Estimates Of Carbon Stocks Research Articles

Species Diversity and Carbon Stock of Mangrove in Guiuan, Eastern Samar, Philippines

Mangrove forests sequester substantial amounts of atmospheric carbon dioxide, store carbon in their biomass, and have the highest carbon pool of any forest. The Philippines is one of the mangrove-rich countries, with almost 1.9% of the global mangroves. No current published works on mangroves exist in Guiuan, Eastern Samar, Philippines. This study utilized a non-destructive technique through quadrat sampling to identify and measure mangroves’ biodiversity and carbon stock. The allometric equation was applied to quantify the carbon stock and Shannon-Wiener diversity index to determine the biodiversity. Twelve species were identified from the 36 sampling plot with 10 × 10 meter. Results showed the mean species evenness of 0.28 and the Shannon-Wiener diversity value of 0.92, indicating very low diversity. The overall aboveground, belowground, total carbon stock, and CO2 sequestration of 209 t/ha, 76 t/ha, 157 t/ha, and 575 CO2 t/ha, respectively. The test revealed that comparing the six barangays regarding species diversity and carbon stocks gives no statistically significant difference of 0.032 and 0.046, respectively. Considering the estimated carbon stock and CO2 equivalent accounted from the tree biomass, the mangrove stands in Guiuan have the potential to store and sequester a large amount of carbon dioxide.

Economic Valuation of Sumber Semen Nature Tourism Park in Rembang Regency

The Sumber Semen Nature Tourism Park is the only conservation area in Rembang Regency. This area is designated as a protected area by two ministries mainly because this area is the largest water catchment area in Rembang Regency, which is part of the North Kendeng Karst Mountains. However, some people dos not realize the value of the Sumber Semen area resulting low management participation level. This study aims to identify the area potential, estimate the total economic value of the Sumber Semen area, and determine the level of community dependence. The exploration results showed that there were 124 species of flora, six species of birds, one species of mammal, two species of herpetofauna, and butterflies. Water is utilized for tourism activities, agricultural irrigation, and household necessities. The minimum estimated carbon stock is 468.31 tons/ha. The estimated total economic value is IDR 12,486,219,121/year. The highest economic value is obtained from agricultural activities and the fulfillment of household clean water. The analysis results of economic benefits in the income sharing from total income are included in the very low category (18%). Meanwhile, the income to household expenditures is included in the low category (25%).

Allometric model based estimation of biomass and carbon stock for individual and overlapping trees using terrestrial LiDAR

Allometric model based estimation of biomass and carbon stock for individual and overlapping trees using terrestrial LiDAR

Middle-aged forests in the Eastern U.S. have significant climate mitigation potential

Middle-aged forests in the Eastern U.S. are broadly defined to consist of forests between the regeneration phase following harvest or disturbance, and an older phase with forests composed of large trees, complex structure, and increasing natural mortality. Study objectives were to develop new carbon accumulation curves based on recent inventory data that represent the actual growth rates of forests as they advance beyond middle age; quantify the projected carbon sink from reducing or increasing harvest of middle-aged forests; and disseminate credible estimates of future carbon stocks in ecosystems and harvested wood products to support policies designed to enhance the role of forests in removing carbon dioxide from the atmosphere and storing it securely. We found that middle-aged Eastern U.S. forests could continue to accumulate carbon for many decades or several centuries in the absence of harvesting, with relatively low risk of natural disturbances. Compared with a recent study that estimated a potential increase in biomass of only 22%, and some analyses that anticipate significant increases in risks from natural disturbances, our results indicate a potential increase of about 100% over current biomass stocks by 2100. Temperate Continental forests have greater potential to increase carbon stock over longer time periods than Subtropical Humid forests. Results from scenario analyses showed that in the near term of 20–40 years, reducing harvest will yield the greatest reduction in net greenhouse gas emissions compared with business as usual. Under an extreme “no-harvest” scenario, C sequestration could increase by about 20 TgC yr−1 in Temperate Continental forests by 2050, and by about 30 TgC yr−1 in Subtropical Humid forests over the same time period. In contrast, a scenario of tripling harvest would increase C emissions by 30 and 60 TgC yr−1 in the Temperate Continental and Subtropical Humid ecozones by 2050, respectively. In the longer term of 80 or so years, all scenarios and “business as usual” converge toward similar impacts on greenhouse gas emissions, nearing neutrality in the Subtropical Humid ecozone but separated by about 30 TgC yr−1 in the Temperate Continental ecozone. Our estimated annual emission reduction from stopping harvest over the 73 million ha of middle-aged forests amounts to 117 Mg CO2 per year in 2050, equivalent to about 7% of the emissions from using fossil energy in the two ecozones. Increasing harvest would have the opposite effect of increasing emissions by a similar magnitude in 2050. How middle-aged forests in the Eastern U.S. are managed can clearly affect their contribution to achieving net-zero GHG emissions by 2050 and beyond.

