Above-ground biomass estimation using airborne discrete-return and full-waveform LiDAR data in a coniferous forest

The estimation of forest aboveground biomass (AGB) is critical for quantifying carbon stocks and essential for evaluating global carbon cycle. Many previous studies have estimated forest AGB using airborne discrete-return Light Detection and Ranging (LiDAR) data, while fewer studies predicted forest AGB using airborne full-waveform LiDAR data. The objective of this work was to evaluate the utility of airborne discrete-return and full-waveform LiDAR data in estimating forest AGB. To fulfill the objective, airborne discrete-return LiDAR-derived metrics (DR-metrics), full-waveform LiDAR-derived metrics (FW-metrics) and structure parameters (combining height metrics and canopy cover) were used to estimate forest AGB. Additionally, the combined use of DR- and FW-metrics through a nonlinear way was also evaluated for AGB estimation in a coniferous forest in Dayekou, Gansu province of China. Results indicated that both height metrics derived from discrete-return and full-waveform LiDAR data were stronger predictors of forest AGB compared with other LiDAR-derived metrics. Canopy cover derived from discrete-return LiDAR data was not sensitive to forest AGB, while canopy cover estimated by full-waveform LiDAR data (CC_WF) showed moderate correlation with forest AGB. Structure parameters derived from full-waveform LiDAR data, such as H75_FW * CC_FW, were closely related to forest AGB. In contrast, structure parameters derived from discrete-return LiDAR data were not suitable for estimating forest AGB due to the less sensitivity of canopy cover CC_DR2 to forest AGB. This research also concluded that the synergistic use of DR- and FW-metrics can provide better AGB estimates in coniferous forest.     

Airborne LiDAR technique for estimating biomass components of maize: A case study in Zhangye City,Northwest China

Crop biomass is an important ecological indicator of growth, light use efficiency, and carbon stocks in agro-ecosystems. Light detection and ranging (LiDAR) or laser scanning has been widely used to estimate forest structural parameters and biomass. However, LiDAR is rarely used to estimate crop parameters because the short, dense canopies of crops limit the accuracy of the results. The objective of this study is to explore the potential of airborne LiDAR data in estimating biomass components of maize, namely aboveground biomass (AGB) and belowground biomass (BGB). Five biomass-related factors were measured during the entire growing season of maize. The field-measured canopy height and leaf area index (LAI) were identified as the factors that most directly affect biomass components through Pearson's correlation analysis and structural equation modeling (SEM). Field-based estimation models were proposed to estimate maize biomass components during the tasseling stage. Subsequently, the maize height and LAI over the entire study area were derived from LiDAR data and were used as input for the estimation models to map the spatial pattern of the biomass components. The results showed that the LiDAR-estimated biomass was comparable to the field-measured biomass, with root mean squared errors (RMSE) of 288.51 g/m² (AGB), and 75.81 g/m² (BGB). In conclusion, airborne LiDAR has great potential for estimating canopy height, LAI, and biomass components of maize during the peak growing season.     

中亚热带森林单木地上生物量的机载激光雷达估测

以雪峰山武冈林场为研究对象,利用遥感数据和地面实测样地数据,研究机载激光雷达（LiDAR）估测中亚热带森林乔木层单木地上生物量的能力.利用条件随机场和最优化方法实现LiDAR点云的单木分割,以单木尺度为对象提取的植被点云空间结构、回波特征以及地形特征等作为遥感变量,采用回归模型估测乔木层地上生物量.结果表明: 针叶林、阔叶林和针阔混交林的单木识别率分别为93%、86%和60%;多元逐步回归模型的调整决定系数分别为0.83、0.81和0.74,均方根误差分别为28.22、29.79和32.31 t·hm^-2;以冠层体积、树高百分位值、坡度和回波强度值构成的模型精度明显高于以树高为因子的传统回归模型精度.以单木为对象从LiDAR点云中提取的遥感变量有助于提高森林生物量估测精度.
     

