Biomass Carbon Stocks Research Articles

Carbon Stock Estimation of Poplar Plantations Based on Additive Biomass Models

Accurate estimation of biomass and carbon stocks in forest ecosystems is critical for understanding their roles in carbon sequestration and climate change mitigation. Currently, the development of stand biomass models and carbon stock estimation at the regional scale has emerged as a prominent research priority. In this study, 225 Populus spp. (poplar) trees in Shandong Province, China, were destructively sampled to obtain the biomass of their components. Two models (MS1 and MS2) were developed using allometric equations and the seemingly unrelated regression (SUR) method to ensure additive properties across tree components. The model evaluation employed the leave-one-out jackknife (LOO) method, considering statistics such as adjusted R-squared (Ra2), root mean square error (RMSE), mean absolute percent error (MAPE), and mean absolute error (MAE). The results from our models demonstrated high accuracy, with MS2 slightly outperforming MS1 after incorporating tree height as an independent variable. The models reliably estimated component-specific biomass and carbon stocks, with distinct variations observed in the carbon content among foliage (47.14 ± 2.07%), branches (47.26 ± 2.48%), stems (47.67 ± 2.21%), and roots (46.37 ± 2.78%). Carbon stocks in poplar plantations increased with the diameter class, ranging from 5 to 35 cm and correspondingly from 3.670 to 172.491 Mg C ha−1. As the diameter class increases, the carbon allocation strategy of poplars aligns with the CSR strategy, transitioning from prioritizing growth competition to emphasizing self-stabilization. Our research proposes a robust framework for assessing biomass and carbon stocks in poplar plantations, which is essential for evidence-based forest management strategies.

The biogeochemical model Biome-BGCMuSo v6.2 provides plausible and accurate simulations of the carbon cycle in central European beech forests

Abstract. Process-based ecosystem models are increasingly important for predicting forest dynamics under future environmental conditions, which may encompass non-analogous climate coupled with unprecedented disturbance regimes. However, challenges persist due to the extensive number of model parameters, scarce calibration data, and trade-offs between the local precision and the applicability of the model over a wide range of environmental conditions. In this paper, we describe a protocol that allows a modeller to collect transferable ecosystem properties based on ecosystem characteristic criteria and to compile the parameters that need to be described in the field. We applied the procedure to develop a new parameterisation for European beech (Fagus sylvatica L.) for the Biome-BGCMuSo model, the most advanced member of the Biome-BGC family. For model calibration and testing, we utilised multiyear forest carbon data from 87 plots distributed across five European countries. The initial values of 48 new ecophysiological parameters were defined based on a literature review. The final values of six calibrated parameters were optimised for single sites as well as for multiple sites using generalised likelihood uncertainty estimation (GLUE) and model output conditioning that ensured plausible simulations based on user-defined ranges of carbon stock output variables (carbon stock in aboveground wood biomass, soil, and litter) and finding the intersections of site-specific plausible parameter hyperspaces. To support the model use, we tested the model performance by simulating aboveground tree wood, soil, and litter carbon across a large geographical gradient of central Europe and evaluated the trade-offs between parameters tailored to single plots and parameters estimated using multiple sites. Our findings indicated that parameter sets derived from single sites provided an improved local accuracy of simulations of aboveground wood, soil, and litter carbon stocks by 35 %, 55 %, and 11 % in comparison to the a priori parameter set. However, their broader applicability was very limited. A multi-site optimised parameter set, on the other hand, performed satisfactorily across the entire geographical domain studied here, including on sites not involved in the parameter estimation, but the errors were, on average, 26 %, 35 % and 9 % greater for the aboveground wood, soil, and litter carbon stocks than those obtained with the site-specific parameter sets. Importantly, model simulations demonstrated plausible responses across large-scale environmental gradients, featuring a clear production optimum of beech that aligns with empirical studies. These findings suggest that the model is capable of accurately simulating the dynamics of European beech across its range and can be used for more comprehensive experimentations.

