Aboveground Biomass Stocks Research Articles

Geostatistical estimation of forest biomass in interior Alaska combining Landsat-derived tree cover, sampled airborne lidar and field observations

Lidar provides critical information on the three-dimensional structure of forests. However, collecting wall-to-wall laser altimetry data at regional and global scales is cost prohibitive. As a result, studies employing lidar for large area estimation typically collect data via strip sampling, leaving large swaths of the forest unmeasured by the instrument. The goal of this research was to develop and examine the performance of a coregionalization modeling approach for combining field measurements, strip samples of airborne lidar and Landsat-based remote sensing products to predict aboveground biomass (AGB) in interior Alaska's Tanana Valley. The proposed modeling strategy facilitates mapping of AGB density across the domain. Additionally, the coregionalization framework allows for estimation of total AGB for arbitrary areal units within the study area—a key advance to support diverse management objectives in interior Alaska. This research focuses on characterization of prediction uncertainty in the form of posterior predictive coverage intervals and standard deviations. Using the framework detailed here, it is possible to quantify estimation uncertainty for any spatial extent, ranging from point-level predictions of AGB density to estimates of AGB stocks for the full domain. The lidar-informed coregionalization models consistently outperformed their counterpart lidar-free models in terms of point-level predictive performance and total (mean) AGB precision. Additionally, including a Landsat-derived forest cover covariate further improved precision in regions with lower lidar sampling intensity. Findings also demonstrate that model-based approaches not explicitly accounting for residual spatial dependence can grossly underestimate uncertainty, resulting in falsely precise estimates of AGB. The inferential capabilities of AGB posterior predictive distribution (PPD) products extend beyond simply mapping AGB density. We show how PPD products can provide insight regarding drivers of AGB heterogeneity in boreal forests, including permafrost and fire, highlighting the range of potential applications for Bayesian geostatistical methods to integrate field, airborne and satellite data.

Estimation of Above Ground Biomass in a Tropical Mountain Forest in Southern Ecuador Using Airborne LiDAR Data

A reliable estimation of Above Ground Biomass (AGB) in Tropical Mountain Forest (TMF) is still complicated, due to fast-changing climate and topographic conditions, which modifies the forest structure within fine scales. The variations in vertical and horizontal forest structure are hardly detectable by small field plots, especially in natural TMF due to the high tree diversity and the inaccessibility of remote areas. Therefore, the present approach used remotely sensed data from a Light Detection and Ranging (LiDAR) sensor in combination with field measurements to estimate AGB accurately for a catchment in the Andes of south-eastern Ecuador. From the LiDAR data, information about horizontal and vertical structure of the TMF could be derived and the vegetation at tree level classified, differentiated between the prevailing forest types (ravine forest, ridge forest and Elfin Forest). Furthermore, topographical variables (Topographic Position Index, TPI; Morphometric Protection Index, MPI) were calculated by means of the high-resolution LiDAR data to analyse the AGB distribution within the catchment. The field measurements included different tree parameters of the species present in the plots, which were used to determine the local mean Wood Density (WD) as well as the specific height-diameter relationship to calculate AGB, applying regional scale modelling at tree level. The results confirmed that field plot measurements alone cannot capture completely the forest structure in TMF but in combination with high resolution LiDAR data, applying a classification at tree level, the AGB amount (Mg ha−1) and its distribution in the entire catchment could be estimated adequately (model accuracy at tree level: R2 > 0.91). It was found that the AGB distribution is strongly related to ridges and depressions (TPI) and to the protection of the site (MPI), because high AGB was also detected at higher elevations (up to 196.6 Mg ha−1, above 2700 m), if the site is situated in depressions (ravine forest) and protected by the surrounding terrain. In general, highest AGB is stored in the protected ravine TMF parts, also at higher elevations, which could only be detected by means of the remote sensed data in high resolution, because most of these areas are inaccessible. Other vegetation units, present in the study catchment (pasture and subpáramo) do not contain large AGB stocks, which underlines the importance of intact natural forest stands.

