Forest Biomass Stocks Research Articles

Variation in tree biomass and carbon stocks in three tropical dry deciduous forest types of Madhya Pradesh, India

Variation in tree biomass and carbon (C) stocks in tropical dry forests is important at both regional and global scale to know their contribution in global C cycle. In Indian tropical forests, the information on variation in tree biomass and C stocks at forest type level is scarce and fragmented. In the present study, three tropical dry deciduous forest types: dry deciduous teak forest (DDTF), dry deciduous mixed forest (DDMF) and Boswellia forest (BF) have been selected in Madhya Pradesh, Central India, to assess the biomass and C stocks. The total tree biomass (aboveground + belowground) ranged from 70.4 (DDTF) to 296.6 (BF) Mg/ha with a mean of 184.1 Mg/ha, whereas the tree C ranged from 35.3 (DDTF) to 140.9 (BF) Mg C/ha with a mean value of 87.4 Mg C/ha. The greatest biomass (220.4 Mg/ha) and C stocks (104.7 Mg C/ha) have been observed in BF type, whereas the least biomass (160.9 Mg/ha) and C (76.4 Mg C/ha) have been observed in DDTF type, with the mean biomass and C stock values of 184.1 Mg/ha and 87.4 Mg C/ha, respectively. In both DDTF and DDMF types, Tectona grandis contributed 76.5 and 26.4% of C, whereas in BF type, Boswellia serrata contributed 73.6% of C to the total C stock. A significant positive (R2 = 0.951; p < 0.01) relationship has been observed between basal area and tree C stock. The present study would improve our understanding of C stocks present in these forest types and could be used to enhance the C sequestration potential through conservation, monitoring and management of such forests.

Tree-Level Above-Ground Biomass Equations for Pinus sylvestris L. in Mongolia

Allometric equations have been used as a main tool to quantify forest above-ground biomass. Therefore, the development of species-specific allometric equations is essential to accurately estimate forest biomass. The present study aims at developing species-specific equations for above-ground biomass (AGB) estimation for Pinus sylvestris L. in northern Mongolia. A total of 35 sample trees were harvested from natural Scots pine forests in the northeastern Khangai and western Khentii forest regions. Evaluating the statistical relationships of AGB against predictor variables, three allometric equations were formulated. Stem diameter at breast height (D) and height (H) were measured, and the biomass of stem, branches and needles were weighed, separately. AGB was regressed against stem D and H individually, and in combination. The best-fitting equations with a higher coefficient of determination and lower residual standard error were selected using model performance statistics. The most well performing biomass model was the logarithmic equation lnŶ = lna + b x lnD + c x lnH for the estimation of the total AGB, the stem, branch and needle masses. The biomass model is the important tool for accurate estimation of Scots pine forest biomass and carbon stocks in Mongolia.

Drivers of above‐ground understorey biomass and nutrient stocks in temperate deciduous forests

AbstractThe understorey in temperate forests can play an important functional role, depending on its biomass and functional characteristics. While it is known that local soil and stand characteristics largely determine the biomass of the understorey, less is known about the role of global change. Global change can directly affect understorey biomass, but also indirectly by modifying the overstorey, local resource availability and growing conditions at the forest floor.In this observational study across Europe, we aim at disentangling the impact of global‐change drivers on understorey biomass and nutrient stocks, from the impact of overstorey characteristics and local site conditions. Using piecewise structural equation modelling, we determine the main drivers of understorey biomass and nutrient stocks in these forests and examine potential direct and indirect effects of global‐change drivers.Tree cover, tree litter quality and differences in former land use were the main drivers of understorey biomass and nutrient stocks, via their influence on understorey light and nitrogen availability and soil acidity. Other global‐change drivers, including climate and nitrogen deposition, had similar indirect effects, but these were either weak or only affecting nutrient concentrations, not stocks.Synthesis.We found that direct effects of global‐change drivers on understorey biomass and nutrient stocks were absent. The indirect effects of global change, through influencing resource availability and growing conditions at the forest floor, were found to be less important than the effects of overstorey cover and composition. These results suggest that understorey biomass and nutrient stocks might respond less to global change in the presence of a dense overstorey, highlighting the buffering role of the overstorey in temperate forests.

