Stock Estimates Research Articles

A Novel Workflow for Mapping Forest Canopy Height by Synergizing ICESat-2 and Multi-Sensor Data

Precise information on forest canopy height (FCH) is critical for forest carbon stocks estimation and management, but mapping continuous FCH with satellite data at regional scale is still a challenge. By fusing ICESat-2, Sentinel-1/2 images and ancillary data, this study aimed to develop a workflow to obtain an FCH map using a machine learning algorithm over large areas. The vegetation-type map was initially produced by a phenology-based spectral feature selection method. A forest characteristic-based model was then proposed to map spatially continuous FCH after a multivariate quality control. Our results show that the overall accuracy (OA) and average F1 Score (F1) for eight main vegetation types were more than 90% and 89%, respectively, and the vegetation-type map agreed well with the census areas. The forest characteristic-based model demonstrated a greater potential in FCH prediction, with an R-value 60.47% greater than the traditional single model, suggesting that the addition of the multivariate quality control and forest structure characteristics could positively contribute to the prediction of FCH. We generated a 30 m continuous FCH map by the forest characteristic-based model and evaluated the product with about 35 km2 of airborne laser scanning (ALS) validation data (R = 0.73, RMSE = 2.99 m), which were 45.34% more precise than the China FCH, 2019. These findings demonstrate the potential of our proposed workflow for monitoring regional continuous FCH, and will greatly benefit accurate forest resources assessment.

Integration of Optical Remote Sensing and Laser Point Cloud for Forest Stock Estimation in Karst Mountainous Areas

Integration of Optical Remote Sensing and Laser Point Cloud for Forest Stock Estimation in Karst Mountainous Areas

Integrating Ward’s Clustering Stratification and Spatially Correlated Poisson Disk Sampling to Enhance the Accuracy of Forest Aboveground Carbon Stock Estimation

In forest resource surveys, using sampling methods to estimate aboveground carbon stock (ACS) can significantly reduce survey costs. This study improves the accuracy of ACS estimation by optimizing the stratified sampling design. The sampling process was divided into two stages: stratification and intra-stratum sampling. For stratification, remote sensing features were used as stratification variables, and a spatial clustering stratification method was introduced. For intra-stratum sampling, a composite method, Spatially Correlated Poisson Disk Sampling (SCPDS), was proposed. Using Random Forest (RF) and the sample points selected by SCPDS, the ACS was estimated and compared with traditional sampling methods for Pinus densata in Shangri-La, Yunnan, China. The results showed that (1) by selecting effective stratification variables (e.g., texture features), the required sample size was reduced by up to 19.35% compared to that of simple random sampling; (2) the Ward clustering method greatly improved stratification heterogeneity; (3) for intra-stratum sampling, the SCPDS method ensured spatial independence within strata, particularly at low sampling rates (1%–5%), where its error was significantly lower than that of other methods, indicating greater stability and improved accuracy; (4) the SCPDS-based model achieved the best fitting accuracy, with R2 = 0.886. The total carbon stock of Pinus densata using RF was 7,872,787.5 t, closely matching forest management inventory (FMI) data. Through sampling, even with a relatively small sample size, the representative plots can still accurately reflect ACS estimates that are consistent with those derived from large-scale plot surveys. Thus, the optimized stratified sampling method effectively reduced sampling costs while significantly enhancing the stability and accuracy of the results.

Estimation of carbon stock and economic value of Sanjiangyuan National Park, China

