Forest Biomass Research Articles

Potential and assessment of urea-based SNCR in a small-scale multi-fuel biomass furnace

The aim of this work was to assess the potential NOx reduction achieved with urea-based SNCR in a small-scale biomass grate furnace operated with miscanthus pellets and forest biomass residues. Therefore, experiments were performed to test the main parameters of SNCR efficiency, namely the urea amount (normalised stoichiometric ratio), temperature and residence time, and the results were compared with those of a kinetic simulation applying ideal reactors in conjunction with a detailed nitrogen chemistry mechanism. The urea decomposition was estimated using different NH3:HNCO ratios, which were compared in the course of this work. The experimental and simulation results confirm an optimum temperature of around 950 °C, enabling a NOx reduction of over 90 % for both investigated fuels in this biomass furnace. However, at steady state, the NOx reduction was limited to 60–70 % due to the legal limits for NH3. At lower temperatures or residence times, a significant increase in NH3 emissions was observed. Due to the use of urea in this SNCR system, an increase in N2O emissions was detected, and the simulations also predicted non-negligible HNCO emissions. For a simulation of these two species, a suitable NH3:HNCO ratio for urea decomposition is needed. Finally, the optimum operating point for achieving 60–70 % NOx reduction and for complying with the NH3 legal limits shows that a great potential for using SNCR exists in small-scale biomass furnaces, if the required temperature and residence time can be provided.

Forest above-ground biomass estimation based on strongly collinear variables derived from airborne laser scanning data

Airborne laser scanning technique (ALS) is the most appealing remote sensing technique for the precise estimation of forest above-ground biomass (AGB). Significantly strong correlations (collinearity) among the independent variables derived from ALS data decrease the accuracy of developed AGB models. To address this issue, we propose a novel variable selection algorithm-an improved sure independence screening (SPV), which integrates the Pearson correlation coefficient, threshold μ, and variance inflation factor. We further compared the performance of SPV-based, stepwise feature selection (SFS)-based, and least absolute shrinkage and selection operator (LASSO)-based AGB models developed with different regression approaches that were sensitive to strong collinearity. Field-measured data and corresponding ALS data, acquired from 1002 sample plots distributed across four distinct forest types within the Guangxi Zhuang Autonomous Region in Southern China, were used to evaluate variable selection techniques and develop AGB models. Results indicated that ALS variables selected by SPV exhibited weaker collinearity compared to those selected by SFS and LASSO. SPV-based AGB models outperformed SFS-based AGB models with higher leave-one-out cross-validation adjusted R2 (LOOCV Radj2) by 0.1% − 27.8%. SPV-based AGB models outperformed LASSO-based AGB models with higher LOOCV Radj2 by 0.4% − 16.3%. Hence, for variable selection in constructing AGB models (linear regression model, log–log regression model, and generalized additive model (GAM)) based on strongly collinear ALS variables, SPV is most preferred, followed by SFS and LASSO. The smooth curves from our GAMs developed using SPV-selected variables revealed that five canopy height variables (whp40, hmean, hp25, hp70, hskew), one canopy density variable (whp85), three density-related variables (nVegPts, bcc, and mcc), and one vertical structural variable (LADmean) were positively correlated with AGB. The canopy height variables (whp40, hmean, hp25, and hp70) were identified as the most important variables in estimating AGB for four forest types. The canopy density variable wdp85 showed a strong effect on AGB of the coniferous forests, whereas it had almost no effect on the AGB of broadleaved forests. Overall, this manuscript proposes a novel variable selection algorithm named SPV, aimed at addressing collinearity of variables derived from ALS data, which has significant implications for the application of ALS in forest inventory and forest modeling.