Control of "blue carbon" storage by mangrove ageing: Evidence from a 66-year chronosequence in French Guiana.

The role of mangroves in the blue carbon stock is critical and requires special focus. Mangroves are carbon-rich forests that are not in steady-state equilibrium at the decadal time scale. Over the last decades, the structure and zonation of mangroves have been largely disturbed by coastal changes and land use conversions. The amount of time since the last disturbance is a key parameter determining forest structure, but it has so far been overlooked in mangrove carbon stock projections. In particular, the carbon sequestration rates among mangrove successional ages after (re)establishment are poorly quantified and not used in large-scale estimations of the blue carbon stock. Here, it is hypothesized that ecosystem age structure significantly modulates mangrove carbon stocks. We analysed a 66-year chronosequence of the aboveground and belowground biomass and soil carbon stock of mangroves in French Guiana, and we found that in the year after forest establishment on newly formed mud banks, the aboveground, belowground and soil carbon stocks averaged 23.56±7.71, 13.04±3.37 and 84.26±64.14 (to a depth of 1m) Mg C/ha, respectively. The mean annual increment (MAI) in the aboveground and belowground reservoirs was 23.56×Age-0.52 and 13.20×Age-0.64 Mg Cha-1 year-1 , respectively, and the MAI in the soil carbon reservoir was 3.00±1.80Mg Cha-1 year-1 . Our results show that the plant carbon sink capacity declines with ecosystem age, while the soil carbon sequestration rate remains constant over many years. We suggest that global projections of the above- and belowground reservoirs of the carbon stock need to account for mangrove age structures, which result from historical changes in coastal morphology. Our work anticipates joint international efforts to globally quantify the multidecadal mangrove carbon balance based on the combined use of age-based parametric equations and time series of mangrove age maps at regional scales.

Potential environmental benefits from woodfuel transitions in Haiti: Geospatial scenarios to 2027

Woodfuels constitute nearly 80% of Haiti’s primary energy supply. Forests are severely degraded and the nation has long been considered an archetypal case of woodfuel-driven deforestation. However, there is little empirical evidence that woodfuel demand directly contributes to deforestation, but may contribute to degradation. We use MoFuSS (Modeling Fuelwood Sustainability Scenarios), a dynamic landscape model, to assess whether current woodfuel demand is as impactful as it is often depicted by simulating changes in land cover that would result if current demand continues unabated. We also simulate several near-term interventions focused on woodfuel demand reduction to analyze the land cover impacts of different energy trajectories. We find that current demand may contribute to moderate levels of degradation, but it is not as severe as is typically portrayed. Under a business-as-usual scenario, the simulated regenerative capacity of woody biomass is insufficient to meet Haiti’s increasing demand for wood energy and, as a result, between 2017 and 2027 stocks of above-ground (woody) biomass could decline by 4% ± 1%. This is an annual loss of 302 ± 29 kton of wood and would emit 555 ± 54 kton CO2 yr−1. Aggressive interventions to reduce woodfuel demand could slow or even reverse woodfuel-driven degradation, allowing woody biomass to recover in some regions. We discuss the policy implications and propose steps to reduce uncertainty and validate the model.

Relationships among phenology, climate and biomass across subtropical forests in Argentina