Sanjiangyuan National Park (SNP) is one of China’s national parks with high ecological significance and ecological vulnerability. In order to promote the healthy development of national park ecosystems, it is extremely important to investigate the carbon sequestration potential and the economic value of carbon sequestration in the SNP. Here, we comprehensively analyzed the characteristics of land use change from 1985 to 2022 based on the land use transfer matrix and land use dynamic degree, calculated the ecosystem carbon storage based on the InVEST model, and estimated the economic value of carbon storage in each period combined with compound interest present value method. The results show that: (1) From 1985 to 2022, carbon stocks generally went through three phases: increasing, stabilizing, and decreasing. (2) Low carbon density areas were concentrated in the Kekexili Natural Reserve in the Yangtze River Source Park and a small part of the Yellow River Source Park and Lancang River Source Park; The high carbon density area was concentrated in the water area and the forest land in the southeast of the Lancang River Source Park. (3) Land use change in SNP had a decisive impact on carbon storage change, and the economic value of carbon storage in SNP continued to increase from 1985 to 2022. In order to provide a scientific basis for the sustainable management of carbon sinks and the reduction of carbon emissions in the SNP area, it is necessary to further study the carbon cycle process, carbon storage and carbon emissions in the region in the future.

Spatial Variation and Stock Estimation of Soil Organic Carbon in Cropland in the Black Soil Region of Northeast China

Soil organic carbon (SOC) sequestration in cropland is not only instrumental in combating climate change, but it also significantly enhances soil fertility. It is imperative to precisely and accurately quantify the SOC sequestration potential and assess the relative significance of various multiple explanatory factors in a timely manner. We studied 555 soil samples from the cropland topsoil (0–15 cm) across the black soil region in Northeast China between the years 2021 and 2022, and we identified 16 significant impact factors using one-way ANOVA and Pearson correlation coefficient analysis. In addition, the Random Forest (RF) model outperformed the Cubist model in predicting the spatial distribution of SOC contents. The predicted ranges of SOC contents span from 5.24 to 43.93 g/kg, with the average SOC content using the RF model standing at 17.24 g/kg in Northeast China. Stepwise regression and structural equation modeling revealed climate and topography as key factors affecting SOC distribution. The SOC density in the study area varied from 0.51 to 9.11 kg/m2, averaging 3.30 kg/m2, with a total SOC stock of 1226.64 Tg. The SOC sequestration potential in the study area was estimated at 3057.65 Tg by the categorical maximum method, with a remaining sequestration capacity of 1831.01 Tg. The study area has great potential for SOC sequestration. We hope to transform the theoretical value of SOC sequestration potential into actual SOC sequestration capacity by promoting sustainable agriculture and additional strategies. Our findings provide insights into the global soil conditions, SOC storage capacities, and effective SOC management strategies.

Quantifying sedimentary ’blue carbon’ in relation to canopy cover in the seagrass meadows of Turneffe Atoll, Belize

IntroductionSeagrass sediments are important ‘blue carbon’ reservoirs which store climatically significant quantities of organic carbon (Corg) at the global scale. Seagrass meadows that overly these sediments also provide a range of critical ecosystem services including shoreline stabilization, storm surge protection, and fisheries nursery grounds. However, the controls over accumulation and the sources of organic C to these sediments beds are highly variable and poorly understood with the relative importance of hydrodynamic setting, species composition and canopy density being unclear.MethodsHere we address these questions using the first observation-based estimates of Corg stocks and provenance on Turneffe Atoll, Belize, made via remotely-sensed habitat extent, local Corg data and isotopic data. Sedimentary Corg was highest in sediments underlying the most sheltered meadows and decreased with increasing exposure to wind and wave energy with the seagrass meadows in the central lagoon containing an extensive deposit of mangrove derived organic carbon, stabilized and protected by the overlying seagrass meadow.ResultsThe influence of species composition appeared weak with the ubiquitous species T. testudinum occurring across a wide range of hydrodynamic regimes ranging from the most sheltered to the most energetic and being associated with a wide range of sedimentary organic C concentrations. Importantly from the perspective of remote sensing, org C concentrations were unrelated to canopy density. We hypothesize that this decoupling of organic C concentration from seagrass canopy cover reflects a much longer timescale for carbon storage in the sediments than the lifespan of the seagrass plants themselves and/or a substantial non seagrass derived organic C burden in seagrass sediments. Overall, we conservatively estimate that the top 30cm of sediments underlying the seagrass meadows overlying carbonate sediments on the atoll exterior store 0.58 x 106 Mg Corg, most of which is seagrass-derived, whilst the sediments underlying the meadows within the central lagoon store an additional 1.28 x 106 Mg Corg. When the maximum possible extent of seagrass is considered, this estimate increases to 3.54 x 106 Mg Corg. Substantial Corg stocks extending >1m depth were observed across all sites, and so these inventories are considered conservative.DiscussionA preliminary ‘cost of loss’ for sedimentary Corg in the top 30 cm of Turneffe Atoll’s seagrass meadows, based on a carbon trading value of €60 tCO2 (eq), is estimated at €42 million for the outer atoll, increasing to €136 million when the mangrove-derived sediments of the central atoll are considered and €260 million when turbid areas are assumed to contain seagrass.

