Forest Biomass Research Articles

Optimization of Combined Hydrothermal and Mechanical Refining Pretreatment of Forest Residue Biomass for Maximum Sugar Release during Enzymatic Hydrolysis

This study aimed to investigate the effect of chemical-free two-stage hydrothermal and mechanical refining pretreatment on improving the sugar yields during enzymatic hydrolysis of forest residue biomass (FRB) and optimize the pretreatment conditions. Hot-water pretreatment experiments were performed using a central composite design for three variables: temperature (160–200 °C), time (10–20 min), and solid loading (10–20%). Hydrothermally pretreated biomass was subsequently pretreated using three cycles of disk refining. The combined pretreatment was found to be highly effective in enhancing sugar yields during enzymatic hydrolysis, with almost 99% cellulose conversion for biomass pretreated at 213.64 °C, 15 min, and 15% solid loading. However, the xylose concentrations in the hydrolysate were found to be low under these conditions due to sugar degradation. Thus, less severe optimum pretreatment conditions (194.78 °C, 12.90 min, and 13.42% solid loading) were predicted using a second-order polynomial model. The response surface model optimized the hydrothermal pretreatment of FRB and predicted the glucan, xylan, and overall conversions of 94.57%, 79.78%, and 87.84%, respectively, after the enzymatic hydrolysis. The model-predicted biomass conversion values were validated by the experimental results.

Combining multiple feature selection methods and structural equation modelling for exploring factors affecting stand biomass of natural coniferous-broad leaved mixed forests

Recognition of biotic and abiotic factors affecting biomass of natural mixed forests is of great importance for forest carbon estimation and management. When estimating stand biomass using models, different variable selection methods often yield inconsistent results, and there is lack of systematic analysis. This study aimed to combine multiple feature selection methods with structural equation modelling (SEM) to identify a set of variables affecting stand biomass more reasonably. Eight methods were applied for feature selection based on data from 286 permanent sample plots in natural coniferous-broad leaved mixed forests in northeast China. These methods included Pearson correlation analysis, two methods derived from principal component analysis (PCA), stepwise regression, redundancy analysis (RDA), generalized additive model (GAM), random forest (RF), and boosted regression tree (BRT). A total of 56 candidate variables were considered, covering stand, biodiversity, climate and soil features. Significant variability was observed in the variables selected, however, there were 6 variables consistently identified across all methods, including tree species diversity (N_Sp_Div), stand structural diversity (N_ Size_ Div), nearest taxon index (NRI), community weighted mean based on dry matter mass of leaves (CWM.LDMC), soil pH, and degree-days above 18 °C (DD18). Then, these variables were included in the SEM with stand average age and additive stand density index (aSDI) to explore the direction and magnitude of their impacts on stand biomass. The SEM results showed that aSDI and average age had the greatest positive effects on stand biomass, and structural diversity also had a significant positive effect. DD18 affected stand biomass both directly and indirectly, with the total negative effect. Soil pH indirectly affected stand biomass via aSDI. Our findings demonstrated that combining multiple feature selection methods with SEM was an effective approach for understanding multiple factors affecting stand biomass, and provided valuable insights for forest biomass estimation and carbon management.

Forest structure explains spatial heterogeneity of decadal carbon dynamics in a cool-temperate forest

Abstract Accurate evaluation of forest biomass distribution and its long-term change over wide areas is required for effective forest carbon management and prediction of landscape-scale forest dynamics. We evaluated a landscape-scale (225 km2) decadal forest carbon budget at a 1 ha spatial resolution in a cool-temperate forest, by repeating airborne laser observations 10 years apart and partitioning net forest biomass change (FBC) into growth and mortality. Using >10 000 samples, we revealed that naturally regenerated forests have large spatial heterogeneity in net biomass change, and 3/4 of the photosynthetically acquired carbon stock moved to necromass even without anthropogenic disturbances. Actual carbon residence time as living tree biomass was estimated by dividing biomass by growth or mortality rates. The residence time was 107 and 106 years, respectively with large spatial variation among stands (48 and 42 years, respectively, as the difference between 25 and 75 percentile), although studied forest stands have small variation in the forest functional type in a landscape-scale. The best predictors of subsequent decadal biomass changes were two forest structural factors, mean canopy height and canopy height variation in addition to one environmental factor, elevation. Considering the long lifetime of trees, these structural factors may be an indicator of forest soundness rather than a cause of forest growth or mortality. However, in any cases, these structural factors can be powerful predictors of subsequent FBC.

