Explore the vast range of titles available.

\"This book introduces readers to second-generation biofuels obtained from non-food biomass, such as forest residue, agricultural residue, switch grass, and corn stover\"--Provided by publisher.

Purpose of ReviewFor the past few decades, consumers have increasingly demanded biodegradable, petroleum-free, and safe products for the environment, humans, and animals, with improved performance. In terms of energy consumption, modern society has progressively sought to reduce fossil fuel utilization and greenhouse gas emissions. This review presents and discusses the possibilities of using biomass residues that are derived from forest operations and wood manufacturing to produce biofuels and biomaterials as sustainable alternatives that could boost the development of renewable technologies and bio-economy.Recent FindingsForest biomass residues are composed primarily of cellulose, hemicellulose, and lignin in varying proportions depending upon the species. Residues from forest operations have heterogeneous compositions due to the presence of branches, foliage, tree tops, and bark, compared with those derived from wood manufacturing industries. Several technological approaches have been developed to add value to forest biomass residues through their conversion to biomaterials such as wood-based composite panels, wood-plastic composites, wood pellets, and biofuels, such as biochar, bio-oil, syngas (thermochemical approach), and biogas (biochemical approach).SummaryForest biomass residues are valuable lignocellulosic materials, but research is still required regarding their conversion into value-added products given their heterogeneous compositions and varied physicochemical properties. Obstacles such as transportation costs and their complex structural and chemical mechanisms that resist decomposition need to be better overcome in developing high-quality and economically viable biofuels and biomaterials. In contrast, wood-based panels, composites, pellets, and biofuels produced by the wood manufacturing industries exhibit superior properties and characteristics for commercialization. Recent studies regarding valorization of forest biomass residues are a welcome recognition of the need to transition to a sustainable economy, and a definitive strategy for achieving objectives that have been set for reducing greenhouse gas emissions.

Earth Observation data are uniquely positioned to estimate forest aboveground biomass density (AGBD) in accordance with the United Nations Framework Convention on Climate Change (UNFCCC) principles of ‘transparency, accuracy, completeness, consistency and comparability’. However, the use of space-based AGBD maps for national-level reporting to the UNFCCC is nearly non-existent as of 2023, the end of the first global stocktake (GST). We conduct an evidence-based comparison of AGBD estimates from the NASA Global Ecosystem Dynamics Investigation and ESA Climate Change Initiative, describing differences between the products and National Forest Inventories (NFIs), and suggesting how science teams must align efforts to inform the next GST. Between the products, in the tropics, the largest differences in estimated AGBD are primarily in the Congolese lowlands and east/southeast Asia. Where NFI data were acquired (Peru, Mexico, Lao PDR and 30 regions of Spain), both products show strong correlation to NFI-estimated AGBD, with no systematic deviations. The AGBD-richest stratum of these, the Peruvian Amazon, is accurately estimated in both. These results are remarkably promising, and to support the operational use of AGB map products for policy reporting, we describe targeted ways to align products with Intergovernmental Panel on Climate Change (IPCC) guidelines. We recommend moving towards consistent statistical terminology, and aligning on a rigorous framework for uncertainty estimation, supported by the provision of open-science codes for large-area assessments that comprehensively report uncertainty. Further, we suggest the provision of objective and open-source guidance to integrate NFIs with multiple AGBD products, aiming to enhance the precision of national estimates. Finally, we describe and encourage the release of user-friendly product documentation, with tools that produce AGBD estimates directly applicable to the IPCC guideline methodologies. With these steps, space agencies can convey a comparable, reliable and consistent message on global biomass estimates to have actionable policy impact.

The largest area of tropical rainforests in China is on Hainan Island, and it is an important part of the world’s tropical rainforests. The structure of the tropical rainforests in Hainan is complex, the biomass density is high, and conducting ground surveys is difficult, costly, and time-consuming. Remote sensing is a good monitoring method for biomass estimation. However, the saturation phenomenon of such data from different satellite sensors results in low forest biomass estimation accuracy in tropical rainforests with high biomass density. Based on environmental information, the biomass of permanent sample plots, and forest age, this study established a tropical rainforest database for Hainan. Forest age and 14 types of environmental information, combined with an enhanced vegetation index (EVI), were introduced to establish a tropical rainforest biomass estimation model for remote sensing that can overcome the saturation phenomenon present when using remote sensing data. The fitting determination coefficient R2 of the model was 0.694. The remote sensing estimate of relative bias was 2.29%, and the relative root mean square error was 35.41%. The tropical rainforest biomass in Hainan Island is mainly distributed in the central mountainous and southern areas. The tropical rainforests in the northern and coastal areas have been severely damaged by tourism and real estate development. Particularly in low-altitude areas, large areas of tropical rainforest have been replaced by economic forests. Furthermore, the tropical rainforest areas in some cities and counties have decreased, affecting the increase in tropical rainforest biomass. On Hainan Island, there were few tropical rainforests in areas with high rainfall. Therefore, afforestation in these areas could maximize the ecological benefits of tropical rainforests. To further strengthen the protection, there is an urgent need to establish a feasible, reliable, and effective tropical rainforest loss assessment system using quantitative scientific methodologies.

