Tree allometry and improved estimation of carbon stocks and balance in tropical forests

Tropical forests hold large stores of carbon, yet uncertainty remains regarding their quantitative contribution to the global carbon cycle. One approach to quantifying carbon biomass stores consists in inferring changes from long-term forest inventory plots. Regression models are used to convert inventory data into an estimate of aboveground biomass (AGB). We provide a critical reassessment of the quality and the robustness of these models across tropical forest types, using a large dataset of 2,410 trees ≥ 5 cm diameter, directly harvested in 27 study sites across the tropics. Proportional relationships between aboveground biomass and the product of wood density, trunk cross-sectional area, and total height are constructed. We also develop a regression model involving wood density and stem diameter only. Our models were tested for secondary and old-growth forests, for dry, moist and wet forests, for lowland and montane forests, and for mangrove forests. The most important predictors of AGB of a tree were, in decreasing order of importance, its trunk diameter, wood specific gravity, total height, and forest type (dry, moist, or wet). Overestimates prevailed, giving a bias of 0.5–6.5% when errors were averaged across all stands. Our regression models can be used reliably to predict aboveground tree biomass across a broad range of tropical forests. Because they are based on an unprecedented dataset, these models should improve the quality of tropical biomass estimates, and bring consensus about the contribution of the tropical forest biome and tropical deforestation to the global carbon cycle. Electronic Supplementary Material Supplementary material is available for this article at     

MODIS Based Estimation of Forest Aboveground Biomass in China

Accurate estimation of forest biomass C stock is essential to understand carbon cycles. However, current estimates of Chinese forest biomass are mostly based on inventory-based timber volumes and empirical conversion factors at the provincial scale, which could introduce large uncertainties in forest biomass estimation. Here we provide a data-driven estimate of Chinese forest aboveground biomass from 2001 to 2013 at a spatial resolution of 1 km by integrating a recently reviewed plot-level ground-measured forest aboveground biomass database with geospatial information from 1-km Moderate-Resolution Imaging Spectroradiometer (MODIS) dataset in a machine learning algorithm (the model tree ensemble, MTE). We show that Chinese forest aboveground biomass is 8.56 Pg C, which is mainly contributed by evergreen needle-leaf forests and deciduous broadleaf forests. The mean forest aboveground biomass density is 56.1 Mg C ha⁻¹, with high values observed in temperate humid regions. The responses of forest aboveground biomass density to mean annual temperature are closely tied to water conditions; that is, negative responses dominate regions with mean annual precipitation less than 1300 mm y⁻¹ and positive responses prevail in regions with mean annual precipitation higher than 2800 mm y⁻¹. During the 2000s, the forests in China sequestered C by 61.9 Tg C y⁻¹, and this C sink is mainly distributed in north China and may be attributed to warming climate, rising CO₂ concentration, N deposition, and growth of young forests.     

Estimating the Aboveground Biomass of Bornean Forest

We apply a process‐based model, called 3‐PG (physiological principles for predicting growth), to estimate aboveground biomass for the primary forests of Borneo. Using publicly available soil and climate data, and parameterized with physiological traits of Bornean forest, the modeled aboveground biomass and basal area showed statistically significant relationships with field‐measured data from 85 sites across four major forest types. Our results highlight the possibility to expand the application of 3‐PG to forests of varying condition, which would facilitate inclusion of modeled forest biomass data for developing a Tier 3 carbon inventory system for Borneo.     

Topographic Variation in Aboveground Biomass in a Subtropical Evergreen Broad-Leaved Forest in China

The subtropical forest biome occupies about 25% of China, with species diversity only next to tropical forests. Despite the recognized importance of subtropical forest in regional carbon storage and cycling, uncertainties remain regarding the carbon storage of subtropical forests, and few studies have quantified within-site variation of biomass, making it difficult to evaluate the role of these forests in the global and regional carbon cycles. Using data for a 24-ha census plot in east China, we quantify aboveground biomass, characterize its spatial variation among different habitats, and analyse species relative contribution to the total aboveground biomass of different habitats. The average aboveground biomass was 223.0 Mg ha⁻¹ (bootstrapped 95% confidence intervals [217.6, 228.5]) and varied substantially among four topographically defined habitats, from 180.6 Mg ha⁻¹ (bootstrapped 95% CI [167.1, 195.0]) in the upper ridge to 245.9 Mg ha⁻¹ (bootstrapped 95% CI [238.3, 253.8]) in the lower ridge, with upper and lower valley intermediate. In consistent with our expectation, individual species contributed differently to the total aboveground biomass of different habitats, reflecting significant species habitat associations. Different species show differently in habitat preference in terms of biomass contribution. These patterns may be the consequences of ecological strategies difference among different species. Results from this study enhance our ability to evaluate the role of subtropical forests in the regional carbon cycle and provide valuable information to guide the protection and management of subtropical broad-leaved forest for carbon sequestration and carbon storage.     