Modeling the Effects of Increased Hurricane Frequency on the Tropical Forest Carbon Cycle

AbstractModels project that climate change is increasing the frequency of severe storm events such as hurricanes. Hurricanes are an important driver of ecosystem structure and function in tropical coastal and island regions and thus impact tropical forest carbon (C) cycling. We used the DayCent model to explore the effects of increased hurricane frequency on humid tropical forest C stocks and fluxes at decadal and centennial timescales. The model was parameterized with empirical data from the Luquillo Experimental Forest (LEF), Puerto Rico. The DayCent model replicated the well-documented cyclical pattern of forest biomass fluctuations in hurricane-impacted forests such as the LEF. At the historical hurricane frequency (60 years), the dynamic steady state mean forest biomass was 80.9 ± 0.8 Mg C/ha during the 500-year study period. Increasing hurricane frequency to 30 and 10 years did not significantly affect net primary productivity but resulted in a significant decrease in mean forest biomass to 61.1 ± 0.6 and 33.2 ± 0.2 Mg C/ha, respectively (p < 0.001). Hurricane events at all intervals had a positive effect on soil C stocks, although the magnitude and rate of change of soil C varied with hurricane frequency. However, the gain in soil C stocks was insufficient to offset the larger losses from aboveground biomass C over the time period. Heterotrophic respiration increased with hurricane frequency by 1.6 to 4.8%. Overall, we found that an increasing frequency of tropical hurricanes led to a decrease in net ecosystem production by − 0.2 ± 0.08 Mg C/ha/y to − 0.4 ± 0.04 Mg C/ha/y for 30–10-year hurricane intervals, respectively, significantly increasing the C source strength of this forest. These results demonstrate how changes in hurricane frequency can have major implications for the tropical forest C cycle and limit the potential for this ecosystem to serve as a net C sink.

Timber and carbon sequestration potential of Chinese forests under different forest management scenarios

Developing forestry action plans with the goal of carbon neutrality is a critical task to identify carbon sink potential and balance the pathways of China's forests. Current research that predicts forest biomass carbon stock and sink potential has shortcomings, such as an incomplete assessment of China's forest carbon sink range, assumptions based on unchanged forest area in the base year and insufficient consideration of natural and human disturbance factors. This study utilised the national forest inventory (NFI) data to construct a model of forest growth and consumption using a machine learning algorithm (i.e. random forest), identified suitable areas for future forest expansion by integrating multi-source data, and set up three future forest management scenarios: business as usual (BAU), enhanced policy scenario (EPS) and maximum potential scenario (MPS). In addition, changes in the area, volume stock and biomass carbon stock in China's forests between 2020 and 2060 were predicted under three forestry activities (i.e. existing forest, afforested/reforested (AR) forest and forest conversion) and three climate scenarios (i.e. SSP126, SSP370 and SSP585) based on the 9th NFI (2014–2018). According to China's relevant planning goals and suitable forest space, the area of AR forests is predicted to be 55.55 × 106 hm2 by 2060, and the forest coverage rate is predicted to increase from 23% in 2018 to 28% by 2060. Biomass carbon sequestration (BCS) between 2020 and 2060 in AR forests is predicted to be 36.00 TgC per year. By 2060, the average BCS is predicted to be approximately 140.00–287.56 TgC per year in China's forests, which is mainly owing to arbor forest management. Under the BAU and EPS scenarios, BCS in China's forests is expected to decline from 2020 to 2060. However, under the MPS, BCS in China's forests is projected to increase and be maintained at 322 TgC per year or above by 2060, with wood production reaching 4.71 × 108 m3 per year. China's forests are predicted to experience an increase in biomass carbon stock in the future and play a role as a carbon sink. By taking measures to achieve the maximum growth potential of all forest types, China's forests will achieve a win‒win situation between carbon sinks and timber production.