Abstract:Phenology is a key ecosystem process that reflects climate–vegetation functioning, and is an indicator of global environmental changes. Recently, it has been suggested that land-use change and timber extraction promote differences in forest phenology. We use remote-sensing data to describe regional leaf phenological patterns in combination with field data from 131 plots in old-growth and disturbed forests distributed over subtropical forests of Argentina (54–65°W). We assessed how climate is related to phenological patterns, and analysed how changes in forest structural characteristics such as stock of above-ground biomass relate to the observed phenological signals across the gradient. We found that the first three axes of a principal component analysis explained 85% of the variation in phenological metrics across subtropical forests, ordering plots mainly along indicators of seasonality and productivity. At the regional scale, the relative importance of forest biomass in explaining variation in phenological patterns was about 15%. Climate showed the highest relative importance, with temperature and rainfall explaining Enhanced Vegetation Index metrics related to seasonality and productivity patterns (27% and 47%, respectively). Within forest types, climate explains the major fraction of variation in phenological patterns, suggesting that forest function may be particularly sensitive to climate change. We found that forest biomass contributed to explaining a proportion of leaf phenological variation within three of the five forest types studied, and this may be related to changes in species composition, probably as a result of forest use.

Long rotation swidden systems maintain higher carbon stocks than rubber plantations

Conversion of shifting cultivation to rubber (Hevea brasiliensis) plantations is one of the dominant land use changes in montane mainland areas of Southeast Asia, with the area of rubber expected to quadruple by 2050. However, the impacts of this transition on total ecosystem carbon stocks are poorly quantified. We undertook a chronosequence study to quantify changes in ecosystem carbon stocks following conversion from swidden agriculture to rubber plantations in Northern Laos. We measured above-ground biomass stocks and collected volume specific soil samples across rubber plantations established between 2 and 18 years prior to the study, and fallows used in a swidden system. The carbon stock in the upper 40 cm of the soil was almost 20% lower after 18 years of rubber than in the swidden system fallows, suggesting a SOC loss of 0.74 ± 0.2 Mg C ha−1 yr−1. Rates of biomass accumulation in fallows were 1.5 ± 0.12 Mg C ha−1 yr−1 and 1.9 ± 0.14 Mg C ha−1 yr−1 in rubber plantations. When comparing time-averaged carbon stocks of swidden systems to rubber plantations with 30 year rotation periods, the stocks of swidden systems with rotation times of 5 and 10 years were 19% and 13% lower respectively; the stock of swidden systems with a rotation time of ∼15 years was approximately equal to rubber; and the stock of swidden systems with a rotation time of 30 years was 11% higher than in rubber. Therefore, we conclude that the replacement of swidden agriculture with rubber leads to soil carbon losses, but the overall effects on ecosystem carbon stocks depend on the rotation intensity of the swidden system that is being replaced.

Mixed-species allometric equations and estimation of aboveground biomass and carbon stocks in restoring degraded landscape in northern Ethiopia

Accurate biomass estimation is critical to quantify the changes in biomass and carbon stocks following the restoration of degraded landscapes. However, there is lack of site-specific allometric equations for the estimation of aboveground biomass (AGB), which consequently limits our understanding of the contributions of restoration efforts in mitigating climate change. This study was conducted in northwestern Ethiopia to develop a multi-species allometric equation and investigate the spatial and temporal variation of C-stocks following the restoration of degraded landscapes. We harvested and weighed 84 trees from eleven dominant species from six grazing exclosures and adjacent communal grazing land. We observed that AGB correlates significantly with diameter at stump height D30 (R2 = 0.78; P < 0.01), and tree height H (R2 = 0.41, P < 0.05). Our best model, which includes D30 and H as predictors explained 82% of the variations in AGB. This model produced the lowest bias with narrow ranges of errors across different diameter classes. Estimated C-stock showed a significant positive correlation with stem density (R2 = 0.80, P < 0.01) and basal area (R2 = 0.84, P < 0.01). At the watershed level, the mean C-stock was 3.8 (±0.5) Mg C ha−1. Plot-level C-stocks varied between 0.1 and 13.7 Mg C ha−1. Estimated C-stocks in three- and seven-year-old exclosures exceeded estimated C-stock in the communal grazing land by 50%. The species that contribute most to C-stocks were Leucaena sp. (28%), Calpurnia aurea (21%), Euclea racemosa (20.9%), and Dodonaea angustifolia (15.8%). The equations developed in this study allow monitoring changes in C-stocks and C-sequestration following the implementation of restoration practices in northern Ethiopia over space and time. The estimated C-stocks can be used as a reference against which future changes in C-stocks can be compared.

