Changes In Carbon Stocks Research Articles

Tracking the Dynamics and Uncertainties of Soil Organic Carbon in Agricultural Soils Based on a Novel Robust Meta-Model Framework Using Multisource Data

Monitoring and estimating spatially resolved changes in soil organic carbon (SOC) stocks are necessary for supporting national and international policies aimed at assisting land degradation neutrality and climate change mitigation, improving soil fertility and food production, maintaining water quality, and enhancing renewable energy and ecosystem services. In this work, we report on the development and application of a data-driven, quantile regression machine learning model to estimate and predict annual SOC stocks at plow depth under the variability of climate. The model enables the analysis of SOC content levels and respective probabilities of their occurrence as a function of exogenous parameters such as monthly temperature and precipitation and endogenous, decision-dependent parameters, which can be altered by land use practices. The estimated quantiles and their trends indicate the uncertainty ranges and the respective likelihoods of plausible SOC content. The model can be used as a reduced-form scenario generator of stochastic SOC scenarios. It can be integrated as a submodel in Integrated Assessment models with detailed land use sectors such as GLOBIOM to analyze costs and find optimal land management practices to sequester SOC and fulfill food–water–energy–-environmental NEXUS security goals.

Changes in Wuhan’s Carbon Stocks and Their Spatial Distributions in 2050 under Multiple Projection Scenarios

Urbanization in the 21st century has reshaped carbon stock distributions through the expansion of cities. By using the PLUS and InVEST models, this study predicts land use and carbon stocks in Wuhan in 2050 using three future scenarios. Employing local Moran’s I, we analyze carbon stock clustering under these scenarios, and the Getis–Ord Gi* statistic identifies regions with significantly higher and lower carbon-stock changes between 2020 and 2050. The results reveal a 2.5 Tg decline in Wuhan’s carbon stock from 2000 to 2020, concentrated from the central to the outer city areas along the Yangtze River. By 2050, the ecological conservation scenario produced the highest carbon stock prediction, 77.48 Tg, while the economic development scenario produced the lowest, 76.4 Tg. High-carbon stock-change areas cluster in the north and south, contrasting with low-change area concentrations in the center. This research provides practical insights that support Wuhan’s sustainable development and carbon neutrality goals.

Landscape fragmentation and regularity lead to decreased carbon stocks in basins: Evidence from century-scale research

Landscapes evolution have significantly altered the Earth's energy balance and biogeochemical cycles, thereby exacerbating climate change. This, in turn, affects surface characteristics and the provision of ecosystem services, especially carbon storage. While recent centuries have witnessed unprecedented landscape changes, limited long-term studies have offered insights into the comparison between present-day features and historical conditions. This study utilized historical reconstruction data and remote sensing imagery to assess landscape evolution and its consequences for carbon stocks over 300 years. Employing multiple regression and random forest models were selected to quantify the influence of key landscape metrics on carbon stocks in the Dongting Lake basin, allowing for a thorough analysis across different sub-basins and land types. The results revealed that intensified human disturbances led to increased landscape fragmentation (+82%), regularity (+56%), and diversity (+37%) within the basin. Moreover, carbon stocks decreased from 4.13 Gt to 3.66 Gt, representing an 11.4% loss, with soil carbon stock experiencing the most considerable reduction (0.24 Gt, 51%). These changes in carbon stock metrics corresponded to shifts in landscape patterns, both undergoing significant transitions at the turn of the 21st century. Meanwhile, fragmentation and regularity played a vital role in explaining carbon stock changes, as their increase contributes to greater carbon losses. Likewise, an increase in landscape diversity correlated with decreased carbon stocks, challenging the prevailing notion that enhanced diversity promotes carbon stocks. The influence of landscape patterns on carbon stocks varies notably across distinct land types. An increase in the dominance of farmland and built-up land led to decreased carbon stocks, while the opposite holds true for forestland. Similarly, a decrease in regularity for farmland, forestland, and built-up land benefits carbon storage, while grassland demonstrates the opposite trend. These findings offer insights for countries and regions in the early stages of development or approaching development, suggesting improvements in land use practices and strategies to address climate change. This involves offsetting land-based carbon emissions through changes in landscape spatial configuration.

