Temporal and Spatial Dynamics of Carbon Fixation by Moso Bamboo (Phyllostachys pubescens) in Subtropical China

To study the temporal and spatial dynamics of carbon fixation by Moso bamboo (Phyllostachys pubescens) in subtropical China, carbon fixation of leaves within the canopy of P. pubescens was measured with a LI-6400 portable photosynthesis system. The results showed that the capability of carbon fixation of P. pubescens leaves had obvious temporal and spatial dynamic variations. It was revealed that there were two peak periods and two low periods in the season variation of carbon fixation capability. Data also revealed that the capability of carbon fixation by five-year-old P. pubescens was more than that of one-year-old and three-year-old. Daily and seasonal carbon fixation showed a negative correlation with the CO2 concentration. The temporal and spatial dynamics of carbon fixation by P. pubescens described above provided a scientific basis for development of technologies in bamboo timber production

Improving Estimations of Spatial Distribution of Soil Respiration Using the Bayesian Maximum Entropy Algorithm and Soil Temperature as Auxiliary Data

This study was supported by the NSF China Programs (Grant No. 31300539 and 31570629) and the Public Welfare Technology Application Research Program of Zhejiang province (Grant No. 2015C31004).Soil respiration inherently shows strong spatial variability. It is difficult to obtain an accurate characterization of soil respiration with an insufficient number of monitoring points. However, it is expensive and cumbersome to deploy many sensors. To solve this problem, we proposed employing the Bayesian Maximum Entropy (BME) algorithm, using soil temperature as auxiliary information, to study the spatial distribution of soil respiration. The BME algorithm used the soft data (auxiliary information) effectively to improve the estimation accuracy of the spatiotemporal distribution of soil respiration. Based on the functional relationship between soil temperature and soil respiration, the BME algorithm satisfactorily integrated soil temperature data into said spatial distribution. As a means of comparison, we also applied the Ordinary Kriging (OK) and Co-Kriging (Co-OK) methods. The results indicated that the root mean squared errors (RMSEs) and absolute values of bias for both Day 1 and Day 2 were the lowest for the BME method, thus demonstrating its higher estimation accuracy. Further, we compared the performance of the BME algorithm coupled with auxiliary information, namely soil temperature data, and the OK method without auxiliary information in the same study area for 9, 21, and 37 sampled points. The results showed that the RMSEs for the BME algorithm (0.972 and 1.193) were less than those for the OK method (1.146 and 1.539) when the number of sampled points was 9 and 37, respectively. This indicates that the former method using auxiliary information could reduce the required number of sampling points for studying spatial distribution of soil respiration. Thus, the BME algorithm, coupled with soil temperature data, can not only improve the accuracy of soil respiration spatial interpolation but can also reduce the number of sampling points.Yeshttp://www.plosone.org/static/editorial#pee

Carbon Footprint Analysis of Bamboo Scrimber Flooring—Implications for Carbon Sequestration of Bamboo Forests and Its Products

Bamboo forest is characterized by large carbon sequestration capability and it plays an important role in mitigating climate change and global carbon cycling. Previous studies have mostly focused on carbon cycling and carbon stocks in bamboo forest ecosystems, whereas the carbon footprints of bamboo products have not received attention. China is the largest exporting country of bamboo flooring in the world. Estimating the carbon footprint of bamboo flooring is of essential importance for the involved enterprises and consumers to evaluate their own carbon footprints. In this study, we investigated the production processes of bamboo scrimber flooring for outdoor use, a typical bamboo flooring in China. Based on business-to-business (B2B) evaluation method, we assessed CO2 emission and carbon transfer ratio in each step of the production process, including transporting bamboo culms and producing and packing the products. We found that to produce 1 m3 of bamboo scrimber flooring, direct carbon emissions from fossil fuels during transporting raw materials/semi-finished products, from power consumptions during production, and indirect emissions from applying additives were 30.94 kg CO2 eq, 143.37 kg CO2 eq, and 78.34 kg CO2 eq, respectively. After subtracting the 267.54 kg CO2 eq carbon stocks in the product from the 252.65 kg CO2 eq carbon emissions derived within the defined boundary, we found that the carbon footprint of 1 m3 bamboo scrimber flooring was −14.89 kg CO2 eq. Our results indicated that the bamboo scrimber flooring is a negative carbon-emission product. Finally, we discussed factors that influence the carbon footprint of the bamboo flooring and gave suggestions on carbon emission reduction during production processes. This study provided a scientific basis for estimating carbon stocks and carbon footprints of bamboo products and further expanded knowledge on carbon cycling and lifespan of carbon in the bamboo forest ecosystem

Characteristics of Aerosol Optical Thickness as Well as the Relationship with NDVI in the Yangtze River Delta, China

A long term (2000 - 2008) dataset of aerosol products retrieved from MODerate Resolution Imaging Spectroradiometer (MODIS) is used to investigate the temporal and spatial variations of aerosol optical thickness (AOT) over the Yangtze River Delta (YRD). Ground-based observations (sun photometers) were used to validate the MODIS L2 aerosol products, and the Normalized Difference Vegetation Index (NDVI) derived from SPOT-VGT is used to analyze the relationship with AOT. The results illustrate that the highest correlation (R = 0.92) of CE-318 AOT vs. MODIS AOT is in Hangzhou with the slope and intercept of 1.04 and -0.01. The largest value of AOT appears in June, and has the largest spatial differences in July. There are two different aerosol patterns. One includes smaller AOT over dense vegetation cover areas dominated by natural aerosols and the other involves larger AOT over urban areas where anthropogenic aerosols dominate. The correlation analysis indicates that there are moderate negative correlation coefficients (R) for AOT vs. NDVI, and the R values vary as seasons shift. The range of R values is 0 ~ -0.8 and the mean values are about -0.5. The relationship between AOT and NDVI is more obvious in dense vegetation areas