Evolutionary diversity impacts tropical forest biomass and productivity through disturbance‐mediated ecological pathways

Abstract Significant research efforts have been made to uncover links between biodiversity and biomass productivity in forest ecosystems. However, the causal link between these two ecosystem components, and the underlying mediation role of disturbance, are yet poorly understood for hyper‐diverse tropical forests, because multiple ecological mechanisms are sequentially or simultaneously in play, leading to contradictory results in observational studies. Here, we introduce a novel framework for inferring the expected effects of evolutionary diversity on biomass stocks and productivity within forest ecosystems using observational field data. This framework involves an analytical decomposition of stand biomass into three key components: the number of trees, the mean size of trees and the mean wood density. Through this approach, we can distinguish structure‐ and compositional‐based diversity effects, which likely have distinct ecological origins. We tested this framework in one of the oldest tropical forest experiments, where different levels of silvicultural disturbances were applied in the 1980s, with regular monitoring since then. Our results revealed that disturbance history mediates the effect of evolutionary diversity on forest biomass dynamics and that several Biodiversity Ecosystem Function (BEF) relationships may be hidden behind the composite biomass variable. We specifically found an overall significant negative relationship between evolutionary diversity and biomass productivity soon after disturbances (~5–8 years), mostly via mean tree size, despite a positive evolutionary diversity effect on mean wood density. This result reflects that the productivity of disturbed forests is driven by a few dominant and disturbance‐prone species with low wood density and large potential stature, and not by niche complementarity among species. However, these effects rapidly vanished with time, with non‐significant overall effect of evolutionary diversity on productivity both ~30 years after disturbance and in the undisturbed plots. Synthesis. By disentangling the effects of evolutionary diversity on the different components of forest biomass, our framework unveiled how evolutionary diversity impacts forest productivity through different ecological mechanisms, and suggests that it plays a major role, albeit mainly negative, only soon after a disturbance.

A Multi-Baseline Forest Height Estimation Method Combining Analytic and Geometric Expression of the RVoG Model

As an important parameter of forest biomass, forest height is of great significance for the calculation of forest carbon stock and the study of the carbon cycle in large-scale regions. The main idea of the current forest height inversion methods using multi-baseline P-band polarimetric interferometric synthetic aperture radar (PolInSAR) data is to select the best baseline for forest height inversion. However, the approach of selecting the optimal baseline for forest height inversion results in the process of forest height inversion being unable to fully utilize the abundant observation data. In this paper, to solve the problem, we propose a multi-baseline forest height inversion method combining analytic and geometric expression of the random volume over ground (RVoG) model, which takes into account the advantages of the selection of the optimal observation baseline and the utilization of multi-baseline information. In this approach, for any related pixel, an optimal baseline is selected according to the geometric structure of the coherence region shape and the functional model for forest height inversion is established by the RVoG model’s analytic expression. In this way, the other baseline observations are transformed into a constraint condition according to the RVoG model’s geometric expression and are also involved in the forest height inversion. PolInSAR data were used to validate the proposed multi-baseline forest height inversion method. The results show that the accuracy of the forest height inversion with the algorithm proposed in this paper in a coniferous forest area and tropical rainforest area was improved by 17% and 39%, respectively. The method proposed in this paper provides a multi-baseline PolInSAR forest height inversion scheme for exploring regional high-precision forest height distribution. The scheme is an applicable method for large-scale, high-precision forest height inversion tasks.

Tree richness increased biomass carbon sequestration and ecosystem stability of temperate forests in China: Interacted factors and implications