Soil Microbial Community Structure and Carbon Stocks Following Fertilization with Organic Fertilizers and Biological Inputs.

The application of organic fertilizers and biological inputs to soil inevitably affects its quality, agrochemical indicators, and microbiota. Sustainable agriculture is based on continuously learning about how to properly manage available soil, water, and biological resources. The aim of the study was to determine changes in microorganism communities and carbon stocks in infertile soils for fertilization using different organic fertilizers and their combinations with bio-inputs. Genetic analysis of microorganism populations was performed using the NGS approach. Our study showed that the application of organic fertilizers affects the soil microbiota and the taxonomic structure of its communities. Specific groups of bacteria, such as Bacillota, were promoted by organic fertilization, meanwhile the abundance of Pseudomonadota and Ascomycota decreased in most treatments after the application of poultry manure. Metagenomic analysis confirmed that the use of bio-inputs increased the relative abundance of Trichoderma spp. fungi; meanwhile, a significant change was not found in the representatives of Azotobacter compared to the treatments where the bio-inputs were not used. The positive influence of fertilization appeared on all the studied agrochemical indicators. Higher concentrations of Corg and Nmin accumulated in the soil when we used granulated poultry manure, and pHKCl when we used cattle manure.

Carbon footprint of mixed farming crop-livestock rotational-based grazing beef systems using long term experimental data

In the context of ever-growing demand for food and associated concerns regarding the environmental impacts of high-input agricultural systems, there is growing interest in mixed farm enterprises to deliver greater sustainability compared with mono-enterprise production systems. However, assessments of such systems are complex and require high-resolution data to determine the true value and interconnectivity across enterprises. Given the scarcity of information on mixed crop–livestock systems and the difficulties of its analysis, we perform life cycle assessment using temporally high-resolution data (2019–2022) from a long-term experiment in South America to evaluate the ‘cradle-to-farmgate exit’ greenhouse gas emissions intensities of four rotational crop–livestock systems. Systems evaluated were continuous cropping: 2 years of continuous cropping; short rotation: 2-year continuous cropping plus 2-year pasture; long rotation: 2-year continuous cropping followed by 4-year pasture; and forage rotation: continuous pasture. Emissions intensities for beef throughput were reported as kilograms of carbon dioxide equivalents (CO2-eq) per kilogram of liveweight gain (LWG) using the Intergovernmental Panel for Climate Change’s Sixth Assessment Report (AR6 2021) CO2 characterisation factors. Point estimate results were found to be 11.3, 11.8, 11.8 and 16.4 kg CO2-eq/kg/LWG for continuous cropping, short rotation, long rotation and forage rotation, respectively. Emission averages arising from crops, which were separated from animal-based emissions using economic allocation, were 1.23, 0.53 and 0.52 kg CO2-eq/kg for soybean, wheat and oat, respectively. The inclusion of soil organic carbon stock changes had notable effects on reducing each system’s emissions: by 22.4%, 19.2%, 25.3% and 42.1% under continuous cropping, short rotation, long rotation and forage rotation, respectively, when soil organic carbon was included. Given there are few life cycle assessment studies available on such mixed-enterprise ‘semi-circular’ systems, particularly with novel primary data, this study adds critical knowledge to agri-food-related sustainability literature by addressing environmental issues in complex production systems compared to extant and broad coverage of mono-enterprise systems.