Timber and carbon sequestration potential of Chinese forests under different forest management scenarios

Developing forestry action plans with the goal of carbon neutrality is a critical task to identify carbon sink potential and balance the pathways of China's forests. Current research that predicts forest biomass carbon stock and sink potential has shortcomings, such as an incomplete assessment of China's forest carbon sink range, assumptions based on unchanged forest area in the base year and insufficient consideration of natural and human disturbance factors. This study utilised the national forest inventory (NFI) data to construct a model of forest growth and consumption using a machine learning algorithm (i.e. random forest), identified suitable areas for future forest expansion by integrating multi-source data, and set up three future forest management scenarios: business as usual (BAU), enhanced policy scenario (EPS) and maximum potential scenario (MPS). In addition, changes in the area, volume stock and biomass carbon stock in China's forests between 2020 and 2060 were predicted under three forestry activities (i.e. existing forest, afforested/reforested (AR) forest and forest conversion) and three climate scenarios (i.e. SSP126, SSP370 and SSP585) based on the 9th NFI (2014–2018). According to China's relevant planning goals and suitable forest space, the area of AR forests is predicted to be 55.55 × 106 hm2 by 2060, and the forest coverage rate is predicted to increase from 23% in 2018 to 28% by 2060. Biomass carbon sequestration (BCS) between 2020 and 2060 in AR forests is predicted to be 36.00 TgC per year. By 2060, the average BCS is predicted to be approximately 140.00–287.56 TgC per year in China's forests, which is mainly owing to arbor forest management. Under the BAU and EPS scenarios, BCS in China's forests is expected to decline from 2020 to 2060. However, under the MPS, BCS in China's forests is projected to increase and be maintained at 322 TgC per year or above by 2060, with wood production reaching 4.71 × 108 m3 per year. China's forests are predicted to experience an increase in biomass carbon stock in the future and play a role as a carbon sink. By taking measures to achieve the maximum growth potential of all forest types, China's forests will achieve a win‒win situation between carbon sinks and timber production.

Carbon stock assessment of a reforestation site within Mt. Arayat protected landscape, Pampanga, Philippines

The Philippines has very few studies in terms of biomass production and carbon stocks in reforestation sites and the level of emission that has been avoided due to forest protection activities. Most of carbon stock estimates being used in the country are based on secondary data or studies in other countries using the same species. The study aimed to provide a reliable assessment on the contribution of reforestation sites in biomass production and carbon stocks and the potential of these plantation to contribute to the climate change mitigation efforts in the Philippines. The guideline prepared by the Philippines’ Department of Environment and Natural Resources – Forest Management Bureau (DENR-FMB) in 2021 on Forest Resources Assessment (FRA) which follows the manual of Food and Agriculture Organization of the United Nations (FAO) Forestry Department in conducting FRA was used during data collection. A tract was established in the southern portion of the reforestation site wherein the species planted in 2012 are already very prominent and their diameter at breast height (DBH) and total height were measured and their corresponding wood specific gravity were taken into account. Carbon stocks were estimated using Chave et al.’s and Brown’s allometric equations. The estimated carbon stocks using Chave et al.’s formula is 542.3 tC/ha while the estimated carbon stock using Brown’s formula is 2001.6 tC/ha. The results of computation using Brown’s equation are considerably higher than the results using Chave et al.’s equation considering that Brown’s equation only took into consideration the DBH of trees while Chave et al.’s equation included the height and wood specific gravity of each species. The study demonstrated how important our forests in terms of their roles as carbon sinks and therefore contribute to our country’s efforts in climate change mitigation.

