Estimates of the magnitude and distribution of aboveground carbon in Earth's forests remain uncertain, yet knowledge of forest carbon content at a global scale is critical for forest management in ...support of climate mitigation. In light of this knowledge gap, several upcoming spaceborne missions aim to map forest aboveground biomass, and many new biomass products are expected from these datasets. As these new missions host different technologies, each with relative strengths and weaknesses for biomass retrieval, as well as different spatial resolutions, consistently comparing or combining biomass estimates from these new datasets will be challenging. This paper presents a demonstration of an inter-comparison of biomass estimates from simulations of three NASA missions (GEDI, ICESat-2 and NISAR) over Sonoma county in California, USA. We use a high resolution, locally calibrated airborne lidar map as our reference dataset, and emphasize the importance of considering uncertainties in both reference maps and spaceborne estimates when conducting biomass product validation. GEDI and ICESat-2 were simulated from airborne lidar point clouds, while UAVSAR's L-band backscatter was used as a proxy for NISAR. To estimate biomass for the lidar missions we used GEDI's footprint-level biomass algorithms, and also adapted these for application to ICESat-2. For UAVSAR, we developed a locally trained biomass model, calibrated against the ALS reference map. Each mission simulation was evaluated in comparison to the local reference map at its native product resolution (25 m, 100 m transect, and 1 ha) yielding RMSEs of 57%, 75%, and 89% for GEDI, NISAR, and ICESat-2 respectively. RMSE values increased for GEDI's power beam during simulated daytime conditions (64%), coverage beam during nighttime conditions (72%), and coverage beam daytime conditions (87%). We also test the application of GEDI's biomass modeling framework for estimation of biomass from ICESat-2, and find that ICESat-2 yields reasonable biomass estimates, particularly in relatively short, open canopies. Results suggest that while all three missions will produce datasets useful for biomass mapping, tall, dense canopies such as those found in Sonoma County present the greatest challenges for all three missions, while steep slopes also prove challenging for single-date SAR-based biomass retrievals. Our methods provide guidance for the inter-comparison and validation of spaceborne biomass estimates through the use of airborne lidar reference maps, and could be repeated with on-orbit estimates in any area with high quality field plot and ALS data. These methods allow for regional interpretations and filtering of multi-mission biomass estimates toward improved wall-to-wall biomass maps through data fusion.
•GEDI, ICESat-2 and NISAR will collect useful data for estimating forest biomass.•All three missions have increased errors with canopy cover and slope.•Airborne Lidar biomass maps allow consistent multi-mission accuracy assessment.•These missions will produce naturally synergistic datasets.•GEDI models can be applied to ICESat-2 data.
In recent years, the production of pellets derived from forestry biomass to replace coal for electricity generation has been increasing, with over 10 million tonnes traded internationally—primarily ...between United States and Europe but with an increasing trend to Asia. Critical to this trade is the classification of woody biomass as ‘renewable energy’ and thus eligible for public subsidies. However, much scientific study on the net effect of this trend suggests that it is having the opposite effect to that expected of renewable energy, by increasing atmospheric levels of carbon dioxide for substantial periods of time. This review, based on recent work by Europe's Academies of Science, finds that current policies are failing to recognize that removing forest carbon stocks for bioenergy leads to an initial increase in emissions. Moreover, the periods during which atmospheric CO2 levels are raised before forest regrowth can reabsorb the excess emissions are incompatible with the urgency of reducing emissions to comply with the objectives enshrined in the Paris Agreement. We consider how current policy might be reformed to reduce negative impacts on climate and argue for a more realistic science‐based assessment of the potential of forest bioenergy in substituting for fossil fuels. The length of time atmospheric concentrations of CO2 increase is highly dependent on the feedstocks and we argue for regulations to explicitly require these to be sources with short payback periods. Furthermore, we describe the current United Nations Framework Convention on Climate Change accounting rules which allow imported biomass to be treated as zero emissions at the point of combustion and urge their revision to remove the risk of these providing incentives to import biomass with negative climate impacts. Reforms such as these would allow the industry to evolve to methods and scales which are more compatible with the basic purpose for which it was designed.