Quantifying forest aboveground biomass (AGB) is essential for elucidating the global carbon cycle and the response of forest ecosystems to climate change. Over the past five decades, remote-sensing techniques have played a vital role in forest AGB estimation at different scales. Here, we present an overview of the progress in remote sensing-based forest AGB estimation. More in detail, we first describe the principles of remote sensing techniques in forest AGB estimation: that is, the construction and use of parameters associated with AGB (rather than the direct measurement of AGB values). Second, we review forest AGB remotely sensed data sources (including passive optical, microwave, and LiDAR) and methods (e.g., empirical, physical, mechanistic, and comprehensive models) alongside their limitations and advantages. Third, we discuss possible sources of uncertainty in resultant forest AGB estimates, including those associated with remote sensing imagery, sample plot survey data, stand structure, and statistical models. Finally, we offer forward-looking perspectives and insights on prospective research directions for remote sensing-based forest AGB estimation. Remote sensing is anticipated to play an increasingly important role in future forest AGB estimation and carbon cycle studies. Overall, this comprehensive review may (1) benefit the research communities focused on carbon cycle, remote sensing, and climate change elucidation, (2) provide a theoretical basis for the study of the carbon cycle and global climate change, (3) inform forest ecosystems and carbon management, and (4) aid in the elucidation of forest feedbacks to climate change.

Forest biomass quantification is essential to the global carbon cycle and climate studies. Many studies have estimated forest biomass from a variety of data sources, and consequently generated some regional and global maps. However, these forest biomass maps are not well known and evaluated. In this paper, we reviewed an extensive list of currently available forest biomass maps. For each map, we briefly introduced the data sources, the algorithms used, and the associated uncertainties. Large-scale biomass datasets were compared across Europe, the conterminous United States, Southeast Asia, tropical Africa and South America. Results showed that these forest biomass datasets were almost entirely inconsistent, particularly in woody savannas and savannas across these regions. The uncertainties in biomass maps could be from a variety of sources including the chosen allometric equations used to calculate field data, the choice and quality of remotely sensed data, as well as the algorithms to map forest biomass or extrapolation techniques, but these uncertainties have not been fully quantified. We suggested the future directions for generating more accurate large-scale forest biomass maps should concentrate on the compilation of field biomass data, novel approaches of forest biomass mapping, and comprehensively addressing the accuracy of generated biomass maps.

Accurate forest biomass estimation at the individual tree scale is the foundation of timber industry and forest management. It plays an important role in explaining ecological issues and small-scale processes. Remotely sensed images, across a range of spatial and temporal resolutions, with their advantages of non-destructive monitoring, are widely applied in forest biomass monitoring at global, ecoregion or community scales. However, the development of remote sensing applications for forest biomass at the individual tree scale has been relatively slow due to the constraints of spatial resolution and evaluation accuracy of remotely sensed data. With the improvements in platforms and spatial resolutions, as well as the development of remote sensing techniques, the potential for forest biomass estimation at the single tree level has been demonstrated. However, a comprehensive review of remote sensing of forest biomass scaled at individual trees has not been done. This review highlights the theoretical bases, challenges and future perspectives for Light Detection and Ranging (LiDAR) applications of individual trees scaled to whole forests. We summarize research on estimating individual tree volume and aboveground biomass (AGB) using Terrestrial Laser Scanning (TLS), Airborne Laser Scanning (ALS), Unmanned Aerial Vehicle Laser Scanning (UAV-LS) and Mobile Laser Scanning (MLS, including Vehicle-borne Laser Scanning (VLS) and Backpack Laser Scanning (BLS)) data.