Methodology for evaluating soil carbon stock changes based on regional geochemical survey data: A case study in Huangpi, China

Soil carbon pools play a crucial role in the Earth's ecosystem, acting as significant carbon sinks and sources. Through a detailed analysis of soil carbon content using data from the Huangpi District in Wuhan, China, this research employs geochemical survey data, field replicates, and spatial autocorrelation information to establish an assessment model for soil carbon stocks. The model addresses the sources of errors and their effects on carbon pool changes, using both traditional statistical theories and geostatistical models to detect changes in carbon density and estimate carbon sources and sinks with minimized error ranges. Key findings indicate that sampling errors, influenced by small-scale spatial variability, are the primary source of observational inaccuracies in assessing total soil carbon and organic carbon, accounting for over 90% of the variation. Meanwhile, analytical errors are more significant when quantifying soil inorganic carbon content due to its lower concentrations. From 2001 to 2022, no significant changes were observed in the soil organic carbon stock in Huangpi District, while a modest increase in inorganic carbon was noted. The study highlights that increasing sample density beyond a certain threshold does not significantly affect carbon stock estimates or their error ranges, emphasizing the stability of the block kriging method in estimating regional carbon stocks.

Greenhouse gas emissions and nutrient use efficiency assessment of 6 New York organic dairies

Improving nutrient use efficiency and reducing GHG emissions are important environmental priorities for organic-certified dairy operations. The objectives of this research were to quantify annual nutrient use and GHG emissions in 6 organic New York dairy farms. Farm-gate nutrient mass balances (NMB) were estimated with the Cornell NMB calculator. Whole-farm GHG emissions were estimated using the Cool Farm Tool (CFT) and COMET. Farm-gate NMB were low, ranging from -6.5 to 19 kg N/ha for N1 (without legume N fixation), 26 to 71 kg N/ha for N2 (including N fixation), -2.4 to 8.2 kg P/ha for P, and 1.1 to 19.8 kg K/ha for K. Additional nutrient imports, coupled with nutrient management planning, adequate legume stands, and diet balancing may help improve P balances and ensure no N deficiencies in the system. Estimates of annual GHG emissions intensity ranged from 0.98 to 2.10 kg of CO2-equivalents (CO2-eq) per kilogram of fat- and protein-corrected milk (FPCM) estimated by CFT, and from 0.69 to 2.48 kg CO2-eq/kg FPCM estimated by COMET. Enteric fermentation, feed production, and fuel and energy use represented the largest sources of GHG. For farms with liquid manure storages, manure management was also a significant source. Estimates of soil carbon (C) stock changes from CFT were in agreement with or smaller than previous studies, and estimates from COMET were in agreement or greater. Variability and uncertainty in the results for soil C stock change indicate that more research and new protocols are needed. The effects of individual management changes on GHG emissions intensity were small, ranging from -8 to +7% in CFT, and -8% to +8% in COMET. The management changes that resulted in the largest reductions in GHG emissions intensity included increasing individual cow productivity and ratio of milk to total feed, and implementation of manure treatment systems.

Are soil carbon credits empty promises? Shortcomings of current soil carbon quantification methodologies and improvement avenues

AbstractAs the consequences of climate change are looming large, agricultural soil carbon credits have emerged as an increasingly advocated lever to incentivize the reduction of greenhouse gas emissions and promote carbon storing farming practices. These credits are exchanged on self‐regulated voluntary carbon markets, each of them using distinct protocols to assess the changes in soil carbon stocks and convert them into carbon credits. Although serious discrepancies between protocols have already been noted regarding general carbon credit accounting principles, an in‐depth evaluation of how changes in soil organic carbon stocks are calculated is still lacking. In this context, the primary objective of our study was to investigate how changes in soil organic carbon stock are estimated by the major carbon credit protocols worldwide. We evaluated the requirements of each protocol regarding the estimation of the initial SOC stock as well as the modelling and/or measurement of changes in stock with time. We found that existing protocols vary greatly in their scientific rigour. We showed in particular that some protocols do not require in situ soil analyses to estimate initial soil carbon stocks but rely on regional values, leading them to potentially overestimate these stocks by up to 2.5 times. Our study also found that the protocols relying on models require different farming practices and different levels of information for each practice to estimate SOC stock changes. The protocols relying, at least partly, on soil sampling also displayed different requirements for the sampling design, sampling tools, SOC analysis methods and SOC stock calculation methods. On this basis, we suggest reforms designed to improve and standardize the quantification of carbon stock changes in soils and to improve the reliability of soil carbon credits.