Characterizing 40 years of natural Pinus sylvestris var. mongolica carbon stocks in Northeast China using stand age from remote sensing time series

ABSTRACTRemote sensing (RS)-assisted estimates of forest carbon stocks are essential for modelling carbon budgets at large scales. Research on the dynamic variations in forest biomass and carbon stocks is important to improve the sustainable use of forest resources and understand forest carbon budgets in China. The Honghuaerji region of the Inner Mongolia Autonomous Region is where Pinus sylvestris var. mongolica Litv. (MP) originated. MP forests contained the most important coniferous silvicultural tree species and were later introduced to the desert regions of northern China. However, there has been very little research on carbon stocks (vegetation and soil carbon) over time in the original MP forests. Our goal was to estimate the changes in the carbon stocks of the natural MP forests in the Honghuaerji region using a 40-year RS time series, forest survey data and laboratory data. First, we mapped MP forests and estimated forest ages based on Key Hole 9 and Landsat time series stacks from 1975 to 2015, and the estimated forest ages were shown to be consistent with the surveyed tree ages with a coefficient of determination (R2) of 0.96. Second, fifteen vegetation indices were evaluated as indicators of diameter at breast height (DBH). Of these, tasselled cap greenness provided the best relationships, and an RS-based DBH equation was constructed with an R2 of 0.65. Third, the carbon densities in the different ages of MP forests were obtained at regional scales. Finally, the dynamics of the carbon stocks in the natural MP forest ecosystem (including stems, branches, leaves, roots and soils) over 40 years were obtained. The results of this study indicated that the total MP forest ecosystem carbon stocks increased significantly from 7.36 ± 1.92 Tg C in 1975 to 11.52 ± 2.51 Tg C in 2015 at a rate of 1.42% year–1, among which vegetation carbon stocks increased 5.54% year–1 and soil carbon stocks increased 0.45% year–1. The results reveal a notable carbon increase in the MP forests in the Honghuaerji region over the past four decades and provide information on the role of natural forest conservation and management in China in mitigating global climate change.

Above Ground Biomass Estimation Methods and Challenges: A Review

Forest ecosystems play an important role in global change on the earth. However, continued forest degradation and deforestation will results in the loss of forest biomass or carbon stock. Hence, current concerns for global change and ecosystem functioning require accurate biomass estimation and examination of its dynamics. In this end we reviewed the present scenarios of above ground biomass estimation, focusing predominantly on field measurement (destructive and non-destructive) and remote sensing (optical remote sensing, radar and light detection and ranging (LiDAR)) biomass estimation methods and identifying some important point or research findings in detail. In addition, we discuss the critical uncertainties or the source of errors in all methods. In the field methods the source of error encountered mainly from sampling error, measurement error and statistical or model error. In the remote sensing methods; optical sensor data is not suitable for estimation of vertical vegetation structures such as canopy height, Radar have the following uncertainty: costly data, no time-composite data as the case for optical data, limited area coverage and LiDAR also faces the following challenges spatially limited, data intensive, and expensive, can't applied extensively to larger areas, limited usage in harsh weather. Finally, we suggest that using both field measurement and remote sensing methods will increase the accuracy of biomass estimation. Keywords: biomass, estimation, remote sensing, uncertainty, LiDAR, Radar and optical DOI : 10.7176/JETP/9-8-02 Publication date: November 30 th 2019

