Estimates Of Carbon Stocks Research Articles

Carbon stocks estimation in the KGPAA Mangkunagoro I Forest Park at collection zone area

Abstract Mangkunagoro I Forest Park is a nature conservation area for plant collection purposes and preserving biodiversity. Moreover, Forest park as a forest also can absorb and store carbon as biomass. It makes forest park play a role in the mitigation of climate change. This research aimed to determine the amount of carbon stock in the Mangkunagoro I Forest Park collection zone. There are 35 plots in total with 20 x 20 m in size taken using systematic sampling. A non-destructive method with allometric equations was used to estimate total biomass and carbon stocks. In the Mangkunagoro I forest park collection zone, 11 species of trees and 3 species of bamboo were found with a total density of 79 trees/ha. Estimated biomass, carbon stocks, and carbon sequestration in the site were 304.24 ton/ha, 142 ton/ha, and 299.68 ton/ha, respectively.

New Zealand's planted forests–Carbon stocks and yield in fast growing exotic tree plantations of the Southern Hemisphere

Every year New Zealand measures 1/5th of the permanent sample plots of the National Planted Forest Inventory administered by the Ministry for the Environment (MFE). This means that all plots are measured once during a five-year cycle to provide the full inventory, while each measurement year in its own right provides a random nationally-representative sample of New Zealand's plantation forests.Land-use change and planted forest area are highly dynamic in New Zealand compared to forests in many northern hemisphere countries and every year new plots are added to the inventory due to new afforestation areas. Over time, old plot data representing stands that have been recently harvested will be replaced with measurements of the new forests growing on these sites or will drop out altogether due to improvements in mapping or deforestation. This means that estimates from a single year's plot measurements can give different results from the previous year due to the changes that the plantation forest estate experiences from year to year.In this paper we analyse results for the latest measurement cycle (Panels 2016 - 2020) and identify any issues or trends in carbon stocks. We use all panel data plus data from previous periodic forest inventories to generate a fully representative yield table for all plantation forests in the sub-categories of Kyoto Protocol-compliant afforestation and other planted forests (“non-Kyoto”). Furthermore, we use an imputation approach to provide a statistically accurate representation of stock changes over time. The yield tables generated are used in MFE's Land Use Carbon Analysis System (LUCAS) to generate estimates of carbon stocks and stock changes for international reporting.The estimated mean carbon sequestration rate over the typical rotation for New Zealand's dominant plantation species is higher than the default range provided by the IPCC (Intergovernmental Panel on Climate Change). Predicted carbon sequestration in non-Kyoto planted forests (10.5 tC ha−1 yr−1) exceeds the estimated carbon sequestration in Kyoto-compliant planted forests (9.8 tC ha−1 yr−1) with both at the upper range of estimates for temperate plantation forests (IPCC). Current mean carbon stocks are significantly larger in Kyoto-compliant plantation forests (169 tC ha−1) than in non-Kyoto planted forests (108 tC ha−1) across New Zealand, largely because they are on average five years older. Estimated Mean annual increment, m3 ha−1 yr−1 expressed as a comparable index (“300Index”) indicates that Kyoto-compliant forest land has slightly higher productivity than non-Kyoto planted forests, but because the latter are composed of younger stands, greater carbon sequestration rates but lower current stocks are estimated. Plots with slower growing tree species such as Douglas-fir, cypresses and coastal redwood are now an increasing part of the Kyoto-compliant estate, reducing the overall sequestration rates for this forest type.

Pattern of plant biomass and carbon stock along different elevational forests in eastern Nepal