Landscape‐scale lidar analysis of aboveground biomass distribution in secondary Brazilian Atlantic Forest

Secondary forests account for more than half of tropical forests and represent a growing carbon sink, but rates of biomass accumulation vary by a factor of two or more even among plots in the same landscape. To better understand the drivers of this variability, we used airborne lidar to measure forest canopy height and estimate biomass over 4529 ha at Serra do Conduru Park in Southern Bahia, Brazil. We measured trees in 30 georeferenced field plots (0.25‐ha each) to estimate biomass using allometry. Then we estimated aboveground biomass density (ABD) across the lidar study area using a statistical model developed from our field plots. This model related the 95th percentile of the distribution of lidar return heights to ABD. We overlaid this map of ABD on a Landsat‐derived forest age map to determine rates of biomass accumulation. We found rapid initial biomass regeneration (~6 Mg/ha yr), which slowed as forests aged. We also observed high variability in both height and biomass across the landscape within forests of similar age. Nevertheless, a regression model that accounted for spatial autocorrelation and included forest age, slope, and distance to roads or open areas explained 62 and 77 percent of the landscape variation in ABD and canopy height, respectively. Thus, while there is high spatial heterogeneity in forest recovery, and the drivers of this heterogeneity warrant further investigation, we suggest that a relatively simple set of predictor variables is sufficient to explain the majority of variance in both height and ABD in this landscape.     

黄瓜砧用白籽南瓜对不同盐胁迫的耐性评价

采用营养液栽培,研究Ca(NO₃)₂和NaCl胁迫对黄瓜嫁接用砧木南瓜幼苗生长和抗氧化酶活性的影响,并用隶属函数法综合评价其耐盐性.结果表明： 低浓度盐30 mmol·L^-1Ca(NO₃)₂和等渗的45 mmol·L^-1 NaCl处理促进砧木幼苗生长;高浓度盐60、120 mmol·L^-1Ca(NO₃)₂和等渗的90、180 mmol·L^-1NaCl胁迫下,各砧木幼苗的生长和抗氧化酶系统均受到不同程度的抑制,其中,‘青砧1号’的盐害指数最小,生物量及超氧化物歧化酶(SOD)、过氧化物酶(POD)和过氧化氢酶(CAT)活性的下降幅度以及相对电导率的上升幅度均小于其他砧木.高盐Ca(NO₃)₂胁迫下,各砧木SOD、POD和CAT酶活性均高于等渗的NaCl,而盐害指数和相对电导率低于NaCl,表明Ca(NO₃)₂对砧木南瓜幼苗生长的危害小于NaCl.4个砧木品种的耐盐性顺序为‘青砧1号’>‘佐木南瓜’>‘丰源铁甲’>‘超霸南瓜’.     

Aboveground biomass increments over 26 years (1993–2019) in an old-growth cool-temperate forest in northern Japan

Assessing long-term changes in the biomass of old-growth forests with consideration of climate effects is essential for understanding forest ecosystem functions under a changing climate. Long-term biomass changes are the result of accumulated short-term changes, which can be affected by endogenous processes such as gap filling in small-scale canopy openings. Here, we used 26 years (1993–2019) of repeated tree census data in an old-growth, cool-temperate, mixed deciduous forest that contains three topographic units (riparian, denuded slope, and terrace) in northern Japan to document decadal changes in aboveground biomass (AGB) and their processes in relation to endogenous processes and climatic factors. AGB increased steadily over the 26 years in all topographic units, but different tree species contributed to the increase among the topographic units. AGB gain within each topographic unit exceeded AGB loss via tree mortality in most of the measurement periods despite substantial temporal variation in AGB loss. At the local scale, variations in AGB gain were partially explained by compensating growth of trees around canopy gaps. Climate affected the local-scale AGB gain: the gain was larger in the measurement periods with higher mean air temperature during the current summer but smaller in those with higher mean air temperature during the previous autumn, synchronously in all topographic units. The influences of decadal summer and autumn warming on AGB growth appeared to be counteracting, suggesting that the observed steady AGB increase in KRRF is not fully explained by the warming. Future studies should consider global and regional environmental factors such as elevated CO₂ concentrations and nitrogen deposition, and include cool-temperate forests with a broader temperature range to improve our understanding on biomass accumulation in this type of forests under climate change.

