Forest Biomass Research Articles

The key role of forest disturbance in reconciling estimates of the northern carbon sink

AbstractNorthern forests are an important carbon sink, but our understanding of the driving factors is limited due to discrepancies between dynamic global vegetation models (DGVMs) and atmospheric inversions. We show that DGVMs simulate a 50% lower sink (1.1 ± 0.5 PgC yr−1 over 2001–2021) across North America, Europe, Russia, and China compared to atmospheric inversions (2.2 ± 0.6 PgC yr−1). We explain why DGVMs underestimate the carbon sink by considering how they represent disturbance processes, specifically the overestimation of fire emissions, and the lack of robust forest demography resulting in lower forest regrowth rates than observed. We reconcile net sink estimates by using alternative disturbance-related fluxes. We estimate carbon uptake through forest regrowth by combining satellite-derived forest age and biomass maps. We calculate a regrowth flux of 1.1 ± 0.1 PgC yr−1, and combine this with satellite-derived estimates of fire emissions (0.4 ± 0.1 PgC yr−1), land-use change emissions from bookkeeping models (0.9 ± 0.2 PgC yr−1), and the DGVM-estimated sink from CO2 fertilisation, nitrogen deposition, and climate change (2.2 ± 0.9 PgC yr−1). The resulting ‘bottom-up’ net flux of 2.1 ± 0.9 PgC yr−1 agrees with atmospheric inversions. The reconciliation holds at regional scales, increasing confidence in our results.

Combining Sentinel-2 and diverse environmental data largely improved aboveground biomass estimation in China's boreal forests.

Accurately mapping aboveground biomass (AGB) in China's boreal forests is crucial for assessing global carbon stock and formulating forest management strategies but remains challenging as the environmental heterogeneity complicates AGB estimation. Here, we investigated the relative gains of integrating Sentinel-2 and environmental data, as well as synthetic aperture radar (SAR) images to map AGB in China's boreal forests. We used two machine learning algorithms, random forest and gradient boosting regression (GBR), and four dataset combinations to develop the AGB models, then evaluated the AGB map by carrying on uncertainty analysis and comparing it with existing AGB products. Results showed that the GBR model based on Sentinel-2 and environmental data presented the best AGB estimation capability (R2: 0.75, RMSE: 23.60 Mg/ha), while further adding SAR images had negative effects on the model improvement. The Tasseled Cap Distance, short-wave infrared from Sentinel-2, Black dragon fire disturbance, Elevation, and Geographic locations were found to be significant contributors to AGB prediction. Our AGB estimates exhibited moderate to low uncertainty and outperformed other existing AGB maps in China's boreal forests based on independent validation assessment. The AGB distribution presented a noticeable south-north gradient difference, ranging from 3.23 to 346.37 Mg/ha. This study provides new insight into AGB estimation through the integration of Sentinel-2 imagery and multiple environmental data and offers a basis for sustainable management in China's boreal forests.

Assessing the potential of polarimetric decomposition of Sentinel-1 SAR for the estimation of mangrove forest biomass

Assessing the potential of polarimetric decomposition of Sentinel-1 SAR for the estimation of mangrove forest biomass

Disentangling the Complex Effects of Seasonal Drought, Floor Mass, and Roots on Soil Microbial Biomass in a Subtropical Moist Forest

Severe seasonal droughts driven by global climate change significantly alter the cycling of carbon and nutrients in forest ecosystems, while the investigation into the impacts of floor mass and plant roots on soil microbial biomass within the context of recurrent seasonal droughts is still rare. To investigate the environmental determinants governing soil microbial biomass with the escalating severity of seasonal droughts, we conducted a study in a montane subtropical moist evergreen broad-leaved forest in southwestern China from June 2019 to May 2023. The study results revealed that soil microbial biomass, as well as soil moisture, floor mass, and plant roots, showed an apparent single-hump modal within one year. In the comparative analysis of the soil microbial biomass fluctuation amplitudes across control and watered plots, a discernible disparity was observed, indicating significant differences in microbial biomass dynamics between the respective experimental conditions. The pooled data revealed a statistically significant influence of seasonal drought, floor mass, plant roots, and their reciprocal interactions on the soil microbial biomass, highlighting these factors as pivotal determinants of microbial community dynamics. This study elucidates the interactive regulatory mechanisms by which seasonal drought, floor mass, and plant roots collectively modulate soil microbial biomass within tropical and subtropical forests, offering insights into the complex ecological processes governing microbial community dynamics. This interactive regulation might influence the trajectory of plant species and soil microbial communities, facilitating their adaptive development and evolutionary responses.