The primary aim of this investigation was to determine the biomass and carbon stock distribution pattern among different forest stands of diverse elevations in the Morang district of East Nepal. It is noteworthy to estimate carbon stock and biomass of relatively least underexplored forests in east Nepal. The data for estimating the biomass and carbon stocks of the five different forest sites, viz. Bhaunne, Raja-Rani, Murchungi, Adheri, and Sagma located between 100-1300m above the mean sea level, were acquired through the measurement of inventory plots selected randomly. Altogether, 50 sample plots were established within five forest stands located on different elevational zone; within each forest site, 10 sample plots of 20m × 20m size, were laid out for the measurement of trees. In the case of shrubs and herbs, nested plots of 5m × 5m and 1m ×1m, respectively were established. Calculation of the biomass of trees and shrubs was facilitated through the application of an allometric equation, while the biomass of herbs was determined by the harvest method. The carbon concentration in the plant materials was estimated using ash content method. The comprehensive analysis of the stand biomass in the Bhaunne, Raja-Rani, Murchungi, Adheri, and Sagma forest sites were: 815.86 Mg ha-1, 414.19 Mg ha-1, 606.81 Mg ha-1, 519.20 Mg ha-1, and 299.96 Mg ha-1, respectively, with minimum at the Sagma site (high-altitude forest) and maximum at the Bhaunne site (low-altitude forest). As per the variation in stand biomass, the carbon stocks in the forest sites also showed the same trend, but the values ranged from 140.19 Mg C ha-1 to 333.63 Mg C ha-1, with the minimum in the Sagma site and the maximum in the Bhaunne site. The application of the Friedman Test revealed statistically significant variation in the tree biomass between the Murchungi and Sagma sites and also in the shrub biomass between the Adheri and Sagma sites. Similarly, noteworthy variations were observed in the herb biomass of the Bhaunne, Raja-Rani, Murchungi, and Adheri sites as compared to that of the Sagma site. The present study contributes to the understanding of forest ecosystems in context to carbon management.

On the detailed mapping of peat (raised bogs) using airborne radiometric data

This study concerns the applied use of the natural radioactivity in soils. The relevance of airborne radiometric (gamma ray) survey data to peat mapping is now well established and such data have been used in a stand-alone sense and as covariates in machine learning algorithms. Here we present a method to use these data to accurately map the boundaries of peat (raised bogs). This has the potential to assist with the estimation of carbon stocks using a property-based assessment of soil. The significance of such regionally-uniform survey data lies in the subsurface information carried by the measurement which contrasts with the surficial nature of many other covariates. Soils attenuate radiometric flux by virtue of their bulk density (and associated carbon content) and water saturation level. The high attenuation levels in low density, wet peat materials give rise to a distinctive soil response. Here an entirely physics-based assessment of flux attenuation is carried out both theoretically and empirically. Radiometric data from the ongoing Tellus airborne survey of Ireland are used. The study area is characterised by an extensive assemblage of discrete raised peat bogs in a framework of largely mineral soils. Peat is detected by a property contrast with adjacent soils and so we consider all soils within the study area. The relatively low lateral resolution of the airborne data is demonstrated by modelling and we examine the behaviour of a combined spatial derivative of the data. The procedure allows the identification of the edges of the 128 peat polygons considered and indicates other additional potential areas of subsurface peat. The data appear to resolve the differences that exist across three available soil/peat databases that are used for the validation of the results obtained.

Research on Estimation Model of Carbon Stock Based on Airborne LiDAR and Feature Screening

The rapid and accurate estimation of forest carbon stock is important for analyzing the carbon cycle. In order to obtain forest carbon stock efficiently, this paper utilizes airborne LiDAR data to research the applicability of different feature screening methods in combination with machine learning in the carbon stock estimation model. First, Spearman’s Correlation Coefficient (SCC) and Extreme Gradient Boosting tree (XGBoost) were used to screen out the variables that were extracted via Airborne LiDAR with a higher correlation with carbon stock. Then, Bagging, K-nearest neighbor (KNN), and Random Forest (RF) were used to construct the carbon stock estimation model. The results show that the height statistical variable is more strongly correlated with carbon stocks than the density statistical variables are. RF is more suitable for the construction of the carbon stock estimation model compared to the instance-based KNN algorithm. Furthermore, the combination of the XGBoost algorithm and the RF algorithm performs best, with an R2 of 0.85 and an MSE of 10.74 on the training set and an R2 of 0.53 and an MSE of 21.81 on the testing set. This study demonstrates the effectiveness of statistical feature screening methods and Random Forest for carbon stock estimation model construction. The XGBoost algorithm has a wider applicability for feature screening.

