Forest Carbon Stock Estimates Research Articles

Comparison of Global Aboveground Biomass Estimates From Satellite Observations and Dynamic Global Vegetation Models

AbstractThe global forest carbon stocks represent the amount of carbon stored in woody vegetation and are important for quantifying the ability of the global forests to sequester atmospheric CO2 and to provide ecosystem services (e.g., timber) under climate change. The forest ecosystem carbon pool estimates are highly variable and poorly quantified in areas lacking forest inventory estimates. Here, we compare and analyze aboveground biomass (AGB) estimates from five satellite‐based global data sets and nine dynamic global vegetation models (DVGMs). We find that across the data sets, mean AGB exhibits the largest variability around the tropical area. In addition, AGB shows a similar latitudinal trend but large variability among the data sets. Satellite‐based AGB estimates are lower than those simulated by DVGMs. The divergence among the satellite‐based AGB estimates can be driven by the methodology, input satellite products, and the forested areas used to estimate AGB. The modeled NPP, autotrophic respiration, and carbon allocation mostly drive the variability of AGB simulated by DGVMs. The future availability of a high‐quality global forest area map is anticipated to improve AGB estimate accuracy and to reduce the discrepancies among different satellite‐ and model‐based AGB estimates. We suggest the carbon‐modeling community reexamine the methodology used to estimate AGB and forested areas for a more robust global forest carbon stock estimation.

Climate changes have alleviated constraints on forest carbon storage capacity since 1970 in most of the Northern Hemisphere

Global climate action is urgent, with forest carbon stock critical for mitigating climate change, yet vulnerable to its impacts. However, the long-term dynamics of climate-driven forest carbon stock have not fully been expressed. Here, we introduce the Forest Carbon Stock Accumulated by Single Tree growth (FAST) framework and constructed a counterfactual scenario to isolate and quantify the climate-driven changes in forest total carbon stock for 1901–2022. Results show that breakpoints in climate-driven forest carbon stock occurred post-1970 are observed over 62% of the study areas, with Europe experiencing the latest, followed by Asia, and North America the earliest. Furthermore, we observe a prevailing increasing trend in climate-driven forest carbon stock, especially in post-breakpoints period (from 53% to 68%), indicating that climate changes have alleviated constraints on forest carbon storage capacity in most areas. FAST can be utilized for historical, current and future forest carbon stock estimation, providing scientific support for sustainable forest management decisions.

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.

Advancing forest carbon stocks’ mapping using a hierarchical approach with machine learning and satellite imagery

Remote sensing of forests is a powerful tool for monitoring the biodiversity of ecosystems, maintaining general planning, and accounting for resources. Various sensors bring together heterogeneous data, and advanced machine learning methods enable their automatic handling in wide territories. Key forest properties usually under consideration in environmental studies include dominant species, tree age, height, basal area and timber stock. Being proxies of stand productivity, they can be utilized for forest carbon stock estimation to analyze forests’ status and proper climate change mitigation measures on a global scale. In this study, we aim to develop an effective machine learning-based pipeline for automatic carbon stock estimation using solely freely available and regularly updated satellite observations. We employed multispectral Sentinel-2 remote sensing data to predict forest structure characteristics and produce their detailed spatial maps. Using the Extreme Gradient Boosting (XGBoost) algorithm in classification and regression settings and management-level inventory data as reference measurements, we achieved quality of predictions of species equal to 0.75 according to the F1-score, and for stand age, height, and basal area, we achieved an accuracy of 0.75, 0.58 and 0.56, respectively, according to the R2. We focused on the growing stock volume as the main proxy to estimate forest carbon stocks on the example of the stem pool. We explored two approaches: a direct approach and a hierarchical approach. The direct approach leverages the remote sensing data to create the target maps, and the hierarchical approach calculates the target forest properties using predicted inventory characteristics and conversion equations. We estimated stem carbon stock based on the same approach: from Earth observation imagery directly and using biomass and conversion factors developed for the northern regions. Thus, our study proposes an end-to-end solution for carbon stock estimations based on the complexation of inventory data at the forest stand level, Earth observation imagery, machine learning predictions and conversion equations for the region. The presented approach enables more robust and accurate large-scale assessments using limited annotated datasets.