The regional variation of aboveground live biomass in old-growth Amazonian forests

The biomass of tropical forests plays an important role in the global carbon cycle, both as a dynamic reservoir of carbon, and as a source of carbon dioxide to the atmosphere in areas undergoing deforestation. However, the absolute magnitude and environmental determinants of tropical forest biomass are still poorly understood. Here, we present a new synthesis and interpolation of the basal area and aboveground live biomass of old‐growth lowland tropical forests across South America, based on data from 227 forest plots, many previously unpublished. Forest biomass was analyzed in terms of two uncorrelated factors: basal area and mean wood density. Basal area is strongly affected by local landscape factors, but is relatively invariant at regional scale in moist tropical forests, and declines significantly at the dry periphery of the forest zone. Mean wood density is inversely correlated with forest dynamics, being lower in the dynamic forests of western Amazonia and high in the slow‐growing forests of eastern Amazonia. The combination of these two factors results in biomass being highest in the moderately seasonal, slow growing forests of central Amazonia and the Guyanas (up to 350 Mg dry weight ha^?1) and declining to 200–250 Mg dry weight ha^?1 at the western, southern and eastern margins. Overall, we estimate the total aboveground live biomass of intact Amazonian rainforests (area 5.76 × 10⁶ km² in 2000) to be 93±23 Pg C, taking into account lianas and small trees. Including dead biomass and belowground biomass would increase this value by approximately 10% and 21%, respectively, but the spatial variation of these additional terms still needs to be quantified.     

基于TM影像、森林资源清查数据和人工神经网络的森林碳空间分布模拟

森林是陆地生态系统中最大的碳库,在全球碳平衡和减缓全球气候变化方面发挥着不可替代的作用。当前主要利用森林资源清查数据和优势树种材积源-生物量的关系进行碳储量估算,在此基础上有效结合遥感影像数据将会更好的满足相关部门对国家和区域森林碳储量计算的需求。利用临安市2004年森林资源清查的930个样地数据和同年度Landsat TM影像数据,提取6个波段灰度值以及与碳储量相关性相对较大的3个波段组合,结合人工神经网络对研究区森林碳储量及其分布进行有效模拟。结果显示,用误差反向传播算法训练神经网络较好的重建了森林碳密度空间分布和变化,森林碳地上部分模拟结果与样地实测值之间的一致性好,全区域模拟结果森林碳平均值为0.98Mg(10.89Mg/hm²),总体森林碳密度模拟结果低于样地平均值约13%,进一步验证了人工神经网络在对大范围森林碳估算与模拟上具有较好的效果,为区域森林碳储量的估测研究提供有效的方法支持。     

林火干扰对大兴安岭主要林分类型地上生物量预测的影响模拟研究

林火干扰是北方森林最主要的自然干扰之一,对北方森林地上生物量影响是一个长期的过程。因此,在预测地上生物量动态变化时需要考虑林火的影响。运用空间直观景观模型LANDIS PRO,模拟大兴安岭林区林火对不同树种地上生物量预测的影响。选取研究区5种主要树种林分(兴安落叶松、樟子松、云杉、白桦和山杨),以无干扰情景为参考预案,在验证模型模拟结果的基础上,模拟林火在短期(0—50a)、中期(50—150a)和长期(150—300a)对地上生物量的定量化影响,及其对不同立地类型地上生物量的动态变化。结果表明:(1)基于森林调查数据参数化的2000年森林景观模拟结果能够较好地代表2000年真实森林景观,模拟的2010年森林林分密度和胸高断面积与2010年森林调查数据无显著性差异(P0.05),当前林火干扰机制模拟结果能够较好地与样地调查数据匹配,说明林火模拟能够代表当前研究区林火发生情况;(2)与无干扰预案相比,整个模拟时期内景观水平上林火减少了1.7—5.9 t/hm2地上生物量;(3)与无干扰预案相比,林火预案下主要树种生物量在短期、中期和长期变化显著(P0.05);(4)在不同模拟时期,林火显著地改变了地上生物量空间分布,其中以亚高山区地上生物量降低最为明显。研究可为长期森林管理以及森林可持续发展提供参考。     