Boreal tree species diversity increases with global warming but is reversed by extremes.

Tree species diversity is essential to sustaining stable forest ecosystem functioning. However, it remains unclear how boreal tree species diversity has changed in response to climate change and how it is associated with productivity and the temporal stability of boreal forest ecosystems. By combining 5,312 field observations and 55,560 Landsat images, here we develop a framework to estimate boreal tree species diversity (represented by the Shannon diversity index, H') for the years 2000, 2010 and 2020. We document an average increase in H' by 12% from 2000 to 2020 across the boreal forests. This increase accounts for 53% of all boreal forest areas and mainly occurs in the eastern forest-boreal transition region, the Okhotsk-Manchurian taiga and the Scandinavian-Russian taiga. Tree species diversity responds positively to increasing temperatures, but the relationship is weakened for higher temperature changes, and in areas of extreme warming (>0.065 °C yr-1), a negative impact on tree species diversity is found. We further show that the observed spatiotemporal increase in diversity is significantly associated with increased productivity and temporal stability of boreal forest biomass. Our results highlight climate-warming-driven increases in boreal tree species diversity that positively affect boreal ecosystem functioning but are countered in areas of extreme warming.

Evaluation of statistical and machine learning models using satellite data to estimate aboveground biomass: A study in Vietnam Tropical Forests

The combination of machine learning models with satellite imagery is becoming a popular data-modeling tool for biomass prediction, supporting land cover management. This study aims to select the most suitable model to estimate tropical forest aboveground biomass in Vietnam, helping to manage and monitor changes in biomass at regional and local scales. The study identified the optimal model for estimating forest aboveground biomass and minimizing the number of input variables while achieving satisfactory model performance. A total of 59 input variables, including topography, texture features, and vegetation indices, from satellite data were used in four non-parametric algorithms and a conventional parametric model, Artificial Neural Networks (ANN), Support Vector Machine (SVM), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Multiple Linear Regression (MLR) to predict biomass and evaluate changes aboveground biomass over 10 years in two tropical forests in Vietnam. The results indicated that all models had good estimation performance with R2 ranging from 0.615 to 0.754. For RF, MLR, and XGBoost, vegetation indices contributed the highest model weights, occupying 77.71% – 92.48%. For ANN and SVM, textural and topographic features were the majority of the model weights (73.74 – 96.36%). The RF model performed the best using 59 variables (R2 = 0.754, MAE = 78.5 Mg·ha−1, and %RMSE = 13.57%) and ten variables (R2 = 0.745, MAE = 85.8 Mg·ha−1, and %RMSE = 16.17%). The biomass map using the RF and ten variables achieved a good degree of fitting of 0.76, so it was suitable for managing and monitoring forest biomass in Vietnam. The results indicated a sharp decrease in the areas of dense and very dense forests from 2013 to 2021 and a gradual increase in 2023.