Soil organic carbon stocks as driven by land use in Mato Grosso State: the Brazilian Cerrado agricultural frontier

To address national and global demand for agro-based products, agricultural expansion has rapidly become a norm in Brazil since 1950s to date. In recent decades, agricultural expansion and technological advancement have placed the country among the top producers and exporters of agricultural products. The paradigm shifts in farming system from conventional to integrated approach has brought significant changes in land use, which consequently influenced carbon sequestration and soil organic carbon (SOC). This is more prevalent in the State of Mato Grosso, one of the most producers of food in Brazil. On this background, we hypothesized that though forests have potential for SOC stock, which decreases due to conversion to cropland but in longer-term with sustainable management, carbon might accrual significantly in cropland. Therefore, this paper aimed to unveil the nexus between long term land use and carbon stock changes and estimate future SOC stocks in Mato Grosso State of Brazil. To achieve this aim, a hybridization of machine learning and the InVEST prediction models was applied to estimate the land use changes and the SOC stocks between 1990 and 2020 and estimate for 2050. The study revealed that between 1990, 2020, and 2050, croplands increased significantly by at least 78%, pastures decreased by 32%, while forests decreased marginally by about 4% due to agricultural expansions. However, in 1990 and 2020, the SOC stock was slightly up to 147.34 Mg ha−1, it recorded an increase after a longer-time (i.e., in 2050). This increase was substantially under the forests, and marginally in the croplands. Climate-smart agricultural systems such as crop-livestock forest, integrated crop-livestock, and other conservation agricultural practices have great potential to contribute to sustainable development by increasing the levels of carbon in agricultural soils especially, after a longer period. Therefore, agricultural policies geared towards low carbon agriculture should be fully integrated into the various government decision making processes as this will guarantee food security and maximize soil carbon sequestration and stocks in the long term. Simultaneously, it is crucial to promote the dissemination of best practices for implementing and sustaining conservation efforts, thereby safeguarding the carbon stocks established to prevent their depletion. This will also support the Brazilian government in achieving its Nationally determined contributions (NDCs) through agricultural soils.

A rough calculation of the litter carbon in agarwood small scale forest in Asahan Regency, North Sumatra, Indonesia

Abstract One of the sources of forest carbon is the forest floor, such as dead organic material and litter. The aim of this research is to analyze estimates of litter and soil carbon stocks in small-scale forest agarwood in Asahan, North Sumatra. Laboratory analysis was carried out at the Agricultural Laboratory of the Universitas Sumatera Utara. The analysis of litter biomass is carried out in the laboratory. The average litter carbon on land in Hessa Perlompongan Village is 0.36 tons/ha, which is greater than the average litter carbon on land in Bunut Village, which is 0.19 tons/ha. There is a significant difference between the average carbon biomass of litter carbon in Hessa Perlompongan Village and Bunut Village.

Green turtle tracking leads the discovery of seagrass blue carbon resources.