Biodiversity loss and forest degradation have received increasing attention worldwide, and their effects on forest biomass carbon storage and stability have not yet been well defined. This study examined 1275 tree plots using the field survey method to quantify the effects of tree diversity, tree sizes, and mycorrhizal symbiont abundance on biomass carbon storages (Cs) and NDVI (Normalized Difference Vegetation Index)-based ecosystem stability (standard deviation/mean NDVI = NDVI_S) during the field survey period from 2008 to 2018. Our data showed Cs and NDVI_S averaged at 31–108 t ha−1 and 32.04–49.28, respectively, and positive relations between Cs and NDVI_S were observed (p < 0.05). Large forest-type and regional variations were found in these two parameters. Broadleaf forests had 74% of Cs (p < 0.05) of the conifer forests, but no differences were in NDVI_S. Cold regions at high latitudes had 71% of NDVI_S in the warm regions at low latitudes, while no differences were in Cs. Moist regions at high longitudes had 2.04 and 1.28-fold higher Cs and NDVI_S (p < 0.05). The >700 m a.s.l. regions had 1.24-fold higher Cs (p < 0.01) than the <700 m a.s.l. regions, but similar NDVI_S (p > 0.05). Nature Reserves had 1.94-fold higher Cs but 30% lower NDVI_S than outside Reserves (p < 0.001). > 40-year-old forests had 1.3- and 2-fold higher Cs and NDVI_S than the young forests. Structural equation modeling and hierarchical partitioning revealed the driving paths responsible for these variations. Tree richness was positively associated with Cs and ecosystem stability, contributing 21.6%–30.6% to the total effects on them; tree sizes significantly promoted the Cs, but had negligible impacts on NDVI_S. MAT's total effects on NDVI_S of conifer forests were 40% higher than that of broadleaf forests, MAP's total effects on Cs varied with forest types; arbuscular mycorrhizal tree dominance exhibited a smaller positive impact on Cs and ecosystem stability in comparison to other factors. Our findings underscore that the significance of climatic-adapted forest management, diversity conservation, and big-sized tree protections can support the achievement of carbon neutrality in China from biomass carbon sequestration and ecosystem stability.

Mapping aboveground biomass in Indonesian lowland forests using GEDI and hierarchical models

Spaceborne lidar (light detection and ranging) instruments such as the Global Ecosystem Dynamics Investigation (GEDI) provide a unique opportunity for global forest inventory by generating broad-scale measurements sensitive to the vertical arrangement of plant matter as a supplement to in situ measurements. Lidar measurables are not directly relatable to most physical attributes of interest, including biomass, and therefore must be related through statistical models. Further, GEDI observations are not spatially complete, necessitating methods to convert the incomplete samples to predictions of area averages/totals. Such methods can face challenges in equatorial and persistently cloudy areas, such as Indonesia, where the density of quality observations is diminished. We developed and implemented a hierarchical model to produce gap-free maps of aboveground biomass density (AGBD) at various resolutions within the lowlands of Jambi province, Indonesia. A biomass model was trained between local field plots and a metric from GEDI waveforms simulated with coincident airborne laser scanning (ALS) data. After selecting a locally suitable ground-finding algorithm setting, we trained an error model depicting the discrepancies between the simulated and GEDI-observed waveforms. Finally, a geostatistical model was used to model the spatial distribution of the on-orbit GEDI observations. These three models were nested into a single hierarchical model, relating the spatial distribution of GEDI observations to field-measured AGBD. The model allows spatially complete predictions at arbitrary resolutions while accounting for uncertainties at each stage of the relationship. The model uncertainties were low relative to the predicted biomass, with a median relative standard deviation of 8% at the 1 km resolution and 26% at the 100 m resolution. The spatially consistent information on AGBD provided by our model is beneficial in support of sustainable forest management, carbon sequestration initiatives and the mitigation of climate change. This is particularly relevant in a dynamic tropical landscape like Jambi, Indonesia in order to understand the impacts of land-use transformations from forests to cash crops like oil palm and rubber. More generally, we advocate for the use of hierarchical models as a framework to account for multiple stages of relationships between field and sensor data and to provide reliable uncertainty audits for final predictions.