Partitioning direct and indirect human-induced effects on carbon sequestration of managed coniferous forests using model simulations and forest inventories

Temperate forest ecosystems have recently been identified as an important net sink in the global carbon budget. The factors responsible for the strength of the sinks and their permanence, however, are less evident. In this paper, we quantify the present carbon sequestration in Thuringian managed coniferous forests. We quantify the effects of indirect human‐induced environmental changes (increasing temperature, increasing atmospheric CO₂ concentration and nitrogen fertilization), during the last century using BIOME‐BGC, as well as the legacy effect of the current age‐class distribution (forest inventories and BIOME‐BGC). We focused on coniferous forests because these forests represent a large area of central European forests and detailed forest inventories were available. The model indicates that environmental changes induced an increase in biomass C accumulation for all age classes during the last 20 years (1982–2001). Young and old stands had the highest changes in the biomass C accumulation during this period. During the last century mature stands (older than 80 years) turned from being almost carbon neutral to carbon sinks. In high elevations nitrogen deposition explained most of the increase of net ecosystem production (NEP) of forests. CO₂ fertilization was the main factor increasing NEP of forests in the middle and low elevations. According to the model, at present, total biomass C accumulation in coniferous forests of Thuringia was estimated at 1.51 t C ha^?1 yr^?1 with an averaged annual NEP of 1.42 t C ha^?1 yr^?1 and total net biome production of 1.03 t C ha^?1 yr^?1 (accounting for harvest). The annual averaged biomass carbon balance (BCB: biomass accumulation rate‐harvest) was 1.12 t C ha^?1 yr^?1 (not including soil respiration), and was close to BCB from forest inventories (1.15 t C ha^?1 yr^?1). Indirect human impact resulted in 33% increase in modeled biomass carbon accumulation in coniferous forests in Thuringia during the last century. From the forest inventory data we estimated the legacy effect of the age‐class distribution to account for 17% of the inventory‐based sink. Isolating the environmental change effects showed that these effects can be large in a long‐term, managed conifer forest.     

停止商业性采伐对大兴安岭森林结构与地上生物量的影响

采伐是北方森林最主要的人为干扰之一,过去高强度采伐导致森林植被组成单一化和均质化。为提高森林的生态功能和经济效益,国家先后于2000年实施"天然林资源保护工程"、2014年实施全面停止天然林商业性采伐。为评价这两种政策下不同的采伐干扰对森林的直接影响,以大兴安岭林区为研究对象,采用空间直观景观模型LANDIS PRO,模拟比较2000—2100年"天然林资源保护工程"、全面停止商业性采伐政策下森林树种组成、年龄结构及森林地上生物量的长期变化特征及其差异性。研究结果表明:1)模型初始化的林分密度、地上生物量与2000年野外调查数据相吻合(P0.01),模型模拟结果具有较高的可靠性;2)对比分类经营,全面停止商业性采伐的实施:增大了优势树种(落叶松与白桦)的树种组成比例,减小了保护树种(云杉与樟子松)的比例;对树种组成在中长期影响显著(P0.05),降低了树种组成结构的多样性;总体上增加了林分平均胸高断面积,减小了林分密度;3)模型模拟100年,全面停止商业性采伐下中幼龄林向成熟林过渡,改善森林年龄结构;4)与分类经营相比,整个模拟时期内全面停止商业性采伐增加森林地上生物量,提高森林恢复速率,有助于森林地上总生物量的恢复与累积。但保护树种(云杉与樟子松)森林地上生物量在一定程度上有所下降,不利于提高珍贵树种的丰度。对评估森林管理方案在森林资源恢复上的作用和有效实施森林生态系统管理有重要的参考意义。     