Prediction of Soil Inorganic Carbon at Multiple Depths Using Quantile Regression Forest and Digital Soil Mapping Technique in the Thar Desert Regions of India

ABSTRACT Soil inorganic carbon (SIC) is important carbon reservoirs in desert ecosystems. However, little attention was paid to estimate carbon stock in these regions. In the present study, the distribution of SIC stock was investigated using digital soil mapping in Bikaner district, Rajasthan, India. A total of 187 soil profiles were used for SIC estimation by Quantile regression forest model. Landsat data, terrain attributes and bioclimatic variables were used as environmental variables. Ten-fold cross-validation was used to evaluate model. Equal-area quadratic splines were fitted to soil profile datasets to estimate SIC at six standard soil depths (0–5, 5–15, 15–30, 30–60, 60–100 and 100–200 cm). The SIC in the study area ranged from 0.27 to 27.85 g kg−1 in 0–5 cm and 0.31 to 27.84 g kg−1 in 5–15 cm, respectively. The model could capture reasonable variability (R2 = 11–21%) while predicting SIC for different depths. The Lin’s concordance correlation coefficient values ranged from 0.20 to 0.32, indicating poor relationship between the predicted and observed values. The values of prediction interval coverage probability (PICP) recorded 86.4–91.1% for SIC at different depths. Annual precipitation and precipitation seasonality were the most important covariates in soil below the 30 cm depth. The predicted SIC stocks were 10.3 ± 0.01, 81.6 ± 0.07 and 186.7 ± 0.13 Mg ha−1 at 0–15, 0–100 and 0–200 cm, depth, respectively. The uncertainty analysis suggests that there is room to improve the current spatial predictions of SIC. It is anticipated that this digital mapping of SIC will be useful for assessment of carbon cycle in arid regions.

Statistical Estimation of Soil Carbon Stocks in Chungcheong Province through Digital Soil Mapping and Multiple Linear Regression

Statistical Estimation of Soil Carbon Stocks in Chungcheong Province through Digital Soil Mapping and Multiple Linear Regression

Estimation of Carbon Stocks and Carbon Sequestration Rates in Abandoned Agricultural Soils of Northwest Russia

The fallow agricultural soils of Northwest Russia represent an evolutionary model of the development of ecosystem components in time and space with multidirectional dynamics of agrogenic impact during the long history of agricultural land development. There has been both large-scale land development and uncontrolled conversion of arable lands to a fallow state along with their removal in recent times. All this has led to the formation of a chrono-series of different-age soils with varying degrees of exposure of agrogenic factors. This paper presents a current review of the humus state of fallow soils in Northwest Russia, and examines the main factors (self-restoration, humus transformation, acidification) influencing the transformation of the soil cover under the process of post-agrogenesis. Effective farming techniques aimed at fixing carbon in soils as part of increasing the sequestration potential to mitigate the impact of climate change are considered. The ongoing process of the transition of lands into a fallow state could lead to organic carbon losses and changes in the main physical and chemical parameters, which negatively affects the self-restoration of fallow lands. We offer some recommendations for the effective rewetting of fallow lands in Northwest Russia with the purpose of carbon sequestration in the soil cover.

