Comparison of forest above‐ground biomass from dynamic global vegetation models with spatially explicit remotely sensed observation‐based estimates

Gaps in our current understanding and quantification of biomass carbon stocks, particularly in tropics, lead to large uncertainty in future projections of the terrestrial carbon balance. We use the recently published GlobBiomass data set of forest above‐ground biomass (AGB) density for the year 2010, obtained from multiple remote sensing and in situ observations at 100 m spatial resolution to evaluate AGB estimated by nine dynamic global vegetation models (DGVMs). The global total forest AGB of the nine DGVMs is 365 ± 66 Pg C, the spread corresponding to the standard deviation between models, compared to 275 Pg C with an uncertainty of ~13.5% from GlobBiomass. Model‐data discrepancy in total forest AGB can be attributed to their discrepancies in the AGB density and/or forest area. While DGVMs represent the global spatial gradients of AGB density reasonably well, they only have modest ability to reproduce the regional spatial gradients of AGB density at scales below 1000 km. The 95th percentile of AGB density (AGB₉₅) in tropics can be considered as the potential maximum of AGB density which can be reached for a given annual precipitation. GlobBiomass data show local deficits of AGB density compared to the AGB₉₅, particularly in transitional and/or wet regions in tropics. We hypothesize that local human disturbances cause more AGB density deficits from GlobBiomass than from DGVMs, which rarely represent human disturbances. We then analyse empirical relationships between AGB density deficits and forest cover changes, population density, burned areas and livestock density. Regression analysis indicated that more than 40% of the spatial variance of AGB density deficits in South America and Africa can be explained; in Southeast Asia, these factors explain only ~25%. This result suggests TRENDY v6 DGVMs tend to underestimate biomass loss from diverse and widespread anthropogenic disturbances, and as a result overestimate turnover time in AGB.     

基于MODIS数据的松嫩平原西部芦苇湿地地上生物量遥感估算

芦苇作为湿地生态系统中重要的群落类型,其地上生物量是衡量湿地生态系统质量的关键指标。应用面向对象的土地覆盖分类技术,基于多季相Landsat8 OLI遥感数据,提取松嫩平原西部芦苇湿地分布信息;依托野外实测芦苇地上生物量数据(AGB)和同期MODIS数据源的NDVI、EVI、RVI、MSAVI和WDVI 5种光谱植被指数,探讨不同光谱植被指数对芦苇AGB的敏感性,进而构建松嫩平原西部芦苇AGB遥感估算最优模型,并进行芦苇AGB遥感反演及空间格局分析。结果表明:2014年松嫩平原西部地区芦苇总面积为1653 km~2,其中扎龙湿地自然保护区内芦苇分布面积最大(1178km~2),占区域芦苇总面积的71.3%;所选取的5种植被指数均与芦苇AGB呈极显著正相关(P0.01),基于EVI构建的指数曲线模型为松嫩平原西部芦苇AGB反演的最优模型(R2=0.55)。研究区芦苇平均AGB为372.1g/m~2,AGB总量为6.14×105t,其中扎龙湿地自然保护区内芦苇AGB总量为4.38×105t;各保护区芦苇平均AGB由大到小依次为:向海保护区(469.7 g/m~2)大布苏保护区(454.1 g/m~2)莫莫格保护区(373.0 g/m~2)扎龙保护区(372.4 g/m~2)查干湖保护区(369.8 g/m~2);松嫩平原西部芦苇AGB总体呈现南高北低的分布格局,将为湿地生态系统管理与保护及芦苇资源的合理利用提供科学依据。     