Modeling Forest Above-Ground Biomass of Teak (Tectona grandis L. F.) Using Field Measurement and Sentinel-2 Imagery

Over the last few decades, remote sensing has emerged as a dependable and cost-effective method for collecting precise data on forest biophysical parameters, aiding in sustainable forest management and global initiatives to combat climate change. This research aimed to develop a model for estimating the above-ground biomass (AGB) of Teak (Tectona grandis L. F.) by combining field measurements with Sentinel-2 earth observation data. The study took place in 36-year-old teak plantation areas within the Sagarnath Forest Development Project in Nepal’s Sarlahi district. Field measurements were conducted using a destructive systematic sampling method, employing 10 × 10 m2 sample plots, and the volume of logs was determined using Newton’s formula. A total of 30 sample plots were used for calibration, while 10 were utilized for validation purposes. The findings revealed that the average AGB per plot was 814 kg (equivalent to 81.4 t ha−1), with a minimum value of 716 kg (71.6 t ha−1) and a maximum value of 1,060 kg (106 t ha−1). The study utilized five independent variables, namely, the Red band, Green band, Blue band, near-infrared (NIR), and normalized difference vegetation index (NDVI) values from Sentinel-2 imagery data, to develop estimation models. Among the 12 models examined, model M10 proved to be the best fit for accurate AGB estimation (adjusted R2 = 0.9809, RMSE = 0.01269, AIC = −170, and p-value = < 8.39e−21). The equation of the best-fitted model was ln (AGB) = A + B × Red + C × Green + D × Blue2 + E × ln (NIR) + F × ln (NDVI), providing an accurate estimate of AGB. Model validation involved a t-test comparing the observed and calculated AGB values for ten sample plots, demonstrating no significant difference (p-value = 0.3662 > 0.05). This model has the potential to facilitate AGB biomass calculations and carbon stock estimates for teak plantations of similar age groups.

Global carbon balance of the forest: satellite-based L-VOD results over the last decade

Monitoring forest carbon (C) stocks is essential to better assess their role in the global carbon balance, and to better model and predict long-term trends and inter-annual variability in atmospheric CO2 concentrations. On a national scale, national forest inventories (NFIs) can provide estimates of forest carbon stocks, but these estimates are only available in certain countries, are limited by time lags due to periodic revisits, and cannot provide spatially continuous mapping of forests. In this context, remote sensing offers many advantages for monitoring above-ground biomass (AGB) on a global scale with good spatial (50–100 m) and temporal (annual) resolutions. Remote sensing has been used for several decades to monitor vegetation. However, traditional methods of monitoring AGB using optical or microwave sensors are affected by saturation effects for moderately or densely vegetated canopies, limiting their performance. Low-frequency passive microwave remote sensing is less affected by these saturation effects: saturation only occurs at AGB levels of around 400 t/ha at L-band (frequency of around 1.4 GHz). Despite its coarse spatial resolution of the order of 25 km × 25 km, this method based on the L-VOD (vegetation optical depth at L-band) index has recently established itself as an essential approach for monitoring annual variations in forest AGB on a continental scale. Thus, L-VOD has been applied to forest monitoring in many continents and biomes: in the tropics (especially in the Amazon and Congo basins), in boreal regions (Siberia, Canada), in Europe, China, Australia, etc. However, no reference study has yet been published to analyze L-VOD in detail in terms of capabilities, validation and results. This paper fills this gap by presenting the physical principles of L-VOD calculation, analyzing the performance of L-VOD for monitoring AGB and reviewing the main applications of L-VOD for tracking the carbon balance of global vegetation over the last decade (2010–2019).

Carbon pools in forest systems and new estimation based on an investigation of carbon sequestration