Urban carbon stock estimation based on deep learning and UAV remote sensing: a case study in Southern China

Urban carbon stock estimation based on deep learning and UAV remote sensing: a case study in Southern China

Deep-Learning-Based Point Cloud Semantic Segmentation: A Survey

With the rapid development of sensor technologies and the widespread use of laser scanning equipment, point clouds, as the main data form and an important information carrier for 3D scene analysis and understanding, play an essential role in the realization of national strategic needs, such as traffic scene perception, natural resource management, and forest biomass carbon stock estimation. As an important research direction in 3D computer vision, point cloud semantic segmentation has attracted more and more researchers’ attention. In this paper, we systematically outline the main research problems and related research methods in point cloud semantic segmentation and summarize the mainstream public datasets and common performance evaluation metrics. Point cloud semantic segmentation methods are classified into rule-based methods and point-based methods according to the representation of the input data. On this basis, the core ideas of each type of segmentation method are introduced, the representative and innovative algorithms of each type of method are elaborated, and the experimental results on the datasets are compared and analyzed. Finally, some promising research directions and potential tendencies are proposed.

Stok Karbon Organik Sedimen di Kawasan Ekosistem Mangrove Pesisir Kabupaten Kolaka Sulawesi Tenggara

Mangrove ecosystem can absorb and store carbon stock in their biomass and sediment. This ecosystem plays an important role in the carbon cycle. This study aimed to analyze the C-organic content and organic carbon stock of sediment, also CO2 absorption. The sediment sampling was conducted in the Kolaka Coastal line mangroves using a Peat Auger for 1 m of sediment depth. Then the sediment was separated into four layers. Then the sediment samples were oven dried at 50°C for 1 week for sediment bulk density analysis (SBD). The analysis of the C-organic content of sediment was done by the LOI method. The estimation of sediment carbon stock using sediment C-organic data and SBD data. The results showed that the highest average of C-organic content was in station 3 (4.12±0.15%). In the othher hand, the highest average of SBD was in station 1 (0.67±0.02 g cm-3). Overall, the total of sediment C-organic stock in station 1 was higher than other station (233.67±11.90 Mg C ha-1). The total capacity of CO2 absorption by sediment in the station 1 was also higher than other station (856.77±37.66 Mg CO2 ha-1). The sediment C-organic stock was influenced by C-organic content and SBD value.

Simulation of LUCC Dynamics and Estimation of Carbon Stock under Different SSP-RCP Scenarios in Heilongjiang Province

An accurate estimation of carbon stocks in terrestrial ecosystem and their future changes in relation to land use and land cover change (LUCC) is important for regional territorial spatial optimization and low-carbon development. In this paper, we integrated the System Dynamics (SD) model, PLUS model and InVEST model to simulate land use dynamics and corresponding carbon stocks in Heilongjiang Province from 2030 to 2050 under three SSP-RCP scenarios proposed by the CMIP6. The findings revealed significant variations in land use demand projections across different SSP-RCP scenarios, with increases observed in farmland, construction land and unused land while decreases in woodland and grassland, and the SSP585 scenario showed the highest increment or decrease. Under the SSP126 scenario, the expansion of farmland was due to a reduction in construction land, with little change observed in woodland and grassland, which resulted in a carbon stock increase of 102.71 × 106 Mg at the highest rate; conversely, under the SSP585 scenario, rapid expansion of farmland, construction land and unused land came at the expense of forest and grassland, leading to a significant carbon stock decrease of 204.64 × 106 Mg. The increase in farmland and the decrease in woodland under the SSP245 scenario was relatively moderate with little change observed in construction, resulting in a carbon stock increase of 108.10 × 106 Mg. Regardless of any scenario considered here, forests remain an important carbon sink contributing significantly to carbon sequestration as well as other ecosystem services in Heilongjiang Province. Enhancing territorial spatial planning and ecological environment construction, while promoting an eco-economic development model, will significantly contribute to the achievement of carbon neutrality and regional sustainable development goals.