To improve estimates of neotropical forest carbon stocks more direct measurements are needed: An example from the Southwestern Amazon

Tropical forests play a critical role in the global carbon cycle, storing 40–55 % of terrestrial plant carbon and significantly contributing to primary productivity. However, uncertainties persist in estimating carbon stocks and fluxes, exhibiting variation across the Neotropics, Africa, and Asia tropical forest regions. Despite hosting some of the most densely sampled forests, significant uncertainties persist in biomass and forest carbon stock estimates in the Neotropics. Although the Southwestern Amazon (SWA) forests span over 20 million hectares, no specific biomass or above- and below-ground carbon model has been calibrated for this region thus far. In our study, we conducted direct forest inventories in the SWA to address the following question: Do the allometric patterns, biomass, and carbon stocks observed in the Southwestern Amazon differ from those found in other regions of the Amazon or Pantropical? Our research reveals substantial differences in water and carbon content, biomass stocks, above- and below-ground oven-dry biomass ratios, and allometric patterns between SWA forests and other Amazonian and Pantropical forests. We have demonstrated that these differences result in overestimations of forest biomass when applying allometric equations developed for other Amazonian and Pantropical regions to the open forests of Southwestern Amazonia. This overestimation can reach up to 37 % when using equations from the eastern Amazon, and between 26 % and 46 % depending on the applied Pantropical equation. The use of an inappropriate factor for the root-to-shoot ratio in the Southwestern Amazon (SWA) can lead to overestimates of belowground oven-dry biomass by up to 20 %. To reduce uncertainties related to estimates of forest carbon stock and flux in Neotropical forests, it is necessary to enhance the density of direct biomass measurements, particularly in southwestern Amazonia.

Chronic anthropogenic disturbance causes prolific resprouting and dwarfing – A case study of a widely distributed subtropical tree

Chronic anthropogenic disturbance (CAD) comprises the additive effect of multiple anthropogenic stressors that lead to subtle but continuous alterations to the structure, composition and functioning of natural ecosystems. However, this effect has been little studied at the tree species level. Here, we examined CAD effects on Chinese beech (Fagus engleriana), a common species in northern subtropical forests that utilizes both vegetative resprouting and seeding as regenerative modes. We computed an index of CAD for each plot based on livestock grazing intensity, wood extraction and miscellaneous resource use. Over 60 20 × 20 m plots, we measured the height and DBH of a total of 1261 Chinese beech individuals (21.02 ± 13.06 per plot, [mean ± SD]), including 593 adults (9.88 ± 4.89), 576 saplings (9.60 ± 8.91) and 92 seedlings (1.53 ± 3.91), and assessed the proportions of each that exhibited resprouting (0.91 ± 0.11, 0.69 ± 0.29, and 0.09 ± 0.25, respectively). CAD was associated with a greater number of individual trees and a proliferation of stems, and with a greater proportion of resprouting seedlings, but had no effect on adult-sized stem DBH. Chinese beech thus appears generally tolerant of CAD, which may ultimately result in its increasing contribution to the maintenance of forest biomass. CAD was also associated with shorter adult and sapling stems, but taller resprouting seedling stems, implying that CAD induced dwarfism as Chinese beech matures. Further analysis showed that the proportion of resprouting was related to small scale wood extraction and miscellaneous resource use, but not to grazing. In the context of better understanding cryptic effects of CAD, our findings show that CAD can alter tree morphology and regenerative processes. These coppicing-type effects of CAD on tree morphology structure are typically associated with higher community biodiversity in managed forest coppice, and future work should investigate the biodiversity consequences of CAD at our study site. Our study shows that CAD has important implications for forest resource utilization, planning, and management, with effects on forest biomass and carbon storage in human-modified landscapes.

A Comparative Analysis of Remote Sensing Estimation of Aboveground Biomass in Boreal Forests Using Machine Learning Modeling and Environmental Data