Forests, the ancient wooden giants, are both symbols of natural beauty and reservoirs of carbon stocks. The current climate crisis has created an urgent need for an in-depth study of forest ecosystems and carbon stocks. Based on forest inventory data from field surveys and four bioclimatic zones [Zone 1 (Z1, humid forest), Zone 2 (Z2, semi-humid forest), Zone 3 (Z3, semi-humid to semi-arid forest-grassland), and Zone 4 (Z4, semi-arid typical grassland)], two methods [Method 1 (M1) and Method 2 (M2)] were used to estimate carbon stocks in forest ecosystems in Shaanxi Province, China, and explored the spatial patterns of carbon pools and potential influences. The total forest ecosystem carbon pool amounted to 520.80 Tg C, of which 53.60% was stored aboveground, 17.16% belowground, and 29.24% in soil (depth of 0–10 cm). Spatially, there were marked north-south gradients in both biomass (Z2 > Z3 > Z1 > Z4) and soil organic carbon densities (Z1 > Z2 > Z3 > Z4). The differences between aboveground and belowground biomass carbon density across broadleaf, needle-leaf, and broadleaf and needle-leaf mixed forest were not pronounced, while soil organic carbon density had the order of broadleaf (18.38 Mg C/ha) > needle-leaf (11.29 Mg C/ha) > broadleaf and needle-leaf mixed forest (10.33 Mg C/ha). Under an ideal scenario that excludes external factors, mainly forest growth, the sequestration potential of forest biomass by 2032 was estimated by M1 as 85.43 Tg, and by M2 to be substantially higher at 176.21 Tg. As of 2062, M1 estimated 155.97 Tg of sequestration potential for forest biomass. The spatial patterns of forest biomass and soil carbon density were closely related to climatic factors, and these relationships allowed the spatial division into two distinct climatic regions. Moreover, biomass carbon density was significantly correlated with the normalized difference vegetation index, soil silt, and elevation. This study provides key information for promoting the strategic shift from light-green to deep-green forest systems in Shannxi Province and updates the estimation methods of forest ecosystems’ carbon pools based on field surveys.

Empirical Analysis of a Super-SBM-Based Framework for Wetland Carbon Stock Safety Assessment

With climate change and urbanization expansion, wetlands, which are some of the largest carbon stocks in the world, are facing threats such as shrinking areas and declining carbon sequestration capacities. Wetland carbon stocks are at risk of being transformed into carbon sources, especially those of wetlands with strong land use–natural resource conservation conflict. Moreover, there is a lack of well-established indicators for evaluating the health of wetland carbon stocks. To address this issue, we proposed a novel framework for the safety assessment of wetland carbon stocks using the Super Slack-Based Measure (Super-SBM), and we then conducted an empirical study on the Quanzhou Bay Estuary Wetland (QBEW). This framework integrates the unexpected output indicator (i.e., carbon emissions), the expected output indicators, including the GDP per capita and carbon stock estimates calculated via machine learning (ML)-based remote sensing inversion, and the input indicators, such as environmental governance investigations, climate conditions, socio-economic activities, and resource utilization. The results show that the annual average safety assessment for carbon pools in the QBEW was a meager 0.29 in 2015, signaling a very poor state, likely due to inadequate inputs or excessive unexpected outputs. However, there has been a substantial improvement since then, as evidenced by the fact that all the safety assessments have exceeded the threshold of 1 from 2018 onwards, reflecting a transition to a “weakly effective” status within a safe and acceptable range. Moreover, our investigation employing the Super-SBM model to calculate the “slack variables” yielded valuable insights into optimization strategies. This research advances the field by establishing a safety measurement framework for wetland carbon pools that leverages efficiency assessment methods, thereby offering a quantitative safeguard mechanism that supports the achievement of the “3060” dual-carbon target.

Ecosystem type affects how Amazonian tree species invest in stem and twig wood

Abstract Wood density (WD) is a key functional trait for its importance in tree performance and in biomass calculations of forests. Yet, the variation of WD among different woody tree parts, how this varies across ecosystems, and how this influences estimates of forest carbon stocks remains little understood, particularly for diverse tropical forests such as the Amazon. We assembled a dataset on stem and twig wood density from 119 tree species in three different Amazonian ecosystem types that differ considerably in soil nutrition and flooding: non‐flooded forest (Terra Firme), white‐water floodplain forest (Várzea) and black‐water floodplain forest (Igapó) to investigate (i) variation of stem and twig wood density across ecosystems, (ii) the relationships between stem and twig wood density and how these relationships vary across ecosystems. Wood density varied substantially across ecosystems. Várzea species showed lower mean WD for stems compared to Terra firme, while Igapó species showed higher WD for twigs compared to the other ecosystems. Twig and stem wood density were positively related (R2adj = 0.47) with similarly increasing rates across ecosystems, although average WD values differed between Terra firme and Igapó. For any given twig density, stem density tends to be lower in floodplain environments but higher in Terra firme, a habitat‐specific pattern of wood density variation within trees that may emerge from differences in the function of stem and twig wood for growth and survival in ecologically differentiated environments. Our results show how ecosystem has strong impacts on how trees allocate resources to different woody tissues, suggesting contrasting ecological strategies linked to ecosystem constraints. Our results suggest that greater consideration of the variation of WD within trees and how these changes across ecosystems might lead to more accurate estimates of above‐ground biomass in Amazonia. Read the free Plain Language Summary for this article on the Journal blog.