Estimation of the Aboveground Biomass of Forests in Complex Mountainous Areas Using Regression Kriging

The forest area in China’s plateaus and mountainous regions accounts for as much as 43% of the country’s total forest area. Accurately estimating the aboveground biomass (AGB) in these plateau and mountain forests is significant for global carbon sink assessment and climate change. However, the complexity of the natural environment poses significant challenges to the accurate estimation of forests’ aboveground biomass (AGB), and the accuracy of both AGB estimation and spatial mapping needs further improvement. This study utilized support vector regression, backpropagation neural networks, and random forests to predict trends in AGB and establish an optimal original model for forest AGB estimation. Further calibration was performed using regression kriging on the optimal model. The results indicated that (1) random forests achieved the highest coefficient of determination (R2 for cypress = 0.63, R2 for fir = 0.66, R2 for cryptomeria = 0.64, and R2 for mixed forest = 0.54), showing greater potential in predicting AGB in complex mountainous mixed forests; (2) the residual kriging method significantly improved the estimation accuracy, increasing the R2 values of the original RF model by 25%, 24%, and 22%, and improving the accuracy of mixed plot estimates from 54% to 81%; and (3) the residual kriging method effectively addressed the underestimation of high values and overestimation of low values in AGB estimates, broadening the range of AGB values and allowing for a more detailed spatial distribution of forests’ aboveground biomass.

Bioplastics from Waste Biomass: Paving the Way for a Sustainable Future

Abstract: Bioplastics originating from waste biomass present a viable remedy to the environmental dilemmas associated with traditional plastics, thereby reducing dependency on fossil fuels and mitigating carbon emissions. This review investigates the utilization of diverse waste biomass sources, encompassing agricultural residues, food waste, industrial by-products, municipal solid waste, and forest biomass residues, in the synthesis of bioplastics. Technological innovations in feedstock pretreatment, bioconversion methodologies, and polymerization techniques have markedly enhanced the efficiency and material characteristics of bioplastics, establishing them as formidable alternatives to petroleum-derived plastics. Notwithstanding these advancements, obstacles such as feedstock inconsistency, scalability issues, technological limitations, and consumer acceptance persistently impede the extensive adoption of bioplastics. The ecological advantages of bioplastics, particularly concerning their diminished carbon footprint and potential for biodegradability, are underscored through life cycle assessments (LCAs). Government policies, the increasing market demand for sustainable products, and advancements in emerging technologies, including synthetic biology and AI-enhanced process optimization, are propelling the commercialization of bioplastics derived from waste biomass. The incorporation of bioplastics within a circular economy framework, coupled with considerations for long-term sustainability, positions them as a pivotal element in fostering a more sustainable and environmentally friendly future. This review offers a thorough examination of the present landscape, challenges, and future prospects of bioplastics production from waste biomass.

Design and performance of the Climate Change Initiative Biomass global retrieval algorithm

The increase in Earth observations from space in recent years supports improved quantification of carbon storage by terrestrial vegetation and fosters studies that relate satellite measurements to biomass retrieval algorithms. However, satellite observations are only indirectly related to the carbon stored by vegetation. While ground surveys provide biomass stock measurements to act as reference for training the models, they are sparsely distributed. Here, we addressed this problem by designing an algorithm that harnesses the interplay of satellite observations, modeling frameworks and field measurements, and generated global estimates of above-ground biomass (AGB) density that meet the requirements of the scientific community in terms of accuracy, spatial and temporal resolution. The design was adapted to the amount, type and spatial distribution of satellite data available around the year 2020. The retrieval algorithm estimated AGB annually by merging estimates derived from C- and L-band Synthetic Aperture Radar (SAR) backscatter observations with a Water Cloud type of model and does not rely on AGB reference data at the same spatial scale as the SAR data. This model is integrated with functions relating to forest structural variables that were trained on spaceborne LiDAR observations and sub-national AGB statistics. The yearly estimates of AGB were successively harmonized using a cost function that minimizes spurious fluctuations arising from the moderate-to-weak sensitivity of the SAR backscatter to AGB. The spatial distribution of the AGB estimates was correctly reproduced when the retrieval model was correctly set. Over-predictions occasionally occurred in the low AGB range (<50 Mg ha−1) and under-predictions in the high AGB range (>300 Mg ha−1). These errors were a consequence of sometimes too strong generalizations made within the modeling framework to allow reliable retrieval worldwide at the expense of accuracy. The precision of the estimates was mostly between 30% and 80% relative to the estimated value. While the framework is well founded, it could be improved by incorporating additional satellite observations that capture structural properties of vegetation (e.g., from SAR interferometry, low-frequency SAR, or high-resolution observations), a dense network of regularly monitored high-quality forest biomass reference sites, and spatially more detailed characterization of all model parameters estimates to better reflect regional differences.