Analyze QRF model for soil organic carbon map building with digital soil mapping (case study: Sumatra island and Java island)

Abstract In the current era of global warming, soil organic content is one of the most important soil properties. The goal of the entire globe is for carbon neutrality to be achieved and regularly assessed. It is hoped that a dynamic, quick and effective soil organic carbon mapping method will be able to distribute the presence of soil organic carbon to support calculations for changes in carbon stocks and carbon sequestration so that carbon neutrality can be achieved. Digital Soil Mapping (DSM) recently has become the ultimate framework for accurately representing spatial distribution based on its quantitative result and uncertainty analysis. These advantages allow DSM to be replicated uniquely in each mapped area. Digital soil mapping requires input in the form of laboratory and field observation results that are spatially modeled using machine learning techniques. Field observations and laboratory data for Sumatra and Java Island from the Indonesian Center for Agricultural Land Resources Standard Testing (1970-2022) were used in this study, and the results were modeled using Quantile Regression Forests (QRF) in the R Software. Evaluation results from this model with 5738 observation points covering a 47.3 million-hectare-sized island of Sumatra and 3398 observation points covering a 12.8 million-hectare-sized island of Java show an RMSE value of 0.78 with a coefficient of determination (R2) of 0.31 for Sumatra Island and RMSE value of 0.68 with a coefficient of determination (R2) of 0.71 for Java Island. These findings indicate that the neighborhoods for the organic carbon content on the islands of Sumatra and Java differ quite noticeably. This may be due to the relatively wide range in some soils in the Sumatra region, which are peat soils with relatively high carbon content values compared to regions in Java where mineral soils predominate. In conclusion, the evaluation results for digital mapping with the QRF model for soil organic carbon content in Indonesia referring to these 2 large islands show good results with sufficient coefficients of determination in mineral soil areas and there is a need a different modeling approach in areas where peat soil predominates.

Soil amendment and N fertilization strategies to improve C sequestration and storage in soil aggregates

AbstractManagement can affect soil quality through changes in carbon (C) stock, especially in protected C fractions of soil aggregates. Soil aggregation and C sequestration may be improved with soil correction (liming and gypsum) and nitrogen (N) fertilization, but it is not clear how these factors might interact to affect humic substances. The objective of the study was to evaluate the effects of calcium (Ca), magnesium (Mg), and N fertilizatilizer amendments on the accumulation of C, N, and humic substances in soil aggregates from an Oxisoil in Brazil. The production system was no‐till soybean [Gycine max (L.) Merr] double‐cropped with maize (Zea mays L.) and intercropped with forage grass since 2016. Treatments were no amendment, lime only, and lime + phosphogypsum, factorially arranged with and without annual N fertilization of maize. Soil was collected at 0–10, 10–20, 20–40, and 40–60 cm 6 years after initiation of the experiment. The combination of lime, gypsum, and N led to 13% greater total C stock and 20% greater total N stock within the 0–60 cm soil profile than the control. Levels of fulvic acid (FA) and humic acid (HA) were similar among aggregate classes, but humin was greatest in larger aggregates. Liming plus N decreased FA and HA, but gypsum application mitigated this negative effect. Application of limestone, gypsum, and N increased humin, mainly in the soil surface (0–10 cm). Soil C storage and stabilization was enhanced in large soil aggregates with the combined use of lime, gypsum, and N fertilization in humid tropical soils.