Biochar has emerged as a promising strategy for carbon sequestration in the context of climate change and carbon neutrality goals. Among various feedstocks, underutilized forest biomass (UFB) holds significant potential for conversion into high-value carbon materials. However, the heterogeneity of UFB and the high cost of conventional analyses highlight the need for rapid prediction techniques for key carbon indicators, such as carbon content, atomic oxygen-to-carbon ratio, and atomic hydrogen-to-carbon ratio. This study proposes a chemometric model that non-destructively predicts the carbonization characteristics of biochar using attenuated total reflectance infrared (ATR-IR) spectroscopy combined with partial least squares regression (PLSR). Twenty biochar samples were produced from UFB at carbonization temperatures of 200 °C, 300 °C, and 400 °C. The ATR-IR spectra were preprocessed using normalization and second-derivative transformation before being used to construct the predictive models. The optimized PLSR models, which were validated through cross-validation and outlier removal, achieved high prediction accuracy for all three carbon indices (R² > 0.94). Variable importance in projection (VIP) analysis further identified the key spectral regions contributing to the model performance. These findings demonstrate that high predictive power and interpretability can be achieved without the use of complex machine learning algorithms, providing a practical analytical tool for assessing the quality of biochar and for the efficient utilization of forest residues.

Background Estimation of forest biomass stocks in vast and heterogeneous mountain ranges is critical in the context of climate change mitigation and remains challenging because of limited field observations and unknown relationships between variation in forest biomass and environmental heterogeneity. We addressed this challenge by using forest inventory plot observations and a novel spatial modelling approach. In the first step of our approach, we employ a rigorous clustering process to identify a homogeneous group of locations based on tree species and topoclimatic variables and predict potential forest aboveground biomass (AGB). Subsequently, in the second step, we incorporate finer-scale variables, including proxies of forest structure, disturbance likelihood, and elevation zones, to model deviations from the predicted potential AGB. Results Our method significantly improves forest AGB estimation in heterogeneous mountain landscapes, achieving a 25% reduction in prediction error compared to the best-performing existing model. The final forest AGB map, generated at 30 m resolution, reveals distinct spatial patterns, with the Central Himalayas emerging as a key carbon reservoir, harbouring forest patches exceeding 1000 t ha -1 . Aggregation of these predictions yielded a total forest AGB of 1982 Mt. In addition, we produced a 250 m resolution potential forest AGB map with associated prediction standard error. Conclusion The spatially explicit estimates of actual and potential forest biomass presented is important step towards elucidation of spatial distribution patterns of forest carbon pools and environmental controls. It also provides support for critical initiatives, including climate change mitigation strategies, monitoring forest landscape restoration, and combatting forest degradation challenges. The proposed approach, integrating both broad-scale environmental controls and fine-scale deviations, offers a robust method that is potentially applicable other mountainous regions and contributes for tracking changes in forest carbon over time, essential for REDD+ initiatives.

Climate change is posing new challenges to forestry management practices. Thinning reduces competitive pressure in the forest by repeatedly reducing the tree density of forest stands, thereby increasing the productivity of plantations. Considering the impact of thinning on vegetation and physiological and ecological traits, for this study, we used Norway spruce (Picea abies) data from three sites in the PROFOUND dataset to parameterize the 3-PG model in stages. The calibrated 3-PG model was used to simulate the stand diameter at breast height and the stem, root, and leaf biomass data on a monthly scale. The 3PG-MT-LSTM model uses 3-PG simulation data as the input variable. The model uses a long short-term memory neural network (LSTM) as a shared layer and introduces multi-task learning (MTL). Based on the compatibility rules, the interpretability of the model was further improved. The models were trained using single-site and multi-site data, respectively, and multiple indicators were used to evaluate the model accuracy and generalization ability. Our preliminary results show that, compared with the process model and LSTM algorithm without MTL and compatibility rules, the hybrid model has higher biomass simulation accuracy and shows a more realistic biomass response to environmental driving factors. To illustrate the potential applicability of the model, we applied light (10%), moderate (20%), and heavy thinning (30%) at intervals of 10, 15, 20, 25, 30 years. Then, we used three climate scenarios—SSP1-2.6, SSP2-4.5, and SSP5-8.5—to simulate the growth of Norway spruce. The hybrid model can effectively capture the impact of climate change and artificial management on stand growth. In terms of climate, temperature and solar radiation are the most important factors affecting forest growth, and under warm conditions, the positive significance of forest management is more obvious. In terms of forest management practices, less frequent light-to-moderate thinning can contribute more to the increase in forest carbon sink potential; high-intensity thinning can support large-diameter timber production. In summary, moderate thinning should be carried out every 10 years in the young-aged forest stage. It is also advisable to perform light thinning procedures after the forest has progressed into a middle-aged forest stage. This allows for a better trade-off of the growth relationship between stand yield and diameter at breast height (DBH). The physical constraint-based hybrid modeling approach is a practical and effective tool. It can be used to measure long-term dynamic changes in forest production and then guide management activities such as thinning to achieve sustainable forest management.