CARBON STORAGE OF MANGROVE ECOSYSTEM IN NORTH SULAWESI AND ITS IMPLICATIONS TO CLIMATE CHANGE MITIGATION ACTION

North Sulawesi is one of the provinces that implements national / regional action plan (RAN / RAD) policies as part of a national effort to climate change mitigation. One of the land-based mitigation activities in North Sulawesi is to measure and monitore biomass and carbon stocks in forests including coastal forests where the measurement area is still limited. In 2013-2015, Blue Carbon Research Group conducted research in four locations in North Sulawesi, which aims to analyze the ecological conditions and the ability of coastal ecosystems, especially mangrove in sequestering carbon. Study sites situated in Ratatotok - Southeast Minahasa Regency, Kema - North Minahasa Regency, Lembeh Island – Bitung City and Sangihe Island – Sangihe Regency. A total of 17 species identified where B. gymnorrhiza, R. mucronata and S. alba found in all locations. Species diversity ranges from low to moderate, where species distribution is uneven. Carbon storage capacity is equal to 343.85 Mg C ha-1 in Ratatotok, 254,35 Mg C ha-1 in Lembeh, 387,95 Mg C ha-1 in Kema and 594,83 Mg C ha-1 in Sangihe. More than 59% of carbon storage are in the sediment. The average value of carbon storage in the four research sites is 456,86 Mg C ha-1 or 5,70 Tg C after converted to a total area of mangrove ecosystems in North Sulawesi. The value is equal to absorption of atmospheric CO2 by 20.70 Tg CO2e. Potential emission due to changes in mangrove conversion reach 0.42 Tg CO2e. Efforts to increase the contribution of reducing North Sulawesi emissions can be achieved by implementing emission reduction interventions through the rehabilitation and conservation of mangrove ecosystems.

Effects of forest management on biomass stocks in Romanian beech forests

BackgroundForest management aims at obtaining a sustainable production of wood to be harvested to generate products or energy. However, the quantitative influence of forest management and of removals by harvest on biomass stocks has rarely been analysed on a large scale based on measurements. Two hypotheses prevail: management induces a reduction of wood stocks due to cuttings, versus no impact because of increased growth of the remaining trees.Using data collected for 2840 permanent plots across Romania from the National Forest Inventory representing ~ 2.5 Mha, we have tested to what extent different management types and treatments can influence the biomass stock and productivity of beech forests, and attempt to quantify these effects both on the short and long terms.Three main types of beech forest management are implemented in Romania with specific objectives: intensive wood production in production forests, protection of ecosystem services (e.g. watersheds, avalanche protection) in protection forests, and protection of the forest and its biodiversity in protected forests. Production forests encompass two treatments differing according to the stand regeneration method: the age class rotation management and the group shelterwood management.ResultsWe show that forest management had little influence on the biomass stocks at a given stand age. The highest stocks at stand age 100 were observed in production forests (the most intensively managed forests). Consequences of early cuttings were very short-termed because the increase in tree growth rapidly compensated for tree cuttings. The cumulated biomass of production forests exceeded that of protected and protection forests. Regarding the treatment, the group shelterwood forests had a markedly higher production over a full rotation period.The total amount of deadwood was primarily driven by the amount of standing deadwood, and no management effect was detected.ConclusionsGiven the relatively low-intensity management in Romania, forest management had no negative impact on wood stocks in beech forests biomass stocks at large scale. Stand productivity was very similar among management types or treatments. However cumulated biomass in production forests was higher than in protection or protected forests, and differed markedly according to treatments with a higher cumulated biomass in shelterwood forests.

Accounting tropical forest carbon stock with synergistic use of space-borne ALOS PALSAR and COSMO-Skymed SAR sensors