Mapping peat thickness and carbon stock of a degraded peatland in West Sumatra, Indonesia

AbstractTropical peatlands store a large amount of carbon and act as carbon sinks, thus have crucial roles in regulating climate by preventing CO2 emission and enhancing carbon sequestration. However, the conversion of these peatlands for agricultural purposes, such as oil palm plantations, leads to carbon loss and increased CO2 emissions. To effectively protect and manage vulnerable areas, accurate mapping of peatland thickness and carbon stocks is essential, aligning with Indonesia's National Determined Contribution to climate action. This research article aims to assess the status of peatland in Agam‐West Pasaman, Western Sumatra, which was cleared between 1990 and 2000 for oil palm cultivation. Digital Soil Mapping (DSM) approaches were employed to map peat thickness, carbon stock and estimate carbon loss resulting from land use change. The study area, spanning 54,000 hectares, was covered by peat‐swamp forest in 1989. A grid of 2 km was used for a soil survey, resulting in 134 observation points of peat thickness, water level and subsidence. Various spatial prediction methods, including geostatistics, machine learning (ML) and their combination, were tested to map peat thickness, carbon stocks, subsidence rate and carbon loss. The covariates considered in the analysis were elevation, nearest distance from rivers and Sentinel 1a radar images. The results obtained through 10‐fold cross‐validations revealed that ordinary kriging exhibited the best performance, with an R2 of 0.44 for peat thickness and 0.39 for carbon stocks. The superior performance of ordinary kriging can be attributed to the severe impact of human activities in the area, which disrupted the clear relationship between peat parameters and environmental covariates. The estimated carbon stock of the area was 107 Mt C (std. dev. 0.143 Mt), while the carbon loss since the establishment of oil palm plantations was estimated to be 19.50 Mt C (std. dev. 0.017 Mt C) based on subsidence data. These findings provide insights into the degradation of the peatland and the magnitude of carbon loss over the past three decades. This information supports informed decision‐making and contributes to efforts aimed at preserving and restoring peatlands.

Small carbon stocks in sediments of Baltic Sea eelgrass meadows

Seagrass meadows act as an effective carbon sink and store carbon in the sediments for substantial periods of time. The drivers of carbon sequestration are complex, and global and regional estimates of carbon stocks have large uncertainties. Here, we report new carbon stock estimates from 14 sites along the Swedish coast and compile existing literature to estimate the magnitude of carbon stocks of Zostera marina (eelgrass) meadows in the Baltic Sea. Eelgrass meadows in the Baltic Sea have considerably lower carbon content and lower stocks (0.25 ± 0.21% DW, 635 ± 321 g C m-2) than in the Kattegat-Skagerrak region (3.25 ± 2.78% DW, 3457 ± 3382 g C m-2) and the average for temperate regions in general (1.4 ± 0.4% DW, 2721 ± 989 g C m-2). Unfavorable growing conditions for eelgrass in the Baltic Sea often lead to meadows occurring in areas of high hydrodynamics, preventing significant carbon accumulation. Stable isotopes revealed that the dominating source of organic carbon in the meadows was planktonic, further highlighting that Baltic Sea eelgrass meadows are not major carbon reservoirs in comparison to unvegetated sediments and other seagrass areas. The results also highlight that environmental conditions drive intraspecific variation of carbon sequestration on large spatial scales. Overall, the carbon stocks and sequestration potential in eelgrass meadows of the Baltic Sea are small compared to other temperate regions.

Using Functional Traits to Improve Estimates of Height–Diameter Allometry in a Temperate Mixed Forest

Accurate estimates of tree height (H) are critical for forest productivity and carbon stock assessments. Based on an extensive dataset, we developed a set of generalized mixed-effects height–DBH (H–D) models in a typical natural mixed forest in Northeastern China, adding species functional traits to the H–D base model. Functional traits encompass diverse leaf economic spectrum features as well as maximum tree height and wood density, which characterize the ability of a plant to acquire resources and resist external disturbances. Beyond this, we defined expanded variables at different levels and combined them to form a new model, which provided satisfactory estimates. The results show that functional traits can significantly affect the H–D ratio and improve estimations of allometric relationships. Generalized mixed-effects models with multilevel combinations of expanded variables could improve the prediction accuracy of tree height. There was an 82.42% improvement in the accuracy of carbon stock estimates for the studied zone using our model predictions. This study introduces commonly used functional traits into the H–D model, providing an important reference for forest growth and harvest models.