Tree carbon stock and seasonal influence on soil properties in shivpuri-nagarjun national park, nepal

The carbon stock stored within the biomass of tree species is vital in the forest ecosystem as it contributes significantly to the carbon balance. In addition, the physicochemical properties of soil play a critical role, in influencing overall ecosystem health. This study has analyzed the carbon stock and influence of seasonal change on soil physicochemical properties along soil depths in the Shivpuri Nagarjun National Park (SNNP), Nepal. The above-ground biomass carbon stock was found to be 227.09 t/ha and below-ground stock was 45.42 t/ha. Tree species Castanopsis tribuloides exhibited the highest values of above and below-ground tree carbon stock. The soil of the study site was sandy loam and slightly acidic. High temperature and moisture in the monsoon season were followed by an increased bulk density during the pre-monsoon with deeper soil layers. The sand, silt, and clay contents did not differ significantly across the seasons and depths. The key soil nutrients, like carbon, total nitrogen, phosphorus, and potassium were high during the monsoon season at the topsoil layer, which gradually declined with increasing depth in all seasons. The study highlights that the total tree carbon stock in the study site is 272.51 t/ha, with significant seasonal and depth-related variations in soil attributes. The monsoon season, characterized by maximum soil moisture and higher concentrations of essential soil components, is crucial in influencing soil physicochemical properties and offers important insights for forest conservation and management. Bangladesh J. Bot. 53(3): 567-576, 2024 (September)

Carbon sequestration potential of tree planting in China

China’s large-scale tree planting programs are critical for achieving its carbon neutrality by 2060, but determining where and how to plant trees for maximum carbon sequestration has not been rigorously assessed. Here, we developed a comprehensive machine learning framework that integrates diverse environmental variables to quantify tree growth suitability and its relationship with tree numbers. Then, their correlations with biomass carbon stocks were robustly established. Carbon sink potentials were mapped in distinct tree-planting scenarios. Under one of them aligned with China’s ecosystem management policy, 44.7 billion trees could be planted, increasing forest stock by 9.6 ± 0.8 billion m³ and sequestering 5.9 ± 0.5 PgC equivalent to double China’s 2020 industrial CO2 emissions. We found that tree densification within existing forests is an economically viable and effective strategy and so it should be a priority in future large-scale planting programs.

Relationship between seagrass community structure and carbon stocks on the coasts of Karimunjawa Marine National Park, Indonesia

The seagrass ecosystem is considered one of the most effective coastal ecosystems in storing carbon. Carbon stock estimation for a certain ecosystem is highly affected by factors such as species diversity and habitat type. This study aims to investigate the relationship between plant community structure and carbon stocks in the seagrass ecosystem using a case study of six coastal sites in Karimunjawa Marine National Park, Indonesia. In this region, eight seagrass species were recorded, i.e., Enhalus acoroides, Thalassia hemprichii, Cymodocea rotundata, Halodule pinifolia, Halophila ovalis, Halophila minor, Syringodium isoetifolium, and Oceana serrulata. From the six study sites, the highest estimated carbon stock was 426.2 Mg C ha−1 (Site 5; Telaga, dominated by E. acoroides). Meanwhile, the lowest estimated carbon stock was 127.4 Mg C ha−1 (Site 4; Koin, dominated by T. hemprichii). The density of E. acoroides was positively correlated with the total seagrass biomass carbon stocks (r = 0.97; p < 0.01), while its dominance was positively correlated with sediment carbon stocks (r = 0.92; p < 0.05) and total seagrass ecosystem carbon stocks (r = 0.92; p < 0.05). Seagrass ecosystems with different community structures showed different carbon storage capacities. Seagrass ecosystems dominated by large-sized species such as E. acoroides showed higher estimated carbon stocks thus suggesting the importance of considering the variability of community structure in managing seagrass ecosystems for carbon sequestration and storage.

Assessing the of carbon and nitrogen storage potential in Khaya spp. stands in Southeastern Brazil

The objective of this study was to assess the dynamics of carbon and nitrogen in soil, forest floor, and aboveground biomass in 9.5 years-old planted stands of three Khaya spp. (K. grandifoliola, K. ivorensis, and K. senegalensis). The study was conducted at the Reserva Natural Vale (RNV), Brazil. The stands were planted at 5 × 5 m spacing, distributed over rectangular plots of 1250 m2. Soil bulk density at the evaluated depths, as well nitrogen contents, were similar among the species. However, K. ivorensis exhibited higher carbon concentration in the soil. In general, there were no differences in carbon and nitrogen content in soil between the three species; however, the values obtained are comparable to those of the reference area–Native Forest. The carbon stocks in the aboveground biomass for K. grandifoliola, K. ivorensis, and K. senegalensis averaged 37.97, 33.66 and 33.86 Mg ha−1, respectively (p ≤ 0.05). These values collectively represent about 28% of the total carbon stocks across the observed compartments. Notably, the nitrogen content within the aboveground biomass did not differ among these species. Therefore, African mahogany possesses a robust potential to store both carbon and nitrogen.