Estimating mangrove aboveground biomass from airborne LiDAR data: a case study from the Zambezi River delta

Mangroves are ecologically and economically important forested wetlands with the highest carbon (C) density of all terrestrial ecosystems. Because of their exceptionally large C stocks and importance as a coastal buffer, their protection and restoration has been proposed as an effective mitigation strategy for climate change. The inclusion of mangroves in mitigation strategies requires the quantification of C stocks (both above and belowground) and changes to accurately calculate emissions and sequestration. A growing number of countries are becoming interested in using mitigation initiatives, such as REDD+ (reducing emissions from deforestation and forest degradation), in these unique coastal forests. However, it is not yet clear how methods to measure C traditionally used for other ecosystems can be modified to estimate biomass in mangroves with the precision and accuracy needed for these initiatives. Airborne Lidar (ALS) data has often been proposed as the most accurate way for larger scale assessments but the application of ALS for coastal wetlands is scarce, primarily due to a lack of contemporaneous ALS and field measurements. Here, we evaluated the variability in field and Lidar-based estimates of aboveground biomass (AGB) through the combination of different local and regional allometric models and standardized height metrics that are comparable across spatial resolutions and sensor types, the end result being a simplified approach for accurately estimating mangrove AGB at large scales and determining the uncertainty by combining multiple allometric models. We then quantified wall-to-wall AGB stocks of a tall mangrove forest in the Zambezi Delta, Mozambique. Our results indicate that the Lidar H100 height metric correlates well with AGB estimates, with R2 between 0.80 and 0.88 and RMSE of 33% or less. When comparing Lidar H100 AGB derived from three allometric models, mean AGB values range from 192 Mg ha−1 up to 252 Mg ha−1. We suggest the best model to predict AGB was based on the East Africa specific allometry and a power-based regression that used Lidar H100 as the height input with an R2 of 0.85 and an RMSE of 122 Mg ha−1 or 33%. The total AGB of the Lidar inventoried mangrove area (6654 ha) was 1 350 902 Mg with a mean AGB of 203 Mg ha−1 ±166 Mg ha−1. Because the allometry suggested here was developed using standardized height metrics, it is recommended that the models can generate AGB estimates using other remote sensing instruments that are more readily accessible over other mangrove ecosystems on a large scale, and as part of future carbon monitoring efforts in mangroves.

Carbon stocks in the Guinea savanna of Ghana: estimates from three protected areas

AbstractSavannas are widespread in sub‐Saharan Africa (SSA) and play a major role in the global carbon balance. Extensive quantification of savanna carbon stocks inSSAwill therefore contribute to better accounting of the global carbon budget in the era of climate change. In this study, we investigated the spatial distribution of carbon stocks of different soil fractions and aboveground biomass within three forest reserves in the Guinea savanna zone of Ghana. Soil carbon stocks (SCSs) ranged from 4.80 to 12.61 Mg C/ha in surface soils (0–10 cm depth). HigherSCSs were associated with the silt +clay fraction than microaggregates and small macroaggregates in all three reserves. Relative to the dominant tree species (Vitellaria paradoxa), the highestSCSs were recorded under the sub‐canopy (SC), drip line (DL), and interspace (2 *SC+DL) zones for the Klupene, Sinsablegbinni, and Kenikeni forest reserves, respectively. The highest tree carbon stock was 60.01 Mg C/ha in Kenikeni. Sinsablegbinni had an average stock of 26.74 Mg C/ha and had the highest tree density. Average carbon capture by a single tree ranged from 0.04 to 0.34 Mg C. Aboveground grass carbon stock ranged from 0.08 to 0.47 Mg C/ha, while the belowground carbon stock ranged from 0.03 to 0.44 Mg C/ha. Accumulation of carbon in the aboveground grass biomass was greater at Klupene with low forest cover.