Quantifying past forest cover and biomass changes in the Ecuadorian Amazon.

Here, we developed and applied models to quantitatively reconstruct forest cover and biomass changes at three lakes in northwestern Amazonia over the past > 1500 yr. We used remotely sensed data and a modern dataset of 50 Amazonian lakes to develop generalized linear models that predict aboveground biomass, using phytolith morphotypes and forest cover as predictor variables. Also, we applied a published beta regression model to predict forest cover within 200 m of each lake, using Poaceae phytoliths. Charcoal and maize phytoliths were analysed to identify past land use. Results showed forest cover and biomass changes at our study sites ranged between 48-84% and 142-438 Mg ha-1, respectively. Human occupation was discontinuous, with major changes in forest cover and biomass coinciding with periods of land use. Forest cover and biomass decreased notably after fire (at all sites) or cultivation events (Lakes Zancudococha, Kumpaka). The timing and ecological impact of past land use were spatially and temporally variable. Our results suggest past human impact was small-scaled and heterogenous in northwestern Amazonia, with a significant impact of fire on forest cover and biomass changes.

Displacement Factors for Aerosol Emissions From Alternative Forest Biomass Use

ABSTRACTSubstituting alternative materials and energy sources with forest biomass can cause significant environmental consequences, such as alteration in the released emissions which can be described by displacement factors (DFs). Until now, DFs of wood‐based materials have included greenhouse gas (GHG) emissions and have been associated with lower fossil and process‐based emissions than non‐wood counterparts. In addition to GHGs, aerosols released in combustion processes, for example, alter radiative forcing in the atmosphere and consequently have an influence on climate. In this study, the objective was to quantify the changes in the most important aerosol emission components for cases when wood‐based materials and energy were used to replace the production of high‐density polyethylene (HDPE) plastic, common fossil‐based construction materials (concrete, steel and brick), non‐wood textile materials and energy produced by fossil fuels and peat. For this reason, we expanded the DF calculations to include aerosol emissions of total suspended particles (TSP), respirable particulate matter (PM10), fine particles (PM2.5), black carbon (BC), nitrogen oxides (NOx), sulphur dioxide (SO2) and non‐methane volatile organic compounds (NMVOCs) based on the embodied energies of materials and energy sources. The DFs for cardboard implied a decrease in BC, SO2 and NMVOC emissions but an increase in the other emission components. DFs for sawn wood mainly indicated higher emissions of both particles and gaseous emissions compared to non‐wood counterparts. DFs for wood‐based textiles demonstrated increased particle emissions and reduced gaseous emissions. DFs for energy biomass mainly implied an increase in emissions, especially if biomass was combusted in small‐scale appliances. Our main conclusion highlights the critical need to thoroughly assess how using forest biomass affects aerosol emissions. This improved understanding of the aerosol emissions of the forestry sector is crucial for a comprehensive evaluation of the climate and health implications associated with forest biomass use.

Tree species diversity and spatial distribution of carbon stock in forests under different management regimes in Nepal's Western Terai Arc Landscape

Reliable tree species diversity and carbon stock data are crucial for establishing a baseline and aiding monitoring and management decision-making to ensure ecosystem functions and services, including carbon management. However, such data are not available for many primary forests, especially in developing countries like Nepal. For this study, we generated primary baseline data on tree species diversity, biomass, and carbon stock for forests under different management regimes in one of the critical landscapes of western Nepal. We sampled 94 clusters of concentric circular sample plots placed systematically at the nodes of 3 km by 3 km square grids in biological corridors (managed by community forest user groups) and a national park's buffer zones (managed by buffer zone user groups) to study the tree species composition and estimate the carbon stock. The number of tree species and botanical families reported in different sites did not differ significantly. The basal area was found to be highest in Brahmadev Corridor where tree density per unit area was also highest. Our study sites showed a basal area ranging from 26.42 to 40.65 m2/ha, which is higher than previously reported from similar forests. The total biomass (302.12 to 496 tons/ha) and carbon stock (142 to 233.12 tons/ha), both being highest in Brahmadev Corridor, were within the range of comparable data and national averages. The number of stems in all sites showed the reverse J-shaped pattern, indicating a stable population and good species recruitment. The buffer zones had a large proportion of forest area with the highest carbon stock classes of 300–400 tons/ha. The basal area and diameter at breast height showed a significant positive correlation with carbon stock, indicating that the basal area is a key determinant of carbon stock. Our study provides disaggregated information on tree species diversity, the quality of forests in terms of stem density and size-class distribution, and carbon stock and its distribution in different carbon stock classes in two forest management regimes. In this sense, our study is the first of its kind in providing important insights into forest and carbon management because areas with different carbon stocks need different management prescriptions.