It is crucial to have precise and current maps of aboveground biomass (AGB) in boreal forests to accurately track global carbon levels and develop effective plans for addressing climate change. Remote sensing as a cost-effective tool offers the potential to update AGB maps for boreal forests in real time. This study evaluates different machine learning algorithms, namely Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Support Vector Regression (SVR), for predicting AGB in boreal forests. Conducted in the Qilian Mountains, northwest China, the study integrated field measurements, space-borne LiDAR, optical remote sensing, and environmental data to develop a training dataset. Among 34 variables, 22 were selected for AGB estimation modeling. Our findings revealed that the LightGBM AGB model had the highest level of accuracy (R2 = 0.84, RMSE = 15.32 Mg/ha), outperforming the XGBoost, RF, and SVR AGB models. Notably, the LightGBM AGB model effectively addressed issues of underestimation and overestimation. We also observed that the disparity in accuracy among the models widens with increasing altitude. Remarkably, the LightGBM AGB model consistently demonstrates optimal performance across all elevation gradients, with residuals generally below 25 Mg/ha for low-value overestimation and below −38 Mg/ha for high-value underestimation. The model developed in this study presents a viable and alternative approach for enhancing AGB estimation accuracy in boreal forests based on remote sensing technology.

Forestat: An R package for computing forest carbon sequestration and potential productivity

1. Forest ecosystem is a basic component of terrestrial ecosystem that mitigates the impact of climate change through absorbing significant amount of carbon dioxide. Forest is crucial in maintaining ecological equilibrium, reducing greenhouse gas emission, and preserving biodiversity. Thus, standardised and precise methodologies for measuring forest carbon sequestration and assessing its potential are of paramount importance in evaluating the impacts of climate change.2. We presented the “forestat”, an approach for assessing forest carbon sequestration and its potential based on annual growth of biomass, considering site class and forest age conditions, wrapped in an R package, which is applicable to all forest types. By using a comprehensive system of forest stand models, “forestat” contributes to a refined comprehension of relationships between forest biomass and carbon sequestration and offers an evaluation of the carbon sequestration potential in forest ecosystem, unifying the methods of carbon measurement and its potential estimation in both natural and plantation forests.3. Based on the two applications, this study (a) introduces and demonstrates the methods in R package “forestat”; (b) uses the 6th, 7th, 8th, and 9th National Forest Inventory data to investigate carbon sequestration and potential productivity of degraded forests in China; and (c) conducts suitability analysis for tree species in the Yellow River Basin.4. “forestat” provides a unified, efficient, and standardized analytical tool for forest carbon sequestration and its potential. This study also highlights the primary forest functionalities.

Getting allometry right at the Oak Ridge free‐air CO2 enrichment experiment: Old problems and new opportunities for global change experiments

Societal Impact StatementFree‐air CO2 enrichment (FACE) experiments provide essential data on forest responses to increasing atmospheric CO2 for evaluations of climate change impacts on humanity. Understanding and reducing the uncertainty in the experimental results is critical to ensure scientific and public confidence in the models and policy initiatives that derive therefrom. One source of uncertainty is the estimation of tree biomass using mathematical relationships between biomass and easily obtained and non‐destructive measurements (allometry). We evaluated the robustness of the allometric relationships established at the beginning of a FACE experiment and discuss the challenges and opportunities for the new generation of FACE experiments.Summary Long‐term field experiments to elucidate forest responses to rising atmospheric CO2 concentration require allometric equations to estimate tree biomass from non‐destructive measurements of tree size. We analyzed whether the allometric equations established at the beginning of a free‐air CO2 enrichment (FACE) experiment in a Liquidambar styraciflua plantation were still valid at the end of the 12 year experiment. Aboveground woody biomass was initially predicted by an equation that included bole diameter, taper, and height, assuming that including taper and height as predictors would accommodate changes in tree structure that might occur over time and in response to elevated CO2. At the conclusion of the FACE experiment, we harvested 23 trees, measured dimensions and dry mass of boles and branches, and extracted and measured the woody root mass of 10 trees. Although 10 of the harvested trees were larger than the trees used to establish the allometric relationship, measured aboveground woody biomass was well predicted by the original allometry. The initial linear equation between bole basal area and woody root biomass underestimated final root biomass by 28%, but root biomass was just 21% of total wood mass, and errors in aboveground and belowground estimates were offsetting. The allometry established at the beginning of the experiment provided valid predictions of tree biomass throughout the experiment. New allometric approaches using terrestrial laser scanning should reduce an important source of uncertainty in decade‐long forest experiments and in assessments of centuries‐long forest biomass accretion used in evaluating carbon offsets and climate mitigation.