Effects of Grazing on Plant Diversity and Their Carbon Stocks in Different Types of Grasslands

With the drying and warming of the climate and irrational grazing, various types of grasslands in Inner Mongolia have been degraded to different degrees, and different management modes will inevitably affect the plant diversity and vegetation carbon stock of soil grasslands. To clarify the changes and influencing factors of plant diversity and carbon stock in different types of grasslands under different management modes, plant species composition, aboveground biomass, and vegetation carbon were analyzed based on 18 sentinel monitoring stations across three different types of grasslands in Inner Mongolia. The results showed that grazing increased the dominance of typical grassland and desert grassland, whereas meadow grassland decreased, and the evenness index and Shannon Wiener diversity index increased less in meadow grassland and desert grassland. Grazing decreased graminaceous biomass in meadow grassland and typical grassland, whereas it increased in desert grassland. Above-ground vegetation and below-ground root carbon stocks were much higher than those in grazing areas, 1.5 and 1.2 higher, respectively, but vegetation carbon stocks in long-term grazing sites were significantly lower than those in short-term grazing. Further, the structural equations showed that the effects of geographic location, climatic factors, and soil factors on the biomass and vegetation carbon stocks of the three grassland types differed significantly. The results can provide a reference for the ecologically sustainable development of grassland and the optimization of management mode.

To improve estimates of neotropical forest carbon stocks more direct measurements are needed: An example from the Southwestern Amazon

Tropical forests play a critical role in the global carbon cycle, storing 40–55 % of terrestrial plant carbon and significantly contributing to primary productivity. However, uncertainties persist in estimating carbon stocks and fluxes, exhibiting variation across the Neotropics, Africa, and Asia tropical forest regions. Despite hosting some of the most densely sampled forests, significant uncertainties persist in biomass and forest carbon stock estimates in the Neotropics. Although the Southwestern Amazon (SWA) forests span over 20 million hectares, no specific biomass or above- and below-ground carbon model has been calibrated for this region thus far. In our study, we conducted direct forest inventories in the SWA to address the following question: Do the allometric patterns, biomass, and carbon stocks observed in the Southwestern Amazon differ from those found in other regions of the Amazon or Pantropical? Our research reveals substantial differences in water and carbon content, biomass stocks, above- and below-ground oven-dry biomass ratios, and allometric patterns between SWA forests and other Amazonian and Pantropical forests. We have demonstrated that these differences result in overestimations of forest biomass when applying allometric equations developed for other Amazonian and Pantropical regions to the open forests of Southwestern Amazonia. This overestimation can reach up to 37 % when using equations from the eastern Amazon, and between 26 % and 46 % depending on the applied Pantropical equation. The use of an inappropriate factor for the root-to-shoot ratio in the Southwestern Amazon (SWA) can lead to overestimates of belowground oven-dry biomass by up to 20 %. To reduce uncertainties related to estimates of forest carbon stock and flux in Neotropical forests, it is necessary to enhance the density of direct biomass measurements, particularly in southwestern Amazonia.

Woody species’ carbon sequestration and soil seed bank conservation potentials of traditional rangeland management strategies in Western Guji, Southern Ethiopia