     

Environmental and Biotic Controls over Aboveground Biomass Throughout a Tropical Rain Forest

The environmental and biotic factors affecting spatial variation in canopy three-dimensional (3-D) structure and aboveground tree biomass (AGB) are poorly understood in tropical rain forests. We combined field measurements and airborne light detection and ranging (lidar) to quantify 3-D structure and AGB across a 5,016 ha rain forest reserve on the northeastern flank of Mauna Kea volcano, Hawaii Island. We compared AGB among native stands dominated by Metrosideros polymorpha found along a 600–1800 m elevation/climate gradient, and on three substrate-age classes of 5, 20, and 65 kyr. We also analyzed how alien tree invasion, canopy species dominance and topographic relief influence AGB levels. Canopy vertical profiles derived from lidar measurements were strong predictors (r ² = 0.78) of AGB across sites and species. Mean AGB ranged from 48 to 363 Mg ha⁻¹ in native forest stands. Increasing elevation corresponded to a 53–84% decrease in AGB levels, depending upon substrate age. Holding climate constant, changes in substrate age from 5 to 65 kyr corresponded to a 23–53% decline in biomass. Invasion by Psidium cattleianum and Ficus rubiginosa trees resulted in a 19–38% decrease in AGB, with these carbon losses mediated by substrate age. In contrast, the spread of former plantation tree species Fraxinus uhdei corresponded to a 7- to 10-fold increase in biomass. The effects of topographic relief at both local and regional scales were evident in the AGB maps, with poorly drained terrain harboring 76% lower biomass than forests on well-drained relief. Our results quantify the absolute and relative importance of environmental factors controlling spatial variation in tree biomass across a rain forest landscape, and highlight the rapid changes in carbon storage incurred following biological invasion. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users. Author Contributions  GPA and RFH conceived of or designed the study. GPA, RFH, TAV, DEK, and TKB performed research and analyzed data. GPA, RFH, DEK, and TKB contributed new methods or models. GPA wrote the article.     

Effects of <Emphasis Type="Italic">Morella faya</Emphasis> tree invasion on aboveground carbon storage in Hawaii

The negative effects of biological invasion are often the focus of ecological studies, but few have considered potential positive impacts, such as increased carbon storage, resulting from invasion. We combined airborne imaging spectrometer and LiDAR (light detection and ranging) observations with field measurements to assess if the highly invasive nitrogen-fixing tree Morella faya alters canopy 3-D structure and aboveground biomass (AGB) along a 1,500 mm precipitation gradient in Hawaii. Airborne analysis of canopy water content, leaf nitrogen concentration, fractional canopy cover, and vegetation height facilitated mapping of native- and Morella-dominated canopies in rainforest, woodland–savanna and shrubland ecosystems, with Morella detection errors ranging from 0 to 13.4%. Allometric equations were developed to relate the combined LiDAR and spectral data to field-based AGB estimates (r ² = 0.97, P < 0.01), and to produce a map of biomass stocks throughout native and invaded ecosystems. The structure of the invasive Morella canopies varied by ecosystem type, and the invader shaded out native understory plants in rainforest zones. Despite a 350% increase in AGB going from shrubland to rainforest, Morella did not increase average AGB in any ecosystem it invaded. Furthermore, spatial distributions of biomass indicated that Morella decreased maximum AGB in the woodland–savanna ecosystems. We conclude that Morella tree invasion does not enhance aboveground carbon stocks in any ecosystem it invades in Hawaii, thereby minimizing its contribution to this potentially important ecosystem service. We also found that the fusion of spectral and LiDAR remote sensing provided canopy chemical and structural data facilitating a landscape assessment of how biological invasion alters on carbon stocks and other ecosystem properties.     

