Forest Biomass Research Articles

More Accurately Estimating Aboveground Biomass in Tropical Forests With Complex Forest Structures and Regions of High‐Aboveground Biomass

AbstractAccurately estimating aboveground biomass (AGB) in tropical forests is vital for managing the threats posed by deforestation, degradation, and climate change. However, challenges persist in accurately estimating AGB in high AGB regions. This study aims to accurately estimate the AGB of regions with high AGB by using spatial statistical analyses based on AGB estimates made by machine‐learning fusion of multisource data. We hypothesize that incorporating dominant auxiliary factors in the analysis increases the estimation accuracy. This study focuses on tropical forests located in Longyan, Fujian Province, China, covering an area of 19,028 km2. Multisource data are used, including airborne laser scanning, the Shuttle Radar Topography Mission digital elevation model, the Landsat Operational Land Imager, and the National Forest Inventory. Based on GeogDetector's spatial covariance matrix and the spatial similarity principle, we identify key auxiliary factors (dominant tree species, canopy closure, and herbaceous cover) and investigated how auxiliary variables can improve estimation accuracy. Empirical Bayesian kriging regression prediction introduces the main auxiliary factors to refine AGB estimates. These refinements significantly enhance the accuracy of AGB estimates, particularly for high AGB, resulting in a 0.1 increase in R2, a 7.0% reduction in root mean square error, a 13.5% reduction in mean square error, and a 6.6% reduction in mean absolute error when compared with the AGB estimates obtained by using machine learning to fuse multisource data. Thus, incorporating spatial statistical analysis into the integration of multisource data and machine learning for AGB estimation can enhance the accuracy of high‐AGB estimates in intricate forest structures, resulting in precise AGB maps.

Imbalances in Dissolved Elemental Export Fluxes Disclose “Hidden” Critical Zone Compartments

AbstractIn streams, short‐term element‐specific solute fluxes are often not balanced with long‐term chemical weathering fluxes determined in the residual solids from fractional element loss and denudation rate. The ratio of both estimates—the “Dissolved Export Efficiency” (DEE)—is frequently <1, indicating deficits in the stream dissolved load. To explore the cause of the stream deficits, we performed a daily water sampling campaign for one year in a forested headwater watershed in Southern Germany. We sampled surface runoff, above‐canopy and below‐canopy precipitation, subsurface flow from the organic soil layer, upper, and deep mineral soil, and groundwater. Regolith samples were obtained from a drill core and revealed the weathering front to lie between 7 and 15 m depth. We found a DEE < 1 for K, Si, Al, Fe. These elements are characterized by shallow slopes in C‐Q relationships, and the imbalances were found to originate in the deep saprolite. Their export pathway potentially includes “hidden” Critical Zone compartments or fluxes, presumably unsampled colloids that are exported preferentially during rare flushing events with stochastic temporal distribution. The DEE of nutritive elements like Ca, Mg, and P is also <1. These elements are characterized by steeper C‐Q slopes, and their imbalance can be explained by deep nutrient uptake followed by nutrient retainment in re‐growing forest biomass or export in plant debris. The collective evidence for these imbalances, including previous evidence from metal stable isotopes, suggests that the deep Critical Zone represents the location for chemical or biogenic retention and release of solutes.

Stepwise Calibration of Age‐Dependent Biomass in the Integrated Biosphere Simulator (IBIS) Model

AbstractMany land surface models (LSMs) assume a steady‐state assumption (SS) for forest growth, leading to an overestimation of biomass in young forests. Parameters inversion under SS will potentially result in biased carbon fluxes and stocks in a transient simulation. Incorporating age‐dependent biomass into LSMs can simulate real disequilibrium states, enabling the model to simulate forest growth from planting to its current age, and improving the biased post‐calibration parameters. In this study, we developed a stepwise optimization framework that first calibrates “fast” light‐controlled CO2 fluxes (gross primary productivity, GPP), then leaf area index (LAI), and finally “slow” growth‐controlled biomass using the Global LAnd Surface Satellite (GLASS) GPP and LAI products, and age‐dependent biomass curves for the 25 forests. To reduce the computation time, we used a machine learning‐based model to surrogate the complex integrated biosphere simulator LSM during calibration. Our calibrated model led to an error reduction in GPP, LAI, and biomass by 28.5%, 35.3% and 74.6%, respectively. When compared with net biome productivity (NBP) using no‐age‐calibrated parameters, our age‐calibrated parameters increased NBP by an average of 50 gC m−2 yr−1 across all forests, especially in the boreal needleleaf evergreen forests, the NBP increased by 118 gC m−2 yr−1 on average, increasing the estimate of the carbon sink in young forests. Our work highlights the importance of including forest age in LSMs, and provides a novel framework for better calibrating LSMs using constraints from multiple satellite products at a global scale.