Seagrass meadows are natural carbon sinks, and their conservation and restoration play a crucial role in climate change mitigation and adaptation. However, blue carbon projects are hindered, in most nations, by major gaps in understanding the distribution and extent of seagrasses. Here, we show how satellite tracking of green turtles (Chelonia mydas) provided a major advance in identifying novel seagrass blue carbon resources in the Red Sea. By tracking 53 nesting green turtles, we identified 38 distinctive foraging sites. All ground-truthed foraging sites (100%) identified a seagrass meadow, surpassing the 40% (n = 30) accuracy of satellite imagery-based inferences. Sampling from these turtle-derived locations represents a greater range of depths than previously sampled in the Red Sea providing a carbon stock estimate of 4.89 ± 0.83 kg Corg (organic carbon) m-2. By improving estimates of seagrass extent and associated blue carbon, our approach can support the conservation of blue carbon resources in data-deficient regions worldwide.

Carbon Stocks in Two Aquatic Marshes on the Caribbean and Pacific Coast of Panama

Wetlands are critical ecosystems globally, boasting significant ecological and economic value. They play a crucial role in the hydrological cycle by storing water and carbon, thereby helping to mitigate climate variability. But in Panama, little is known about the carbon stored in freshwater wetlands. This research presents the estimation of the carbon stocks of two freshwater wetlands in Panama, located on both sides of the Caribbean (Portobelo) and Pacific (Tonosi) coasts. The methodology consisted of transects of 125 m and 40 m wide, with six circular plots every 25 m; in each transect, the diameter of the tree trunk was measured at breast height (1.3 m) and the species was recorded, and in the same plots, soil samples were collected in triplicate by depth intervals. The average total ecosystem carbon storage (TECS) for the aquatic wetlands of Tonosí was 106.26 ± 18.3 Mg C ha−1, and for Portobelo, it was 355.09 ± 70.02 Mg C ha−1. These recorded values can contribute to the conservation of wetlands, supporting Panama’s nationally determined NDC contributions. However, despite the acceptance that wetlands are important nature-based solutions, national data on soil carbon stocks in freshwater wetlands are still scarce and their protection should be increased.

Carbon stocks in Norwegian eelgrass meadows across environmental gradients

Seagrass meadows are well-known for their capacity to capture and store blue carbon in sediments. However carbon stocks vary significantly between meadows, spanning more than three orders of magnitude on both local and global scales. Understanding the drivers of seagrass carbon stocks could help improve strategies for incorporating blue carbon into management plans. Here, we measured sediment carbon stocks in eelgrass (Zostera marina) meadows and unvegetated areas along the Norwegian coast, spanning wide gradients in temperature, wave exposure, water depth, salinity, and eelgrass biomass. Carbon stocks were generally higher in eelgrass meadows than in adjacent unvegetated areas, yet they displayed considerable variation (400 − 30 000 g C m−2 at 50 cm sediment depth) even among nearby sites. Overall, the highest carbon stocks were found in deeper, muddier, sheltered meadows near river mouths. These sites likely have the highest input and retention of carbon from different sources. Consequently, they should be prioritized as conservation targets for preserving coastal blue carbon stocks. Despite ever-increasing efforts to quantify seagrass blue carbon globally, high uncertainties still persist, partly due to differing methodologies, processes, and environmental context. Blue carbon stock estimates could be improved through the coordination of standardised mapping and sampling methods.

Seafloor sediment characterization improves estimates of organic carbon standing stocks: an example from the Eastern Shore Islands, Nova Scotia, Canada

Abstract. Continental shelf sediments contain some of the largest stocks of organic carbon (OC) on Earth and play a vital role in influencing the global carbon cycle. Quantifying how much OC is stored in shelf sediments and determining its residence time is key to assessing how the ocean carbon cycle will be altered by climate change and possibly human activities. Spatial variations in terrestrial carbon stocks are well studied and mapped at high resolutions, but our knowledge of the distribution of marine OC in different seafloor settings is still very limited, particularly in dynamic and spatially variable shelf environments. This lack of knowledge reduces our ability to understand and predict how much and for how long the ocean sequesters CO2. In this study, we use high-resolution multibeam echosounder (MBES) data from the Eastern Shore Islands offshore Nova Scotia (Canada), combined with OC measurements from discrete samples, to assess the distribution of OC content in seafloor sediments. We derive four different spatial estimates of organic carbon stock: (i) OC density estimates scaled to the entire study region assuming a homogenous seafloor, (ii) interpolation of OC density estimates using empirical Bayesian kriging, (iii) OC density estimates scaled to areas of soft substrate estimated using a high-resolution classified substrate map, and (iv) empirical Bayesian regression kriging of OC density within areas of estimated soft sediment only. These four distinct spatial models yielded dramatically different estimates of standing stock of OC in our study area of 223 km2: 80 901, 58 406, 16 437 and 6475 t of OC, respectively. Our study demonstrates that high-resolution mapping is critically important for improved estimates of OC stocks on continental shelves and for the identification of carbon hotspots that need to be considered in seabed management and climate mitigation strategies.