Fabricating whole pine needles biomass with phenylhydrazine-4-sulphonic acid for effective removal of cationic dyes and heavy metal ions from wastewater

We applied a holistic, sustainable, and green approach to develop an effective multipurpose adsorbent from whole pine needles (PNs), a forest waste lignocellulosic biomass. The PNs were oxidized and modified with phenylhydrazine-4-sulphonic acid (ɸHSO3H) to OPN-ɸHSO3H. The latter was characterized and tested as an adsorbent for cationic dyes, malachite green (MG), methylene blue (MB), crystal violet (CV), and metal ions (Hg2⁺ and Pb2⁺). The adsorption followed different kinetic models: Elovich for MG and MB, pseudo-second-order for CV, and pseudo-first-order for Hg2⁺ and Pb2⁺. Langmuir isotherm indicated maximum adsorption capacities of 303.4 ± 8.91 mgg−1 (MG), 331.4 ± 17.50 mgg−1 (MB), 376.6 ± 22.47 mgg−1 (CV), 210.8 ± 28.86 mgg−1 (Hg2⁺), and 172.9 ± 20.93 mgg−1 (Pb2⁺) within 30 min. Maximum removal efficiencies were 99.0% (MG), 98.0% (MB), 96.04% (CV), 95.5% (Hg2⁺), and 89.8% (Pb2⁺). The adsorbent demonstrated significant regeneration and reusability over ten cycles, proving highly efficient for both cationic dyes and metal ions, with wide potential for practical applications where more than one adsorbate is present.

Application of the wildland fire emissions inventory system to estimate fire emissions on forest lands of the United States

BackgroundForests are significant terrestrial biomes for carbon storage, and annual carbon accumulation of forest biomass contributes offsets affecting net greenhouse gases in the atmosphere. The immediate loss of stored carbon through fire on forest lands reduces the annual offsets provided by forests. As such, the United States reporting includes annual estimates of direct fire emissions in conjunction with the overall forest stock and change estimates as a part of national greenhouse gas inventories within the United Nations Framework Convention on Climate Change. Forest fire emissions reported for the United States, such as the 129 Tg CO2 reported for 2022, are based on the Wildland Fire Emissions Inventory System (WFEIS). Current WFEIS estimates are included in the Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2022 published in 2024 by the United States Environmental Protection Agency. Here, we describe WFEIS the fire emissions inventory system we used to address current information needs, and an analysis to confirm compatibility of carbon mass between estimated forest fire emissions and carbon in forest stocks.ResultsThe summaries of emissions from forests are consistent with previous reports that show rates and interannual variability in emissions and forest land area burned are generally greater in recent years relative to the 1990s. Both emissions and interannual variability are greater in the western United States. The years with the highest CO2 emissions from forest fires on the 48 conterminous states plus Alaska were 2004, 2005, and 2015. In some years, Alaska emissions exceed those of the 48 conterminous states, such as in 2022, for example. Comparison of forest fire emission to forest carbon stocks indicate there is unlikely any serious disconnect between inventory and fire emissions estimates.ConclusionsThe WFEIS system is a user-driven approach made available via a web browser. Model results are compatible with the scope and reporting needs of the annual national greenhouse gas inventories.