Estimation of Forest Growing Stock Volume with Synthetic Aperture Radar: A Comparison of Model-Fitting Methods

Satellite-based estimation of forest variables including forest biomass relies on model-based approaches since forest biomass cannot be directly measured from space. Such models require ground reference data to adapt to the local forest structure and acquired satellite data. For wide-area mapping, such reference data are too sparse to train the biomass retrieval model and approaches for calibrating that are independent from training data are sought. In this study, we compare the performance of one such calibration approach with the traditional regression modelling using reference measurements. The performance was evaluated at four sites representative of the major forest biomes in Europe focusing on growing stock volume (GSV) prediction from time series of C-band Sentinel-1 and Advanced Land Observing Satellite Phased Array L-band Synthetic Aperture Radar (ALOS-2 PALSAR-2) backscatter measurements. The retrieval model was based on a Water Cloud Model (WCM) and integrated two forest structural functions. The WCM trained with plot inventory GSV values or calibrated with the aid of auxiliary data products correctly reproduced the trend between SAR backscatter and GSV measurements across all sites. The WCM-predicted backscatter was within the range of measurements for a given GSV level with average model residuals being smaller than the range of the observations. The accuracy of the GSV estimated with the calibrated WCM was close to the accuracy obtained with the trained WCM. The difference in terms of root mean square error (RMSE) was less than 5% units. This study demonstrates that it is possible to predict biomass without providing reference measurements for model training provided that the modelling scheme is physically based and the calibration is well set and understood.

Forest biomass recovery twenty-four years after conventional and reduced-impact logging in Eastern Amazon

Given the mounting global concerns about mitigating climate change and curbing greenhouse gas emissions, it becomes increasingly crucial to comprehend the effects of logging techniques on biomass dynamics in tropical forests. This understanding is essential for fostering greater carbon retention and sequestration, aligning with the objectives of initiatives like REDD+ (Reducing Emissions from Deforestation and forest Degradation plus sustainable forest management and the conservation and enhancement of forest carbon stocks) and other conservation goals. In this context, this study investigated the effects of two wood harvesting methods, reduced-impact logging (RIL) and conventional logging (CL), on above-ground biomass (AGB) recovery rates 24 years after harvesting. The experimental design was based on three treatments: RIL, CL, and an unlogged control plot, situated in the municipality of Paragominas, State of Pará, in the Eastern Amazon region of Brazil. All trees with diameter at breast height (DBH) ≥25 cm, as well as all trees of commercial species with a DBH ≥10 cm, were monitored in a 24.5 ha plot within each treatment. Additionally, a 5.25 ha subplot within each treatment was designated for the monitoring of all trees with DBH ≥10 cm. The biomass data were generated from 11 measurements carried out from 1993 to 2017 (24-year period). Pre-logging AGB stocks were estimated at 181 Mg ha-1 in the RIL plot, 187 Mg ha-1 in the CL plot, and 174 Mg ha-1 in the control plot. One year after logging, AGB decreased by 19 % under RIL and 30 % under CL, while the control forest remained unchanged. By 13 years after harvest, the RIL plot achieved 102 % AGB recovery, while the CL plot recovered 86 % of the original pre-harvest stock. Over the 24-year post-logging period, AGB stocks recovered to 128 % in the RIL plot compared to only 90 % in the CL plot, while the control forest maintained 93 % of its original stock. The average annual ABG increment rates were 3.56 Mg ha-1 year-1 after RIL and 2.33 Mg ha-1 year-1 after CL. Our findings demonstrate that implementing RIL is a more effective strategy for maintaining post-logging AGB stocks and accelerating AGB recovery rates, serving as a significant mitigation measure against climate change.