Trade in wood pellets from forestry biomass to replace coal for electricity generation is increasing dramatically. Critical to this trade is the classification of woody biomass as ‘renewable energy’ and thus eligible for public subsidies. However, scientific studies show that it is having the opposite effect and is increasing atmospheric levels of carbon dioxide for substantial periods of time. We argue that EU regulations and the current United Nations Framework Convention on Climate Change accounting, which both allow imported biomass to be treated as zero emissions at the point of combustion, should be revised to remove incentives to import biomass with negative climate impacts.
Lignocellulosic biomass (LCB) is globally available and sustainable feedstock containing sugar-rich platform that can be converted to biofuels and specialty products through appropriate processing. ...This review focuses on the efforts required for the development of sustainable and economically viable lignocellulosic biorefinery to produce carbon neutral biofuels along with the specialty chemicals. Sustainable biomass processing is a global challenge that requires the fulfillment of fundamental demands concerning economic efficiency, environmental compatibility, and social responsibility. The key technical challenges in continuous biomass supply and the biological routes for its saccharification with high yields of sugar sources have not been addressed in research programs dealing with biomass processing. Though many R&D endeavors have directed towards biomass valorization over several decades, the integrated production of biofuels and chemicals still needs optimization from both technical and economical perspectives. None of the current pretreatment methods has advantages over others since their outcomes depend on the type of feedstock, downstream process configuration, and many other factors. Consolidated bio-processing (CBP) involves the use of single or consortium of microbes to deconstruct biomass without pretreatment. The use of new genetic engineering tools for natively cellulolytic microbes would make the CBP process low cost and ecologically friendly. Issues arising with chemical characteristics and rigidity of the biomass structure can be a setback for its viability for biofuel conversion. Integration of functional genomics and system biology with synthetic biology and metabolic engineering undoubtedly led to generation of efficient microbial systems, albeit with limited commercial potential. These efficient microbial systems with new metabolic routes can be exploited for production of commodity chemicals from all the three components of biomass. This paper provides an overview of the challenges that are faced by the processes converting LCB to commodity chemicals with special reference to biofuels.
Objective The purpose of this study is to explore the reproductive allocation (RA) of Tetracentron sinense Oliv. and the relationship between reproductive and vegetative investment, and thus ...investigate the life history strategies and reasons causing this species endangered. Method Using fixed-area sampling plot in Meigu Dafengding Nature Reserve, the reproductive modules biomass of individuals with different DBH(diameter at breast height)class were collected and measured. Multiple comparison analysis was conducted to compare the differences in reproductive investment and allocation among different DBH class. The relationships between RA, reproductive modules biomass and the nutrient modules biomass were analyzed by correlation and linear regression analysis. Result (1) With increasing DBH, the reproductive and vegetative investment showed the same changing trend, and no trade off appeared between them; (2) At the level of modules, the RA value increased at the beginning and then decreased with increasi
Objective To study the effect of slow-release fertilizer (SLF) N/P ratio and loading on the growth of Taxus wallichiana var. mairei, Phoebe chekiangensis and Cinnamomum chekiangense.Method In this ...study, with two factors of N/P ratio (1.75:1, 2.25:1, 2.75:1 and 3.25:1) and SLF loading (1.5 kg·m-3, 2.5 kg·m-3, 3.5 kg·m-3 and 4.5 kg·m-3), a factorial experimental design was applied to study the Growth (seedling height, root diameter and biomass accumulation), growth rhythm and SPAD of two-year-old container seedlings of the three species.Result The growth and SPAD value of the container seedlings (two-year-old) of the 3 species as affected by N/P ratio and SLF loading were different. The biomass accumulation and root-shoot ratio of T. wallichiana var. mairei, Ph. chekiangensis and C. chekiangense varied slightly in N/P ratio. With the increase of N/P ratio, the growth of Ph. chekiangensis was promoted, but the growth of T. wallichiana var. mairei was inhibited. The effect of SRF on the growth and biomass
NASA’s Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass ...density (AGBD). This paper presents the development of the models used to create GEDI’s footprint-level (~25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI’s waveform-to-biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available.