Using airborne laser scanning to model potential abundance and assemblages of forest passerines

Modelling and forecasting of the distribution and abundance of organisms using environmental variables is a major focus of applied ecological research. High-resolution airborne laser scanning is a recently developed remote-sensing method that provides data that can be used as surrogates for the vertical structure of the vegetation. These data can be used for modelling the occurrence and abundance of species or species assemblages. Until now, few studies evaluated the potential of these data for use in such models, or compared the suitability of data obtained by airborne systems with data gained by alternative methods. To fill part of this gap, we used forest passerine bird species to evaluate airborne laser scanning data for statistical modelling of potential bird abundances and composition of assemblages. Birds were counted in a mixed montane forest, on 223 1-ha plots along four transects. In the same period, these areas were scanned using Light Detection And Ranging (LiDAR) to characterise canopy structure. Additionally, we used visual interpretations of aerial photographs and field measurements on the same plots to derive habitat variables for comparison. We found clear correlations between the LiDAR variables and the other two variable sets using canonical correlation analysis. With a few exceptions, predictive power of the LiDAR data set for modelling abundances of single species, with up to 40% explained variance, was superior to that of the other two data sets. Models agreed with existing ecological knowledge for these species. For modelling of species composition with redundancy analysis, LiDAR was also superior to the other two data sets with more than 20% unique contribution to the explained variance. Our results clearly showed that LiDAR provides valuable data for describing and modelling single species as well as assemblages of forest organisms.     

Fusion of airborne LiDAR data and hyperspectral imagery for aboveground and belowground forest biomass estimation

Vegetation biomass is a key biophysical parameter for many ecological and environmental models. The accurate estimation of biomass is essential for improving the accuracy and applicability of these models. Light Detection and Ranging (LiDAR) data have been extensively used to estimate forest biomass. Recently, there has been an increasing interest in fusing LiDAR with other data sources for directly measuring or estimating vegetation characteristics. In this study, the potential of fused LiDAR and hyperspectral data for biomass estimation was tested in the middle Heihe River Basin, northwest China. A series of LiDAR and hyperspectral metrics were calculated to obtain the optimal biomass estimation model. To assess the prediction ability of the fused data, single and fused LiDAR and hyperspectral metrics were regressed against field-observed belowground biomass (BGB), aboveground biomass (AGB) and total forest biomass (TB). The partial least squares (PLS) regression method was used to reduce the multicollinearity problem associated with the input metrics. It was found that the estimation accuracy of forest biomass was affected by LiDAR plot size, and the optimal plot size in this study had a radius of 22 m. The results showed that LiDAR data alone could estimate biomass with a relative high accuracy, and hyperspectral data had lower prediction ability for forest biomass estimation than LiDAR data. The best estimation model was using a fusion of LiDAR and hyperspectral metrics (R² = 0.785, 0.893 and 0.882 for BGB, AGB and TB, respectively, with p < 0.0001). Compared with LiDAR metrics alone, the fused LiDAR and hyperspectral data improved R² by 5.8%, 2.2% and 2.6%, decreased AIC value by 1.9%, 1.1% and 1.2%, and reduced RMSE by 8.6%, 7.9% and 8.3% for BGB, AGB and TB, respectively. These results demonstrated that biomass accuracies could be improved by the use of fused LiDAR and hyperspectral data, although the improvement was slight when compared with LiDAR data alone. This slight improvement could be attributed to the complementary information contained in LiDAR and hyperspectral data. In conclusion, fusion of LiDAR and other remotely sensed data has great potential for improving biomass estimation accuracy.     