Effect of bioactive extracts from Eucalyptus globulus leaves in experimental models of Alzheimer's disease.

Current therapies for Alzheimer's disease (AD) do not delay its progression, therefore, novel disease-modifying strategies are urgently needed. Recently, an increasing number of compounds from natural origin with protective properties against AD have been identified. Mixtures or extracts obtained from natural products containing several bioactive compounds have multifunctional properties and have drawn the attention because multiple AD pathways can be simultaneously modulated. This study evaluated the in vitro and in vivo effect of the essential oil (EO) obtained from the hydrodistillation of Eucalyptus globulus leaves, and an extract obtained from the hydrodistillation residual water (HRW). It was observed that EO and HRW have anti-inflammatory effect in brain immune cells modeling AD, namely lipopolysaccharide (LPS)- and amyloid-beta (Aβ)-stimulated microglia. In cell models that mimic AD-related neuronal dysfunction, HRW attenuated Aβ secretion and Aβ-induced mitochondrial dysfunction. Since the HRW's major components did not cross the blood-brain barrier, both EO and HRW were administered to the APP/PS1 transgenic AD mouse model by an intranasal route, which reduced cortical and hippocampal Aβ levels, and to rescue memory deficits and anxiety-like behaviors. Finally, HRW and EO were found to regulate cholesterol levels in aged mice after intranasal administration, suggesting that these extracts can reduce hypercholesterolemia and avoid risk for AD development. Overall, findings support a protective role of E. globulus extracts against AD‑like pathology and cognitive impairment highlighting the underlying mechanisms. These extracts obtained from underused forest biomass could be useful to develop nutraceutical supplements helpful to avoid AD risk and to prevent its progression.

Both Biotic and Abiotic Factors Shape the Spatial Distribution of Aboveground Biomass in a Tropical Karst Seasonal Rainforest in South China

Forest biomass accumulation is fundamental to ecosystem stability, material cycling, and energy flow, and pit lays a crucial role in the carbon cycle. Understanding the factors influencing aboveground biomass (AGB) is essential for exploring ecosystem functioning mechanisms, restoring degraded forests, and estimating carbon balance in forest communities. Tropical karst seasonal rainforests are species-rich and heterogeneous, yet the impact mechanisms of biotic and abiotic factors on AGB remain incompletely understood. Based on the survey data of a 15 ha monitoring plot in a karst seasonal rainforest in Southern China, this study explores the distribution characteristics of AGB and its intrinsic correlation with different influencing factors. The results show that the average AGB of the plot is 125.7 Mg/ha, with notable variations among habitats, peaking in hillside habitats. Trees with medium and large diameters at breast height (DBH ≥ 10 cm) account for 83.94% of the aboveground biomass (AGB) and are its primary contributors; dominant tree species exhibit higher AGB values. Both biotic and abiotic elements substantially influence AGB, with biotic factors exhibiting the largest influence. Among abiotic factors, topographic factors have a strong direct or indirect influence on AGB, while soil physicochemical properties have the smallest indirect impact. This research provides a comprehensive understanding of AGB distribution and its influencing factors in tropical karst forests (KFs), contributing to the management of carbon sinks in these ecosystems.

Acidity and Salinization of Soil Following the Application of Ashes from Biomass Combustion Under Different Crop Plant Species Cultivation

Ashes from biomass combustion (BAs) are a valuable source of plant nutrients, making them suitable for fertilizing crops. BAs also contain components that directly affect soil environmental conditions, leading to improved growth and development of plants. Their deacidifying properties allow BAs to serve as a substitute for calcium fertilizers. However, they contain substantial amounts of components that can increase soil salinity, which can have negative effects. The aim of this study was to assess the impact of BAs on changes in pH and salinity of haplicluvisol soil under the cultivation of various plant species. The study also analyzed the effects of BAs on the content of total forms of calcium (Ca), magnesium (Mg), potassium (K), and sodium (Na) in the soil. The BAs used in the experiment were sourced from a combined heat and power plant that combusts forest and agricultural biomass. These BAs are distributed as a product for fertilizing agricultural land. However, their application is not subjected to further monitoring. The results indicated that the application of different doses of BAs significantly affected the pH of the analyzed soil. Compared to control objects, a significant increase in pH was observed, with these changes dependent on the species of the cultivated plant. Additionally, even the smallest doses of BAs caused an increase in the electrolytic conductivity (EC) of soil solutions, which serves as a measure of soil salinity. Despite the increase in the average EC value, the application of BAs did not alter the salinity class. The use of BAs also significantly influenced other analyzed parameters. An increase in the average content of total forms of Ca, Mg, K, and Na in the soil was observed, along with a higher degree of soil saturation with alkaline cations, compared to the control and the soil condition before the experiment. The changes in the analyzed soil parameters were significantly influenced not only by the different doses of BAs but also by the species of the cultivated plant. The greatest fluctuations in the obtained values were observed in soil under winter rape cultivation, while the smallest fluctuations were noted in soil under spring barley and potato cultivation.