The spatial distribution of tree-tree interaction effects on soil microbial biomass and respiration.

The capacity of forests to sequester carbon in both above- and belowground compartments is a crucial tool to mitigate rising atmospheric carbon concentrations. Belowground carbon storage in forests is strongly linked to soil microbial communities that are the key drivers of soil heterotrophic respiration, organic matter decomposition and thus nutrient cycling. However, the relationships between tree diversity and soil microbial properties such as biomass and respiration remain unclear with inconsistent findings among studies. It is unknown so far how the spatial configuration and soil depth affect the relationship between tree richness and microbial properties. Here, we studied the spatial distribution of soil microbial properties in the context of a tree diversity experiment by measuring soil microbial biomass and respiration in subtropical forests (BEF-China experiment). We sampled soil cores at two depths at five locations along a spatial transect between the trees in mono- and hetero-specific tree pairs of the native deciduous species Liquidambar formosana and Sapindus saponaria. Our analyses showed decreasing soil microbial biomass and respiration with increasing soil depth and distance from the tree in mono-specific tree pairs. We calculated belowground overyielding of soil microbial biomass and respiration - which is higher microbial biomass or respiration than expected from the monocultures - and analysed the distribution patterns along the transect. We found no general overyielding across all sampling positions and depths. Yet, we encountered a spatial pattern of microbial overyielding with a significant microbial overyielding close to L. formosana trees and microbial underyielding close to S. saponaria trees. We found similar spatial patterns across microbial properties and depths that only differed in the strength of their effects. Our results highlight the importance of small-scale variations of tree-tree interaction effects on soil microbial communities and functions and are calling for better integration of within-plot variability to understand biodiversity-ecosystem functioning relationships.

Improving Forest Above-Ground Biomass Estimation by Integrating Individual Machine Learning Models

The accurate estimation of forest above-ground biomass (AGB) is crucial for sustainable forest management and tracking the carbon cycle of forest ecosystem. Machine learning algorithms have been proven to have great potential in forest AGB estimation with remote sensing data. Though many studies have demonstrated that a single machine learning model can produce highly accurate estimations of forest AGB in many situations, efforts are still required to explore the possible improvement in forest AGB estimation for a specific scenario under study. This study aims to investigate the performance of novel ensemble machine learning methods for forest AGB estimation and analyzes whether these methods are affected by forest types, independent variables, and spatial autocorrelation. Four well-known machine learning models (CatBoost, LightGBM, random forest (RF), and XGBoost) were compared for forest AGB estimation in the study using eight scenarios devised on the basis of two study regions, two variable types, and two validation strategies. Subsequently, a hybrid model combining the strengths of these individual models was proposed for forest AGB estimation. The findings indicated that no individual model outperforms the others in all scenarios. The RF model demonstrates superior performance in scenarios 5, 6, and 7, while the CatBoost model shows the best performance in the remaining scenarios. Moreover, the proposed hybrid model consistently has the best performance in all scenarios in spite of some uncertainties. The ensemble strategy developed in this study for the hybrid model substantially improves estimation accuracy and exhibits greater stability, effectively addressing the challenge of model selection encountered in the forest AGB forecasting process.