Modelling above-ground live trees biomass and carbon stock estimation of tropical lowland Dipterocarp forest: integration of field-based and remotely sensed estimates

ABSTRACTCarbon exists as carbon dioxide (CO2) which is one of the greenhouse gases (GHG) in the atmosphere that has an enormous influence on the impact of climate change. Therefore, the forest plays an undeniably pivotal role as a carbon sink, which absorbs carbon dioxide from the atmosphere. This research aims to develop allometric equation for above-ground live tree biomass (AGB) by combining field-based, combination of field data observation and technology (WV-3 and light detection and ranging (lidar)) and by using only technology derivation. The independent predictor was induced based on the literature review and theories, and an ordinary least square (OLS) estimator will be used to develop multiple linear regression models. During model selection, the best model fit was selected by calculating statistical parameters such as residual of the coefficient of determination (R2) selection methods, adjusted coefficient of determination (R2adj), root mean square error, graphical analysis of the residuals, standard error (Syx), and Akaike information criterion. An allometric equation of this research was developed using carbon stocks as dependent variables, and four of the predictor’s variables: diameter at breast height (DBH); total height observed at field (hF); total height derived from airborne lidar (hL); and morphometric variables of the crown projection area (CPA). Based on the statistic indicators, the most suitable model is Model 1, ln (Sc) = – β0 + β1 ln (hL) + β2 ln (DBH) + β3 ln (CPA) for the combination of remote sensing and field observation; ln (Sc) = – β0 + β1 ln (hF) + β2 ln (DBH) for field inventory only; and ln (Sc) = – β0 + β1 ln (hL) + β2 ln (CPA) for remote sensing only. This model is reliable in forest management to estimate the AGB and carbon stock estimation using a selection of variable sources.

Biomass of a fragment of the montane seasonal deciduous forest the south Brazil

The objective the work was to quantify the biomass using the destructive method of a forest fragment located in the South of Brazil. The study was conducted in fragment of Montane Seasonal Deciduous Forest with approximately 55 ha, centered at the coordinates S 27˚23’44” and W 53˚25’59”. For that, three plots of 12x12 m were installed. All plants with diameter at breast height (DBH) greater than 5 cm were identified at the species level. All trees with DBH>10 cm had the following information collected: DBH, total height (h), height at the morphological inversion point (MIP) and the diameter at 0%, 25%, 50%, 75% and 90% of the MIP height. As result, we verified that trees with DBH> 10 cm represent more than 90% of the total biomass (345.2 Mg ha-1). The estimated biomass of leaf litter was 12.4 Mg ha-1. The stock of dry aboveground biomass and litter for the study area was 380.5 Mg ha-1.

Aboveground biomass and carbon stock assessment in forest stands of Gmelinaarborea Roxb. in Mizoram, North-East India

Aboveground biomass and carbon stock in tropical forest play an important role in global carbon cycle. Assessment of biomass and carbon pool in different forest stands may provide information in making decisions about the carbon management within the forest. Gmelina arborea , a fast growing species that is widely distributed and an important timber species of Mizoram has been chosen to assess its biomass and carbon stock. The present study was carried out to estimate the aboveground biomass and carbon stock in G. arborea in different forest stands of Mamit District, Mizoram, north-east India. The result shows that the total aboveground biomass ranged between 66-108 Mg ha -1 and carbon stock (30.00-53.20 mg C ha -1 ). The aboveground biomass and carbon stock was maximum in forest stands (site-III) with highest tree density and diameter class of 30-40cm and 40-50cm indicating the forest site was mature and undisturbed. The result demonstrates that G. arborea contribute in carbon sequestration and helps in mitigating global warming. Further, the aboveground biomass and carbon sequestration potential was greatly affected by the tree composition, population pressure and anthropogenic activities. Keywords: Aboveground biomass, carbon stock, diameter class,Gmelina arborea, tropical forest.