Circular Economy Practices in Biomass-Fired Power Plants in Brazil: An Assessment Using the ReSOLVE Framework

This study aimed to identify the adoption of circular economy (CE) practices at thermoelectric power plants (TPPs) fueled by forest biomass in Brazil and determine the degree of implementation; social, environmental, and economic impacts; motivations; challenges; and facilitating factors for the adoption of such practices. Data were collected through a questionnaire applied to a sample of 32 TPPs in Brazil employing the ReSOLVE framework from October 2023 to January 2024. The data were analyzed using descriptive statistics, with the assignment of scores, and principal component analysis. The results indicate that optimization practices are the most widespread (6.7) and sharing practices were the least adopted (4.0). The greatest motivators for adopting CE practices are promoting sustainability (9.5) and enhancing corporate image (9.5). One of the most relevant positive impacts of such practices is the improvement in social relations between companies and the community (9.1). A major challenge to CE adoption is government neglect, whereas certification systems constitute one of the major facilitators (with 20 indications). This study provides indicators for decision-makers in the private sector and public managers interested in promoting sustainable practices in the renewable energy industry.

Industrial Symbiosis and Circular Economy Practices Towards Sustainability in Forest-Based Clusters: Case Studies in Southern Brazil

This study aimed to investigate how Industrial Symbiosis (IS) built around thermoelectric power plants (TPPs) located in forest-based clusters in the southern plateau region of the state of Santa Catarina, Brazil, can stimulate the adoption of circular economy (CE) practices. Data were collected to identify the presence of forest-based clusters and the purchase and sale of logs, processed wood, and forest residues from companies within the clusters, and to investigate CE practices using the ReSOLVE framework adopted in two TPPs, which used residues and black liquor as fuel. The results indicate that the nature of the forest-based activities enhances the formation of clusters, especially when TPPs use forest residues for energy production with IS intensified in this process. Brazil has 140 TTPs that use forest biomass with the capacity to generate 5.5 million wK. Three clusters were identified in the study region with purchase and sale, respectively, of logs (78.6 and 100%), processed wood (75 and 66.7%), and wood residues (45.5 and 83.3%) within the region. The case studies indicated the strong occurrence of CE practices in the cases analyzed that contribute to four of the Sustainable Development Goals (SDG) proposed by the United Nations (7, 8, 9, and 12).

Estimation of Tree Diameter at Breast Height from Aerial Photographs Using a Mask R-CNN and Bayesian Regression

A probabilistic estimation model for forest biomass using unmanned aerial vehicle (UAV) photography was developed. We utilized a machine-learning-based object detection algorithm, a mask region-based convolutional neural network (Mask R-CNN), to detect trees in aerial photographs. Subsequently, Bayesian regression was used to calibrate the model based on an allometric model using the estimated crown diameter (CD) obtained from aerial photographs and analyzed the diameter at breast height (DBH) data acquired through terrestrial laser scanning. The F1 score of the Mask R-CNN for individual tree detection was 0.927. Moreover, CD estimation using the Mask R-CNN was acceptable (rRMSE = 10.17%). Accordingly, the probabilistic DBH estimation model was successfully calibrated using Bayesian regression. A predictive distribution accurately predicted the validation data, with 98.6% and 56.7% of the data being within the 95% and 50% prediction intervals, respectively. Furthermore, the estimated uncertainty of the probabilistic model was more practical and reliable compared to traditional ordinary least squares (OLS). Our model can be applied to estimate forest biomass at the individual tree level. Particularly, the probabilistic approach of this study provides a benefit for risk assessments. Additionally, since the workflow is not interfered by the tree canopy, it can effectively estimate forest biomass in dense canopy conditions.