森林生物量遥感降尺度研究

森林生物量是评价全球碳氧平衡、气候变化的重要指标。目前已有基于星载激光雷达数据的全球森林生物量产品,但空间分辨率较低,不能很好地满足小区域森林调查和动态监测的需要。针对这一现状,以美国马里兰州两个森林分布状况不同的区域为研究区,基于CMS(Carbon Monitoring System)30 m分辨率和GEOCARBON 1 km分辨率森林地上生物量产品以及TM等数据源,通过升尺度模拟低分辨率生物量数据和直接使用低分辨率产品两种方式,分别尝试建立了多光谱地表参数和低分辨率森林地上生物量之间的统计关系,以此作为降尺度模型实现了森林地上生物量空间分辨率从1 km到30 m的转换,并对降尺度结果进行精度评价和误差分析。结果表明:模拟数据降尺度后的30 m分辨率森林地上生物量空间分布和CMS森林地上生物量分布状况大致相同,RMSE=59.2—65.5 Mg/hm~2,相关系数约为0.7;其降尺度结果优于GEOCARBON产品直接降尺度结果RMSE=75.3—79.9 Mg/hm~2;相较于线性模型,非线性模型能更好地呈现森林地上生物量和地表参数间的关系;总体上,降尺度生物量呈现高值区低估,低值区高估的现象。     

Quantifying Tropical Dry Forest Type and Succession: Substantial Improvement with LiDAR

Improved technologies are needed to advance our knowledge of the biophysical and human factors influencing tropical dry forests, one of the world's most threatened ecosystems. We evaluated the use of light detection and ranging (LiDAR) data to address two major needs in remote sensing of tropical dry forests, i.e., classification of forest types and delineation of forest successional status. We evaluated LiDAR‐derived measures of three‐dimensional canopy structure and subcanopy topography using classification‐tree techniques to separate different dry forest types and successional stages in the Guánica Biosphere Reserve in Puerto Rico. We compared the LiDAR‐based results with classifications made from commonly used remote sensing data, including Landsat satellite imagery and radar‐based topographic data. The accuracy of the LiDAR‐based forest type classification (including native‐ and exotic‐dominated forest classes) was substantially higher than those from previously available data (kappa = 0.90 and 0.63, respectively). The best result was obtained when combining LiDAR‐derived metrics of canopy structure and topography, and adding Landsat spectral data did not improve the classification. For the second objective, we observed that LiDAR‐derived variables of vegetation structure were better predictors of forest successional status (i.e., mid‐secondary, late‐secondary, and primary forests) than was spectral information from Landsat. Importantly, the key LiDAR predictors identified within each classification‐tree model agreed with previous ecological knowledge of these forests. Our study highlights the value of LiDAR remote sensing for assessing tropical dry forests, reinforcing the potential for this novel technology to advance research and management of tropical forests in general.     

Detecting successional changes in tropical forest structure using GatorEye drone-borne lidar

Drone-based remote sensing is a promising new technology that combines the benefits of ground-based and satellite-derived forest monitoring by collecting fine-scale data over relatively large areas in a cost-effective manner. Here, we explore the potential of the GatorEye drone-lidar system to monitor tropical forest succession by canopy structural attributes including canopy height, spatial heterogeneity, gap fraction, leaf area density (LAD) vertical distribution, canopy Shannon index (an index of LAD), leaf area index (LAI), and understory LAI. We focus on these variables’ relationship to aboveground biomass (AGB) stocks and species diversity. In the Caribbean lowlands of northeastern Costa Rica, we analyze nine tropical forests stands (seven second-growth and two old-growth). Stands were relatively homogenous in terms of canopy height and spatial heterogeneity, but not in their gap fraction. Neither species density nor tree community Shannon diversity index was significantly correlated with the canopy Shannon index. Canopy height, LAI, and AGB did not show a clear pattern as a function of forest age. However, gap fraction and spatial heterogeneity increased with forest age, whereas understory LAI decreased with forest age. Canopy height was strongly correlated with AGB. The heterogeneous mosaic created by successional forest patches across human-managed tropical landscapes can now be better characterized. Drone-lidar systems offer the opportunity to improve assessment of forest recovery and develop general mechanistic carbon sequestration models that can be rapidly deployed to specific sites, an essential step for monitoring progress within the UN Decade on Ecosystem Restoration.     