Hierarchical integration of individual tree and area-based approaches for savanna biomass uncertainty estimation from airborne LiDAR

Understanding the role that the vast north Australian savannas play in the continental carbon cycle requires reliable quantification of their carbon stock at landscape and regional scales. LiDAR remote sensing has proven efficient and accurate for the fine-scale estimation of above-ground tree biomass (AGB) and carbon stocks in many ecosystems, but tropical savanna remain under studied. We utilized a two-phase LiDAR analysis procedure which integrates both individual tree detection (ITC) and area-based approaches (ABA) to better understand how the uncertainty of biomass estimation varies with scale. We used estimations from individual tree LiDAR measurements as training/reference data, and then applied these data to develop allometric equations related to LIDAR metrics. We found that LiDAR individual tree heights were strongly correlated with field-estimated AGB (R2=0.754, RMSE=90kg), and that 63% of individual trees crowns (ITC) could be accurately delineated with a canopy maxima approach. Area-based biomass estimation (ABA), which incorporated errors from the ITC steps, identified the quadratic mean of canopy height (QMCH) as the best single independent variable for different plot sample sizes (e.g. for 4ha plots: R2=0.86, RMSE=3.4Mgha−1; and 1ha plots: R2=0.83, RMSE=4.0Mgha−1). Our results show how ITC and ABA approached can be integrated to understand how biomass uncertainty varies with scale across broad landscapes. Understanding these scaling relationships is critical for operationalizing regional savanna inventories, monitoring and mapping.

Biodiversity and climate determine the functioning of Neotropical forests

AbstractAimTropical forests account for a quarter of the global carbon storage and a third of the terrestrial productivity. Few studies have teased apart the relative importance of environmental factors and forest attributes for ecosystem functioning, especially for the tropics. This study aims to relate aboveground biomass (AGB) and biomass dynamics (i.e., net biomass productivity and its underlying demographic drivers: biomass recruitment, growth and mortality) to forest attributes (tree diversity, community‐mean traits and stand basal area) and environmental conditions (water availability, soil fertility and disturbance).LocationNeotropics.MethodsWe used data from 26 sites, 201 1‐ha plots and &gt;92,000 trees distributed across the Neotropics. We quantified for each site water availability and soil total exchangeable bases and for each plot three key community‐weighted mean functional traits that are important for biomass stocks and productivity. We used structural equation models to test the hypothesis that all drivers have independent, positive effects on biomass stocks and dynamics.ResultsOf the relationships analysed, vegetation attributes were more frequently associated significantly with biomass stocks and dynamics than environmental conditions (in 67 vs. 33% of the relationships). High climatic water availability increased biomass growth and stocks, light disturbance increased biomass growth, and soil bases had no effect. Rarefied tree species richness had consistent positive relationships with biomass stocks and dynamics, probably because of niche complementarity, but was not related to net biomass productivity. Community‐mean traits were good predictors of biomass stocks and dynamics.Main conclusionsWater availability has a strong positive effect on biomass stocks and growth, and a future predicted increase in (atmospheric) drought might, therefore, potentially reduce carbon storage. Forest attributes, including species diversity and community‐weighted mean traits, have independent and important relationships with AGB stocks, dynamics and ecosystem functioning, not only in relatively simple temperate systems, but also in structurally complex hyper‐diverse tropical forests.

Impact of deforestation and climate on the Amazon Basin\u2019s above-ground biomass during 1993\u20132012

Since the 1960s, large-scale deforestation in the Amazon Basin has contributed to rising global CO2 concentrations and to climate change. Recent advances in satellite observations enable estimates of gross losses of above-ground biomass (AGB) stocks due to deforestation. However, because of simultaneous regrowth, the net contribution of deforestation emissions to rising atmospheric CO2 concentrations is poorly quantified. Climate change may also reduce the potential for forest regeneration in previously disturbed regions. Here, we address these points of uncertainty with a machine-learning approach that combines satellite observations of AGB with climate data across the Amazon Basin to reconstruct annual maps of potential AGB during 1993–2012, the above-ground C storage potential of the undisturbed landscape. We derive a 2.2 Pg C loss of AGB over the study period, and, for the regions where these losses occur, we estimate a 0.7 Pg C reduction in potential AGB. Thus, climate change has led to a decline of ~1/3 in the capacity of these disturbed forests to recover and recapture the C lost in disturbances during 1993–2012. Our approach further shows that annual variations in land use change mask the natural relationship between the El Niño/Southern Oscillation and AGB stocks in disturbed regions.