Rangelands are important for mitigating climate change and rehabilitating land by storing carbon from woody plants and seeds in the soil. However, there is a lack of empirical data on how traditional rangeland management practices (such as enclosure, communal grazing and prescribed fire) affect the carbon storage of woody species and the conservation potential of soil seed banks. This study evaluates the carbon sequestration and soil seed bank conservation potential of traditional rangeland management practices in the Western Guji zone, Ethiopia. A total of 32 sample plots were established using systematic random sampling, with 12 in each management practice, to measure woody biomass and estimate carbon stock using a nondestructive method. Additionally, 72 soil samples were collected for soil seed bank analysis, with 24 samples taken from each type of rangeland management practice. The study found that enclosures had the highest amount of carbon stocks 63.1 tons per hectare (t ha−1), with a corresponding carbon dioxide equivalent (CO2e) sequestration of 231.58 t ha-1, followed by communal grazing areas that stored 27.05 t ha−1 of carbon with a CO2e sequestration of 99.27 t ha−1. Prescribed fire had the lowest carbon stocks at 19.39 t ha−1, with a CO2e sequestration of 71.16 t ha−1. The study also found that different traditional rangeland management practices significantly influenced the diversity, richness, and density of woody species in soil seed banks. The highest Shannon diversity was recorded in enclosures (3.56 ± 0.09), followed by communal grazing (2.87 ± 0.14) and prescribed fire (1.27 ± 0.08). As a result, enclosure promotes the storage of carbon in woody plants and increases the diversity, density, and richness of woody species seeds in the soil, while prescribed fire reduces it. Therefore, it recommend for pastoralists to expand the use of enclosures as it improves the potential for conserving carbon stocks and soil seed banks of woody species.

COMPOSITION, STRUCTURE, AND CARBON SEQUESTRATION OF DIFFERENT RAINFOREST ECOSYSTEMS IN THE GUNUNG GEDE PANGRANGO NATIONAL PARK, INDONESIA

ARTICLE HIGLIGHTS- High demand for environmental services makes the park vulnerable to human activities.- Both ecosystems are well regenerated; seedling &gt; sapling &gt; pole &gt; tree (inverted J)- Both ecosystems show normal diversity conditions and stable species distribution.- Growth of Maesopsis eminii needs monitoring to preserve forest purity.- Montane forests have greater biomass, carbon stocks, less anthropogenic disturbanceABSTRACTThe Gunung Gede Pangrango National Park (GGPNP) area is one of the vital ecosystems that support the environment in West Java Province, Indonesia. It is a unique area that has multiple forest ecosystems, including lowland rainforest and montane rainforest ecosystems. Despite the GGPNP’s status as a conservation area, the high demand for the GGPNP’s environmental services makes the region vulnerable to disturbances from human activities. Several studies have been conducted in the GGPNP area (lowland and montane forest ecosystems), however, the results of this study are still necessary to explain the forest dynamics and forest carbon sequestration in this location. The objective of this research was to analyze the structure, composition, and carbon sequestration of stands in the lowland and montane rainforest ecosystems in the GGPNP area. Data processing and analyses were conducted using diversity indices, biomass-carbon stock estimation, and carbon dioxide sequestration estimation. The results showed that the GGPNP lowland and montane rainforest ecosystems were well regenerated. The number of seedlings &gt; saplings &gt; poles &gt; trees and the graph showed a reverse “J” pattern. The GGPNP lowland rainforest ecosystem was dominated by Neonauclea lanceolata and had relatively higher species diversity. The GGPNP montane rainforest ecosystem was dominated by Castanopsis acuminatissima with a higher individual density, denser canopy, and more complex canopy strata. The lack of regeneration in several species of trees heightens the threat to these species’ existence in the future. Biomass, carbon stocks, and carbon sequestration in the GGPNP montane rainforest were greater than those in the GGPNP lowland rainforest. The GGPNP montane rainforest ecosystem had older forest stands, a larger average tree diameter, and lower potential for anthropogenic disturbances.