Mapping of peatlands in the forested landscape of Sweden using lidar-based terrain indices

Abstract. Globally, northern peatlands are major carbon deposits with important implications for the climate system. It is therefore crucial to understand their spatial occurrence, especially in the context of peatland degradation by land cover change and climate change. This study was aimed at mapping peatlands in the forested landscape of Sweden by modelling soil data against lidar-based terrain indices. Machine learning methods were used to produce nationwide raster maps at 10 m spatial resolution indicating the presence or not of peatlands. Four different definitions of peatlands were examined: 30, 40, 50 and 100 cm thickness of the organic horizon. Depending on peatland definition, testing with a hold-out dataset indicated an accuracy of 0.89–0.91 and Matthew's correlation coefficient of 0.79–0.81. The final maps showed a national forest peatland extent of 60 292–71 996 km2, estimates which are in the range of previous studies employing traditional soil maps. In conclusion, these results emphasize the possibilities of mapping boreal peatlands with lidar-based terrain indices. The final peatland maps are publicly available at https://doi.org/10.17043/rimondini-2023-peatlands-2 (Rimondini et al., 2023) and may be employed for spatial planning, estimating carbon stocks and evaluating climate change mitigation strategies.

Tree height-diameter, aboveground and belowground biomass allometries for two West African mangrove species

Accurate estimation of biomass and carbon stocks in mangrove forests is a prerequisite for a better understanding of their role in climate regulation. Allometric equations remain appropriate tools in this context but are lacking for many mangrove species and sites across Africa. In this study, we destructively sampled 68 individual trees of the two dominant West African mangrove species (Rhizophora racemosa and Avicennia germinans) to (i) determine species-specific height-diameter allometry; (ii) evaluate biomass allocation to stem, branches, leaves and roots components; (iii) establish species-specific aboveground-, belowground- and total biomass allometric equations; and (iv) examine the accuracy of our best biomass model against existing equations for mangroves. Diameter at breast height (Dbh), total height (H), wood density and crown diameter were used as predictors in the models.Results showed that Dbh explained 53% and 62% of height variation for R. racemosa and A. germinans, respectively. Stems stored the highest biomass fractions (84.30% for R. racemosa and 52.80% for A. germinans), followed by branches, while the belowground compartment contributed to 19%–22% of the total biomass. Among the candidate biomass models, the models incorporating Dbh and height as a compound variable (Dbh2H) were the most suitable for estimating aboveground and total biomass, with 87–92% of explained variance. For the root components, wood density and crown diameter were additionally found to improve model performance for R. racemosa and A. germinans, respectively. Our study revealed that biomass in West African mangrove forests was more accurately predicted using the established equations than with the existing models.

The power of integrating proximal and high-resolution remote sensing for mapping SOC stocks in agricultural peatlands

Background and aimsSoil electrical conductivity (ECa) data derived from electromagnetic induction (EMI) is valuable for estimating peat thickness and soil organic carbon stocks (SOCstocks). However, generating ECa maps locally using geostatistics limits the coverage area. This study explores the use of digital soil mapping (DSM) with random forest (RF) and universal kriging (UK) to create high-resolution ECa maps from field survey EMI data. The objective is to enhance the predictive accuracy of SOCstocks models in peatlands by incorporating these ECa maps as environmental variables.MethodsThree scenarios were evaluated, combining different environmental variables and modelling techniques for ECa mapping. Scenario 1 used spectral indices from RapidEye satellite data and RF. Scenario 2 included spectral indices and terrain derivatives from LiDAR, with RF. Scenario 3 integrated spectral indices, terrain derivatives from LiDAR, and UK. Afterwards, we evaluated the effectiveness of adding ECa maps as environmental variables for SOCstocks mapping. Finally, we incorporated ECa maps from scenario 2 and RF in three ways: (a) scenario 2 variables only, (b) ECa2 with scenario 2 variables, and (c) ECa3 with scenario 2 variables.ResultsScenarios 2 (ECa2) and 3 (ECa3) outperformed scenario 1 (ECa1). The inclusion of ECa maps significantly improved the accuracy of SOCstocks models.ConclusionOur study demonstrates that DSM, combined with RF and UK techniques, enables the generation of high-resolution ECa maps from field surveys in peatlands. Incorporating these ECa maps as environmental variables enhances the accuracy of SOCstocks mapping, providing valuable insights for peatland management and carbon stock estimation.Graphical abstract

Estimation of Carbon Stocks on Mangrove Forests at Pulau Kampai Using Destructive and Non-Destructive Methods