Estimation of carbon stocks in the areas of a seasonally dry tropical forest in the Brazilian semi-arid region

In addition to its ecological importance, the Caatinga biome, one of the most extensive seasonally dry tropical forests (SDTF) in the world, has a relevant socioeconomic role as it is used as a primary natural resource by local communities. However, inadequate ecosystem management practices have resulted in gradual loss of natural vegetation in this ecosystem. Carbon stock estimation is a parameter that can contribute as a support tool for managing and maintaining the few remaining natural vegetated areas. In this study, we calibrated and validated the CENTURY model to simulate carbon stocks in areas of the Caatinga, in the state of Pernambuco, and compared the predictive capacity of the CENTURY model with available estimates. In the validation dataset, the average for biomass stocks was 33.1 Mg C ha−1, this value is close to those observed in the literature for the region. The model also performed well when estimating carbon stocks in the soil (r2 = 0.79, p = 0.017). Ecosystem modeling combined with Geographic Information Systems (GIS) is a promising tool for estimating carbon stocks in the Caatinga, where field sampling campaigns are generally expensive and have scarce research funding opportunities. Furthermore, it also allows the evaluation of the effect of environmental changes on C stocks in long-term studies, which is essential for creating and implementing public policies to mitigate and adapt to the impacts of climate change on the ecosystem. However, additional efforts are needed to improve C estimates, especially in areas with a strongly negative water balance.

Aboveground Carbon Stock Estimation Based on Backpack LiDAR and UAV Multispectral Imagery at the Forest Sample Plot Scale

Aboveground carbon stocks (AGCs) in forests play an important role in understanding carbon cycle processes. The global forestry sector has been working to find fast and accurate methods to estimate forest AGCs and implement dynamic monitoring. The aim of this study was to explore the effects of backpack LiDAR and UAV multispectral imagery on AGC estimation for two tree species (Larix gmelinii and Betula platyphylla) and to emphasize the accuracy of the models used. We estimated the AGC of Larix gmelinii and B. platyphylla forests using multivariate stepwise linear regression and random forest regression models using backpack LiDAR data and multi-source remote sensing data, respectively, and compared them with measured data. This study revealed that (1) the diameter at breast height (DBH) extracted from backpack LiDAR and vegetation indices (RVI and GNDVI) extracted from UAV multispectral imagery proved to be extremely effective in modeling for estimating AGCs, significantly improving the accuracy of the model. (2) Random forest regression models estimated AGCs with higher precision (Xing’an larch R2 = 0.95, RMSE = 3.99; white birch R2 = 0.96, RMSE = 3.45) than multiple linear regression models (Xing’an larch R2 = 0.92, RMSE = 6.15; white birch R2 = 0.96, RMSE = 3.57). (3) After combining backpack LiDAR and UAV multispectral data, the estimation accuracy of AGCs for both tree species (Xing’an larch R2 = 0.95, white birch R2 = 0.96) improved by 2% compared to using backpack LiDAR alone (Xing’an larch R2 = 0.93, white birch R2 = 0.94).

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.