基于高分辨率遥感影像的森林地上生物量估算

以南京市紫金山林区为研究区,利用e-Cognition面向对象分类方法,基于光谱和空间信息融合后的IKONOS影像提取单木树冠阳性冠幅(PoCA, Positive crown area)信息,并结合野外实测的样方生物量数据,分别建立了针叶林和阔叶林地上生物量 (AGB, Aboveground Biomass)的遥感估算模型,并利用实测森林生物量数据对模型进行了验证。结果表明,基于遥感影像提取的PoCA与实测AGB存在较好的非线性相关关系,所建针叶林AGB估算模型的可靠性优于阔叶林模型。对建模样本而言,估算的针叶林和阔叶林AGB与观测数据比较的R2分别为0.62 (P<0.01,n=9) 和0.56(P<0.01,n=16)。验证表明,所建AGB估算模型的可靠性较好,估算的针叶林和阔叶林AGB与观测数据比较的R2分别为0.55(P<0.01,n=6) 和0.52(P<0.01,n=10),但当AGB较低时,模型结果偏高,AGB较低时,模型结果偏低。研究说明通过高分辨率遥感数据的融合、提取树冠信息进行生物量估算是可行性的。     

Inter‐relationships between the heterotrimeric Gβ subunit AGB1, the receptor‐like kinase FERONIA,and RALF1 in salinity response

Plant heterotrimeric G proteins modulate numerous developmental stress responses. Recently, receptor‐like kinases (RLKs) have been implicated as functioning with G proteins and may serve as plant G‐protein‐coupled‐receptors. The RLK FERONIA (FER), in the Catharantus roseus RLK1‐like subfamily, is activated by a family of polypeptides called rapid alkalinization factors (RALFs). We previously showed that the Arabidopsis G protein β subunit, AGB1, physically interacts with FER, and that RALF1 regulation of stomatal movement through FER requires AGB1. Here, we investigated genetic interactions of AGB1 and FER in plant salinity response by comparing salt responses in the single and double mutants of agb1 and fer. We show that AGB1 and FER act additively or synergistically depending on the conditions of the NaCl treatments. We further show that the synergism likely occurs through salt‐induced ROS production. In addition, we show that RALF1 enhances salt toxicity through increasing Na⁺ accumulation and decreasing K⁺ accumulation rather than by inducing ROS production, and that the RALF1 effect on salt response occurs in an AGB1‐independent manner. Our results indicate that RLK epistatic relationships are not fixed, as AGB1 and FER display different genetic relationships to RALF1 in stomatal versus salinity responses.     

Variation in wood density determines spatial patterns inAmazonian forest biomass

Uncertainty in biomass estimates is one of the greatest limitations to models of carbon flux in tropical forests. Previous comparisons of field‐based estimates of the aboveground biomass (AGB) of trees greater than 10 cm diameter within Amazonia have been limited by the paucity of data for western Amazon forests, and the use of site‐specific methods to estimate biomass from inventory data. In addition, the role of regional variation in stand‐level wood specific gravity has not previously been considered. Using data from 56 mature forest plots across Amazonia, we consider the relative roles of species composition (wood specific gravity) and forest structure (basal area) in determining variation in AGB. Mean stand‐level wood specific gravity, on a per stem basis, is 15.8% higher in forests in central and eastern, compared with northwestern Amazonia. This pattern is due to the higher diversity and abundance of taxa with high specific gravity values in central and eastern Amazonia, and the greater diversity and abundance of taxa with low specific gravity values in western Amazonia. For two estimates of AGB derived using different allometric equations, basal area explains 51.7% and 63.4%, and stand‐level specific gravity 45.4% and 29.7%, of the total variation in AGB. The variation in specific gravity is important because it determines the regional scale, spatial pattern of AGB. When weighting by specific gravity is included, central and eastern Amazon forests have significantly higher AGB than stands in northwest or southwest Amazonia. The regional‐scale pattern of species composition therefore defines a broad gradient of AGB across Amazonia.     