Carbon stock increase during post-agricultural succession in central France: no change of the superficial soil stock and high variability within forest stages

Forest development following agricultural abandonment concerns extensive areas including the Massif Central region of France where this study was undertaken. This land-use and land-cover change is expected to have effects on biodiversity and ecosystem services, including an increase of carbon sequestration—a major concern in the face of climate change. Nevertheless, uncertainties about carbon stock changes during successions are remaining, especially as to the total stock and the contribution of the different carbon pools. Our work contributes to this field by studying carbon stocks in multiple plots of different successional stages. We measured and estimated carbon stocks in aboveground and belowground vegetation, deadwood, litter and superficial soil, and surveyed plant communities and plot conditions (slope, aspect, soil characteristics). The average total carbon stock increased along the succession from 70.60 at stage 0 to 314.19 tC ha−1 at stage 5. However, the total carbon stocks at the young forest stage (abandoned for 74 years maximum) and the older forest stage (forested for at least 74 years) were not significantly different, and probably reflected strong local heterogeneity in the older forest stage. An increase of the carbon stock was found in all pools, except the soil pool that did not vary significantly between the successional stages. The aboveground carbon stock was found strongly related to the woody species cover, especially the macrophanerophyte cover. This case study supports the view that the succession dynamics of former agricultural plots participates in carbon sequestration, sometimes with great local variations.

Urban trees' potential for regulatory services in the urban environment: an exploration of carbon sequestration.

Urbanisation has emerged as a formidable challenge for urban policymakers, reaching unparalleled heights and unsettling the ecological equilibrium of the cities. Urban areas now grapple with many issues encompassing climate change, resource depletion, population surges and increased pollution levels. Many planned cities have planted trees and other vegetation within the urban sectors to enhance air quality, mitigate climate effects and provide valuable ecosystem services. This study assessed tree species diversity and their potential for carbon sequestration in Panjab University Campus, Chandigarh. We established 188 plots, each comprising randomly selected quadrats measuring 10 m × 10 m, encompassing areas with varying levels of vegetation, ranging from low to moderate and high density. We used four different allometric equations to estimate tree biomass and carbon stock. Our findings revealed that 92 tree species belong to 72 genera and 35 families, with a total tree density of 975 ha-1. The total CO2 sequestration in form of carbon stock was 18,769.46 Mg C ha-1, with Manilkara hexandra (1239.20 Mg C ha-1), Ficus benghalensis (1072.24 Mg C ha-1), Kigelia pinnata (989.89 Mg C ha-1) and Lagerstroemia floribunda (716.88 Mg C ha-1) being the top contributors. Specifically, the equation of Chave et al. (2005) without tree height yielded the highest biomass and carbon stock estimates than other equations. The present study underscores the vital role of trees on the campus as potent carbon reservoirs meet to maintain an aesthetic sense for biotic components and alleviate rising levels of CO2 in the atmospheric environment. By emphasising the role of urban trees as potent carbon reservoirs, the study underscores the importance of integrating green infrastructure into urban planning strategies. Furthermore, it offers valuable guidance for urban planners. It suggests that strategic tree planting and maintenance can enhance green spaces, regulate temperatures and ultimately support regional and global climate change mitigation goals. Incorporating these findings into urban planning processes can aid policymakers in developing resilient, ecologically sustainable cities worldwide.