Increased straw return promoted soil organic carbon accumulation in China's croplands over the past 40 years

Quantifying changes in soil organic carbon (SOC) stocks within croplands across a broad spatiotemporal scale in response to anthropogenic and environmental factors offers valuable insights for sustainable agriculture aimed to improve soil health. Using a validated and widely used soil carbon model RothC, we simulated the SOC dynamics across intensive croplands in China that support ∼22 % of the global population using only 7 % of the global cropland area. The modelling results demonstrate that the optimized RothC effectively captures SOC dynamics measured across 29 long-term field trials during 40 years. Between 1980 and 2020, the average SOC at the top 30 cm in croplands increased from 40 Mg C ha−1 to 49 Mg C ha−1, resulting in a national carbon sequestration of 1100 Tg C, with an average carbon sequestration rate of 27 Tg C yr−1. The annual increase rate of SOC (relative to the SOC stock of the previous year), starting at <0.2 % yr−1 in the 1980s, reached around 0.4 % yr−1 in the 1990s and further rose to about 0.8 % yr−1 in the 2000s and 2010s. Notably, the eastern and southern regions, comprising about 40 % of the croplands, contributed about two-thirds of the national SOC gain. In northeast China, SOC slightly decreased from 58 Mg C ha−1 in 1980 to 57 Mg C ha−1 in 2020, resulting in a total decline of 28 Tg C. Increased organic C inputs, particularly from the straw return, was the crucial factor in SOC increase. Future strategies should focus on region-specific optimization of straw management. Specifically, in northeast China, increasing the proportion of straw returned to fields can prevent further SOC decline. In regions with SOC increase, such as the eastern and southern regions, diversified straw utilization (e.g., bioenergy production), could further mitigate greenhouse gas emissions.

Quantifying Dissolved Organic Compound Efflux from Drained Peatlands in Hemiboreal Latvia

This study evaluated the impact of different land use types on groundwater dissolved organic carbon (DOC) concentrations and annual DOC efflux from drained peatlands to catchment runoff, providing insights into the mechanisms of carbon stock changes in peatland soils. We measured groundwater chemical properties and various environmental variables, and calculated daily runoff and evapotranspiration for 2021 to estimate monthly and annual DOC efflux and analyzed main affecting factors in different peatland land use types. The highest DOC concentrations in groundwater were found in Scots pine forests and active peat extraction sites, with values of 113.7 mg L−1 and 109.7 mg L−1, respectively, and the lowest in silver birch forests and croplands, at 51.9 mg L−1 and 18.6 mg L−1, respectively. There were statistically significant correlations, including a strong negative correlation between DOC concentrations and several groundwater chemical properties, such as pH, electrical conductivity (EC), Ca, Mg, and K concentrations. The concentrations of DOC in the groundwater of drained peatland showed significant variation between different land use types. The highest annual DOC efflux was observed in active peat extraction sites, at 513.1 kg ha−1 y−1, while the lowest was in grasslands, at 61.9 kg ha−1 y−1, where Ca and Mg concentrations, as well as EC, were the highest. Continuous monitoring of these concentration patterns is essential.

Analysis on the dynamic changes of forest carbon stocks and sinks enhancement strategies in key state-owned forest regions： a case study on Longjiang Forest Group

Analysis on the dynamic changes of forest carbon stocks and sinks enhancement strategies in key state-owned forest regions： a case study on Longjiang Forest Group

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.

Changes in carbon stocks according to stand development stages in oriental beech forests in the Marmara Region of Türkiye.

This study was conducted to determine the changes in carbon stocks of oriental beech (Fagus orientalis) according to stand development stage in the Marmara Region of Türkiye. For this purpose, sample plots were taken from a total of 32 areas encompassing four stand development stages (young, middle age, mature and overmature stand). The diameter at breast height and height of all trees in the sample plots were measured, and only three dominant trees's ages per plot were determined. Aboveground carbon stock was calculated using equations developed for beech forests, while the coefficients in the Agriculture, Forestry and Other Land Use guide were used to determine belowground carbon stocks. A soil pit was dug in each plot and soil samples were taken at different depths (0-10, 10-30, 30-60, 60-100cm). In addition, litters were sampled from four different 25 × 25cm sections in each plot, and then the physical and chemical properties of the soil and litters were analysed. The variations in carbon stocks in above- and below-ground tree mass, litter and soil, and in ecosystem carbon stocks according to development stage were examined by analysis of variance and Duncan test, and the relationships between the carbon stocks were investigated by correlation analysis. Aboveground (AG) and belowground (BG) tree, soil and ecosystem carbon stocks showed significant differences between the four stand development stages (P < 0.05), but not the litter carbon stocks (P > 0.05). AG and BG tree and ecosystem carbon stocks increased with progressive stand development stages, while the soil carbon stock was the highest at the young stage. These findings will contribute to the preparation of forest management plans and the national greenhouse gas inventory.