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。     

Forest disturbance and recovery in Peruvian Amazonia

Amazonian forests function as biomass and biodiversity reservoirs, contributing to climate change mitigation. While they continuously experience disturbance, the effect that disturbances have on biomass and biodiversity over time has not yet been assessed at a large scale. Here, we evaluate the degree of recent forest disturbance in Peruvian Amazonia and the effects that disturbance, environmental conditions and human use have on biomass and biodiversity in disturbed forests. We integrate tree-level data on aboveground biomass (AGB) and species richness from 1840 forest plots from Peru's National Forest Inventory with remotely sensed monitoring of forest change dynamics, based on disturbances detected from Landsat-derived Normalized Difference Moisture Index time series. Our results show a clear negative effect of disturbance intensity tree species richness. This effect was also observed on AGB and species richness recovery values towards undisturbed levels, as well as on the recovery of species composition towards undisturbed levels. Time since disturbance had a larger effect on AGB than on species richness. While time since disturbance has a positive effect on AGB, unexpectedly we found a small negative effect of time since disturbance on species richness. We estimate that roughly 15% of Peruvian Amazonian forests have experienced disturbance at least once since 1984, and that, following disturbance, have been increasing in AGB at a rate of 4.7 Mg ha⁻¹ year⁻¹ during the first 20 years. Furthermore, the positive effect of surrounding forest cover was evident for both AGB and its recovery towards undisturbed levels, as well as for species richness. There was a negative effect of forest accessibility on the recovery of species composition towards undisturbed levels. Moving forward, we recommend that forest-based climate change mitigation endeavours consider forest disturbance through the integration of forest inventory data with remote sensing methods.     

Above-ground biomass references for urban trees from terrestrial laser scanning data

Background and AimsWithin extending urban areas, trees serve a multitude of functions (e.g. carbon storage, suppression of air pollution, mitigation of the ‘heat island’ effect, oxygen, shade and recreation). Many of these services are positively correlated with tree size and structure. The quantification of above-ground biomass (AGB) is of especial importance to assess its carbon storage potential. However, quantification of AGB is difficult and the allometries applied are often based on forest trees, which are subject to very different growing conditions, competition and form. In this article we highlight the potential of terrestrial laser scanning (TLS) techniques to extract highly detailed information on urban tree structure and AGB.MethodsFifty-five urban trees distributed over seven cities in Switzerland were measured using TLS and traditional forest inventory techniques before they were felled and weighed. Tree structure, volume and AGB from the TLS point clouds were extracted using quantitative structure modelling. TLS-derived AGB estimates were compared with AGB estimates based on forest tree allometries dependent on diameter at breast height only. The correlations of various tree metrics as AGB predictors were assessed.Key ResultsEstimates of AGB derived by TLS showed good performance when compared with destructively harvested references, with an R² of 0.954 (RMSE = 556 kg) compared with 0.837 (RMSE = 1159 kg) for allometrically derived AGB estimates. A correlation analysis showed that different TLS-derived wood volume estimates as well as trunk diameters and tree crown metrics show high correlation in describing total wood AGB, outperforming tree height.ConclusionsWood volume estimates based on TLS show high potential to estimate tree AGB independent of tree species, size and form. This allows us to retrieve highly accurate non-destructive AGB estimates that could be used to establish new allometric equations without the need for extensive destructive harvesting.     

Arabidopsis G‐protein interactome reveals connections to cell wall carbohydrates and morphogenesis

The heterotrimeric G‐protein complex is minimally composed of Gα, Gβ, and Gγ subunits. In the classic scenario, the G‐protein complex is the nexus in signaling from the plasma membrane, where the heterotrimeric G‐protein associates with heptahelical G‐protein‐coupled receptors (GPCRs), to cytoplasmic target proteins called effectors. Although a number of effectors are known in metazoans and fungi, none of these are predicted to exist in their canonical forms in plants. To identify ab initio plant G‐protein effectors and scaffold proteins, we screened a set of proteins from the G‐protein complex using two‐hybrid complementation in yeast. After deep and exhaustive interrogation, we detected 544 interactions between 434 proteins, of which 68 highly interconnected proteins form the core G‐protein interactome. Within this core, over half of the interactions comprising two‐thirds of the nodes were retested and validated as genuine in planta. Co‐expression analysis in combination with phenotyping of loss‐of‐function mutations in a set of core interactome genes revealed a novel role for G‐proteins in regulating cell wall modification.     

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.