Examining the Impact of Topography and Vegetation on Existing Forest Canopy Height Products from ICESat-2 ATLAS/GEDI Data

Forest canopy height (FCH) is an important variable for estimating forest biomass and ecosystem carbon sequestration. Spaceborne LiDAR data have been used to create wall-to-wall FCH maps, such as the forest tree height map of China (FCHChina), Global Forest Canopy Height 2020 (GFCH2020), and Global Forest Canopy Height 2019 (GFCH2019). However, these products lack comprehensive assessment. This study used airborne LiDAR data from various topographies (e.g., plain, hill, and mountain) to assess the impacts of different topographical and vegetation characteristics on spaceborne LiDAR-derived FCH products. The results show that GEDI–FCH demonstrates better accuracy in plain and hill regions, while ICESat-2 ATLAS–FCH shows superior accuracy in the mountainous region. The difficulty in accurately capturing photons from sparse tree canopies by ATLAS and the geolocation errors of GEDI has led to partial underestimations of FCH products in plain areas. Spaceborne LiDAR FCH retrievals are more accurate in hilly regions, with a root mean square error (RMSE) of 4.99 m for ATLAS and 3.85 m for GEDI. GEDI–FCH is significantly affected by slope in mountainous regions, with an RMSE of 13.26 m. For wall-to-wall FCH products, the availability of FCH data is limited in plain areas. Optimal accuracy is achieved in hilly regions by FCHChina, GFCH2020, and GFCH2019, with RMSEs of 5.52 m, 5.07 m, and 4.85 m, respectively. In mountainous regions, the accuracy of wall-to-wall FCH products is influenced by factors such as tree canopy coverage, forest cover types, and slope. However, some of these errors may stem from directly using current ATL08 and GEDI L2A FCH products for mountainous FCH estimation. Introducing accurate digital elevation model (DEM) data can improve FCH retrieval from spaceborne LiDAR to some extent. This research improves our understanding of the existing FCH products and provides valuable insights into methods for more effectively extracting accurate FCH from spaceborne LiDAR data. Further research should focus on developing suitable approaches to enhance the FCH retrieval accuracy from spaceborne LiDAR data and integrating multi-source data and modeling algorithms to produce accurate wall-to-wall FCH distribution in a large area.

Microwave-Assisted Pyrolysis of Forest Biomass

Microwave-Assisted Pyrolysis of Forest Biomass

Characteristics of Biochar Obtained by Pyrolysis of Residual Forest Biomass at Different Process Scales