Application of Bayesian Causal Inference in the Study of the Relationship Between Biodiversity and Aboveground Biomass of Subtropical Forest in Eastern China

The relationship between biodiversity and ecosystem function is crucial for understanding the structure and processes of subtropical forest ecosystems. However, the extent to which biodiversity influences subtropical forest biomass remains unclear. This study applies Bayesian causal inference to explore causal relationships between forest Aboveground Biomass (AGB) and its potential driving factors (biodiversity factors, biotic factors and abiotic factors) based on Huangshan Forest Dynamics Plots. Furthermore, hypothetical interventions are introduced to these driving factors within the causal network to estimate their potential impact on AGB. The causal relationship network reveals that species diversity and functional diversity are the most direct factors influencing AGB, whereas phylogenetic diversity exerts only an indirect effect. Biotic and abiotic factors also contribute indirect effects on AGB, potentially by influencing other mediating indexes. Intervention analysis shows that with low-level interventions on direct influencing factors, the probability of low AGB is as high as 84%. As the intervention level increases to high, the probability of low AGB decreases by 36%. Moreover, AGB demonstrates a particularly sensitive response to changes in Rao’s quadratic entropy (RaoQ) intervention levels, more so than to other factors, highlighting its critical role in maintaining forest biomass. Therefore, we contend that functional diversity, due to its direct reflection of species’ roles in ecosystem processes, is a more accurate measure of the impact of biodiversity on biomass compared to species or phylogenetic diversity and the interplay between abiotic and biotic factors and biodiversity should not be overlooked. This approach offers a powerful tool for exploring causal relationships, thereby providing a more nuanced and accurate understanding of the relationship between biodiversity and forest ecosystem function.

Elevation, Soil and Environmental Factors Determine the Spatial and Quantitative Distribution of Qinghai Spruce Recruitment Biomass in Mountainous (Alpine) Watersheds

Soil heterogeneity observed in the alpine environment plays a very important role in the growth of forest recruitment. However, the mechanisms by which the biomass accumulation and allocation patterns of forest recruitment respond to such environmental differences are unclear, which hinders a thorough understanding of climate change’s impact on forest biomass. We hypothesized that soil heterogeneity influences the distribution of Qinghai spruce recruitment biomass along with elevation. In the frame of this study, carried out in the northern Tibetan Plateau, forest Qinghai spruce recruitment data were combined with soil data derived from 24 sample plots, while permutation multifactor ANOVA and multiple linear regression were utilized to reveal the characteristics of forest recruits’ above- and below-ground biomass and their allocation patterns in response to soil heterogeneity. According to the results, the soil heterogeneity mainly affected the distribution characteristics of recruits’ above- and below-ground biomass at different elevations, while the recruits’ root–shoot ratio variability was influenced by a combination of soil and other environmental factors. Soil organic carbon (SOC) had the greatest effect on the variability of the above- and below-ground biomass of spruce recruits, with R2 of 0.280 and 0.257, respectively. Soil organic carbon and soil moisture content (SMC) had a significant effect on the variability of the root–shoot ratio, with R2 of 0.168 and 0.165, respectively. Soil total nitrogen (TN) and soil organic carbon were the main influencing factors of the above-ground biomass of forest recruits, with contribution rates of 43.15% and 35.28%, respectively. Soil total nitrogen and soil organic carbon were also the main factors influencing the below-ground biomass of forest recruits, with contribution rates of 42.52% and 37.24%, respectively, and both of them had a positive effect on biomass accumulation, and the magnitude of the influence varied with the elevation gradient. Soil moisture content was the main influence factor of spruce recruits’ root–shoot ratio, with a contribution rate of 54.12%. Decreasing soil moisture content would significantly increase the root–shoot ratio of spruce recruits and promote plants to allocate more biomass to root growth. Changes in elevation not only affected the intensity of the effect of soil factors on spruce recruitment biomass and its allocation pattern but even led to a change in the positive and negative effects.