Shift-share of the international market for energy forest products

The increase in fossil fuel prices and the growing demand for clean energy has encouraged the consumption of forest biomass for energy around the world. The distribution of production factors for forest products is not homogeneous across space and the specific market characteristics of each country influence the bioenergy potential. Thus, this article analyzed the structural-differential composition (shift-share) of the international market for energy forest products (EFP) between 2000 and 2020. The data are available from the Food and Agriculture Organization. The EFP consists of firewood, charcoal, wood chips, wood waste, pellets and other agglomerates. Conducted a standard structural-differential analysis (shift-share) over the period 2000–2020, then extended it by applying the Esteban-Marquillas variation. North America, Australia and Europe led in terms of the share of countries with positive effects. Charcoal and firewood were the products most used in energy production. The countries with the greatest positive structural effect were the United States (16 × 106), Canada (7 × 106), Japan (7 × 106), Finland (6 × 106), and France (5 × 106). The countries with the greatest positive differential effect were Myanmar (38 × 106), China (36 × 106), Ghana (20 × 106), Ethiopia (20 × 106), and Congo (18 × 106). From 2000–2020, 29 countries demonstrated a specialized competitive advantage (SCA), 51 countries demonstrated a non-specialized competitive advantage (NSCA), 39 countries demonstrated a non-specialized disadvantage (NSD) and 48 countries demonstrated a specialized competitive disadvantage (SCD). With these results it was possible to note that the increase in oil, gas and electricity prices has encouraged the consumption of EFPs worldwide. Hybrid industries (oil/gas/pellets) increased forest biomass consumption in the period 2000–2020. The dynamics of EFP consumption worldwide have been driven by increased demand from competitive and specialized countries.

Development of L-band fully polarimetric SAR algorithm for forest biomass retrieval using 7SD and random forest regression

Forest biomass is essential for carbon budgeting, biodiversity health, and climate change research. This study developed a novel L-band fully polarimetric SAR method for estimating aboveground biomass (AGB) in a forested region in India. Biomass was estimated using fully polarimetric ALOS-2/PALSAR-2 data sets and field AGB measurements. To estimate forest AGB, the algorithm employs a seven-component scattering power decomposition (7SD) and a random forest regression (RFR) machine-learning approach. By implementing the 7SD model, the seven scattering powers were extracted from ALOS-2/PALSAR-2 inverted for aboveground biomass. The field data were used to validate the biomass estimated by the model. The 7SD model estimated AGB in Shivamogga was consistent with field measured biomass. The root mean squared error (RMSE) and relative RMSE with respect to mean AGB were 21.94 Mg/ha and 19.46, respectively, which are within acceptable ranges. The 7SD model was also used for cross-validation at Tundi Forest, where the relative RMSE with respect to the mean AGB was 22.9. The scattering powers generated from L-band fully polarimetric data can be useful in tropical forest AGB estimation.

Deep and machine learning prediction of forest above-ground biomass using multi-source remote sensing data in coniferous planted forests in Iran

Deep and machine learning prediction of forest above-ground biomass using multi-source remote sensing data in coniferous planted forests in Iran

Range-wide salamander densities reveal a key component of terrestrial vertebrate biomass in eastern North American forests.

Characterizing the population density of species is a central interest in ecology. Eastern North America is the global hotspot for biodiversity of plethodontid salamanders, an inconspicuous component of terrestrial vertebrate communities, and among the most widespread is the eastern red-backed salamander, Plethodon cinereus. Previous work suggests population densities are high with significant geographic variation, but comparisons among locations are challenged by lack of standardization of methods and failure to accommodate imperfect detection. We present results from a large-scale research network that accounts for detection uncertainty using systematic survey protocols and robust statistical models. We analysed mark-recapture data from 18 study areas across much of the species range. Estimated salamander densities ranged from 1950 to 34 300 salamanders ha-1, with a median of 9965 salamanders ha-1. We compared these results to previous estimates for P. cinereus and other abundant terrestrial vertebrates. We demonstrate that overall the biomass of P. cinereus, a secondary consumer, is of similar or greater magnitude to widespread primary consumers such as white-tailed deer (Odocoileus virginianus) and Peromyscus mice, and two to three orders of magnitude greater than common secondary consumer species. Our results add empirical evidence that P. cinereus, and amphibians in general, are an outsized component of terrestrial vertebrate communities in temperate ecosystems.

