecosystems in central Guizhou Province as an example. The results showed that: 1) the arbor layer was the dominant component of the forest biomass, accounting for more than 66 % of the total forest biomass; 2) the CC of branches was slightly higher than that of leaves and stems, and the CC of the carbon layer ranged from 37.79 % to 48.90 %, with the arbor layer having the highest average CC; 3) the vegetation CC of each forest type differed significantly from the default value in common research, and the adoption of the fixed values may result in errors in carbon stock estimation; 4) the forest ecosystem had an average carbon density of 187.78 t/hm2, and the forest vegetation maintained a higher level of carbon pooling than the soil. Moreover, the topsoil had a higher carbon density than the deep soil. This study makes important contributions to improving the accuracy of carbon stock estimates and maximizing the potential for carbon stock in karst regions.

Investigation of Uncertainty in Organic Carbon Stock Estimates on a Field Scale

The uncertainty sources in the assessment of organic carbon stocks were studied in a layer of 0–30 cm within the sampling site (100 × 100 m) set on soddy-podzolic cultivated soil (Albic Glossic Retisol (Aric, Loamic, Ochric)). In the experiment, two sampling methods were used – the classic sampling in pits, by 10-cm layers, and sampling with an auger to a depth of 0–30 cm. The soil bulk density was determined by the Kachinsky method and the carbon content was analyzed by the Tyurin method. Some samples were additionally analyzed at the Bryansk State Agrarian University. The uncertainties associated with natural variation, sample preparation and the analytical process proper were estimated. The analytical uncertainty of bulk density measurement did not depend on the sampling depth under the experimental conditions and amounted to about 6%. The analytical error of Tyurin’s method did not differ in two laboratories. Its contribution reached 5–9% of the total variation in soil organic carbon content at the test plot. The uncertainty of sample preparation ranged from 11 to 26%, natural variation—from 49 to 68% to the total variance, respectively. Determination of carbon content in the samples taken with an auger from the depth of 0–30 cm is preferable than layer-by-layer sampling as the number of intermediate operations is fewer, and the obtained results are comparable.

Allometric equations and carbon stocks assessment for Bambusa vulgaris Schrad. ex J.C.Wendl. in the bimodal rainfall forest of Cameroon

Over the last two decades, bamboo has received increasing attention owing to its socio-economic and environmental importance. Environmentally, bamboo plays an important role in carbon sequestration, thus enhancing climate change mitigation. In Cameroon, knowledge about the importance of Bambusa vulgaris Schrad. ex J.C.Wendl. to climate change mitigation is deficient, despite the fact that it is the most abundant bamboo species in Cameroon's Bimodal rainforest agroecological zone (Agroecological zone 5 - AEZ5). This study was initiated to develop allometric equations and estimate carbon stocks of B. vulgaris in Cameroon's AEZ5. The destructive, clump-based method was used for bamboo biomass data collection on 40 clumps and 86 culms. Regression analyses were performed to obtain allometric models for B. vulgaris biomass prediction which were used for B. vulgaris carbon stocks estimation in AEZ5. The best allometric model for culms was obtained when all predictive variables including age, diameter and height were considered into the model. For clump, the best model was obtained when the number of culms per clump, girth and average diameter were considered in the model. Model quality adjustment was better for clump aboveground biomass (AGB) compared to those of culm AGB. The model of B. vulgaris of the evergreen rainfall forest was validated with a bias of 45.5 %. Bamboo aboveground biomass proportions were 77 %, 15 % and 8 %, respectively for culms, branches and leaves. The mean density and carbon stocks of B. vulgaris were estimated at 2,0679 culms.ha−1, 257 clumps.ha−1, and 61.65 tC ha−1. B. vulgaris has a veritable carbon sequestration capacity which policymakers should consider in climate change mitigation strategies like those linked to payments for ecosystem services, voluntary carbon stocks market, Bonn Challenge, AFR100 initiative, and the Paris agreement ratified by the government of Cameroon.

LIDAR-Based Forest Biomass Remote Sensing: A Review of Metrics, Methods, and Assessment Criteria for the Selection of Allometric Equations

The increasing level of atmospheric carbon dioxide and its effects on our climate system has become a global environment issue. The forest ecosystem is essential for the stability of carbon in the atmosphere as it operates as a carbon sink and provides a habitat for numerous species. Therefore, our understanding of the structural elements of the forest ecosystem is vital for the estimation of forest biomass or terrestrial carbon stocks. Over the last two decades, light detection and ranging (LIDAR) technology has significantly revolutionized our understanding of forest structures and enhanced our ability to monitor forest biomass. This paper presents a review of metrics for forest biomass estimation, outlines metrics selection methods for biomass modeling, and addresses various assessment criteria for the selection of allometric equations for the aboveground forest biomass estimations, using LIDAR data. After examining one hundred publications written by different authors between 1999 and 2023, it was observed that LIDAR technology has become a dominant data collection tool for aboveground biomass estimation with most studies focusing on the use of airborne LIDAR data for the plot-level analysis on a local scale. Parametric-based models dominated in most studies with coefficient of determination (R2) and root mean square error (RMSE) as assessment criteria. In addition, mean top canopy height (MCH) and quadratic mean height (QMH) were reported as strong predictors for aboveground biomass (AGB) estimation. Pixel-based uncertainty analysis was found to be a reliable method for assessing spatial variations in uncertainties.

Ecological Assessment of the Role of Mangrove Trees in Carbon Sequestration and Biodiversity In Karimunjawa National Park Indonesia

Mangrove ecosystem has an important role in reducing carbon in the environment. There has been massive conversion of mangrove area into ponds and buildings in the current period. Therefore, the species diversity and carbon sequestration capacity of the mangrove ecosystem must be evaluated to monitor its function. This research aims to evaluate the species diversity and the sequestered carbon in the mangrove ecosystem of Karimunjawa National Park (KNP), Indonesia. The species analysis in the 3 research sites, 9 transects, and 27 plots (10 m × 10 m) that was obtained using the quadrat sampling method. Allometric equations, Shannon–Wiener, and evenness indices were used to estimate the standing biomass and carbon, species diversity, and distribution, respectively. The sediment samples were obtained at a depth of 100 cm and divided into three depths, namely, 0–33, 34–67, and 68–100 cm. The carbon content of mangrove sediments was analyzed in the laboratory using the Walkley–Black method. The results revealed that mangroves in the KNP have moderate diversity and even distribution. The estimated carbon in the mangrove stand was 146.22 t C ha–1 and the estimated carbon stock in the sediment was around 360.61 t C ha–1. Although the mangrove ecosystem in Karimunjawa National Park is still in a stable condition, it is necessary to monitor its changes due to the anthropogenic activities.