The implications of biomass and carbon sequestration of exclosure for climate change mitigation in arid areas, Sekota district, northern Ethiopia

Climate dynamics management plays a crucial role in safeguarding the environment from a global to local scale. Land degradation poses a significant threat to the livelihoods of millions of people living in drylands. Arid regions are particularly vulnerable when it comes to finding suitable habitats for ecological processes. Various global initiatives have been developed to restore degraded drylands. In addition, exclosures have important implications in arid areas for regulating environmental services. However, there is a lack of systematic studies on the biomass improvement and climate change mitigation effects of exclosures. A study was carried out in the Sekota district of northern Ethiopia to evaluate the impact of exclosures on biomass production and carbon stock. Three sites—Gateno, Jinqaba, and Mildam—were chosen for the pilot study. Data was collected from 27 quadrats, with different sizes used for sampling saplings, seedlings, litter, and soil. Soil samples were taken at depths of 0–20 cm and 0–40 cm. The data was analyzed using Excel and R Studio, and a linear mixed-effect model was used to determine the differences between land uses. The results indicated significant variations in above-ground biomass carbon, soil organic carbon, and total carbon stock between the two land uses. Furthermore, the observational perspective of exclosures in the Sekota district reveals a sustainable and suitable environment for interested parties. Exclosures prove to be effective in the restoration of degraded lands, as well as in enhancing biomass accumulation and carbon sequestration.

Biomass carbon stock and allocation of planted and natural forests in the Loess Plateau of China

Establishing planted forests (PF) by afforestation and naturally regenerating forests (NF) are important measures of enhance carbon (C) sequestration in terrestrial ecosystems. However, the difference of biomass C stocks and allocation between NF and PF and their determinants in water-limited areas remain unclear. To address this gap, we conducted a synthesis of above-ground biomass C (AGBC), below-ground biomass C (BGBC) stocks and root to shoot ratios (R:S) of PF and NF in the Chinese Loess Plateau. The changes in biomass C stock and R:S along a climate gradient were investigated, and the relationships between R:S and soil properties were also quantified. We found that PF exhibited lower AGBC and BGBC stocks compared to NF, but the average R:S in PF was significantly higher than that of NF (0.48 ± 0.27 vs. 0.30 ± 0.11, p < 0.05). Turning points existed in biomass C allocation along climatic gradient for both PF and NF. More biomass C was allocated to AGBC in PF and NF when mean annual precipitation exceeded 494 mm and 588 mm, respectively. Higher mean annual temperature promoted larger C allocations to BGBC in PF compared to NF. Soil properties had a stronger effect on the biomass C allocation patterns after afforestation, but to a lesser extent on NF. This study indicates that afforestation promotes a larger allocation of biomass C into the below-ground compared to naturally regenerated forests, and using the default R:S estimates may result in a severe underestimation of below-ground C stocks in water-limited areas. The biomass C stocks and allocation are collectively determined by climate conditions and soil properties, and accumulated below-ground biomass C stock induced by dryland afforestation may be an important C sink at a broad geographic scale.

Remote Sensing Methods and GIS Approaches for Carbon Sequestration Measurement: A General Review

Geospatial technologies like Remote Sensing (RS) and Geographic Information Systems (GIS) provide a platform for swiftly evaluating terrestrial Carbon Stock (CS) across extensive regions. Employing an integrated RS-GIS method for estimating Above-Ground Biomass (AGB) and precise carbon management emerges as a timely and economical strategy for implementing effective management plans on a localized and regional level. This study reviews different RS-related techniques utilized in CS assessment, particularly in arid lands, shedding light on the challenges, opportunities, and future trends associated with the process. As global warming poses adverse impacts on major ecosystems through temperature and precipitation changes, professionals have a call to develop evidence-based interventions to mitigate them. Carbon sequestration involves harnessing and storing carbon stocks from the atmosphere to minimize the adverse effects of climate change. The review explores the effectiveness of integrating remote sensing and GIS methodologies in quantifying carbon sequestration within agroforestry landscapes. In addition, this review also assesses the traditional methods, including their limitations, and deeply delves into recent techniques, emphasizing key remote sensing (RS) variables for biophysical predictions. This study showcases the efficacy of geospatial technologies in evaluating terrestrial carbon stock, particularly in arid regions. The study reviews diverse techniques and sensors, like optical Radio Detection and Ranging (RADAR), and Light Detection and Ranging (LiDAR), extensively employed for above-ground biomass (AGB) estimation and carbon stock assessment with RS data, introducing and discussing new methods. Existing literature was examined to present knowledge and evidence on the effectiveness of these technologies in carbon sequestration. The key findings of this review will inform future research and integration of technology, policy formulation, and carbon sequestration management to mitigate the impacts of climate change.