Understanding Disturbance Regimes From Patterns in Modeled Forest Biomass

AbstractNatural and anthropogenic disturbances are important drivers of tree mortality, shaping the structure, composition, and biomass distribution of forest ecosystems. Differences in disturbance regimes, characterized by the frequency, extent, and intensity of disturbance events, result in structurally different landscapes. In this study, we design a model‐based experiment to investigate the links between disturbance regimes and spatial biomass patterns. First, the effects of disturbance events on biomass patterns are simulated using a simple dynamic carbon cycle model based on different disturbance regime attributes, which are characterized via three parameters: μ (probability scale), α (clustering degree), and β (intensity slope). 856,800 dynamically stable biomass patterns were then simulated using combined disturbance regime, primary productivity, and background mortality. As independent variables, we use biomass synthesis statistics from simulated biomass patterns to retrieve three disturbance regime parameters. Results show confident inversion of all three “true” disturbance parameters, with Nash‐Sutcliffe efficiency of 94.8% for μ, 94.9% for α, and 97.1% for β. Biomass histogram statistics primarily dominate the prediction of μ and β, while texture features have a more substantial influence on α. Overall, these results demonstrate the association between biomass patterns and disturbance regimes. Given the increasing availability of Earth observation of biomass, our findings open a new avenue to understand better and parameterize disturbance regimes and their links with vegetation dynamics under climate change. Ultimately, at a large scale, this approach would improve our current understanding of controls and feedback at the biosphere‐atmosphere interface in the present Earth system models.

Above-ground biomass of Moso bamboo forests in China influenced by climate, soil and topography: A meta-analysis

Moso bamboo is an essential forest resource in China, covering the largest area and stock of any bamboo species. Four provinces (Fujian, Hunan, Zhejiang, Jiangxi) each have more than 70 million hectares, collectively accounting for 80 % of the total area of Moso bamboo in China. We named these four provinces as the core area for Moso bamboo and the remaining area in other provinces as non-core area. There has been limited research on how climate, topography, and soil factors collectively affect Moso bamboo biomass in both the core and non-core areas. To better understand the variations, data on DBH and individual biomass were gathered from 348 plots from 25 literature sources and a total of 18 plots from four field surveys conducted in 2019. We found evidence that the above-ground biomass of Moso bamboo forests is influenced by a combination of climate, soil nutrients and topography, with different factors playing a more significant role in the core and non-core areas. Moso bamboo had higher individual above-ground biomass in the core area compared to the non-core area. In the core area, above-ground biomass of Moso bamboo was more influenced by climate than by soil nutrients. In this area, annual minimum temperature had a negative effect on above-ground biomass, while precipitation had a positive effect. In contrast, soil total nitrogen had the largest positive effect on above-ground biomass in non-core area. These findings may contribute to the development of a future biomass model for Moso bamboo forests.

In situ pretreatment of wood samples with deep eutectic solvents for enhanced lignin removal and enzymatic Saccharification efficiency optimization

Deep eutectic solvents (DESs) are commonly utilized as an effective pretreatment method for biomass materials, particularly for enhancing the value of lignocellulosic biomass. However, to the best of our knowledge, the utilization of DESs for in situ pretreatment of wood samples has been seldom reported. In this study, the ability of five DESs systems to in-situ pretreatment of balsa wood samples was investigated. Among of them, the Choline Chloride-Ethylene Glycol-P-Toluenesulfonic Acid (ChCl-EG-PTSA) system showing an excellent lignin removal rate (the lignin removal of 87.34 % and cellulose retention of 86.32 %), which is significantly better than the delignification effect of the traditional acid chlorite method (lignin removal of 72,13 % and cellulose retention of 64.57 %). In addition, in the subsequent enzymatic saccharification experiments, after the wood samples were pretreated in situ by the ChCl-EG-PTSA system, the enzymatic saccharification yield of cellulose reached 75.46 %, and the enzymatic saccharification yield of xylose reached 92 %. At the same time, this study also used SEM, FTIR, XPS, TGA and other testing technologies to conduct detailed analysis and characterization of the pretreated samples. This comprehensive analysis unequivocally demonstrated the viability of the ChCl-EG-PTSA system as an environmentally sustainable and efficient approach for pretreating forest biomass. Significantly, this in-situ wood pretreatment strategy significantly lays a robust research groundwork for the development of multifunctional wood fiber composites in subsequent stages.