气候变化和不同强度造林对大兴安岭主要树种林分信息和地上生物量的长期影响

气候变化及相应火干扰在不同尺度上影响着我国大兴安岭地区森林动态,且在未来的影响可能继续加剧。为了提高森林生态功能和应对气候变暖,国家在分类经营基础上全面实施抚育采伐和补植造林,效果较好,但抚育采伐对森林主要树种的长期影响知之甚少,其在未来气候下的可持续性也有待进一步评估,同时,探讨造林措施对未来森林的影响也显得尤为重要。本文运用森林景观模型LANDIS PRO,模拟气候变化及火干扰、采伐和造林对大兴安岭地区主要树种的长期影响。结果表明:1)模型初始化、短期和长期模拟结果均得到了有效验证,模拟结果与森林调查数据之间无显著性差异(P0.05),基于火烧迹地数据的林火干扰验证亦能够反映当前火干扰的效果,模型模拟结果的可信度较高;2)与当前气候相比,气候变暖及火干扰明显改变了树种组成、年龄结构和地上生物量,B1气候下研究区森林基本上以针叶树种为主要树种,A2气候下优势树种向阔叶树转变;3)与无采伐预案相比,当前气候下,抚育采伐使落叶松的林分密度和地上生物量分别降低了(165±94.9)株/hm~2和(8.5±5.1) Mg/hm~2,增加了樟子松、白桦和云杉等树木株数和地上生物量(3.3—753.4株/hm~2和0.2—4.0 Mg/hm~2),而对山杨的影响较小;B1和A2气候下抚育采伐显著改变林分密度,降低景观尺度地上生物量,进而表现为不可持续;4)B1气候下,推荐实施中低强度造林预案(10%和20%强度),在A2气候下,各强度造林均可在模拟后期增加树种地上生物量。     

Modeling the impact of liana infestation on the demography and carbon cycle of tropical forests

There is mounting empirical evidence that lianas affect the carbon cycle of tropical forests. However, no single vegetation model takes into account this growth form, although such efforts could greatly improve the predictions of carbon dynamics in tropical forests. In this study, we incorporated a novel mechanistic representation of lianas in a dynamic global vegetation model (the Ecosystem Demography Model). We developed a liana‐specific plant functional type and mechanisms representing liana–tree interactions (such as light competition, liana‐specific allometries, and attachment to host trees) and parameterized them according to a comprehensive literature meta‐analysis. We tested the model for an old‐growth forest (Paracou, French Guiana) and a secondary forest (Gigante Peninsula, Panama). The resulting model simulations captured many features of the two forests characterized by different levels of liana infestation as revealed by a systematic comparison of the model outputs with empirical data, including local census data from forest inventories, eddy flux tower data, and terrestrial laser scanner‐derived forest vertical structure. The inclusion of lianas in the simulations reduced the secondary forest net productivity by up to 0.46 t_C ha^?1 year^?1, which corresponds to a limited relative reduction of 2.6% in comparison with a reference simulation without lianas. However, this resulted in significantly reduced accumulated above‐ground biomass after 70 years of regrowth by up to 20 t_C/ha (19% of the reference simulation). Ultimately, the simulated negative impact of lianas on the total biomass was almost completely cancelled out when the forest reached an old‐growth successional stage. Our findings suggest that lianas negatively influence the forest potential carbon sink strength, especially for young, disturbed, liana‐rich sites. In light of the critical role that lianas play in the profound changes currently experienced by tropical forests, this new model provides a robust numerical tool to forecast the impact of lianas on tropical forest carbon sinks.     

A universal airborne LiDAR approach for tropical forest carbon mapping

Airborne light detection and ranging (LiDAR) is fast turning the corner from demonstration technology to a key tool for assessing carbon stocks in tropical forests. With its ability to penetrate tropical forest canopies and detect three-dimensional forest structure, LiDAR may prove to be a major component of international strategies to measure and account for carbon emissions from and uptake by tropical forests. To date, however, basic ecological information such as height–diameter allometry and stand-level wood density have not been mechanistically incorporated into methods for mapping forest carbon at regional and global scales. A better incorporation of these structural patterns in forests may reduce the considerable time needed to calibrate airborne data with ground-based forest inventory plots, which presently necessitate exhaustive measurements of tree diameters and heights, as well as tree identifications for wood density estimation. Here, we develop a new approach that can facilitate rapid LiDAR calibration with minimal field data. Throughout four tropical regions (Panama, Peru, Madagascar, and Hawaii), we were able to predict aboveground carbon density estimated in field inventory plots using a single universal LiDAR model (r ²  = 0.80, RMSE = 27.6 Mg C ha⁻¹). This model is comparable in predictive power to locally calibrated models, but relies on limited inputs of basal area and wood density information for a given region, rather than on traditional plot inventories. With this approach, we propose to radically decrease the time required to calibrate airborne LiDAR data and thus increase the output of high-resolution carbon maps, supporting tropical forest conservation and climate mitigation policy.     