Communities Interaction with Sumatran Tiger (Panthera tigris sumatrae) in the Buffer Zone of Batang Gadis National Park

Bukit Barisan Grand Forest Park Forest is a forest area formed from forests with protected forest status. In this forest area there is a main road connecting the cities of Medan and Berastagi, which is a tourist destination for people in North Sumatra. Therefore this area is very unique to study but is also vulnerable to logging disturbances, etc. Apart from that, tourist activities also put pressure on this area, especially during holiday periods. The aim of this research is to determine the composition of stands and the carbon content stored in Dolay Rayat Village, Karo Regency. The method used is a combination of stripes and plotted lines where the plots are placed using systematic sampling with random start. Meanwhile, allometric equations are used to estimate carbon stocks. The research used 50 sample plots (2 Ha) arranged in 10 transect lines. The results of the vegetation analysis that has been carried out show that there are 29 types of standing vegetation. The dominant species found at the seedling level are Eurya accuminata (IVI: 30.05), saplings are Altingia excelsa (IVI: 34.44), poles are Lithocarpus edulis (IVI: 44.61) and trees are Lithocarpus edulis (IVI: 30.82). This forest has moderate species diversity with an H’ value ranging from 2.41-2.76. Carbon reserves stored in forest areas in the administrative area of Dolat Rayat Village are quite high, namely 14,639.04 tons.

Forest composition and carbon reserves in Bukit Barisan Forest Park, Dolat Rayat Village, Karo District

Bukit Barisan Grand Forest Park Forest is a forest area formed from forests with protected forest status. In this forest area there is a main road connecting the cities of Medan and Berastagi, which is a tourist destination for people in North Sumatra. Therefore this area is very unique to study but is also vulnerable to logging disturbances, etc. Apart from that, tourist activities also put pressure on this area, especially during holiday periods. The aim of this research is to determine the composition of stands and the carbon content stored in Dolay Rayat Village, Karo Regency. The method used is a combination of stripes and plotted lines where the plots are placed using systematic sampling with random start. Meanwhile, allometric equations are used to estimate carbon stocks. The research used 50 sample plots (2 Ha) arranged in 10 transect lines. The results of the vegetation analysis that has been carried out show that there are 29 types of standing vegetation. The dominant species found at the seedling level are Eurya accuminata (IVI: 30.05), saplings are Altingia excelsa (IVI: 34.44), poles are Lithocarpus edulis (IVI: 44.61) and trees are Lithocarpus edulis (IVI: 30.82). This forest has moderate species diversity with an H’ value ranging from 2.41-2.76. Carbon reserves stored in forest areas in the administrative area of Dolat Rayat Village are quite high, namely 14,639.04 tons.

Wood Basic Density in Large Trees: Impacts on Biomass Estimates in the Southwestern Brazilian Amazon

Wood basic density (WD) plays a crucial role in estimating forest biomass; moreover, improving wood-density estimates is needed to reduce uncertainties in the estimates of tropical forest biomass and carbon stocks. Understanding variations in this density along the tree trunk and its impact on biomass estimates is underexplored in the literature. In this study, the vertical variability of WD was assessed along the stems of large trees that had a diameter at breast height (DBH) ≥ 50 cm from a dense ombrophilous forest on terra firme (unflooded uplands) in Acre, Brazil. A total of 224 trees were sampled, including 20 species, classified by wood type. The average WD along the stem was determined by the ratio of oven-dry mass to saturated volume. Five models were tested, including linear and nonlinear ones, to fit equations for WD, selecting the best model. The variation among species was notable, ranging from 0.288 g cm−3 (Ceiba pentandra, L., Gaertn) to 0.825 g cm−3 (Handroanthus serratifolius, Vahl., S. Grose), with an average of 0.560 g cm−3 (±0.164, standard deviation). Significant variation was observed among individuals, such as in Schizolobium parahyba var. amazonicum (H. ex D.), which ranged from 0.305 to 0.655 g cm−3. WD was classified as low (≤0.40 g cm−3), medium (0.41–0.60 g cm−3), and high (≥0.61 g cm−3). The variability in WD along the stem differs by wood type. In trees with low-density wood, density shows irregular variation but tends to increase along the stem, whereas it decreases in species with medium- and high-density wood. The variation in WD along the stem can lead to underestimations or overestimations, not only in individual trees and species but also in total stocks when estimating forest biomass. Not considering this systematic bias results in significant errors, especially in extrapolations to vast areas, such as the Amazon.