In this work, the pyrolysis process and the characteristics of biochar produced using a bench-scale fixed-bed reactor and a prototype-scale auger reactor were studied. Residual forest biomass (RFB) from acacia, broom, gorse, and giant reed was used as feedstock. Besides information on pyrolysis characteristics of these specific biomass species from the Iberian Peninsula, new knowledge on the understanding of how results from small-scale reactors can be used to predict the behavior of higher-scale and continuous-operation reactors is offered. Batch pyrolysis was carried out using 40 g of biomass sample in a fixed-bed reactor with a heating rate of 20 °C∙min−1, pyrolysis temperature of 450 and 550 °C, and a residence time of 30 min, while for the continuous process it was used a prototype of an auger reactor with continuous operation with a biomass flow rate up to 1 kg/h, with temperatures of 450 and 550 °C, and a solids residence time of 5 min. The biochar yield was in the range of 0.26 to 0.36 kg/kg biomass dry basis, being similar for both types of reactors and slightly lower when using the auger reactor. The proximate analysis of the biochar shows volatile matter in the range 0.10 to 0.27 kg/kg biochar dry basis, fixed carbon in the range 0.65 to 0.84 kg/kg biochar dry basis, and ash in the range 0.04 to 0.08 kg/kg biochar dry basis. The carbon, oxygen, and hydrogen content of the biochar was in the range of 0.71 to 0.81, 0.09 to 0.22, and 0.02 to 0.03 kg/kg biochar dry basis, respectively. The results show that the up-scaling of the reactor and regime of operation does not have an important influence on the yield and characteristics of the biochar produced. The biochar obtained in the two types of reactors has characteristics appropriate for environmental applications, such as an additive to improve soil properties. It is possible to see that the characteristics of the biochar are influenced by the type of biomass and the conditions and parameters of the process; therefore, it is of major importance to control and know of these conditions, especially when considering upscaling scenarios.

Деревна лісова біомаса: проблеми та перспективи сталого використання

The article is devoted to the study of the state, problems and prospects of ensuring the sustainable use of woody forest biomass. Most of the bioenergy resources used in Ukraine for energy production are woody forest biomass. At the same time, there is a dangerous tendency to exceed the use of woody forest biomass above its potential. The norms governing the use of woody forest biomass contain various normative legal acts of Ukraine. However, the general concept of “woody forest biomass” is not defined in domestic legislation. The definition of “forest biomass” is contained in Directive (EU) 2018/2001 of the European Parliament and of the Council on the promotion of the use of energy fromrenewable sources of December 11, 2018. Besides, Directive (EU) 2018/2001 of the European Parliament and of the Council on the promotion of the use of energy from renewable sources of December 11, 2018 (RED II Directive) and its amendments (RED III Directive) – Directive (EU) 2023/2413 of the European Parliament and of the Council of October 18, 2023 amending Directive (EU) 2018/2001, Regulation (EU) 2018/1999 and Directive 98/70/EC as regards the promotion of energy from renewable sources, and repealing Council Directive (EU) 2015/652 contains criteria for the sustainability of forest biomass in the EU. The European concept and practice of close to nature silviculture is an important reference point for ensuring the sustainable use of woody forest biomass in Ukraine. A special (limited) approach is required for the harvesting and use of woody forest biomass, which is a component of the ecosystem. Among the main problems of ensuring the sustainable use of woody forest biomass are noncompliance with the sustainability criteria of woody forest biomass at all stages (from harvesting to consumption), burning of forest wood waste in the forest, non-compliance with the proper ratio between leaving dead wood in the forest and its harvesting for energy production etc. To ensure the sustainable use of woody forest biomass, it is important not only to implement relevant European standards into national legislation, in particular the criteria for the sustainability of forest biomass, but also to introduce European practices that are close to nature.

Economic valuation of carbon sequestration in Quito’s Metropolitan Park using Sentinel-2 and neural networks

This study introduces an innovative method for assessing the economic value of carbon stored by the urban park forest in Quito, Ecuador. The method uses Sentinel-2 remote sensing data and advanced deep learning techniques. A large forest study area was chosen, and detailed field measurements were taken to gather biomass and carbon data. Sentinel-2 satellite images were processed to correct for radiation and atmospheric conditions, and vegetation indices such as NDVI and EVI were calculated. To model forest biomass, a convolutional neural network (CNN) was created and trained using Sentinel-2 spectral bands and vegetation indices. The model was validated with independent field data and demonstrated high accuracy in estimating biomass and carbon, as indicated by evaluation metrics like RMSE and R². The findings include detailed maps showing the spatial distribution of biomass and carbon in the study area, which can be a valuable tool for forest management and the implementation of policies for valuing stored carbon. This approach combines the high resolution of Sentinel-2 data with the predictive power of neural networks, providing a robust and scalable method for estimating carbon across large forest areas. The study's conclusions emphasize the feasibility and accuracy of this approach and its potential for application in various forestry and geographical contexts.