Biomass Equations and Carbon Stock Estimates for the Southeastern Brazilian Atlantic Forest

Tropical forests play an important role in mitigating global climate change, emphasizing the need for reliable estimates of forest carbon stocks at regional and global scales. This is essential for effective carbon management, which involves strategies like emission reduction and enhanced carbon sequestration through forest restoration and conservation. However, reliable sample-based estimations of forest carbon stocks require accurate allometric equations, which are lacking for the rainforests of the Atlantic Forest Domain (AFD). In this study, we fitted biomass equations for the three main AFD forest types and accurately estimated the amount of carbon stored in their above-ground biomass (AGB) in Rio de Janeiro state, Brazil. Using non-destructive methods, we measured the total wood volume and wood density of 172 trees from the most abundant species in the main remnants of rainforest, semideciduous forest, and restinga forest in the state. The biomass and carbon stocks were estimated with tree-level data from 185 plots obtained in the National Forest Inventory conducted in Rio de Janeiro. Our locally developed allometric equations estimated the state’s biomass stocks at 70.8 ± 5.4 Mg ha−1 and carbon stocks at 35.4 ± 2.7 Mg ha−1. Notably, our estimates were more accurate than those obtained using a widely applied pantropical allometric equation from the literature, which tended to overestimate biomass and carbon stocks. These findings can be used for establishing a baseline for monitoring carbon stocks in the Atlantic Forest, especially in the context of the growing voluntary carbon market, which demands more consistent and accurate carbon stock estimations.

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.

Old-growth mapping in Patagonia’s evergreen forests must integrate GEDI data to overcome NFI data limitations and to effectively support biodiversity conservation

Structural metrics from the NASA Global Ecosystem Dynamics Investigation (GEDI) are vital to support monitoring, restoration and protection of high biodiversity spots. The Chilean Patagonia is home of pristine and unique ecosystems where GEDI data can provide knowledge on structure and biomass and expand the current limitation in Patagonia on field measurements and open lidar data. Conservation efforts are ramping up across our study area in the Aysén Region. Ongoing and future conservation efforts precise from unbiased estimates of forest carbon stocks to value and to protect forest biodiversity. We evaluate GEDI structural metrics and biomass estimates in the evergreen forests of the Chilean Patagonia using National Forest Inventory (NFI) data and unmanned aerial vehicle (UAV) lidar mapping to assess aboveground biomass density (AGBD) across six conservation projects. Estimates from GEDI ranged between 186 and 255 Mg ha-1. Both AGBD distributions for NFI plots and the gridded map were similarly centered as both converged in the median value (100 Mg ha-1). However, the NFI gridded map turned asymptotic after 200 Mg ha-1, showing the lack of representation in the NFI sampling of dense high-C forests, whereas GEDI estimates were able to sense these areas of old-growth forests. The distribution of GEDI tracks, the scale extension of the projects and the available NFI information explained the differences between using GEDI samples and the NFIcalibrated solution using UAV-lidar. Our results showed the limitations of the Chilean NFI over steep locations and uplands where old-growth forests dominate, and where only GEDI can depict structural metrics and reliable estimates of ADGB and forest carbon. This study is highly relevant to support conservation projects in Patagonia using publicly available GEDI data science, and to enforce ambitious legislation in Chile designed to protect biodiversity and for which quantification, reporting and verification of forest carbon stocks is crucial.

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

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

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

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

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

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

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

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

Carbon stock projection for four major forest plantation species in Japan

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

Global Forest Plantations Mapping and Biomass Carbon Estimation

AbstractManagement of forest plantations is a natural based solution to the global‐scale mitigation of climate change; however, the role of carbon sequestration remains poorly understood, and this is hampered by a lack of detailed distribution on the global forest plantations. For the first time, we generated a global spatial distribution for forest plantations (GSDFP) during 2015 at a spatial resolution of 250 m using hierarchical extraction based on a machine learning algorithm taking time series of MODIS and ALOS PALSAR imagery as the source data, and finally estimates the biomass carbon stored in forest plantations. The resultant map was validated using reference samples visually interpreted, as well as reference samples collected from previous studies, and inter‐compared with statistical forest plantation area and regional forest plantation maps, and the GSDFP was found to accurately represent the spatial distribution of forest plantations around the globe. Globally, forest plantations account for 7.35% (360.22 × 104 km2) of the global forest area and are estimated to contribute 4.60% (12.85 Pg C) of global forest biomass carbon. The forest plantation map and biomass carbon stock estimates will assist forest management and provide a benchmark for the estimation of the forest plantation carbon sink.