Remote Sensing Estimation of Forest Carbon Stock Based on Machine Learning Algorithms

Improving the precision of remote sensing estimation and implementing the fusion and analysis of multi-source data are crucial for accurately estimating the aboveground carbon storage in forests. Using the Google Earth Engine (GEE) platform in conjunction with national forest resource inventory data and Landsat 8 multispectral remote sensing imagery, this research applies four machine learning algorithms available on the GEE platform: Random Forest (RF), Classification and Regression Trees (CART), Gradient Boosting Trees (GBT), and Support Vector Machine (SVM). Using these algorithms, the entire Yunnan Province is classified into seven categories, including broadleaf forest, coniferous forest, mixed broadleaf-coniferous forest, water bodies, built-up areas, cultivated land, and other types. After a thorough comparison, the research reveals that the RF algorithm surpasses others in terms of accuracy and reliability, making it the most suitable choice for estimating aboveground carbon storage in forests using remote sensing data. Therefore, the study used the RF algorithm for both forest classification and the estimation of carbon storage. By extracting remote sensing factors; by using the Pearson correlation coefficient to select the most relevant factors; and by utilizing multiple linear regression, RF regression, and decision tree regression, a model for estimating aboveground carbon stocks in forests was developed. The results indicate that among the four classification algorithms, the RF classifier demonstrates superior performance, with an overall accuracy of 84.96% and a Kappa coefficient of 76.46%. In the RF regression models, the R2 values for the coniferous forest, broadleaf forest, and mixed needle-broadleaf forest models are 0.636, 0.663, and 0.638, respectively. In both RF and CART, the R2 values for the three forest-type models are greater than 0.6, indicating satisfactory model fitting performance. This study aims to explore the possibility of improving the estimation of forest carbon stocks in large-scale areas through fine land use classification. Additionally, the data sources used are completely free, and medium to low resolution can provide a better reference value for practical applications, thereby reducing the cost of utilization.

Spatial–Temporal Pattern Analysis and Development Forecasting of Carbon Stock Based on Land Use Change Simulation: A Case Study of the Xiamen–Zhangzhou–Quanzhou Urban Agglomeration, China

The spatial–temporal distribution and evolution characteristics of carbon stock under the influence of land use changes are crucial to the scientific management of environmental resources and the optimization of land spatial layout. Taking the Xiamen–Zhangzhou–Quanzhou urban agglomeration in the southeastern coastal region of China as an example, based on seven land use types from 1990 to 2020, including cultivated land, woodland, and construction land, we quantitatively investigate the spatial–temporal patterns of carbon stock development and the spatial correlation of carbon stock distribution. Additionally, two scenarios for the development of urban and ecological priorities in 2060 are established to investigate the effects of land use changes on carbon stock. The results indicate that (1) the research area has formed a land use spatial pattern centered around urban construction in the eastern bay area, with the western forest area and coastal forest belt serving as ecological barriers. Carbon stock is influenced by land use type, and the distribution of total carbon stock exhibits a spatial aggregation phenomenon characterized by “low in the southeast, high in the north, and medium in the center”. (2) Distance of trunk and secondary roads, elevation, slope, watershed borders, population size, and gross domestic product (GDP) factors are the main drivers of the growth of land use types. The primary causes of the reduction in carbon stock are the widespread conversion of cultivated land, woodland, and grassland into construction land, as well as water and unused land. (3) In 2060, there will be a decrease of 41,712,443.35 Mg in the urban priority development scenario compared to 2020, and a decrease of 29,577,580.48 Mg in the ecological priority development scenario. The estimated carbon stock under the two scenarios varies by 12,134,862.88 Mg. The average carbon storage of Zhangpu County, Quangang County, and Jimei County is expected to rise by one level under the ecological protection scenario, indicating that the vast forest area can become a potential area to maintain carbon stock. It is crucial to encourage the coordinated development of peri-urban agroforestry and ecological barriers, as well as to establish a harmonious spatial pattern of land use and carbon stock at the scale of urban agglomerations.