Forest aboveground biomass along an elevational transect in Sulawesi,Indonesia, and the role of Fagaceae in tropical montane rain forests

Aim This study investigates how estimated tree aboveground biomass (AGB) of tropical montane rain forests varies with elevation, and how this variation is related to elevational change in floristic composition, phylogenetic community structure and the biogeography of the dominant tree taxa. Location Lore Lindu National Park, Sulawesi, Indonesia. Methods Floristic inventories and stand structural analyses were conducted on 13 plots (each 0.24 ha) in four old‐growth forest stands at 1050, 1400, 1800 and 2400 m a.s.l. (submontane to upper montane elevations). Tree AGB estimates were based on d.b.h., height and wood specific gravity. Phylogenetic diversity and biogeographical patterns were analysed based on tree family composition weighted by AGB. Elevational trends in AGB were compared with other Southeast Asian and Neotropical transect studies (n = 7). Results AGB was invariant from sub‐ to mid‐montane elevation (309–301 Mg ha^?1) and increased slightly to 323 Mg ha^?1 at upper montane elevation. While tree and canopy height decreased, wood specific gravity increased. Magnoliids accounted for most of the AGB at submontane elevations, while eurosids I (including Fagaceae) contributed substantially to AGB at all elevations. Phylogenetic diversity was highest at upper montane elevations, with co‐dominance of tree ferns, Podocarpaceae, Trimeniaceae and asterids/euasterids II, and was lowest at lower/mid‐montane elevations, where Fagaceae contributed > 50% of AGB. Biogeographical patterns showed a progression from dominant tropical families at submontane to tropical Fagaceae (Castanopsis, Lithocarpus) at lower/mid‐montane, and to conifers and Australasian endemics at upper montane elevations. Cross‐continental comparisons revealed an elevational AGB decrease in transects with low/no presence of Fagaceae, but relatively high AGB in montane forests with moderate to high abundance of this family. Main conclusions AGB is determined by both changes in forest structure and shifts in species composition. In our study, these two factors traded off so that there was no net change in AGB, even though there were large changes in forest structure and composition along the elevational gradient. Southeast Asian montane rain forests dominated by Fagaceae constitute important carbon stocks. The importance of biogeography and species traits for biomass estimation should be considered by initiatives to reduce emissions from deforestation and forest degradation (REDD) and in taxon choice in reforestation for carbon offsetting.     

基于HJ1B和ALOS/PALSAR数据的森林地上生物量遥感估算

森林地上生物量的精确估算能够减小碳储量估算的不确定性。为了探寻一种有效地提高森林生物量估算精度的方法,探讨了基于遥感物理模型和经验统计模型估算山地森林地上生物量的方法。首先,基于Li-Strahler几何光学模型和多元前向模式(MFM)进行模型模拟,结合查找表算法(LUT)从多光谱图像HJ1B估算贺兰山研究区的森林地上生物量。其次,采用统计方法建立了2种回归模型:(1)多光谱图像HJ1B进行混合像元分解(SMA),并与雷达图像ALOS/PALSAR进行图像融合建立生物量回归模型;(2)雷达图像ALOS/PALSAR后向散射系数和实测生物量建立了生物量回归模型。用实测数据对3种算法估算结果进行精度验证。研究结果表明:采用几何光学模型和MFM算法估算的森林地上生物量精度最好(决定系数R2=0.61,均方根误差RMSE=8.33 t/hm2,P0.001),其估算地上生物量与实测值一致性较好,估算生物量精度略优于SMA估算的精度(R2=0.60,RMSE=9.417 t/hm2);ALOS/PALSAR多元回归估算的精度最差(R2=0.39,RMSE=14.89 t/hm2)。由此可见,采用几何光学模型和混合像元分解SMA适合估算森林地上生物量,利用这2种方法进行森林地上生物量遥感监测研究具有一定的应用潜力。     

Damage to living trees contributes to almost half of the biomass losses in tropical forests