The objective of this study is to precisely quantify the forest aboveground biomass (AGB) and carbon stock in a tropical deciduous forest with synergistic use of space-borne L-band ALOS PALSAR and X-band COSMO-Skymed SAR data, along with field inventory data. AGB serves as a decisive parameter for preparing global decision making policy targeting the impact of reducing emissions from deforestation and forest degradation and climate change. The study proposed optimal regression models for assessing above-ground bole biomass over tropical deciduous mixed forests of Munger (Bihar, India) with synergistic use of ALOS PALSAR and COSMO-Skymed sigma nought images. Coefficient of determination (r2) of 0.89 and RMSE of 15.12 Mg ha−1 were calculated for the best fit integrated model. On validation, the integrated model produced a model accuracy of 78%, r2 = 0.89, RMSE = 16.64 Mg ha−1 and Willmott’s index of agreement of 0.934. Resulting modeled AGB were converted to carbon and carbon dioxide equivalents using conversion factors. L-band showed higher accuracy in the estimation in comparison to X-band; but the estimation accuracy improved with the synergistic use of both X- and L-band SAR data. Hence, the study recommends the combined use of X- and L-band SAR with exceptional capabilities for improved assessment of tropical forest stand AGB and carbon with significant contribution towards operational forestry and policy making.

Applicability of semi-destructive method to derive allometric model for estimating aboveground biomass and carbon stock in the Hill zone of Bangladesh

Biomass estimation using allometric models is a nondestructive and popular method. Selection of an allometric model can influence the accuracy of biomass estimation. Bangladesh Forest Department initiated a nationwide forest inventory to assess biomass and carbon stocks in trees and forests. The relationship between carbon storage and sequestration in a forest has implications for climate change mitigation in terms of the carbon sink in Bangladesh. As part of the national forest inventory, we aimed to derive multi-species biomass models for the hill zone of Bangladesh and to determine the carbon concentration in tree components (leaves, branches, bark and stem). In total, 175 trees of 14 species were sampled and a semi-destructive method was used to develop a biomass model, which included development of smaller branch (base dia < 7 cm) biomass allometry and volume estimation of bigger branches and stems. The best model of leaf, branches, and bark showed lower values for adjusted R2 (0.3152–0.8043) and model efficiency (0.436–0.643), hence these models were not recommended to estimate biomass. The best fit model of stem and total aboveground biomass (TAGB) showed higher model efficiency 0.948 and 0.837, respectively, and this model was recommended for estimation of tree biomass for the hill zone of Bangladesh. The best fit allometric biomass model for stem was Ln (Stem) = − 10.7248 + 1.6094*Ln (D) + 1.323*Ln (H) + 1.1469*Ln (W); the best fit model for TAGB was Ln (TAGB) = − 6.6937 + 0.809*Ln (D^2*H*W), where DBH = Diameter at Breast Height, H = Total Height, W = Wood density. The two most frequently used pan-tropical biomass models showed lower model efficiency (0.667 to 0.697) compared to our derived TAGB model. The best fit TAGB model proved applicable for accurate estimation of TAGB for the hill zone of Bangladesh. Carbon concentration varied significantly (p < 0.05) by species and tree components. Higher concentration (48–49%) of carbon was recorded in the tree stem.

Effects of Stand Age on Biomass Allocation and Allometry of Quercus Acutissima in the Central Loess Plateau of China

We studied the effects of stand age on allocation and equation fitting of aboveground and below-ground biomass in four Quercus acutissima stands (14, 31, 46, and 63 years old) in the Central Loess Plateau of China. The stem wood, stem bark, branch, foliage, and belowground biomass of each of the 20 destructive harvesting trees were quantified. The mean total biomass of each tree was 28.8, 106.8, 380.6, and 603.4 kg/tree in the 14-, 31-, 46-, and 63-year-old stands, respectively. Aboveground biomass accounted for 72.25%, 73.05%, 76.14%, and 80.37% of the total tree biomass in the 14-, 31-, 46-, and 63-year-old stands, respectively, and stem wood was the major component of tree biomass. The proportion of stem (with bark) biomass to total tree biomass increased with stand age while the proportions of branch, foliage, and belowground biomass to total tree biomass decreased with stand age. The ratio of belowground biomass to aboveground biomass decreased from 0.39 in the 14-year-old stand to 0.37, 0.31, and 0.24 in the 31-, 46-, and 63-year-old stands, respectively. Age-specific biomass equations in each stand were developed for stem wood, stem bark, aboveground, and total tree. The inclusion of tree height as a second variable improved the total tree biomass equation fitting for middle-aged (31-year-old and 46-year-old) stands but not young (14 years old) and mature (63 years old) stands. Moreover, biomass conversion and expansion factors (BCEFs) varied with stand age, showing a decreasing trend with increasing stand age. These results indicate that stand age alters the biomass allocation of Q. acutissima and results in age-specific allometric biomass equations and BCEFs. Therefore, to obtain accurate estimates of Q. acutissima forest biomass and carbon stocks, age-specific changes need to be considered.