Ecosystem Processes and Biogeochemical Cycles in Secondary Tropical Forest Succession

Secondary tropical forests that are in a state of regeneration following clearing for agriculture are now more abundant than primary forests. Yet, despite their large spatial extent and important role in the global carbon (C) cycle, secondary tropical forests are understudied, which challenges our ability to predict how tropical landscapes will respond to future disturbance and global change. We summarize research advances on alterations to C and nutrient dynamics during reforestation and how these are influenced by ecosystem state factors. During forest succession, aboveground biomass stocks and litter fluxes increase in a predictable way, but patterns in soil C dynamics are highly variable. The heterogeneous response of nutrients to reforestation is influenced by multiple factors, including losses incurred during prior land use and management. In contrast to primary tropical forests, where productivity is often limited by rock-derived nutrients, secondary forest growth may be more limited by nutrients from the atmosphere. Future research should identify which nutrients constrain forest regrowth.

Impacts of Airborne Lidar Pulse Density on Estimating Biomass Stocks and Changes in a Selectively Logged Tropical Forest

Airborne lidar is a technology well-suited for mapping many forest attributes, including aboveground biomass (AGB) stocks and changes in selective logging in tropical forests. However, trade-offs still exist between lidar pulse density and accuracy of AGB estimates. We assessed the impacts of lidar pulse density on the estimation of AGB stocks and changes using airborne lidar and field plot data in a selectively logged tropical forest located near Paragominas, Pará, Brazil. Field-derived AGB was computed at 85 square 50 × 50 m plots in 2014. Lidar data were acquired in 2012 and 2014, and for each dataset the pulse density was subsampled from its original density of 13.8 and 37.5 pulses·m−2 to lower densities of 12, 10, 8, 6, 4, 2, 0.8, 0.6, 0.4 and 0.2 pulses·m−2. For each pulse density dataset, a power-law model was developed to estimate AGB stocks from lidar-derived mean height and corresponding changes between the years 2012 and 2014. We found that AGB change estimates at the plot level were only slightly affected by pulse density. However, at the landscape level we observed differences in estimated AGB change of &gt;20 Mg·ha−1 when pulse density decreased from 12 to 0.2 pulses·m−2. The effects of pulse density were more pronounced in areas of steep slope, especially when the digital terrain models (DTMs) used in the lidar derived forest height were created from reduced pulse density data. In particular, when the DTM from high pulse density in 2014 was used to derive the forest height from both years, the effects on forest height and the estimated AGB stock and changes did not exceed 20 Mg·ha−1. The results suggest that AGB change can be monitored in selective logging in tropical forests with reasonable accuracy and low cost with low pulse density lidar surveys if a baseline high-quality DTM is available from at least one lidar survey. We recommend the results of this study to be considered in developing projects and national level MRV systems for REDD+ emission reduction programs for tropical forests.