Geo-Spatial Approach for Estimating Carbon Stock in Context of Siwalik Forests of Arunachal Pradesh, India: A Review

Climate change has been identified as a global issue that must be controlled at every possible step. Arunachal Pradesh is considered a biodiversity hotspot that needs to be conserved. Carbon stock assessment of an area, especially forests, has emerged as an important aspect of understanding the carbon storage capacities of trees of that forest, helping to mitigate climate change along with promoting sustainable conservation of biodiversity. Siwalik refers to the foothill part of the Himalayas comprising of small hillocks. It is characterised by rugged and undulating topography. Due to increasing anthropogenic activities, this region is growing fragile and needs to be conserved. This review article presents various aspects of carbon stock assessment, highlighting the benefits and the challenges that hinder the process of carbon stock estimation. Various articles based upon assessing forest biomass and carbon stock of Arunachal Pradesh were reviewed. To provide insights about associated opportunities, futuristic tendencies, and challenges in carbon stock assessment using geospatial technology, the present article was planned. Reach to understand the preferred method of estimating forest biomass and related carbon stock. Also to get to know about the obstacles that was confronted during the assessment of the carbon potential of forests. The article will review and highlight the use of field-based data inventory in combination with Remote Sensing technology for carbon stock assessment of an area along with the challenges, benefits, and futuristic scope related to them.

Ecosystem restoration can lead to carbon recovery in semi‐arid savanna grasslands in India

Semiarid savanna grasslands (SG) in India deliver enormous benefits to people and nature but are currently undergoing large‐scale degradation. Soil carbon stocks in degraded SGs vary in response to land use and land management changes. Although there is increasing support for restoring grasslands by planting native grass species, its impact on soil carbon recovery is largely unknown. In this study, we undertake a plot‐level investigation of soil and aboveground biomass carbon stocks to provide robust estimates of carbon densities across sites which have undergone restoration over the last 3 years. We compare these restored sites with a no‐intervention control using a space‐for‐time substitution framework and two reference old‐growth grassland sites. We find that SGs store significant amounts of carbon (11.57–26.76 tC/ha across 1‐ to 3‐year restoration sites, respectively), with most of the carbon stored in soils (7.29–15.67 tC/ha across 1‐ to 3‐year restoration sites, respectively). These estimates still remain well below the soil carbon stocks of the reference sites (range of 22.91–39.49 tC/ha). We demonstrate that soil organic carbon (SOC) stocks progressively increase with the age of grass plantings. The 3‐year site shows an increase of 35% in SOC stocks compared to the no‐intervention control and an increase of 30 and 21% in comparison to the 1‐ and 2‐year sites, respectively. Our study demonstrates a robust approach to estimate soil carbon stocks in these ecosystems and highlights that effective conservation and restoration can enable SGs in India to act as natural carbon sinks at scale.

Unveiling spatial variations of high forest live biomass carbon stocks of Gabon using advanced remote sensing techniques

Gabon is one of 11 high-forest, low-deforestation (HFLD) countries in the world. It has the highest proportion of preserved forests in the Congo Basin and is the first country to create large forest carbon offset credits in the market. However, about 60% of forests in Gabon is allocated to logging concessions, causing concerns for forest degradation and the sustainability of carbon credits. Here, we use a combination of air- and space-borne remote sensing data and the-state-of-the-art gradient boosted regression trees to estimate forest structure and aboveground biomass carbon density (ACD) of trees at 100 m resolution for the year circa 2020. Mapping spatial variations of ACD across floristically diverse landscapes, we estimate average density and total living carbon storage of trees at the national and sub-national levels. The estimated ACD of trees in forestlands within the country was 142.12 ± 7.3 Mg C ha−1 with the highest values found in central Gabon (150.08 ± 5.8 Mg C ha−1) and on highlands (161.18 ± 6.7 Mg C ha−1). On average, in every region, ACD of forests found within logging concessions (149.89 ± 6.1 Mg C ha−1) was higher than unmanaged forests of unprotected areas (122.81 ± 4.4 Mg C ha−1), indicating the combined effects of logging in carbon-rich forests and increased productivity due to management. The country’s total estimated biomass carbon for trees (above and belowground) stored within the forests was 4.14 ± 0.3 Pg C with 68% found within logging concessions and 14% within protected areas. The map provides high precision and comprehensive assessment of carbon stocks of trees in Gabon’s forests, significantly improving the country’s prospects to implement climate mitigation policies and to participate in carbon markets.