Optimization of vegetation carbon content parameters and their application in carbon storage estimation in China

As a key parameter for C pool and flux assessments, vegetation carbon (C) content can be used in ecological models to predict climate-induced changes in the C sequestration capacity of vegetation. However, the differences in methods for upscaling C content from the organ to the community scale and their impact on regional C stock estimates have been ignored. Based on a comprehensive community structure survey of 72 typical natural ecosystems in China and 27,905 measured samples of plant organs (leaves, twigs, trunks, and roots), we first quantified the differences among scaling-up methods for vegetation C content. These methods included the community or dominant species-weighted mean, geometric mean, arithmetic mean, and traditional empirical coefficients (45 % and 50 %), and their impact on C storage estimation at the regional scale. Comparing the accuracy, variability, and response patterns of the different scaling-up methods, the dominant C species biomass-weighted mean (CDWM) method had the highest similarity to the community-weighted C mean (CCWM) method. Concerning vegetation C storage estimation in China's natural terrestrial ecosystems, the relative errors of the other methods ranged from −2.6 % to 8.22 % compared with that of the CCWM method (18.39 Pg C). The empirical coefficients had the highest uncertainty, with a 45 % empirical coefficient underestimating the vegetation C stock by 2.60 %, and a 50 % empirical coefficient overestimating it by 8.22 %. The CDWM method proposed here has high reliability for C storage estimation (overestimated by only 0.44 %), making it a preferable sampling and scaling-up method for regional C content and stock assessment. Additionally, our study provided the C content of plant organs for China's provinces and typical vegetation types based on the CCWM, which could be used for regional C stock assessment and C cycle models.

Proximity to inlet channel drives spatial variation in sediment carbon across a lagoonal seagrass meadow

Seagrass meadows can be sinks for organic carbon, but estimates of global organic carbon stocks are complicated by substantial spatial variability in organic carbon burial observed within meadows. To improve estimates of organic carbon burial in seagrass meadows, it is necessary to understand the causes of the spatial heterogeneity. This study investigated relationships between spatial patterns in sediment organic carbon storage and accretion rates, hydrodynamics, and proximity to sources of organic carbon in a current-dominated Zostera marina Linnaeus meadow in Menemsha Pond, Massachusetts, USA. Sediment and velocity measurements were conducted at six stations along a 150-m transect across the meadow oriented perpendicular to the pond's unvegetated inlet channel. The meadow's edge near the channel had higher organic carbon than the channel as well as the highest organic carbon within the meadow. With increasing distance from the meadow's edge, all of the following decreased: sediment organic and total carbon, sediment accretion rates, peak tidal velocity, sediment trap mass deposition rate, and the relative contribution of non-seagrass sources to sediment organic carbon. Lower tidal velocities farther from the inlet channel reduced sediment resuspension, consistent with lower sediment trap mass deposition, which should enhance organic carbon content and organic carbon accretion rates. However, the opposite trend of decreasing organic carbon content (>50 % across the transect) and decreasing accretion rates with distance from channel was observed. This suggested that the local hydrodynamic intensity was not controlling organic carbon accretion, which was instead constrained by supply limitation and controlled by the lagoon-scale flow circulation.

Mapping Forest Carbon Stock Distribution in a Subtropical Region with the Integration of Airborne Lidar and Sentinel-2 Data

Forest carbon stock is an important indicator reflecting a forest ecosystem’s structures and functions. Its spatial distribution is valuable for managing natural resources, protecting ecosystems and biodiversity, and further promoting sustainability, but accurately mapping the forest carbon stock distribution in a large area is a challenging task. This study selected Changting County, Fujian Province, as a case study to explore a method to map the forest carbon stock distribution using the integration of airborne Lidar, Sentinel-2, and ancillary data in 2022. The Bayesian hierarchical modeling approach was used to estimate the local forest carbon stock based on airborne Lidar data and field measurements, and then the random forest approach was used to develop a regional forest carbon stock estimation model based on the Sentinel-2 and ancillary data. The results indicated that the Lidar-based carbon stock distribution effectively provided sample plots with good spatial representativeness for modeling regional carbon stock with a coefficient of determination (R2) of 0.7 and root mean square error (RMSE) of 12.94 t/ha. The average carbon stocks were 48.55 t/ha, 55.51 t/ha, and 57.04 t/ha for Masson pine, Chinese fir, and broadleaf forests, respectively. The carbon stock in non-conservation regions was 15.2–16.1 t/ha higher than that in conservation regions. This study provides a promising method through the use of airborne Lidar data as a linkage between sample plots and Sentinel-2 data to map the regional carbon stock distribution in those subtropical regions where serious soil erosion has led to a relatively sparse forest canopy density. The results are valuable for local government to make scientific decisions for promoting ecosystem restoration due to water and soil erosion.