Response of Hydrothermal Conditions to the Saturation Values of Forest Aboveground Biomass Estimation by Remote Sensing in Yunnan Province, China

Identifying the key climate variables affecting optical saturation values (OSVs) in forest aboveground biomass (AGB) estimation using optical remote sensing is crucial for analyzing OSV changes. This can improve AGB estimation accuracy by addressing the uncertainties associated with optical saturation. In this study, Pinus yunnanensis forests and Landsat 8 OLI imagery from Yunnan were used as case studies to explain this issue. The spherical model was applied to determine the OSVs using specific spectral bands (Blue, Green, Red, Near-Infrared (NIR), and Short-Wave Infrared Band 2 (SWIR2)) derived from Landsat 8 OLI imagery. Canonical correlation analysis (CCA) uncovered the intricate relationships between climatic variables and OSV variations. The results reveal the following: (1) All Landsat 8 OLI spectral bands showed a negative correlation with the Pinus yunnanensis forest AGB, with OSVs ranging from 104.42 t/ha to 209.11 t/ha, peaking in the southwestern region and declining to the lowest levels in the southeastern region. (2) CCA effectively explained 93.2% of the OSV variations, identifying annual mean temperature (AMT) as the most influential climatic factor. Additionally, the mean temperature of the wettest quarter (MTQ) and annual precipitation (ANP) were significant secondary determinants, with higher OSV values observed in warmer, more humid areas. These findings offer important insights into climate-driven OSV variations, reducing uncertainty in forest AGB estimation and enhancing the precision of AGB estimations in future research.

Carbon sink of forest ecosystems: Concept, time effect and improvement approaches.

The widespread utilization of fossil fuels has emitted large amounts of CO2 into the atmosphere since the Industrial Revolution, leading to climate warming and frequent occurrence of extreme climate events. To effectively alleviate climate change, the international community has made various efforts to reduce carbon emissions and eliminate CO2 from the atmosphere. In 2020, the Chinese government announced that carbon emission peaking and carbon neutrality will be achieved by 2030 and 2060, respectively. According to the current forecast, by the time carbon neutrality is achieved in 2060, even under the minimum conditions of fossil energy use, production, and living emissions, China will still have to emit about 1/4 of the current total emissions. These carbon must primarily be absorbed by ecosystems. Furthermore, approximately 140 ppm increase in CO2 in the atmosphere since the Industrial Revolution still needs to be removed by ecosystems. Forests are the main component of terrestrial ecosystems, contributing more than 80% of the carbon sequestration capacity of all terrestrial ecosystems. However, due to the long periodicity, complexity and dynamic variability of forests, the basic concepts of ecosystem carbon sink and its time effect are still unclear, leading to problems, such as lacking technologies for improving carbon sink capacity and disorganized rules in the carbon sink trading market. In this review, we introduced carbon sink concept according to the processes of absorbing and fixing CO2 by plant photosynthesis in forest ecosystems. Then, we analyzed the processes of time-scale-dependent carbon sinks of forest ecosystems, discussed the time effects of forest carbon sinks, and suggested using "t-year" as the unit of carbon sink (taking 3-6 months as the minimum measurement time, i.e., the beginning of carbon sequestration). Third, we proposed the approaches to improve the carbon sink capacity of forest ecosystems. One way is to improve the carbon sink capacity (expanding forest area, improving forest quality, and increasing forest soil carbon storage) of forest ecosystems. Another approach is to maintain the carbon sink of forest ecosystems as long as possible, i.e., to reduce temporary carbon sink (definition: carbon in the forest ecosystems emit into the atmosphere for a certain period) and to increase persistent carbon sink (definition: carbon in the forest ecosystems no longer emit into the atmosphere for a certain period; according to the relevant provisions of the Paris Agreement, the upper time limit for carbon sink measurement can be considered to be the year 2100. In order to maintain the persistent carbon sink, strateges such as efficient use of wood products (replace steel, cement, plastic with wood), control of forest fires or other disturbances-induced emissions, and turning forest biomass into biochar should be taken. Finally, we proposed to develop climate-smart forestry driven by artificial intelligence (AI), which would provide new theoretical and technical support for improving the carbon sink of forest ecosystems and facilitating sustainable forest management.