Accurate estimates of forest biomass stocks and fluxes are needed to quantify global carbon budgets and assess the response of forests to climate change. However, most forest inventories consider tree mortality as the only aboveground biomass (AGB) loss without accounting for losses via damage to living trees: branchfall, trunk breakage, and wood decay. Here, we use ~151,000 annual records of tree survival and structural completeness to compare AGB loss via damage to living trees to total AGB loss (mortality + damage) in seven tropical forests widely distributed across environmental conditions. We find that 42% (3.62 Mg ha⁻¹ year⁻¹; 95% confidence interval [CI] 2.36–5.25) of total AGB loss (8.72 Mg ha⁻¹ year⁻¹; CI 5.57–12.86) is due to damage to living trees. Total AGB loss was highly variable among forests, but these differences were mainly caused by site variability in damage-related AGB losses rather than by mortality-related AGB losses. We show that conventional forest inventories overestimate stand-level AGB stocks by 4% (1%–17% range across forests) because assume structurally complete trees, underestimate total AGB loss by 29% (6%–57% range across forests) due to overlooked damage-related AGB losses, and overestimate AGB loss via mortality by 22% (7%–80% range across forests) because of the assumption that trees are undamaged before dying. Our results indicate that forest carbon fluxes are higher than previously thought. Damage on living trees is an underappreciated component of the forest carbon cycle that is likely to become even more important as the frequency and severity of forest disturbances increase.     

Net aboveground biomass declines of four major forest types with forest ageing and climate change in western Canada's boreal forests

Biomass change of the world's forests is critical to the global carbon cycle. Despite storing nearly half of global forest carbon, the boreal biome of diverse forest types and ages is a poorly understood component of the carbon cycle. Using data from 871 permanent plots in the western boreal forest of Canada, we examined net annual aboveground biomass change (ΔAGB) of four major forest types between 1958 and 2011. We found that ΔAGB was higher for deciduous broadleaf (DEC) (1.44 Mg ha^?1 year^?1, 95% Bayesian confidence interval (CI), 1.22–1.68) and early‐successional coniferous forests (ESC) (1.42, CI, 1.30–1.56) than mixed forests (MIX) (0.80, CI, 0.50–1.11) and late‐successional coniferous (LSC) forests (0.62, CI, 0.39–0.88). ΔAGB declined with forest age as well as calendar year. After accounting for the effects of forest age, ΔAGB declined by 0.035, 0.021, 0.032 and 0.069 Mg ha^?1 year^?1 per calendar year in DEC, ESC, MIX and LSC forests, respectively. The ΔAGB declines resulted from increased tree mortality and reduced growth in all forest types except DEC, in which a large biomass loss from mortality was accompanied with a small increase in growth. With every degree of annual temperature increase, ΔAGB decreased by 1.00, 0.20, 0.55 and 1.07 Mg ha^?1 year^?1 in DEC, ESC, MIX and LSC forests, respectively. With every cm decrease of annual climatic moisture availability, ΔAGB decreased 0.030, 0.045 and 0.17 Mg ha^?1 year^?1 in ESC, MIX and LSC forests, but changed little in DEC forests. Our results suggest that persistent warming and decreasing water availability have profound negative effects on forest biomass in the boreal forests of western Canada. Furthermore, our results indicate that forest responses to climate change are strongly dependent on forest composition with late‐successional coniferous forests being most vulnerable to climate changes in terms of aboveground biomass.     