How drought events during the last century have impacted biomass carbon in Amazonian rainforests

During the last two decades, inventory data show that droughts have reduced biomass carbon sink of the Amazon forest by causing mortality to exceed growth. However, process-based models have struggled to include drought-induced responses of growth and mortality and have not been evaluated against plot data. A process-based model, ORCHIDEE-CAN-NHA, including forest demography with tree cohorts, plant hydraulic architecture and drought-induced tree mortality, was applied over Amazonia rainforests forced by gridded climate fields and rising CO₂ from 1901 to 2019. The model reproduced the decelerating signal of net carbon sink and drought sensitivity of aboveground biomass (AGB) growth and mortality observed at forest plots across selected Amazon intact forests for 2005 and 2010. We predicted a larger mortality rate and a more negative sensitivity of the net carbon sink during the 2015/16 El Niño compared with the former droughts. 2015/16 was indeed the most severe drought since 1901 regarding both AGB loss and area experiencing a severe carbon loss. We found that even if climate change did increase mortality, elevated CO₂ contributed to balance the biomass mortality, since CO₂-induced stomatal closure reduces transpiration, thus, offsets increased transpiration from CO₂-induced higher foliage area.     

Deadwood stocks increase with selective logging and large tree frequency in Gabon

Deadwood is a major component of aboveground biomass (AGB) in tropical forests and is important as habitat and for nutrient cycling and carbon storage. With deforestation and degradation taking place throughout the tropics, improved understanding of the magnitude and spatial variation in deadwood is vital for the development of regional and global carbon budgets. However, this potentially important carbon pool is poorly quantified in Afrotropical forests and the regional drivers of deadwood stocks are unknown. In the first large‐scale study of deadwood in Central Africa, we quantified stocks in 47 forest sites across Gabon and evaluated the effects of disturbance (logging), forest structure variables (live AGB, wood density, abundance of large trees), and abiotic variables (temperature, precipitation, seasonality). Average deadwood stocks (measured as necromass, the biomass of deadwood) were 65 Mg ha^?1 or 23% of live AGB. Deadwood stocks varied spatially with disturbance and forest structure, but not abiotic variables. Deadwood stocks increased significantly with logging (+38 Mg ha^?1) and the abundance of large trees (+2.4 Mg ha^?1 for every tree >60 cm dbh). Gabon holds 0.74 Pg C, or 21% of total aboveground carbon in deadwood, a threefold increase over previous estimates. Importantly, deadwood densities in Gabon are comparable to those in the Neotropics and respond similarly to logging, but represent a lower proportion of live AGB (median of 18% in Gabon compared to 26% in the Neotropics). In forest carbon accounting, necromass is often assumed to be a constant proportion (9%) of biomass, but in humid tropical forests this ratio varies from 2% in undisturbed forest to 300% in logged forest. Because logging significantly increases the deadwood carbon pool, estimates of tropical forest carbon should at a minimum use different ratios for logged (mean of 30%) and unlogged forests (mean of 18%).     