The Role of Wood Density Variation and Biomass Allocation in Accurate Forest Carbon Stock Estimation of European Beech (Fagus sylvatica L.) Mountain Forests

The European beech (Fagus sylvatica L.) is one of the most common tree species in Romania, with importance both economically and environmentally. Accurate methods of biomass assessment at the tree compartment level (i.e., stump, stem, branches, and leaves) are necessary for carbon stock estimation. Wood density (WD) is an important factor in determining biomass and, ultimately, the tree’s carbon content. The average tree density was found to be 578.6 kg/m3. For this study, WD was evaluated by the weighting method related to tree volume. Also, to investigate a practical approach to determining the weighted wood density (WWDst), models were run using density at the base of the tree (WDBase), density at breast height level using discs (WDDBH), the wood core density (WDic), and the diameter at breast height (DBH) as predictors. The biomass assessment was conducted using different model evaluations for WWDst as well as allometric equations using the destructive method. From the results, it was noted that using the WWDst, the total biomass was underestimated by −0.7% compared to the biomass measured in the field. For allometric equations that included DBH and tree height as independent variables, the explained variability was around 99.3% for total aboveground biomass (AGBtotal), while it was 97.9% for allometric function using just the DBH. Overall, the distribution of biomass across different compartments was as follows: 73.5% in stems, 23.8% in branches, 1.9% in stumps, and 1.3% in leaves. The study findings offer valuable insights into WD, biomass distribution among different components, and biomass allometric quantification in natural beech forest environments in mountainous areas.

Improving Pinus densata Carbon Stock Estimations through Remote Sensing in Shangri-La: A Nonlinear Mixed-Effects Model Integrating Soil Thickness and Topographic Variables

Forest carbon sinks are vital in mitigating climate change, making it crucial to have highly accurate estimates of forest carbon stocks. A method that accounts for the spatial characteristics of inventory samples is necessary for the long-term estimation of above-ground forest carbon stocks due to the spatial heterogeneity of bottom-up methods. In this study, we developed a method for analyzing space-sensing data that estimates and predicts long time series of forest carbon stock changes in an alpine region by considering the sample’s spatial characteristics. We employed a nonlinear mixed-effects model and improved the model’s accuracy by considering both static and dynamic aspects. We utilized ground sample point data from the National Forest Inventory (NFI) taken every five years, including tree and soil information. Additionally, we extracted spectral and texture information from Landsat and combined it with DEM data to obtain topographic information for the sample plots. Using static data and change data at various annual intervals, we built estimation models. We tested three non-parametric models (Random Forest, Gradient-Boosted Regression Tree, and K-Nearest Neighbor) and two parametric models (linear mixed-effects and non-linear mixed-effects) and selected the most accurate model to estimate Pinus densata’s above-ground carbon stock. The results showed the following: (1) The texture information had a significant correlation with static and dynamic above-ground carbon stock changes. The highest correlation was for large-window mean, entropy, and variance. (2) The dynamic above-ground carbon stock model outperformed the static model. Additionally, the dynamic non-parametric models and parametric models experienced improvements in prediction accuracy. (3) In the multilevel nonlinear mixed-effects models, the highest accuracy was achieved with fixed effects for aspect and two-level nested random effects for the soil and elevation categories. (4) This study found that Pinus densata’s above-ground carbon stock in Shangri-La followed a decreasing, and then, increasing trend from 1987 to 2017. The mean carbon density increased overall, from 19.575 t·hm−2 to 25.313 t·hm−2. We concluded that a dynamic model based on variability accurately reflects Pinus densata’s above-ground carbon stock changes over time. Our approach can enhance time-series estimates of above-ground carbon stocks, particularly in complex topographies, by incorporating topographic factors and soil thickness into mixed-effects models.

Developing crown width model for mixed forests using soil, climate and stand factors

Abstract The tree crown is a useful measure of tree vigour and is highly relevant to a tree's environmental adaptability. Crown allometry depends on environmental and stand conditions. Several studies have focussed on the effects of climate change and competitive intensity on the crown, but the regulatory role of soil resources and diversity on crown allometry and carbon allocation has been neglected. Data from 20,994 trees in 232 mixed forests collected between 2011 and 2019 were located near four major mountain ranges in northeast China. The proposed crown width model includes the stand developmental stage, soil, climate, competition intensity, species mixture, species diversity, structural diversity and their interactions. We observed that the cross‐species allometric scaling exponent does not conform to the universal scaling law. Our results showed that crown width increased with increasing soil bulk density, quadratic mean diameter and coefficient of diameter variation but decreased with increasing de Martonne aridity index, basal area, Simpson index and species mixture. The interaction between quadratic mean diameter and soil bulk density had a significant negative effect on crown width. The influence of a particular factor within the interaction term on crown width was modulated by the gradients of other factors. Furthermore, soil bulk density contributed more to crown width modelling than the aridity index, and structural diversity had a greater effect on crown width than species diversity. Synthesis. Our results provide new insights into the environmental variability of crown allometry in mixed forests under global change, which is critical for improving regional and global estimates of forest biomass and carbon stocks.