•NASA’s GEDI collects spaceborne lidar data used for mapping aboveground biomass.•A global database of field and airborne lidar was compiled.•Models stratified by Plant Functional Type and geographic region outperform a global model.•GEDI04_A models are OLS models predicting biomass as a function of RH metrics.•Maximum forest height is an important predictor of biomass across geographic domains.
Increasing maize planting density is a major agronomic practice to enhance population grain yield, however, canopy shadowing at high density limits plant growth and per-plant grain yield. Dry mass ...(DM) accumulation, allocation, and remobilization are crucial factors determining grain yield. However, there is limited understanding regarding these processes in response to increasing planting density.
This study aimed to evaluate how planting density affects DM accumulation, allocation, and remobilization, as affected by plant architecture, nitrogen (N) rates, N fertilization frequency, and water management.
A meta-analysis was conducted, involving 2363 observations from 253 peer-reviewed studies.
Globally, population grain yield increased by 11.2 %, which was attributable to increases in a population pre-silking DM (PrS-DM) accumulation of 22.9 % and remobilization efficiency of 12.6 %. Temporally, under a high planting density, per plant DM production showed a decrease (8.3–16.0 %) during the pre-silking stage, but a greater reduction (24.0–25.4 %) during the post-silking stage. DM allocation to roots was greatly reduced, with a decline of 22.1–25.1 % in the root-to-shoot ratio (R/S), and a dropping rate of 5.2 % in harvest index (HI). Compact plant architecture showed a 12.2 % increase in grain yield and a reduction of 3.4 % in HI. Appropriate N rates coupled with splitting-N applications showed an increase in grain yield (up to 13.9 %) and PrS-DM (up to 27.1 %), but a decline in post-silking DM (PoS-DM) (up to 9.7 %) and HI (up to 9.0 %). Efficient water management, i.e., fertigation increased the grain yield (up to 16.9 %).
Increasing planting density increases grain yield mainly by efficiently utilizing light resources during the vegetative stage to increase population PrS-DM production and its remobilization to grain. In addition, less biomass is allocated to the root so that more assimilation is used for shoot growth.
Field management practices and breeding efforts should focus on facilitating early plant growth to increase population PrS-DM accumulation and developing sound root systems to increase efficiency and canopy-lodging resistance.
Scheme of impacts of increasing planting density on dynamic shoot and root growth per plant (a, b) and per hectare (c, d) in maize during the whole growth period. Numbers in black indicate the ratio of indicators under high planting density (HD) to that of the farmers’ practice (FP, control) respectively. Numbers in red and blue colors indicate the ratio of the dry mass under high planting density to that of the control in shoot and root respectively. Numbers with and without underlines indicate the ratio of indicators under high planting density to that of the control in per-hectare and per-plant level respectively. All the data are pooled for the analysis. HI means harvest index; R/S-R1, R/S-R3, and R/S-R6 indicate root-to-shoot ratio at silking, milk, and maturity stage, respectively. DMRE means remobilization efficiency of dry mass within vegetative tissues. DMRC means the contribution of dry mass remobilization to grain yield. Display omitted
•Globally, an average 57.8 % increase in planting density led to an 11.2 % increase in grain yield.•The increase in yield was supported by greater pre-silking biomass production and remobilization.•Individual biomass was less affected by high density before silking than that during post-silking.•High density displayed less biomass allocation to root and kernel.•This study brings insights into high-density planting by optimizing breeding and field management.
Bio-oils derived from lignocellulosic materials have poor properties for use as fuels and cannot be blended with transportation fuels. Hydrotreating is an effective method for eliminating ...contaminants and saturating double bonds. This article is one of the few that report the hydrotreatment of a biomass-derived oil over a sulfided NiMo/ gamma -Al sub(2)O sub(3) catalyst and the deactivation of the catalyst. The results confirm that hydrotreatment is an effective technology for improving the quality of bio-oil. The total acid number of the upgraded bio-oil decreased from 23 mg KOH g super(-1) (raw bio-oil) to 2 mg KOH g super(-1). Oxygenated functional groups are removed, light liquid products are generated and carbon double bonds are saturated. The catalyst can become deactivated at a high operating temperature due to severe coke deposition. The deactivated catalyst was studied by using multiple analytical methods such as TEM, XRD, BET and TPO to study the deactivation pathways.
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