We Prefer to Stay Grounded!

We have proposed a three-pronged approach to identify sustainable practices during road construction: (1) Route segmentation and identification of drive phases, including loading and dumping stages; (2) Use of Digital Elevation Models (DEM) and digital terrain models(DTM) acquired by UAVs to measure progress of construction from volumetric changes; and (3) Use of vegetation indices derived from satellite imagery to calculate changes in carbon stock. The paper introduces a novel technique for identifying driving phases for heavy trucks based only on GPS data. We also delve into how these approaches can serve as the foundational elements in the creation of digital twins tailored to the construction industry.

Aboveground Spatiotemporal Carbon Storage Model in the Changing Landscape of Jatigede, West Java, Indonesia

Land use and land cover (LULC) change is the variable with the maximum influence on carbon storage in terrestrial ecosystems, due to a fundamental alteration of the ecosystem, structure, function, and variability over time. Understanding the dynamics of aboveground carbon stocks in underway constructions and urban expansions is crucial to provide a basis for land use management and planning. The objective of this study was to analyze the spatiotemporal dynamics of aboveground carbon storage and assess how the LULC change is affected by human intervention, as well as how aboveground carbon stocks respond to these changes in the tropical highland landscape of Jatigede. In this study, changes in aboveground carbon stocks were investigated between 2014 and 2021 by using the integrated valuation of ecosystem services and tradeoffs (InVEST) model. The results revealed that the total aboveground carbon stock decreased between 2014 and 2021. Forests showed the greatest decline in the aboveground carbon stock in terms of space. The primary cause of the reduction in the aboveground carbon stock was the conversion of vegetated land to agricultural and urban land cover. The aboveground carbon stock change was also caused by the continuing construction, which resulted in the extension of construction zones. However, an increase in the aboveground carbon stock was mostly observed in mixed gardens that were close to forest areas. The preservation of mixed gardens as a tree-based agroforestry system can be suggested for enhancing the aboveground carbon stock, as mixed gardens play a significant role in carbon storage in the midst of the increasingly massive deforestation due to the expansion of urban areas.

Impact of fire severity on forest structure and biomass stocks using NASA GEDI data. Insights from the 2020 and 2021 wildfire season in Spain and Portugal

Wildfires have been progressively shrinking the C sink capacity of forest accelerating climate change effects on forest biodiversity, especially where megafires are recurrent and of increased frequency such as in the Mediterranean. Data from The Global Ecosystem Dynamics Investigation (GEDI) mission can inform on changes on forest structure to inform on fire impacts on vegetation. In this study, we assessed the performance of GEDI at measuring biomass and structural change from wildfires using the 2020/21 summer fire seasons in Spain and Portugal. The GEDI hybrid-inference method was used to calculate mean and total biomass in pre- and post-fire stages, while GEDI footprint data was further used to explain the fire severity classes derived from optical data. Our results showed the increasing impact of wildfires on biomass stocks and GEDI ecological metrics by increasing fire severity. More than over biomass stocks, severe fires substantially altered trends in structural metrics such as plant area volume density. The integration of GEDI metrics to explain fire severity had an accuracy of 52% considering five severity classes and an accuracy of 69% when considering the three main classes: unburned, moderate and high. Structural metrics from GEDI can be used to improve optical-based fire severity estimates used globally and to evaluate potential fire impacts based on time-series of GEDI tracks as showcased in the study, but also to measure forest recovery between fire seasons. The extension of GEDI is a major support for wildfire mapping efforts, integrated approaches to capture the increasing impact of fire on forest biodiversity and the monitoring of changes in carbon stocks.