Biomass Allocation of China’s Forests as Indicated by a Literature-Based Allometry Database

Allometry reflects the quantitative relationship between the allocation of resources among different organs. Understanding patterns of forest biomass allocation is critical to comprehending global climate change and the response of terrestrial vegetation to climate change. By collecting and reorganizing the existing allometric models of tree species in China, we established a database containing over 3000 empirical allometric models. Based on this database, we analyzed the model parameters and the effect of climate on forest biomass allocation under the context of ‘optimal allocation theory’. We showed that (1) the average and median exponent of power functions for above-ground biomass were 2.344 and 2.385, respectively, which significantly deviated from the theoretical prediction of 2.667 by metabolic theory (p < 0.01). (2) The parameters of the allometric model were not constant, and not significantly correlated with temperature, precipitation, latitude, and elevation (p > 0.05), but were more closely related to individual size (p < 0.01). (3) Among different types of forests, the proportion of above-ground biomass in tropical rainforests and subtropical evergreen rainforests was significantly higher than that in temperate forests and boreal forests (p < 0.05). The proportion of trunk and branch biomass allocated to tropical rainforest was significantly higher than that of boreal forest (p < 0.05), while the proportion of root and leaf biomass allocated to tropical rainforest was significantly lower than that of boreal forest (p < 0.05). (4) The abiotic environment plays a crucial role in determining the allocation of plant biomass. The ratio of below-ground/above-ground biomass is significantly and negatively correlated with both temperature and rainfall (p < 0.01), and significantly and positively correlated with altitude and latitude (p < 0.01). This means that as temperature and rainfall increase, there is a decrease in the amount of biomass allocated to below-ground structures such as roots. On the other hand, as altitude and latitude increase, there is an increase in below-ground biomass allocation. These findings highlight the importance of considering the influence of abiotic factors on plant growth and development.

Effects of Eucalyptus plantation characteristics and environmental factors on GEDI waveform metrics

ABSTRACT The Global Ecosystem Dynamics Investigation (GEDI) is a high-resolution lidar instrument, facilitating landscape monitoring through forest structure and biomass assessments. Previous research has mainly focused on estimating forest attributes such as height and biomass from GEDI data. In this comprehensive study, we empirically investigate how Eucalyptus plantation characteristics and environmental factors collectively influence GEDI waveform metrics. Using a diverse dataset that includes measurements of canopy height, planting density, foliage characteristics, species composition, understorey presence and environmental conditions such as climate and soil properties, we explore the complex relationships between these attributes and GEDI waveform metrics using a two-step approach that includes assessing the linear relationship between each in-situ parameter and GEDI metrics, and also random forest modelling. Key findings include: 1) While canopy height (Ht) plays a significant role in shaping the GEDI waveform, forest wood volume (Volume), which incorporates height and diameter at breast height (DBH) of the trees and also tree stem density (Ntree/ha), exerts an even greater influence on GEDI metrics, in particular on the waveform extent (WE) and relative heights (RH); 2) The influence of factors such as foliage biomass and wood volume on GEDI waveform varies vertically from the forest floor up to forest top, and the model was more precise to predict the top part of the waveform; 3) No single attribute can solely account for the observed variations in the waveform characteristics, but multiple interactions between different forest and environmental features contribute to the complex patterns in the received waveforms. This research contributes to a deeper understanding of the complex relationships between forest plantation characteristics and GEDI waveform metrics and highlights the need to consider a wide range of forest attributes in the analyse of GEDI waveform to produce more accurate canopy height or volume estimates.

Effects of successional age, plot size, and tree size on the relationship between diversity and aboveground biomass in tropical dry forests

Effects of successional age, plot size, and tree size on the relationship between diversity and aboveground biomass in tropical dry forests

Heterostructure catalyst coupled wood-derived carbon and cobalt-iron alloy/oxide for reversible oxygen conversion

As promising energy-storage devices, zinc–air batteries (ZABs) exhibit slow reaction kinetics for oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) occurring at their electrodes. High-performance bifunctional catalysts must thus be synthesized to accelerate the reversible conversion of oxygen and improve the rate and overall performance of ZABs. Herein, we reported the promising prospects of self-supported composite electrodes composed of wood-derived carbon (WDC) and bimetallic cobalt-iron alloys/oxides (CoFe-CoFe2O4@WDC) as efficient electrocatalysts for alkaline ORR/OER. WDC provided a favorable three-phase interface for heterogeneous reactions owing to its layered porous structure and genetic stability, thereby enabling mass diffusion and improving reaction kinetics. The CoFe2O4 spinel surface was reduced to bimetallic CoFe alloy to form abundant heterostructure interfaces that promote electron transfer. Under alkaline conditions, the optimized composite electrode exhibited a remarkable high half-wave potential of 0.85 V and an exceptionally low overpotential of 1.49 V. It also exhibited stable performance over an impressive 2340 cycles in a ZAB. Theoretical calculations also confirmed that the heterointerface addresses the issue of proton scarcity throughout the reaction and actively facilitates the creation of O–O bonds during the reversible transformation of oxygen. This study introduces a new concept for developing bifunctional and efficient electrocatalysts based on charcoal and encourages the sustainable and high-value use of forest biomass resources.Graphical