The global relationship between forest productivity and biomass

Aim  We aim to determine the empirical relationship between above-ground forest productivity and biomass. There are theoretical reasons to assume a relationship between forest structure and function, as both may be influenced by similar ecological factors such as moisture supply. Also, dynamic global vegetation model simulations imply that any increase in forest productivity driven by climate change will result in increases in biomass and therefore carbon storage. However, few studies have explored the strength and form of the relationship between forest productivity and biomass, whether in space or time.
Location Global scale.
Methods  We collated a large data set of above-ground biomass (AGB) and above-ground net primary productivity (ANPP) and tested the extent to which spatial variation in forest biomass across the Earth can be predicted from forest productivity.
Results  The global ANPP–AGB relationship differs fundamentally from the strongly positive, linear relationship reported in earlier analyses, which mostly lacked tropical sites. AGB begins to peak at c . 15–20 Mg ha⁻¹ year⁻¹ ANPP, plateaus at ANPP > 20–25 Mg ha⁻¹ year⁻¹, and may actually decline at higher levels of production.
Main conclusions  High turnover rates in high-productivity forests may limit AGB by promoting the dominance of species with a low wood density. Predicted increases in ANPP will not necessarily favour increases in forest carbon storage, especially if changes in productivity are accompanied by compositional shifts.     

Effects of endogenous and exogenous processes on aboveground biomass stocks and dynamics in Andean forests

Tropical forests are paramount in regulating the global carbon cycle due to the storage of large amounts of carbon in their biomass. Using repeat censuses of permanent plots located at 15 sites in the Andes Mountains of northwest Colombia, we evaluate: (1) the relationship between aboveground biomass (AGB) stocks, AGB dynamics (mortality, productivity, and net change), and changes in temperature across a ca. 3000-m elevational gradient (≈?16.1 °C); (2) how AGB mortality and AGB productivity interact to determine net AGB change; and (3) the extent to which either fine-grain (0.04-ha) or coarse-grain (1-ha) processes determine the AGB dynamics of these forests. We did not find a significant relationship between elevation/temperature and biomass stocks. The net AGB sequestered each year by these forests (2.21?±?0.51 Mg ha^?1 year^?1), equivalent to approximately 1.09% of initial AGB, was primarily determined by tree growth. Both forest structural properties and global warming influenced AGB mortality and net change. AGB productivity increases with greater inequality of tree sizes, a pattern characteristic of forest patches recovering from disturbances. Overall, we find that global warming is triggering directional changes in species composition by thermophilization via increased tree mortality of species in the lower portions of their thermal ranges and that the inclusion of small-scale forest structural changes can effectively account for endogenous processes such as changes in forest structure. The inclusion of fine-grain processes in assessments of AGB dynamics could provide additional insights about the effects that ongoing climate change has on the functioning of tropical montane forests.     

Disentangling stand and environmental correlates of aboveground biomass in Amazonian forests

Tropical forests contain an important proportion of the carbon stored in terrestrial vegetation, but estimated aboveground biomass (AGB) in tropical forests varies two‐fold, with little consensus on the relative importance of climate, soil and forest structure in explaining spatial patterns. Here, we present analyses from a plot network designed to examine differences among contrasting forest habitats (terra firme, seasonally flooded, and white‐sand forests) that span the gradient of climate and soil conditions of the Amazon basin. We installed 0.5‐ha plots in 74 sites representing the three lowland forest habitats in both Loreto, Peru and French Guiana, and we integrated data describing climate, soil physical and chemical characteristics and stand variables, including local measures of wood specific gravity (WSG). We use a hierarchical model to separate the contributions of stand variables from climate and soil variables in explaining spatial variation in AGB. AGB differed among both habitats and regions, varying from 78 Mg ha^?1 in white‐sand forest in Peru to 605 Mg ha^?1 in terra firme clay forest of French Guiana. Stand variables including tree size and basal area, and to a lesser extent WSG, were strong predictors of spatial variation in AGB. In contrast, soil and climate variables explained little overall variation in AGB, though they did co‐vary to a limited extent with stand parameters that explained AGB. Our results suggest that positive feedbacks in forest structure and turnover control AGB in Amazonian forests, with richer soils (Peruvian terra firme and all seasonally flooded habitats) supporting smaller trees with lower wood density and moderate soils (French Guianan terra firme) supporting many larger trees with high wood density. The weak direct relationships we observed between soil and climate variables and AGB suggest that the most appropriate approaches to landscape scale modeling of AGB in the Amazon would be based on remote sensing methods to map stand structure.