An Integrating Framework for Biomass and Carbon Stock Spatialization and Dynamics Assessment Using Unmanned Aerial Vehicle LiDAR (LiDAR UAV) Data, Landsat Imagery, and Forest Survey Data in the Mediterranean Cork Oak Forest of Maamora

Spatialization of biomass and carbon stocks is essential for a good understanding of the forest stand and its characteristics, especially in degraded Mediterranean cork oak forests. Furthermore, the analysis of biomass and carbon stock changes and dynamics is essential for understanding the carbon cycle, in particular carbon emissions and stocks, in order to make projections, especially in the context of climate change. In this research, we use a multidimensional framework integrating forest survey data, LiDAR UAV data, and extracted vegetation indices from Landsat imagery (NDVI, ARVI, CIG, etc.) to model and spatialize cork oak biomass and carbon stocks on a large scale. For this purpose, we explore the use of univariate and multivariate regression modeling and examine several types of regression, namely, multiple linear regression, stepwise linear regression, random forest regression, simple linear regression, logarithmic regression, and quadratic and cubic regression. The results show that for multivariate regression, stepwise regression gives good results, with R2 equal to 80% and 65% and RMSE equal to 2.59 and 1.52 Mg/ha for biomass and carbon stock, respectively. Random forest regression, chosen as the ML algorithm, gives acceptable results, explaining 80% and 60% of the variation in biomass and carbon stock, respectively, and an RMSE of 2.74 and 1.72 Mg/ha for biomass and carbon stock, respectively. For the univariate regression, the simple linear regression is chosen because it gives satisfactory results, close to those of the quadratic and cubic regressions, but with a simpler equation. The vegetation index chosen is ARVI, which shows good performance indices, close to those of the NDVI and CIG. The assessment of biomass and carbon stock changes in the study area over 35 years (1985–2020) showed a slight increase of less than 10 Mg/ha and a decrease in biomass and carbon stock over a large area.

Modeling the carbon dynamics of ecosystem in a typical permafrost area

Climate change poses mounting threats to fragile alpine ecosystem worldwide. Quantifying changes in carbon stocks in response to the shifting climate was important for developing climate change mitigation and adaptation strategies. This study utilized a process-based land model (Community Land Model 5.0) to analyze spatiotemporal variations in vegetation carbon stock (VCS) and soil organic carbon stock (SOCS) across a typical permafrost area - Qinghai Province, China, from 2000 to 2018. Multiple potential factors influencing carbon stocks dynamics were analyzed, including climate, vegetation, soil hydrothermal status, and soil properties. The results indicated that provincial vegetation carbon storage was 0.22 PgC (0.32 kg/m2) and soil organic carbon pool was 9.12 PgC (13.03 kg/m2). VCS showed a mild increase while SOCS exhibited fluctuating uptrends during this period. Higher carbon stocks were observed in forest (21.74 kg/m2) and alpine meadow (18.08 kg/m2) compared to alpine steppes (9.63 kg/m2). Over 90 % of the carbon was stored in the 0-30 cm topsoil layer. The contribution rates of soil carbon in the 30-60 cm and 60-100 cm soil layers were significantly small, despite increasing stocks across all depths. Solar radiation, temperature, and NDVI emerged as primary influential factors for overall carbon stocks, exhibiting noticeable spatial variability. For SOCS at different depths, the normalized differential vegetation index (NDVI) was the foremost predictor of landscape-level carbon distributions, which explained 52.8 % of SOCS variability in shallow layers (0-30 cm) but dropped to just 12.97 % at the depth of 30-60 cm. However, the dominance of NDVI diminished along the soil depth gradients, superseded by radiation and precipitation. Additionally, with an increase in soil depth, the influence of inherent soil properties also increased. This simulation provided crucial insights for landscape-scale carbon responses to climate change, and offered valuable reference for other climate change-sensitive areas in terms of ecosystem carbon management.