National forest carbon harvesting and allocation dataset for the period 2003 to 2018

Abstract. Forest harvesting is one of the anthropogenic activities that most significantly affect the carbon budget of forests. However, the absence of explicit spatial information on harvested carbon poses a huge challenge in assessing forest-harvesting impacts, as well as the forest carbon budget. This study utilized provincial-level statistical data on wood harvest, the tree cover loss (TCL) dataset, and a satellite-based vegetation index to develop a Long-term harvEst and Allocation of Forest Biomass (LEAF) dataset. The aim was to provide the spatial location of forest harvesting with a spatial resolution of 30 m and to quantify the post-harvest carbon dynamics. The validations against the surveyed forest harvesting in 133 cities and counties indicated a good performance of the LEAF dataset in capturing the spatial variation of harvested carbon, with a coefficient of determination (R2) of 0.83 between the identified and surveyed harvested carbon. The linear regression slope was up to 0.99. Averaged from 2003 to 2018, forest harvesting removed 68.3 ± 9.3 Mt C yr−1, of which more than 80 % was from selective logging. Of the harvested carbon, 19.6 ± 4.0 %, 2.1 ± 1.1 %, 35.5 ± 12.6 % 6.2 ± 0.3 %, 17.5 ± 0.9 %, and 19.1 ± 9.8 % entered the fuelwood, paper and paperboard, wood-based panels, solid wooden furniture, structural constructions, and residue pools, respectively. Direct combustion of fuelwood was the primary source of carbon emissions after wood harvest. However, carbon can be stored in wood products for a long time, and by 2100, almost 40 % of the carbon harvested during the study period will still be retained. This dataset is expected to provide a foundation and reference for estimating the forestry and national carbon budgets. The 30 m × 30 m harvested-carbon dataset from forests in China can be downloaded at https://doi.org/10.6084/m9.figshare.23641164.v2 (Wang et al., 2023).

Estimating Forest Aboveground Biomass Using a Combination of Geographical Random Forest and Empirical Bayesian Kriging Models

Accurately estimating forest aboveground biomass (AGB) is imperative for comprehending carbon cycling, calculating carbon budgets, and formulating sustainable forest management plans. Currently, random forest (RF) and other machine learning models are widely used to estimate forest AGB, as they can effectively handle nonlinear relationships. However, by constructing a global model using all the samples collected from a study area, these models fail to account for the spatial heterogeneity in the AGB and cannot correct the prediction biases, thereby constraining the estimation accuracy. To overcome these limitations, we proposed a novel approach termed geographical random forest and empirical Bayesian kriging (GRFEBK). This hybrid model combines the localized modeling capability of geographical random forest (GRF) with the bias correction strength of empirical Bayesian kriging (EBK). GRF adapts RF to account for the spatial heterogeneity of the AGB, while EBK utilizes the spatial autocorrelation of residuals to correct the prediction deviations. This study was conducted in Hainan Island, utilizing spectral bands, vegetation indices, tasseled cap components derived from Landsat-8 imagery, backscattering coefficients from ALOS-2 synthetic aperture radar, topographic features, and the forest canopy height as the explanatory variables. A total of 195 forest aboveground biomass (AGB) samples were collected for modeling and assessing the predictive accuracy. The results demonstrate that, among the tested models, including GRFEBK, RF, support vector machine (SVM), k-nearest neighbor (KNN), geographically weighted regression (GWR), GRF, and EBK, GRFEBK attains the highest R2 (0.78) and the lowest RMSE (36.04 Mg/ha) and RRMSE (22.87%), significantly outperforming the conventional models and using GRF or EBK alone. These results demonstrate that by accounting for local non-stationarity in AGB and correcting prediction biases, GRFEBK achieves significantly higher accuracy than conventional RF and other models. While the results are promising, the computational cost of GRFEBK and its performance under varying geographical conditions warrant further investigation at larger scales to assess its broader applicability. Nevertheless, GRFEBK provides an innovative and more reliable approach for accurate forest AGB estimation with great potential to support global forest resource monitoring.