Allometric equations for aboveground biomass estimation of Diospyros abyssinica (Hiern) F. White tree species

ABSTRACT Introduction: Quantifying forest biomass requires the application of allometric equations which is a fundamental step. Generalized allometric equations have been applied to quantify aboveground biomass (AGB) of forests. But, adopting generalized allometric equations to quantify AGB of different forests creates uncertainty. Therefore, developing species- and site-specific allometric equations is essential to accurately quantify the biomass. The study was aimed to develop species-specific allometric equations for Diospyros abyssinica (Hiern) F. White in Yayu Coffee Forest Biosphere Reserve using the Semi-destructive method. The vegetation types of Yayu Coffee Forest Biosphere Reserve is categorized to Moist Evergreen Montane Rainforest of Ethiopia. Results and discussion: Evaluating statistical relationships of AGB against predictor variables, eight allometric equations were formulated. AGB was regressed against trunk diameter (D), total height (H), and wood density (ρ) individually and in combination. Selection of allometric equations was employed using model performance statistics. Equations with a higher coefficient of determination (adjusted R 2), lower residual standard error, and Akaike information criterion (AIC) values were found best-fitted. Relationships of AGB and independent variables were found statistically significant (p < 0.000). Overall, formulating species- and site-specific allometric equations is significant for accurate estimation of forest biomass and carbon stock budget.

Forest aboveground biomass estimation using machine learning regression algorithm in Yok Don National Park, Vietnam

Forest biomass is one of the key measurement for carbon budget accounting, carbon flux monitoring, and climate change studies. Hence, it is essential to develop a credible approach to estimate forest biomass and carbon stocks. Our study applied Sentinel-2 satellite imagery combined with field-measured biomass using Random Forest (RF), a machine learning regression algorithm, to estimate forest aboveground biomass (AGB) in Yok Don National Park, Vietnam. A total of 132 spectral and texture variables were extracted from Sentinel-2 imagery (February 7, 2017) to predict AGB of the National Park using RF algorithm. It was found that a combination of 132 spectral and texture variables could predict AGB with an R2 value of 0.94, RMSE of 34.5 Mgha−1 and %RMSE of 18.3%. RF regression algorithm was further used to reduce the number of variables in such a way that a minimum number of selected variables can be able to estimate AGB at a satisfactory level. A combination of 11 spectral and texture variables was identified based on out-of-bag (OOB) estimation to develop an easy-to-use model for estimating AGB. On validation, the model developed with 11 variables was able to predict AGB with R2 = 0.81, RMSE = 36.67 Mg ha−1 and %RMSE of 19.55%. The results found in the present study demonstrated that Sentinel-2 imagery in conjunction with RF-based regression algorithm has the potential to effectively predict the spatial distribution of forest AGB with adequate accuracy.