Carbon stock projection for four major forest plantation species in Japan

Carbon sequestration via afforestation and forest growth is effective for mitigating global warming. Accurate and robust information on forest growth characteristics by tree species, region, and large-scale land-use change is vital and future prediction of forest carbon stocks based on this information is of great significance. These predictions allow exploring forestry practices that maximize carbon sequestration by forests, including wood production. Forest inventories based on field measurements are considered the most accurate method for estimating forest carbon stocks. Japan's national forest inventories (NFIs) provide stand volumes for all Japanese forests, and estimates from direct field observations (m-NFIs) are the most reliable. Therefore, using the m-NFI from 2009 to 2013, we selected four major forest plantation species in Japan: Cryptomeria japonica, Chamaecyparis obtusa, Pinus spp., and Larix kaempferi and presented their forest age–carbon density function. We then estimated changes in forest carbon stocks from the past to the present using the functions. Next, we investigated the differences in the carbon sequestration potential of forests, including wood production, between five forestry practice scenarios with varying harvesting and afforestation rates, until 2061. Our results indicate that, for all four forest types, the estimates of growth rates and past forest carbon stocks in this study were higher than those considered until now. The predicted carbon sequestration from 2011 to 2061, assuming that 100 % of harvested carbon is retained for a long time, twice the rate of harvesting compared to the current rate, and a 100 % afforestation rate in harvested area, was three to four times higher than that in a scenario with no harvesting or replanting. Our results suggest that planted Japanese forests can exhibit a high carbon sequestration potential under the premise of active management, harvesting, afforestation, and prolonging the residence time of stored carbon in wood products with technology development.

Seagrass Community Structure and Ecosystem Carbon Stocks Along the Shoreline of Semujur Island, Bangka Belitung Province, Indonesia

Seagrass meadows serve as vital blue carbon ecosystems, sequestering significant amounts of CO2 and playing a crucial role in climate change mitigation. Semujur Island, located in the Bangka Belitung Province, exemplifies numerous small Indonesian islands boasting extensive seagrass meadows lining their shores. This research seeks to (1) describe the community structure of seagrass on Semujur Island, (2) assess the carbon storage within the seagrass ecosystem, and (3) analyze the relationship between seagrass community structure and carbon reserves across three distinct sites. According to the results of this study, there are eight species of seagrass on Semujur Island, i.e., Cymodocea rotundata, Enhalus acoroides, Halodule uninervis, Halophila ovalis, Oceana serrulata, Syringodium isoetifolium, Thalassia hemprichii, and Thalassodendron ciliatum. Diversity indices varied among sites, ranging from 1.48 to 1.72. Species evenness indices varied between 0.83 and 0.92, while dominance indices varied between 0.20 and 0.28. The highest estimated carbon stock was obtained at the site dominated by the species H. uninervis (75.11 MgC/ha); followed by the site dominated by T. hemprichii (50.55 MgC/ha). The correlation between seagrass community structure, including density and coverage, and carbon stocks demonstrated a moderate positive correlation, with coefficients of 0.430 and 0.528, respectively (p<0.05). This research highlights the significance of integrating ecological dynamics into the management of seagrass ecosystems to enhance climate change mitigation efforts. Additionally, it offers valuable data as a reference for the restoration and conservation of seagrass ecosystems.

The estimation of above-ground oil palm carbon stocks of different ages through vegetation index

Above-ground carbon stocks play an important role to support the sustainability of oil palm plantation, and vary with soil characteristics, climate conditions, and agronomic practices. The common direct approach to assess aboveground carbon stocks may be time-consuming, and laborious, so may not be suitable especially for large scale observation. Therefore, the use of remote sensing may overcome some limitations coming up for conventional methods, yet requires a considerable data validation to obtain a reliable model. In this study, the normalized difference vegetation index (NDVI) based on Sentinel-2 satellite imagery, acquired in September 2023, was used in combination with field measurements, i.e. biomass estimation, investigated in three separated of oil palm plantations differing in ages, i.e. young (< 10 years), middle (10 – 20 years) and old (> 20 years), subjected with similar soil characteristics, and climate condition, in Bojong Datar, Cibungur, and Cikasungka, West Java. Results show that above-ground carbon stocks increase with oil palm ages, i.e. from young to middle ages, with value ranging from 3.40 – 65.70 ton/ha, yet decreases down to 17 ton/ha with ages > 20 years. Similarly, it also occurred for NDVI index, i.e. increase from 0.50 – 0.59 for young to middle of oil palms, and down to 0.51 for oil palm > 20 years old. This study suggests a strong correlation between carbon stocks and NDVI values (R2 = 0.878), pointing out the potential use of vegetation index to estimate above-ground oil palm carbon stocks. In the future, with further validation, the use of NDVI to estimate plant biomass may provide a potential substitute for the field measurement methods with a considerable higher efficiency and accuracy.