Estimating aboveground biomass dynamics of wheat at small spatial scale by integrating crop growth and radiative transfer models with satellite remote sensing data

Aboveground biomass (AGB) of plants is an agroecological indicator that can be used to monitor crop growth status and quantify biomass carbon stock in agricultural ecosystems. Although satellite remote sensing data and crop models are widely employed to estimate AGB dynamics, their application in small-scale research plots is often constrained by the unavailability of freely and timely high-resolution satellite imageries and the challenge of acquiring reliable model inputs like initial soil water and nitrogen content. This study integrated a crop growth model (APSIM-Wheat) and radiative transfer model (PROSAIL) to estimate AGB dynamics at a small plot scale (ca. 5 to 20 m2) within variety trials (ca. 1 to 2 ha) by assimilating trial-level remote sensing data into the hybrid APSIM-PROSAIL model. The APSIM-PROSAIL integration, developed by deriving PROSAIL input parameters from APSIM-Wheat output variables, was verified by evaluating its capacity to reproduce trial-level Sentinel-2 (S2) NDVI dynamics across 30 variety trials in 2020. The comparison of the simulated and observed trial-level S2 NDVI dynamics yielded an overall RRMSE of 18.2% (n = 646 observations), suggesting the potential of using observed S2 NDVI dynamics to constrain APSIM-PROSAIL and estimate environment variables in cropping systems. This constraint was subsequently assessed in 28 variety trials in 2021, where APSIM-PROSAIL inversely estimated unknown trial-specific variables including initial soil water and nitrogen content across the trials. Using the estimated initial soil status and the plot information including genotypes and management as sown, we demonstrated that this method could also facilitate accurate retrievals of plot-level AGB dynamics with an overall RRMSE of 20.9% (n = 976 measurements). The proposed APSIM-PROSAIL demonstrated the capability to accurately retrieve plot-level AGB dynamics and reproduce trial-level S2 NDVI dynamics across a broad and diverse range of variety trials in the Australian Wheatbelt, indicating its potential utility to facilitate biomass carbon stock assessment. It holds the potential as a tool to explore the relationships between canopy spectral information and crop traits in response to genotype-environment-management interactions in agricultural ecosystems. The method enables the examinations of within-field variabilities for crop traits through retrieving them at small spatial scales from coarse remote sensing data.

BIOMASSA DAN SIMPANAN KARBON PADA EKOSISTEM PADANG LAMUN DI WILAYAH NUSA LEMBONGAN

The Seagrass ecosystem is one of the important ecosystems in the ocean in mitigating global warming. This research aims to examine the potential for storing carbon stocks in seagrass biomass. The purposive sampling method was used at three location points. At each location, there are 9 quadrants for a total of 27 quadrants. The types of seagrass found were Thalassia hemprichii, Cymodocea rotundata, Enhalus acoroides, Halodule uninervis, Halophila ovalis, Halodule pinifolia with moderate diversity and moderate community stability. Seagrass conditions are relatively protected between the coast and coral reefs with the highest average density of 225 ind/m2. The type of seagrass with the highest density is Thalassia hemprichii. The types of substrates are sand, coral rubble, and sandy mud. The carbon stock in the Lembongan Beach area has an area of ??89.46 hectares of seagrass beds. Around 56.32% or 3,21 tons of carbon were stored as the bottom carbon stock of the substrate and 43.67% or 2,49 tons of carbon were stored as the top carbon stock of the substrate. Keywords: Thalassia Hemprichii, Seagrass, Substate, Global Warming