Biomass,Carbon, and Nitrogen Pools in Mexican Tropical Dry Forest Landscapes

Tropical dry forest is the most widely distributed land-cover type in the tropics. As the rate of land-use/land-cover change from forest to pasture or agriculture accelerates worldwide, it is becoming increasingly important to quantify the ecosystem biomass and carbon (C) and nitrogen (N) pools of both intact forests and converted sites. In the central coastal region of México, we sampled total aboveground biomass (TAGB), and the N and C pools of two floodplain forests, three upland dry forests, and four pastures converted from dry forest. We also sampled belowground biomass and soil C and N pools in two sites of each land-cover type. The TAGB of floodplain forests was as high as 416 Mg ha^–1, whereas the TAGB of the dry forest ranged from 94 to 126 Mg ha^–1. The TAGB of pastures derived from dry forest ranged from 20 to 34 Mg ha^–1. Dead wood (standing and downed combined) comprised 27%–29% of the TABG of dry forest but only about 10% in floodplain forest. Root biomass averaged 32.0 Mg ha^–1 in floodplain forest, 17.1 Mg ha^–1 in dry forest, and 5.8 Mg ha^–1 in pasture. Although total root biomass was similar between sites within land-cover types, root distribution varied by depth and by size class. The highest proportion of root biomass occurred in the top 20 cm of soil in all sites. Total aboveground and root C pools, respectively, were 12 and 2.2 Mg ha^–1 in pasture and reached 180 and 12.9 Mg ha^–1 in floodplain forest. Total aboveground and root pools, respectively, were 149 and 47 kg ha^–1 in pasture and reached 2623 and 264 kg ha^–1 in floodplain forest. Soil organic C pools were greater in pastures than in dry forest, but soil N pools were similar when calculated for the same soil depths. Total ecosystem C pools were 306. The Mg ha^–1 in floodplain forest, 141 Mg ha^–1 in dry forest, and 124 Mg ha^–1 in pasture. Soil C comprised 37%–90% of the total ecosystem C, whereas soil N comprised 85%–98% of the total. The N pools lack of a consistent decrease in soil pools caused by land-use change suggests that C and N losses result from the burning of aboveground biomass. We estimate that in México, dry forest landscapes store approximately 2.3 Pg C, which is about equal to the C stored by the evergreen forests of that country (approximately 2.4 Pg C). Potential C emissions to the atmosphere from the burning of biomass in the dry tropical landscapes of México may amount to 708 Tg C, as compared with 569 Tg C from evergreen forests.     

Biomass change in an Atlantic tropical moist forest: the ENSO effect in permanent sample plots over a 22-year period

There are a number of controversies surrounding both biomass estimation and carbon balance in tropical forests. Here we use long-term (from 1978 through 2000) data from five 0.5-ha permanent sample plots (PSPs) within a large tract of relatively undisturbed Atlantic moist forest in southeastern Brazil to quantify the biomass increment (M_I), and change in total stand biomass (M_stand), from mortality, recruitment, and growth data for trees 10 cm diameter at breast height (DBH). Despite receiving an average of only 1,200 mm annual precipitation, total forests biomass (334.5±11.3 Mg ha^–1) was comparable to moist tropical forests with much greater precipitation. Over this relatively long-term study, forest biomass experienced rapid declines associated with El Niño events, followed by gradual biomass accumulation. Over short time intervals that overlook extreme events, these dynamics can be misinterpreted as net biomass accumulation. However for the 22 years of this study, there was a small reduction in forest biomass, averaging –1.2 Mg ha^–1 year^–1 (±3.1). Strong climatic disturbances can severely reduce forest biomass, and if the frequency and intensity of these events increases beyond historical averages, these changing disturbance regimes have the capacity to significantly reduce forest biomass, resulting in a net source of carbon to the atmosphere.     

The effect of charcoal production on carbon cycling in African biomes

Using biomass for charcoal production in sub-Saharan Africa (SSA) may change carbon stock dynamics and lead to irreversible changes in the carbon balance, yet we have little understanding of whether these dynamics vary by biome in this region. Currently, charcoal production contributes up to 7% of yearly deforestation in tropical regions, with carbon emissions corresponding to 71.2 million tonnes of CO₂ and 1.3 million tonnes of CH₄. With a projected increased demand for charcoal in the coming decades, even low harvest rates may throw the carbon budget off-balance due to legacy effects. Here, we parameterized the dynamic global vegetation model LPJ-GUESS for six SSA biomes and examined the effect of charcoal production on net ecosystem exchange (NEE), carbon stock sizes and recovery time for tropical rain forest, montane forest, moist savanna, dry savanna, temperate grassland and semi-desert. Under historical charcoal regimes, tropical rain forests and montane forests transitioned from net carbon sinks to net sources, that is, mean cumulative NEE from −3.56 ± 2.59 kg C/m² to 2.46 ± 3.43 kg C/m² and −2.73 ± 2.80 kg C/m² to 1.87 ± 4.94 kg C/m² respectively. Varying charcoal production intensities resulted in tropical rain forests showing at least two times higher carbon losses than the other biomes. Biome recovery time varied by carbon stock, with tropical and montane forests taking about 10 times longer than the fast recovery observed for semi-desert and temperate grasslands. Our findings show that high biomass biomes are disproportionately affected by biomass harvesting for charcoal, and even low harvesting rates strongly affect vegetation and litter carbon and their contribution to the carbon budget. Therefore, the prolonged biome recoveries imply that current charcoal production practices in SSA are not sustainable, especially in tropical rain forests and montane forests, where we observe longer recovery for vegetation and litter carbon stocks.     