Vertical distribution of aboveground biomass in a seasonal deciduous forest

Knowledge of aboveground biomass stock in Seasonal Deciduous Forests is imperative for the implementation of mechanisms to reduce emissions from deforestation, forest degradation and land reclamation. The present study analyzed the vertical distribution of aboveground biomasses in a Seasonal Deciduous Forest in Rio Grande do Sul state, Brazil. Seven 12 x 12 m plots were established, and all trees inside the plots were weighed directly in the field. Subplots of 5 x 5 m and 1 x 1 m were marked within the main plots to quantify the remaining vegetation. Average dry aboveground biomass was 316.5 Mg ha-1, trees with diameter at breast height (DBH) greater than 10 cm accounting for over 89% of this biomass. Therefore, biomass determination of large trees deserves special attention, since they represent a large part of the biomass of this forest ecosystem. Biomass of plants taller than 1.3 m and with diameter at breast height < 5 cm was 6.9 Mg ha-1, and that of plants lower than 1.3 m was 1.5 Mg ha-1. Average litter mass was 15.6 Mg ha-1. Trees of large diameters must be analyzed very carefully for quantify the biomass and carbon in the forests, because few individuals might represent a large part of the biomass of a forest ecosystem.

Spatial heterogeneity of biomass and forest structure of the Amazon rain forest: Linking remote sensing, forest modelling and field inventory

AbstractAimEstimating the current spatial variation of biomass in the Amazon rain forest is a challenge and remains a source of substantial uncertainty in the assessment of the global carbon cycle. Precise estimates need to consider small‐scale variations of forest structures resulting from local disturbances, on the one hand, and require large‐scale information on the state of the forest that can be detected by remote sensing, on the other hand. In this study, we introduce a novel method that links a forest gap model and a canopy height map to derive the biomass distribution of the Amazon rain forest.LocationAmazon rain forest.MethodsAn individual‐based forest model was applied to estimate the variation of aboveground biomass across the Amazon rain forest. The forest model simulated individual trees; hence, it allowed the direct comparison of simulated and observed canopy heights from remote sensing. The comparison enabled the detection of disturbed forest states and the derivation of a simulation‐based biomass map at 0.16 ha resolution.ResultsSimulated biomass values ranged from 20 to 490 t (dry mass)/ha across 7.8 Mio km2 of Amazon rain forest. We estimated a total aboveground biomass stock of 76 GtC, with a coefficient of variation of 45%. We found mean differences of only 15% when comparing biomass values of the map with 114 field inventories. The forest model enables the derivation of additional estimates, such as basal area and stem density.Main conclusionsLinking a canopy height map with an individual‐based forest model captures the spatial variation of biomass in the Amazon rain forest at high resolution. The study demonstrates how this linkage allows for quantifying the spatial variation in forest structure caused by tree‐level to regional‐scale disturbances. It thus provides a basis for large‐scale analyses on the heterogeneous structure of tropical forests and their carbon cycle.

Biomass Stock and Productivity of Primeval and Production Beech Forests: Greater Canopy Structural Diversity Promotes Productivity

Our knowledge of temperate broadleaf forest ecology is based mostly on the study of production forests, which lack the terminal stage of forest development and have a simpler stand structure than old-growth and primeval forests. How primeval and production forests differ in net primary production (NPP) is not well known. In three primeval and three nearby production forests of European beech (Fagus sylvatica) in the Slovakian Carpathians, we measured aboveground biomass stocks (live and dead), aboveground NPP (ANPP) and parameters characterizing canopy structural diversity (leaf area index and its spatial variation). Our study aims were (1) to explore the role of canopy structural diversity for ANPP and (2) to assess evidence of a productivity decline in the terminal stage. While aboveground live biomass stocks were on average 20% greater in the primeval forests (386 vs. 320 Mg ha−1; insignificant difference at two sites), deadwood mass stocks were on average four times larger than in the production forests (86 vs. 19 Mg ha−1). ANPP was similarly high in the primeval and production forests (10.0 vs. 9.9 Mg ha−1 y−1) and did not decrease towards the terminal stage. Production models indicate that, in the primeval forests, about 10% of ANPP (ca. 1 Mg ha−1 y−1) was generated by effects related to leaf area heterogeneity, evidencing a positive effect of structural diversity on forest productivity, even though species diversity was low. This study helps to better understand the impact of forest management on the productivity and carbon storage in temperate woodlands.