A Separable Bootstrap Variance Estimation Algorithm for Hierarchical Model‐Based Inference of Forest Aboveground Biomass Using Data From NASA's GEDI and Landsat Missions

ABSTRACTThe hierarchical model‐based (HMB) statistical method is currently applied in connection with NASA's Global Ecosystem Dynamics Investigation (GEDI) mission for assessing forest aboveground biomass (AGB) in areas lacking a sufficiently large number of GEDI footprints for employing hybrid inference. This study focuses on variance estimation using a bootstrap procedure that separates the computations into parts, thus considerably reducing the computational time required and making bootstrapping a viable option in this context. The procedure we propose uses a theoretical decomposition of the HMB variance into two parts. Through this decomposition, each variance component can be estimated separately and simultaneously. For demonstrating the proposed procedure, we applied a square‐root‐transformed ordinary least squares (OLS) model, and parametric bootstrapping, in the first modeling step of HMB. In the second step, we applied a random forest model and pairwise bootstrapping. Monte Carlo simulations showed that the proposed variance estimator is approximately unbiased. The study was performed on an artificial copula‐generated population that mimics forest conditions in Oregon, USA, using a dataset comprising AGB, GEDI, and Landsat variables.

Improving AGB estimations by integrating tree height and crown radius from multisource remote sensing.

Precise estimation of forest above ground biomass (AGB) is essential for assessing its ecological functions and determining forest carbon stocks. It is difficult to directly obtain diameter at breast height (DBH) based on remote sensing imagery. Therefore, it is crucial to accurately estimate the AGB with features extracted directly from RS. This paper demonstrates the feasibility of estimating AGB from crown radius (R) and tree height (H) features extracted from multi-source RS data. Accurate information on tree height (H), crown radius (R), and diameter at breast height (DBH) can be obtained through point clouds generated by airborne laser scanning (ALS) and terrestrial laser scanning (TLS), respectively. Nine allometric growth equations were used to fit coniferous forests (Larix principis-rupprechtii) and broadleaf forests (Fraxinus chinensis and Sophora japonica). The fitting performance of models constructed using only "H" or "R" was compared with that of models constructed using both combined. The results showed that the quadratic polynomial model constructed with "H+R" fitted the AGB estimation better in each vegetation type, especially in the scenario of mixed tall and short coniferous forests, in which the R2 and RMSE were 0.9282 and 25.30 kg (rRMSE 17.31%), respectively. Therefore, using high-resolution data to extract crown radius and tree height can achieve high-precision, global-scale estimation of forest above ground biomass.

Combining the uncombinable: corporate memories, ethnobiological observations, oceanographic and ecological data to enhance climatic resilience in small-scale fisheries

The global food production system is increasingly strained by abrupt and unpredictable weather events, which hinder communities' ability to adapt to climate variations. Despite advances in meteorological predictions, many communities lack the academic knowledge or infrastructure to interpret these complex models. This gap highlights the need for solutions that make climate forecasts more accessible and actionable, especially for communities reliant on natural resources. This study explores the potential of enhancing seasonal climate forecasts by integrating local ecological knowledge (LEK) with scientific data. Specifically, we combined ethnobiological information gathered between 2022 and 2024 with existing oceanographic and ecological data to create an ethnobiological calendar for four fishing cooperatives. An ethnographic approach was used to understand the population's ethnobiological knowledge and their perceptions of marine heatwaves and climate change impacts. Coastal monitoring data was collected using moorings that recorded temperature over a 14-year period (2010–2024). To characterize giant kelp dynamics, we used an existing dataset of multispectral Landsat images, which estimates the surface canopy biomass of giant kelp forests. Ecological monitoring was conducted annually every summer from 2006 to 2023 to record the in situ abundance of ecologically and economically important invertebrate and fish species. Combining oceanographic, ecological, and ethnographic data, allowed for alligning fishers' observations with recorded marine heatwave events and ecological shifts. Our findings revealed that these observations closely matched documented marine heatwave data and corresponding ecological changes. The integration of LEK with scientific oceanographic data can significantly improved our understanding of dynamic climate regimes, offering contextually relevant information that enhances the reliability and utility of seasonal climate forecasts. By incorporating yearly data into an ethnobiological calendar, we promote more inclusive, community-based approaches to environmental management, advocating for the integration of LEK in climate adaptation efforts, emphasizing its crucial role in strengthening resilience strategies against climatic shocks.