Accuracy of the Copernicus High-Resolution Layer Forest Type (HRL FTY) assessed with domestic NFI sampling plots in Poland

Abstract Over the past years, several remote sensing maps of land cover have been produced, but they still exhibit certain differences compared to the real land use that reduce their value for climate and carbon cycle modelling as well as for national estimates of forest carbon stocks and their change. This paper outlines a straightforward framework for evaluating map accuracy and estimating uncertainty in land cover area, specifically for forest-related land cover maps in Poland for the year 2018. The study compares stratified field-based data from the National Forest Inventory (NFI) with remote sensing data on forest variables, at the pixel level, in order to identify suitable methods for accuracy and area uncertainty estimation. Additionally, the paper introduces and presents a variety of accuracy metrics applicable to assess overall uncertainties in GHG inventories. The results indicate that the High-Resolution Layer Forest Type (HRL FTY) product (part of the broader Copernicus Land Monitoring Service [CLMS] portfolio), assessed using NFI field-based information, achieved an overall accuracy (OA) of 69.2%. This metric varies among particular nature protection forms, with the highest observed ones in Natura 2000 sites of 70.45%. The primary source of map errors was associated with distinguishing between broad-leaved and coniferous forest areas. Improving future maps necessitates more precise differentiation between species to better support national forest monitoring systems for the purpose of greenhouse gas (GHG) inventories where information on the spatial distribution and variability of forests sources, biodiversity assessment, threat prevention, estimation of carbon content is becoming an important part of the associated reporting system.

Precise and unbiased biomass estimation from GEDI data and the US Forest Inventory

Atmospheric CO2 concentrations are dependent on land-atmosphere carbon fluxes resultant from forest dynamics and land-use changes. These fluxes are not well-constrained, in part because reliable baseline estimates of forest carbon stocks and the associated uncertainties are lacking. NASA's Global Ecosystem Dynamics Investigation (GEDI) produces estimates of aboveground biomass density (AGBD) that are unique because GEDI's hybrid estimation framework enables formal uncertainty calculations that accompany the biomass estimates. However, GEDI's estimates are not without issue; a recent validation using design-based AGBD estimates from the US Forest Inventory and Analysis (FIA) program revealed systematic differences between GEDI and FIA estimates within a hexagon tessellation of the continental United States. Here, we explored these differences and identified two issues impacting GEDI's estimation process: incomplete filtering of low quality GEDI observations and regional biases in GEDI's footprint-level biomass models. We developed a solution to each, in the form of improved data filtering and GEDI-FIA fusion AGBD models, developed in a scale-invariant small area estimation framework, that were compatible with hybrid estimation. We calibrated 10 regional Fay-Herriot models at the hexagon scale for application at the unit scale of GEDI footprints, for which we provide a mathematical justification and empirical testing of the models' scale-invariance. These models predicted realistic distributions of unit level AGBD, with equal or improved performance relative to GEDI's L4A models for all regions. We then produced GEDI-FIA fusion estimates that were more precise than the FIA estimates and resulted in a bias reduction of 86.7% relative to the original GEDI estimates: 19.3% due to improved data filtering and 67.5% due to the new AGBD models. Our findings indicate that (1) small area estimation models trained in a scale-invariant framework can produce realistic predictions of AGBD, and (2) there is substantial spatial variability in the relationship between GEDI forest structure metrics and AGBD. This work is a step toward achieving reliable baseline forest carbon stocks, provides a viable methodology for training remote sensing biomass models, and may serve as a reference for other investigations of GEDI AGBD estimates.

Plot-level estimates of aboveground biomass and soil organic carbon stocks from Nepal’s forest inventory

Given the contribution of deforestation and forest degradation to the global carbon cycle, forest resources are critical to mitigating the global climate change effects. Improved forest monitoring across different biomes is important to understand forest dynamics better and improve global projections of future atmospheric CO2 concentration. Better quantification of the forest carbon cycle advances scientific understanding and informs global negotiations about carbon emissions reduction. High-quality estimates of forest carbon stocks are currently scarce in many developing countries. Here, we present the most comprehensive georeferenced data set to date of plot-level forest carbon estimates for Nepal. Based on field observations from Nepal’s national forest inventory of 2010–2014; the data set includes estimates for two major forest carbon pools, aboveground biomass (AGB) and soil organic carbon (SOC) stocks from 2,009 and 1,156 inventory plots, respectively. The dataset fills an important knowledge gap about forest carbon stocks in the Central Himalayas, a region with highly heterogeneous environmental conditions and rich biodiversity that is poorly represented in existing global estimates of forest carbon.