Biomass recovery of coastal young mangrove plantations in Central Thailand

Around one-third of the world’s most carbon-rich ecosystems, mangrove forests, have already been destroyed in Thailand owing to coastal development and aquaculture. Improving these degraded areas through mangrove plantations can restore various coastal ecosystem services, including CO2 absorption and protection against wave action. This study examines the biomass of three coastal mangrove plantations (Avicennia alba) of different ages in Samut Prakarn province, Central Thailand. Our aim was to understand the forest biomass recovery during the early stages of development, particularly fine root biomass expansion. In the chronosequence of the mangrove plantations, woody biomass increased by 40% over four years from 79.7 ± 11.2 Mg C ha-1 to 111.7 ± 12.3 Mg C ha−1. Fine root biomass up to a depth of 100 cm was 4.47 ± 0.33 Mg C ha−1, 4.24 ± 0.63 Mg C ha−1, and 6.92 ± 0.32 Mg C ha−1 at 10, 12, and 14 year-old sites, respectively. Remarkably, the fine root biomass of 14-year-old site was significantly higher than those of the younger sites due to increase of the biomass at 15–30 cm and 30–50 cm depths. Our findings reveal that the biomass recovery in developing mangrove plantations exhibit rapid expansion of fine roots in deeper soil layers.

Short-term effects of understory removal on understory diversity and biomass of temperate forests in northeast China

IntroductionUnderstory removal is a traditional practice in forest management to reduce fire risk and promote seedling regeneration. However, its effect on understory diversity, biomass and soil nutrients in temperate forest ecosystems is less known, which limits our assessment of the effectiveness of understory vegetation management.MethodsWe quantified the composition of the understory species, their diversity, and the biomass of the understory and factors driving changes in these parameters in primary mixed broad-leaved Pinus koraiensis forest (BKF), secondary Betula platyphylla forest (BF), and Larix gmelinii plantation (LF) in northeast China after a 5-year understory removal.ResultsAfter understory removal, the number of shrub and herb species in BKF and LF decreased, while the number of shrub species in BF increased significantly and that of herb species decreased; the species with strong light preference, Equisetum hyemale, Impatiens noli-tangere, and Filipendula Palmata, were dominant in the herb layer of the three forest types; Shannon–Wiener diversity, Pielou evenness, and Simpson diversity of the herb layer in LF increased significantly (P < 0.05), while those of the shrub and herb layers in BF and LF showed no significant changes (P > 0.05). The total understory biomass of understory of BKF and LF decreased by 0.94 t·hm−2 and 1.32 t·hm−2, respectively, while that of BF increased by 1.31 t·hm−2; soil NH4+-N and total phosphorus (TP) were the key factors regulating understory vegetation diversity and biomass, respectively.ConclusionThese results suggest that understory removal is a beneficial management strategy for increasing shrub biomass and diversity in secondary forests, while it should be avoided in primary forests and plantations to prevent the reduction of understory plant diversity and soil nutrient loss.

Generic Carbon Budget Model for Assessing National Carbon Dynamics toward Carbon Neutrality: A Case Study of Republic of Korea