Allometric equations from a non-destructive approach for biomass prediction in natural forest and plantation in West Africa

Allometric equations are required for a rapid estimation of commercial timber volume and forest biomass stocks. In order to preserve the forest ecosystem, this study applied a non-destructive sampling approach to measure biophysical properties of living trees. From these measurements, volume and biomass models were developed for 11 dominant tree species in a semi-deciduous natural forest and for Acacia auriculiformis in a plantation located in southern Benin. The observations were combined to develop also generic models applicable to non-dominant tree species. Wood samples of the tree species were collected with an increment borer and analysed in the laboratory to determine species-specific wood densities. The sample size was composed of 243 trees in natural forest and 21 trees in plantation. The measurements were conducted in 30 plots of 50 m × 50 m. The graphical assessment of correlation between model outputs (biomass and volume) and variables (diameter and height) and the statistical analysis confirmed that the logarithmic model with two variables had the best predictions. The assessment also confirmed that the model using diameter only as a variable had good predictions when observations on height were unavailable. The comparative analysis of model predictions showed that the generic model in this study over-estimated biomass by up to 74.80% for certain species and under-estimated biomass by 21.18% for other species. The study shows that there are no statistically significant differences between the wood densities in this research and that published in previous studies.

BIOMASS ACCUMULATION IN FORESTS WITH HIGH PRESSURE OF FUELWOOD EXTRACTION IN CHIAPAS, MEXICO

ABSTRACT Tropical forests plays a vital role in mitigating atmospheric CO2 but the retention capacity of such ecosystems has changed greatly due to increasing anthropogenic pressures, of which firewood extraction is the main one activity in rural areas. The purpose of this research was to evaluate the biomass stocks of pine and oak forests with different pressure of fuelwood extraction in Chiapas, Mexico. The study was carried out in four locations in the state of Chiapas, southern Mexico; two of them with high extraction levels and the other two with lower extraction levels. Pine and Oak forests are the predominant forest types in the region. A total of sixteen plots of 400 m2 were established to measure the biomass stocks of the trees with &gt; 7.5 cm DBH. Published allometric equations were used to quantify the biomass stocks. The average biomass of the pine forest with low fuelwood extraction was 213.4 Mg ha-1, and that of the oak forest was 189.5 Mg ha-1. On the other hand, the biomass stocks of the pine forest with high fuelwood extraction was 138.2 Mg ha-1, and that of the oak forest was 92.0 Mg ha-1. Communities with agricultural diversification like apiculture and agroforestry practices were found more effective in forest biomass conservation when compared to those who are only dedicated to Milpa cultivation and extensive bovine livestock production. The adoption of silvopasture systems, the use of crop residues and the use of ecological cooking stoves can be the alternatives to reduce forest fuelwood extraction.

ESTOQUE E DINÂMICA DE BIOMASSA ARBÓREA EM FLORESTA OMBRÓFILA DENSA NA FLONA TAPAJÓS: AMAZÔNIA ORIENTAL

A análise das oscilações dos estoques de biomassa em florestas tropicais permite descrever o comportamento desses ecossistemas diante de alterações naturais ou antropogênicas. O objetivo deste trabalho foi avaliar o estoque e a dinâmica de biomassa no período (2007-2010) em Floresta Ombrófila Densa de duas áreas de pesquisa na FLONA - Tapajós: Amazônia oriental, denominadas Área 1 e Área 2, distantes aproximadamente 45 km uma da outra. A biomassa foi estimada através de equação alométrica e medidas de DAP, proveniente de 12 parcelas permanentes, sendo 6 em cada área. Os resultados mostraram que houve diferença significativa no estoque médio anual de biomassa entre a Área 1 (293,19 ± 27,74; 298,21 ± 31,73; 299,60 ± 29,46; 298,11 ± 29,40 Mg.ha-1) e a Área 2 (254,35 ± 69,61; 259,10 ± 70,05; 261,00 ± 69,43; 248,92 ± 61,78 Mg.ha-1). Notou-se variação anual no estoque de biomassa em intervalos de medições superiores a um ano, devido às oscilações na taxa de mortalidade, recrutamento e incremento, além da área basal e abundância dos indivíduos em cada área. A maior concentração de indivíduos ocorreu nas classes diamétricas 10 ≤ DAP ≤ 30 cm, porém, verificou-se que os indivíduos com DAP ≥ 60 cm em concentração menor estocam biomassa equivalente à da classe diamétrica 10 ≤ DAP ≤ 30 cm. Concluiu-se que as oscilações na taxa de mortalidade, recrutamento e incremento, além da abundância e área basal nestes locais afetaram os estoques de biomassa.