Significance of Over-Mature and Decaying Trees for Carbon Stocks in a Central European Natural Spruce Forest

Old-growth forests are important stores for carbon as they may accumulate C for centuries. The alteration of biomass and soil carbon pools across the development stages of a forest dynamics cycle has rarely been quantified. We studied the above- and belowground C stocks in the five forest development stages (regeneration to decay stage) of a montane spruce (Picea abies) forest of the northern German Harz Mountains, one of Central Europe’s few forests where the natural forest dynamics have not been disturbed by man for several centuries. The over-mature and decay stages had the largest total (up to 480 Mg C ha^?1) and aboveground biomass carbon pools (200 Mg C ha^?1) with biomass C stored in dead wood in the decay stage. The soil C pool (220–275 Mg C ha^?1, 0–60 cm) was two to three times larger than in temperate lowland spruce forests and remained invariant across the forest dynamics cycle. On the landscape level, taking into account the frequency of the five forest development stages, the total carbon pool was approximately 420 Mg C ha^?1. The results evidence the high significance of over-mature and decaying stages of temperate mountain forests not only for conserving specialized forest organisms but also for their large carbon storage potential.     

Aboveground Forest Carbon Dynamics in Papua New Guinea: Isolating the Influence of Selective-Harvesting and El Niño

Assessment of forest carbon (C) stock and sequestration and the influence of forest harvesting and climatic variations are important issues in global forest ecology. Quantitative studies of the C balance of tropical forests, such as those in Papua New Guinea (PNG), are also required for forest-based climate change mitigation initiatives. We develop a hierarchical Bayesian model (HBM) of aboveground forest C stock and sequestration in primary, selectively harvested, and El Niño Southern Oscillation (ENSO)-effected lowland tropical forest from 15 years of Permanent Sample Plot (PSP) census data for PNG consisting of 121 plots in selectively harvested forest, and 35 plots in primary forest. Model parameters indicated: C stock in aboveground live biomass (AGLB) of 137 ± 9 (95% confidence interval (CI)) MgC ha^?1 in primary forest, compared with 62 ± 18 MgC ha^?1 for selectively harvested forest (55% difference); C sequestration in primary forest of 0.23 ± 1.70 MgC ha^?1 y^?1, which was lower than in selectively harvested forest, 1.12 ± 3.41 MgC ha^?1 y^?1; ENSO-induced fire resulted in significant C emissions (?6.87 ± 3.94 MgC ha^?1 y^?1). High variability between PSPs in C stock and C sequestration rates necessitated random plot effects for both stock and sequestration. The HBM approach allowed inclusion of hierarchical autocorrelation, providing valid CIs on model parameters and efficient estimation. The HBM model has provided quantitative insights on the C balance of PNG’s forests that can be used as inputs for climate change mitigation initiatives.     

Natural Forest Biomass Estimation Based on Plantation Information Using PALSAR Data

Forests play a vital role in terrestrial carbon cycling; therefore, monitoring forest biomass at local to global scales has become a challenging issue in the context of climate change. In this study, we investigated the backscattering properties of Advanced Land Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) data in cashew and rubber plantation areas of Cambodia. The PALSAR backscattering coefficient (σ⁰) had different responses in the two plantation types because of differences in biophysical parameters. The PALSAR σ⁰ showed a higher correlation with field-based measurements and lower saturation in cashew plants compared with rubber plants. Multiple linear regression (MLR) models based on field-based biomass of cashew (C-MLR) and rubber (R-MLR) plants with PALSAR σ⁰ were created. These MLR models were used to estimate natural forest biomass in Cambodia. The cashew plant-based MLR model (C-MLR) produced better results than the rubber plant-based MLR model (R-MLR). The C-MLR-estimated natural forest biomass was validated using forest inventory data for natural forests in Cambodia. The validation results showed a strong correlation (R² = 0.64) between C-MLR-estimated natural forest biomass and field-based biomass, with RMSE  = 23.2 Mg/ha in deciduous forests. In high-biomass regions, such as dense evergreen forests, this model had a weaker correlation because of the high biomass and the multiple-story tree structure of evergreen forests, which caused saturation of the PALSAR signal.