The global distribution and drivers of wood density and their impact on forest carbon stocks

The density of wood is a key indicator of the carbon investment strategies of trees, impacting productivity and carbon storage. Despite its importance, the global variation in wood density and its environmental controls remain poorly understood, preventing accurate predictions of global forest carbon stocks. Here we analyse information from 1.1 million forest inventory plots alongside wood density data from 10,703 tree species to create a spatially explicit understanding of the global wood density distribution and its drivers. Our findings reveal a pronounced latitudinal gradient, with wood in tropical forests being up to 30% denser than that in boreal forests. In both angiosperms and gymnosperms, hydrothermal conditions represented by annual mean temperature and soil moisture emerged as the primary factors influencing the variation in wood density globally. This indicates similar environmental filters and evolutionary adaptations among distinct plant groups, underscoring the essential role of abiotic factors in determining wood density in forest ecosystems. Additionally, our study highlights the prominent role of disturbance, such as human modification and fire risk, in influencing wood density at more local scales. Factoring in the spatial variation of wood density notably changes the estimates of forest carbon stocks, leading to differences of up to 21% within biomes. Therefore, our research contributes to a deeper understanding of terrestrial biomass distribution and how environmental changes and disturbances impact forest ecosystems.

Estimation of carbon stocks of woody plant species in church forests of West Gojjam zone, Northwestern Ethiopia: Implications for climate change mitigation

Forests, particularly church forests, play a crucial role in mitigating climate change by absorbing and storing CO2, preserving biodiversity, and acting as carbon sinks. This study aimed to estimate the biomass and carbon stocks of various woody species in church forests in the West Gojjam zone. Twenty-six church forests were selected based on agroecology, elevation, size, and proximity to population centers. Vegetation data were collected using a systematic sampling technique, with 20 m x 20 m (400 m2) plots established along transect lines oriented at 120° intervals at 60°, 180°, and 300° within each church forest. Measurements of diameter at breast height (DBH) and height were taken for all matured woody plants with a DBH ≥ 2.5 cm. Data analysis was performed using one-way ANOVA to evaluate the effects of altitude, forest size, and human disturbance on aboveground biomass (AGB) and carbon stocks. Additionally, linear regression was applied to investigate the relationship between vegetation structure (species richness, density, and diversity) and biomass accumulation. The results revealed a total of 111 woody species, dominated by indigenous species particularly from the Fabaceae family. The study church forests had Shannon diversity index and richness ranging from 1.73 to 3.47 and 7 to 45, respectively. The results showed that the 26 church forests had an average aboveground biomass (AGB) of 31.97 ± 3.31 tons ha-1 and a CO2 equivalence of 97.15 ± 10.47 tons ha-1. The AGB and aboveground carbon (AGC) values varied among the church forests, of which Debre Mihret Mesk Kidanemihret had the highest AGB with 99.00 tons ha-1 and 49.50 tons ha-1 AGC, indicating their substantial capacity for carbon storage. Conversely, Korch Silassie church forest displayed the lowest AGB, suggesting ecological challenges that necessitate targeted conservation efforts. These findings underscore the critical role of church forests as carbon sinks, capable of sequestering atmospheric CO2 and contributing to the mitigation of climate change. The findings of the present study suggest the integration of church forests into national and international climate policies, such as REDD+ to leverage their potential in reducing emissions from deforestation and forest degradation.