Forests play a crucial role in South Korea’s carbon neutrality goal and require sustainable management strategies to overcome age-class imbalances. The Generic Carbon Budget Model (GCBM) offers a spatially explicit approach to simulate carbon dynamics at a regional scale. In this study, we utilized the GCBM to analyze the carbon budget of forests in South Korea and produce spatiotemporal maps for distribution of the forest biomass. The growth parameters of five representative tree species (Pinus densiflora Siebold & Zucc., Larix kaempferi Carr., Pinus koraiensis Siebold & Zucc., Quercus mongolica Fisch. ex Ledeb., Quercus variabilis Blume), which are the main species in South Korea, were used to operate the model. In addition, spatial data for harvest and thinning management activities were used to analyze the effects of anthropogenic activities. In 2020, the aboveground and belowground biomass were 112.98 and 22.84 tC ha−1, and the net primary productivity was 8.30 tC ha−1 year−1. These results were verified using comparison with statistics, a literature review, and MODIS NPP. In particular, broadleaf is higher than conifer forest in net primary production. The Canadian GCBM with Korean forest inventory data and yield curves successfully estimated the aboveground and belowground biomass of forests in South Korea. Our study demonstrates that these estimates can be mapped in detail, thereby supporting decision-makers and stakeholders in analyzing the carbon budget of the forests in South Korea and developing novel schemes that can serve regional and national aims related to forest management, wood utilization, and ecological preservation. Further studies are needed to improve the initialization of dead organic matter pools, given the large-scale afforestation efforts in recent decades that have established South Korea’s forests on predominantly non-forest sites.

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.

Repeat GEDI footprints measure the effects of tropical forest disturbances

More of the Amazon rainforest is disturbed each year than completely deforested, but the impact of these disturbances on the carbon cycle remains poorly understood. Recent algorithmic advances using optical and radar remote sensing have improved detection of disturbances at fine spatiotemporal resolution, but quantifying changes in forest structure and biomass associated with these detected disturbances has proven challenging. The Global Ecosystem Dynamics Investigation (GEDI), a spaceborne lidar mission collecting data from 2019 to 2023, provides an opportunity to address this problem. GEDI captured billions of measurements of forest height, plant area, and understory structure within ∼25-m diameter footprints scattered across the tropics. Though the instrument had no guaranteed repeat cycle, it sometimes sampled nearby locations twice; some of these spatially near-coincident footprints happened to measure forest structure before and after a detected disturbance, providing information on how disturbance affected aboveground biomass. In this study, we developed an efficient general-purpose open-source pipeline for identifying spatially coincident footprints, which is a computationally complex task, and used the pipeline to find over 13,700 footprint pairs with intervening disturbance events across the Amazon biome. We also identified a set of ∼65,000 spatially near-coincident footprint pairs that lacked an intervening disturbance but came from regions with similar disturbance threat; these provided a control dataset to evaluate the effectiveness of estimating forest structure and biomass changes with nearby footprints. Analysis of this Amazon-wide dataset demonstrated that GEDI was able to measure statistically significant canopy height and biomass deficits following non-stand-replacing disturbances as small as 900 m2 (30 m × 30 m). GEDI's unique three-dimensional view of forest structure also reflected the effects of different intensities of fire disturbance, including 20% of burned areas where the upper canopy retained most of its height, but the understory suffered substantial foliage losses. Finally, we modeled the relationship between Landsat and Sentinel-1 disturbance detection parameters and GEDI-estimated percent biomass loss, showing that certain satellite-derived intensity metrics are correlated with increasing biomass loss and identifying temporal trends in biomass loss and recovery following disturbance. This work represents an important step towards the development of a pan-tropical, spatially explicit system for tracking carbon losses and structural changes arising from forest disturbance.

Diminishing legacy effects from forest fertilization on stand structure, vegetation community, and soil function

While there is consensus that fertilization with nitrogen (N) is a cost-effective way of increasing both forest biomass yield and timber harvest profitability, the strength and longevity of legacy effects are debated. To quantify legacy effects of past fertilization, we analysed 21 mixed Pinus sylvesteris and Picea abies stands. The stands, on average 23 years old at the time of this study, were either unfertilized (n=7), fertilized with 150 kg N ha−1 once 36 years ago (n=7), or twice, 45 and 36 years ago, respectively (n=7), during the previous stand rotation. We performed measurements on soil N mineralisation and N availability, forest growth, ground vegetation community composition, soil and vegetation C/N ratios and soil C and N stocks, many of which responded to legacy N fertilization earlier in stand development. Our results show that the legacy effects of fertilization during the previous stand rotation have diminished through time, indicating an eventual convergence of stand properties. Specifically, all significant effects present in the previous measurement period (over a decade ago), were weaker or completely absent in the current study (i.e. 36 years after fertilization and 23 years after initiation of the new stands). None-the-less, this indicates a longer legacy effect of N fertilization than what is normally considered and suggests that care should be taken to mitigate unwanted, long-term effects when utilizing N addition to promote tree growth in boreal forests.