Multiscale divergence between Landsat- and lidar-based biomass mapping is related to regional variation in canopy cover and composition

BackgroundSatellite-based aboveground forest biomass maps commonly form the basis of forest biomass and carbon stock mapping and monitoring, but biomass maps likely vary in performance by region and as a function of spatial scale of aggregation. Assessing such variability is not possible with spatially-sparse vegetation plot networks. In the current study, our objective was to determine whether high-resolution lidar-based and moderate-resolution Landsat-base aboveground live forest biomass maps converged on similar predictions at stand- to landscape-levels (10 s to 100 s ha) and whether such differences depended on biophysical setting. Specifically, we examined deviations between lidar- and Landsat-based biomass mapping methods across scales and ecoregions using a measure of error (normalized root mean square deviation), a measure of the unsystematic deviations, or noise (Pearson correlation coefficient), and two measures related to systematic deviations, or biases (intercept and slope of a regression between the two sets of predictions).ResultsCompared to forest inventory data (0.81-ha aggregate-level), lidar and Landsat-based mean biomass predictions exhibited similar performance, though lidar predictions exhibited less normalized root mean square deviation than Landsat when compared with the reference plot data. Across aggregate-levels, the intercepts and slopes of regression equations describing the relationships between lidar- and Landsat-based biomass predictions stabilized (i.e., little additional change with increasing area of aggregates) at aggregate-levels between 10 and 100 ha, suggesting a consistent relationship between the two maps at landscape-scales. Differences between lidar- and Landsat-based biomass maps varied as a function of forest canopy heterogeneity and composition, with systematic deviations (regression intercepts) increasing with mean canopy cover and hardwood proportion within forests and correlations decreasing with hardwood proportion.ConclusionsDeviations between lidar- and Landsat-based maps indicated that satellite-based approaches may represent general gradients in forest biomass. Ecoregion impacted deviations between lidar and Landsat biomass maps, highlighting the importance of biophysical setting in determining biomass map performance across aggregate scales. Therefore, regardless of the source of remote sensing (e.g., Landsat vs. lidar), factors affecting the measurement and prediction of forest biomass, such as species composition, need to be taken into account whether one is estimating biomass at the plot, stand, or landscape scale.

Impacts of Alternative Harvesting and Natural Disturbance Scenarios on Forest Biomass in the Superior National Forest, USA

The amount of biomass stored in forest ecosystems is a result of past natural disturbances, forest management activities, and current structure and composition such as age class distributions. Although natural disturbances are projected to increase in their frequency and severity on a global scale in the future, forest management and timber harvesting decisions continue to be made at local scales, e.g., the ownership or stand level. This study simulated potential changes in natural disturbance regimes and their interaction with timber harvest goals across the Superior National Forest (SNF) in northeastern Minnesota, USA. Forest biomass stocks and stock changes were simulated for 120 years under three natural disturbance and four harvest scenarios. A volume control approach was used to estimate biomass availability across the SNF and a smaller project area within the SNF (Jeanette Project Area; JPA). Results indicate that under current harvest rates and assuming disturbances were twice that of normal levels resulted in reductions of 2.62 to 10.38% of forest biomass across the four primary forest types in the SNF and JPA, respectively. Under this scenario, total biomass stocks remained consistent after 50 years at current and 50% disturbance rates, but biomass continued to decrease under a 200%-disturbance scenario through 120 years. In comparison, scenarios that assumed both harvest and disturbance were twice that of normal levels and resulted in reductions ranging from 14.18 to 29.85% of forest biomass. These results suggest that both natural disturbances and timber harvesting should be considered to understand their impacts to future forest structure and composition. The implications from simulations like these can provide managers with strategic approaches to determine the economic and ecological outcomes associated with timber harvesting and disturbances.