Insights about the use wood waste from the neotropical Amazonian for the generation of renewable energy

The abundance and availability of wood waste in the Amazon region pose a great challenge in terms of their use, such as to generate clean and renewable energy. The use of blends of the most different species constitutes a challenge in view of the energetic characteristics that are unknown for the replacement of fossil fuels. Thus, the objective of this study was to investigate the energy potential of wood waste from Amazonian species (Dinizia excelsa and from Manilkara elata, commonly used as lumber) as a way to support the generation of clean and sustainable energy. The wood waste of two Amazonian species were collected during the mechanical processing of logs; the sawdust of Eucalyptus spp. was collected in a biomass company. The contents of total extractives, total lignin, ash, moisture, bulk density, calorific value and energy density were investigated in different blends of residual biomass. The Dinizia excelsa species stood out for presenting higher values of extractives, total lignin, calorific value and energy density. The waste of Amazonian species showed satisfactory characteristics for energy use, which encourages the use of this biomass from wood processing. In addition, forest biomass waste enable the production of various compacted solid fuels, such as pellets and briquettes.

CNN-based transfer learning for forest aboveground biomass prediction from ALS point cloud tomography

ABSTRACT This study presents a new approach for predicting forest aboveground biomass (AGB) from airborne laser scanning (ALS) data: AGB is predicted from sequences of images depicting vertical cross-sections through the ALS point clouds. A 3D version of the VGG16 convolutional neural network (CNN) with initial weights transferred from pre-training on the ImageNet dataset was used. The approach was tested on datasets from Canada, Poland, and the Czech Republic. To analyse the effect of training sample size on model performance, different-sized samples ranging from 10 to 375 ground plots were used. The CNNs were compared with random forest models (RFs) trained on point cloud metrics. At the maximum number of training samples, the difference in RMSE between observed and predicted AGB of CNNs and RFs ranged from −2 t/ha to 5 t/ha, and the difference in squared Pearson correlation coefficient ranged from −0.05 to 0.06. Additional pre-training on synthetic data derived from virtual laser scanning of simulated forest stands could only improve the prediction performance of the CNNs when only a few real training samples (10–40) were available. While 3D CNNs trained on cross-section images derived from real data showed promising results, RFs remain a competitive alternative.

Spatial pattern of forest aboveground biomass and its environmental influencing factors in Qinling-Daba Mountains, central China

Accurate estimation of forest aboveground biomass (AGB) is crucial for understanding and managing forest ecosystems in the context of global environmental change, and also provides a scientific basis for national and regional ecological planning and carbon emission reduction policies. In order to investigate the regional pattern of forest AGB and its influencing factors in central China, a total of 469 sample plots were measured along four sample transects and on six mountains in field survey. The results showed that: 1) Two longitudinal distribution patterns of forest AGB were found, and one was that the AGB in the Qinling Mountains and the Daba Moutains gradually decreased from east to west, and the other was that the AGB in the areas between the two mountains gradually increased from east to west. The latitudinal distribution pattern of the forest AGB showed an "increasing–decreasing-increasing–decreasing" trend from south to north. The altitudinal distribution pattern showed a "first increasing-then decreasing" pattern with increasing altitude. 2) The influence of each factor on the spatial pattern of forest AGB was manifested as temperature > precipitation > HAI > topography, indicating that the spatial pattern of forest AGB in central China was the result of the interaction of climate, human activities and natural factors.