Preliminary Application of Ground-Penetrating Radar for Reconstruction of Root System Architecture in Moso Bamboo

Root system architecture (RSA) refers to the geometric features and topology of the root system. Ground-penetrating radar (GPR) is a possible method of RSA reconstruction. However, because the topology of the root system is not directly accessible by GPR, GPR-based reconstruction must be complemented by manual connection of root points, resulting in limited accuracy. In this study, we used both GPR and direct excavation to obtain 3D coordinates (XYZ coordinates) and diameters of moso bamboo rhizomes on an orthogonal grid. A score function for selecting the best-connected root points was developed using rhizome diameter, depth, extension angle, and measured line spacing, which was then used to recover the topology of discrete root points. Based on the recovered topology, the 3D RSA of the rhizomes was reconstructed using a smoothing function. Based on the excavation data, the reconstructed RSA was generally consistent with the measured RSA, with 78.13% of root points correctly connected. The reconstructed RSA based on GPR data thus provided a rough approximation of the measured RSA, with errors arising due to missing root points and rhizome displacement. The proposed algorithm for reconstructing 3D RSA further enriches the application of ground-penetrating radar to root detection

Influence of Scale Effect of Canopy Projection on Understory Microclimate in Three Subtropical Urban Broad-Leaved Forests

The canopy is the direct receiver and receptor of external environmental variations, and affects the microclimate and energy exchange between the understory and external environment. After autumn leaf fall, the canopy structure of different forests shows remarkable variation, causes changes in the microclimate and is essential for understory vegetation growth. Moreover, the microclimate is influenced by the scale effect of the canopy. However, the difference in influence between different forests remains unclear on a small scale. In this study, we aimed to analyze the influence of the scale effect of canopy projection on understory microclimate in three subtropical broad-leaved forests. Three urban forests: evergreen broad-leaved forest (EBF), deciduous broad-leaved forest (DBF), and mixed evergreen and deciduous broad-leaved forest (MBF) were selected for this study. Sensors for environmental monitoring were used to capture the microclimate data (temperature (T), relative humidity (RH), and light intensity (LI)) for each forest. Terrestrial laser scanning was employed to obtain the canopy projection intensity (CPI) at each sensor location. The results indicate that the influence range of canopy projection on the microclimate was different from stand to stand (5.5, 5, and 3 m). Moreover, there was a strong negative correlation between T and RH, and the time for T and LI to reach a significant correlation in different urban forests was different, as well as the time for RH and LI during the day. Finally, the correlation between CPI and the microclimate showed that canopy projection had the greatest effect on T and RH in MBF, followed by DBF and EBF. In conclusion, our findings confirm that canopy projection can significantly affect understory microclimate. This study provides a reference for the conservation of environmentally sensitive organisms for urban forest management

Soil respiration of a Moso bamboo forest significantly affected by gross ecosystem productivity and leaf area index in an extreme drought event

Moso bamboo has large potential to alleviate global warming through carbon sequestration. Since soil respiration (R-s) is a major source of CO2 emissions, we analyzed the dynamics of soil respiration (R-s) and its relation to environmental factors in a Moso bamboo (Phllostachys heterocycla cv. pubescens) forest to identify the relative importance of biotic and abiotic drivers of respiration. Annual average R(s )was 44.07 t CO2 ha(-1) a(-1) R-s correlated significantly with soil temperature (P <0.01), which explained 69.7% of the variation in R-s at a diurnal scale. Soil moisture was correlated significantly with R-s on a daily scale except not during winter, indicating it affected R-s. A model including both soil temperature and soil moisture explained 93.6% of seasonal variations in R-s. The relationship between R-s and soil temperature during a day showed a clear hysteresis. R-s was significantly and positively (P <0.01) related to gross ecosystem productivity and leaf area index, demonstrating the significance of biotic factors as crucial drivers of R-s.Peer reviewe

The phytolith carbon sequestration in terrestrial ecosystems: the underestimated potential of bamboo forest

Abstract Background Terrestrial ecosystems contain significant carbon storage, vital to the global carbon cycle and climate change. Alterations in human production activities and environmental factors affect the stability of carbon storage in soil. Carbon sequestration in plant phytoliths offers a sustainable method for long-term carbon stabilization. Carbon occluded in phytoliths (PhytOC) is a kind of carbon that can be stable and not decomposed for a long time, so it is crucial to conduct more in-depth research on it. Results We undertook a meta-analysis on PhytOC across global terrestrial ecosystems, analyzing 60 articles, encapsulating 534 observations. We observed notable differences in phytolith and PhytOC contents across various ecosystems. Bamboo forest ecosystems exhibited the highest vegetation phytolith and PhytOC content, while soil phytolith content was most prominent in bamboo forests and PhytOC content in croplands. Human activities, such as grassland grazing, had a lesser impact on soil PhytOC transport than actions like cutting and tillage in croplands and forests. Our study separated bamboo ecosystems, analyzing their PhytOC content and revealing an underestimation of their carbon sink capacity. Conclusions Notwithstanding our findings, phytoliths’ intricate environmental interactions warrant further exploration, crucial for refining ecosystem management and accurately estimating PhytOC stocks. This deepened understanding lays the foundation for studying phytoliths and the carbon sink dynamics