Mangrove forests are essential in maintaining the balance of coastal ecosystems and climate change mitigation. This study aimed to analyze carbon stocks in 2022 in natural mangrove forests at Pulau Kampai, Pangkalan Susu Sub District, Langkat District, North Sumatra Province, with an area of approximately 1,786.25 ha. Estimating carbon stocks in this study uses destructive and non-destructive methods using allometric equations from various aboveground and belowground studies. The sampling plots used a circle plot with a radius of 5.64 m. The sampling plot was determined by purposive sampling based on NDVI criteria (low, moderate, dense, high dense) with a sampling plot distance of 20 m from the edge of the coast or river. The destructive sample tree was taken from the dominant species which grew in the study area, namely Rhizophora apiculata, with a diameter of 7.1 cm with a total biomass yield of 91 kg consisting of aboveground biomass of 67.55 kg (74.23%) and belowground biomass was 23.45 kg (25.77 %). Meanwhile, the average C-organic content produced 56% aboveground and 56% belowground. The estimated carbon stocks using this study's conversion factor was 62.06 tons C/ha with a total carbon stock of 110,849.28 tons C. Meanwhile, the conversion factor using other studies was 48.20 tons C/ha with a complete carbon stock of 86,095.30 tons C.Keywords: Destructive, Non-destructive, Carbon Stocks, C-organic, and NDVI

Aboveground biomass and carbon stock estimation using UAV photogrammetry in Indonesian mangroves and other competing land uses

Mangrove ecosystem is one of coastal wetlands that have experienced significant anthropogenic driven degradation and conversion, which have resulted in substantial carbon releases to the atmosphere and the oceans. However, carbon stock assessments and monitoring for mangroves are mostly confined to in situ methodologies, which has limited further understanding of wider-scale spatial variations and additional driving factors of carbon loss. Here,we report a novel application using unmanned aerial vehicles (UAVs) to estimate and compare aboveground biomass (AGB) carbon stocks of mangrove forests with two alternate replacement commercial land uses (oil palm and coconut plantations) in Lubuk Kertang and Pulau Sembilan, North Sumatra, Indonesia. We generated high-accuracy digital surface models (DSMs) and digital terrain models (DTMs), as well as elevation data from a global navigation satellite system (GNSS). Through analysis, we highlight how mangrove forest conversion to oil palm and coconut plantations significantly decreases the aboveground biomass carbon (AGB) and carbon stocks (AGC) of selected mangrove ecosystems in Indonesia. In this study, spatially mapped biomass carbon measurements were found to be more accurate when cross-validated with in situ measurements. AGC stocks were found to be over 10- to 12-fold that of converted oil palm and coconut plantations across selected study sites. The generated biomass carbon stock product provides new knowledge of carbon stock distribution in Indonesia across mangrove forests and two alternative commercial land use types within similar topographical landscapes. This combined methodological approach may be a useful tool to assist with blue carbon inventories as well as for selecting specific mangrove conservation and restoration areas across larger spatial scales.

A novel method incorporating large rock fragments for improved soil bulk density and carbon stock estimation

AbstractSoil bulk density (BD) is a principal component in estimating the density of soil nutrients and elements including carbon (C). Current literature states that in soils with rock fragment (RF) content ≥3% of the total sample volume, substantial differences in estimated soil organic carbon density (SOCD) are found, depending on the soil BD calculation method chosen, potentially affecting the accuracy of soil nutrient and C inventories. In many soil surveys, soil BD is not measured directly, or the core method is used as the sole determinant of soil BD, potentially neglecting the soil volume dilution effect of RFs larger than the diameter of the cores used. This study uses the core and quantitative pit methods at 10 forest sites in Ireland to determine the BD and RF mass and volume to a depth of 40 cm. The authors examine how large RFs impact BD and subsequently affect the estimated SOCD values by comparing against reference values from established soil sampling and BD calculation methods. The analysis reveals significant variations in the estimated SOCD values when the RF volume in the soil sample exceeds 8% of the total sample volume. A novel method, hereafter named “core‐scaling,” combines core and pit sampling methods to account for large RF mass and volume in BD calculations. This study suggests that using the core‐scaling method provides results that are strongly correlated with the pit method, thus offering an alternative that can also provide accurate SOCD estimates in soils with a high RF content.