Potential value of combining ALOS PALSAR and Landsat-derived tree cover data for forest biomass retrieval in Madagascar

Reducing carbon emissions from deforestation and degradation (REDD) requires detailed insight into how the forest biomass is measured and distributed. Studies so far have estimated forest biomass stocks using rough assumptions and unreliable data. High-resolution data and robust methods are required to capture the spatial variability of forest biomass with sufficient precision. Here we aim to improve on previous approaches by using radar satellite ALOS PALSAR (25-m resolution) and optical Landsat-derived tree cover (30-m resolution) observations to estimate forest biomass stocks in Madagascar, for the years 2007–2010. The radar signal and in situ biomass were highly correlated (R2 = 0.71) and the root mean square error was 30% (for biomass ranging from 0 to 500 t/ha). Using our map at 25-m resolution for the entire island of Madagascar, we estimated the total above-ground forest carbon for the four years 2007, 2008, 2009 and 2010 to be 1.1173 ± 0.0304, 1.1029 ± 0.0303, 1.0916 ± 0.0301 and 1.0773 ± 0.0298 PgC, respectively. Carbon stocks were found to have decreased constantly over this period due to anthropogenic deforestation and likely also to climate change. The results are expected to serve as a more accurate benchmark for monitoring progress on REDD and to provide strong supports for current and future spaceborne missions such as ALOS-2, SAOCOM and BIOMASS.

Pricing forest carbon: Implications of asymmetry in climate policy

Using an integrated assessment model, we examine the implications of climate policies that do not fully recognize forest carbon. Specifically, we first investigate the impact of an asymmetric policy that recognizes carbon emissions from fossil fuels while fully ignoring forest carbon. Next, we investigate the relative importance of not recognizing emissions from a reduction in the stock of forest biomass compared to not recognizing sequestration from the growth of forest biomass. We show that asymmetric carbon policies lead to lower levels of welfare, as well as higher emissions and carbon prices. This occurs because the forest resource will be allocated inefficiently under these carbon policies. Broadly, we find that when the social planner does not account for emissions or sequestration from the forest, the planner will set bioenergy levels that are too high and afforestation and avoided deforestation levels that are too low. Our results further reveal that not recognizing forest emissions leads to larger welfare losses than not recognizing sequestration.

Field methods for sampling tree height for tropical forest biomass estimation.

Quantifying the relationship between tree diameter and height is a key component of efforts to estimate biomass and carbon stocks in tropical forests. Although substantial site‐to‐site variation in height–diameter allometries has been documented, the time consuming nature of measuring all tree heights in an inventory plot means that most studies do not include height, or else use generic pan‐tropical or regional allometric equations to estimate height.Using a pan‐tropical dataset of 73 plots where at least 150 trees had in‐field ground‐based height measurements, we examined how the number of trees sampled affects the performance of locally derived height–diameter allometries, and evaluated the performance of different methods for sampling trees for height measurement.Using cross‐validation, we found that allometries constructed with just 20 locally measured values could often predict tree height with lower error than regional or climate‐based allometries (mean reduction in prediction error = 0.46 m). The predictive performance of locally derived allometries improved with sample size, but with diminishing returns in performance gains when more than 40 trees were sampled. Estimates of stand‐level biomass produced using local allometries to estimate tree height show no over‐ or under‐estimation bias when compared with biomass estimates using field measured heights. We evaluated five strategies to sample trees for height measurement, and found that sampling strategies that included measuring the heights of the ten largest diameter trees in a plot outperformed (in terms of resulting in local height–diameter models with low height prediction error) entirely random or diameter size‐class stratified approaches.Our results indicate that even limited sampling of heights can be used to refine height–diameter allometries. We recommend aiming for a conservative threshold of sampling 50 trees per location for height measurement, and including the ten trees with the largest diameter in this sample.