Development of site-specific allometric equation and predicting aboveground biomass of natural and plantation forests of Oxytenathera abyssinica (A. Rich.) Munro, Northwestern Ethiopia

Bamboo is an ideal plant for commercial production due to its rapid growth rate, high biomass production, low cost of production and environmental friendliness. Although Ethiopia has the highest bamboo cover in Africa, allometric equations for estimating its biomass are scarce. Most allometric models developed to date have been largely concerned with trees and shrubs. The objective of this study was to generate species- and site-specific allometric models that could be used to estimate the total aboveground dry biomass and culm dry biomass of lowland bamboo in northwest Ethiopia. Three power form-based allometric models were created using diameter at breast height (DBH) and culm height (H) as independent variables. One hundred and eight Oxytenathera abyssinica culms were used to predict the total aboveground biomass and culm biomass. Model one (M1) was the best model to predict the culm and total aboveground biomass of the species, regardless of forest type. The allometric models may provide useful information about aboveground biomass and culm biomass estimation methods to forestry professionals, bamboo producers and other stakeholders, and could help in the calculation of the country’s contribution to global carbon sequestration and trade.

Calibrating Tropical Forest Coexistence in Ecosystem Demography Models Using Multi‐Objective Optimization Through Population‐Based Parallel Surrogate Search

AbstractTropical forest diversity governs forest structures, compositions, and influences the ecosystem response to environmental changes. Better representation of forest diversity in ecosystem demography (ED) models within Earth system models is thus necessary to accurately capture and predict how tropical forests affect Earth system dynamics subject to climate changes. However, achieving forest coexistence in ED models is challenging due to their computational expense and limited understanding of the mechanisms governing forest functional diversity. This study applies the advanced Multi‐Objective Population‐based Parallel Local Surrogate‐assisted search (MOPLS) optimization algorithm to simultaneously calibrate ecosystem fluxes and coexistence of two physiologically distinct tropical forest species in a size‐ and age‐structured ED model with realistic representation of wood harvest. MOPLS exhibits satisfactory model performance, capturing hydrological and biogeochemical dynamics observed in Barro Colorado Island, Panama, and robustly achieving coexistence for the two representative forest species. This demonstrates its effectiveness in calibrating tropical forest coexistence. The optimal solution is applied to investigate the recovery trajectories of forest biomass after various intensities of clear‐cut deforestation. We find that a 20% selective logging can take approximately 40 years for aboveground biomass to return to the initial level. This is due to the slow recovery rate of late successional trees, which only increases by 4% over the 40‐year period. This study lays the foundation to calibrate coexistence in ED models. MOPLS can be an effective tool to help better represent tropical forest diversity in Earth system models and inform forest management practices.

The Carbon Stocks Estimation on Tangga Community Forests of Lombok Utara

Forests are one of the largest carbon dioxide absorbers, the forest's function as a carbon dioxide absorber causes an increase in greenhouse gases in the atmosphere, and carbon dioxide is stored in forest biomass. Forest development to increase carbon dioxide absorption can be carried out in state forest areas or private forests, which also include community forests. This research aims to estimate carbon stocks in the Tangga community forest (HKm) , in the Tangga HKm, Lombok utara, NTB. This research uses quantitative methods. The data collected is standing vegetation data. Standing vegetation data includes saplings, poles and trees by measuring diameter and height using purposive sampling collection techniques. Carbon stocks are estimated using the allometric equation formula. The total carbon reserves in the HKm Tangga obtained from each stand were 9.972,11 ton/ha. The highest carbon stock value is at the tree level with a value of 8.785,08 tons/ha, followed by the sapling level at 731,34 ton/ha, and the pole level at 455,69 tons/ha with the highest amount at the candlenut tree level (Aleurites moluccana) at 2.200,63 ton/ha and the lowest was the breadfruit (Artocarpus altilis) with a value of 1,96 ton/ha.