Fine root and aboveground carbon stocks in riparian forests: the roles of diking and environmental gradients

Patterns of carbon (C) allocation across different stages of stand development in Eucalyptus urophylla × E. grandis plantations are not well understood. In this study, we examined biomass and mineral soil C content in five development stages (1, 2, 3, 4-5, and 6-8 years old) of a Eucalyptus stand in southern China. The tree biomass C pool increased with stand age and showed a high annual rate of accumulation. Stems accounted for the highest proportion of biomass C sequestered. The C pool in mineral soil increased initially after afforestation and then declined gradually, with C density decreasing with soil depth. The upper 50 cm of soil contained the majority (57%-68%) of sequestered C. The other biomass components (shrubs, herbaceous plants, litter, and fine roots) accounted for <5% of the total ecosystem C pool. Total C pools in the Eucalyptus plantation ecosystem were 112.9, 172.5, 203.8, 161.1, in the five developmental stages, respectively, with most of the C sequestered below ground. We conclude that Eucalyptus plantations have considerable biomass C sequestration potential during stand development. OPEN ACCESSForests 2015, 6 1764

Section: Introductionmentioning

confidence: 99%

Carbon Storage in a Eucalyptus Plantation Chronosequence in Southern China

Zeng

Peng

et al. 2015

“…In order to accurately estimate the root biomass and correct the fine root loss caused by digging, we used soil coring to supplement the fine root biomass [29]. A representative root sample was extracted from soil cores of 30 cm in length and 1.8 cm in diameter (76 cm 3 ).…”

Section: Plot Measurementsmentioning

confidence: 99%

Biometric and Eddy Covariance Methods for Examining the Carbon Balance of a Larix principis-rupprechtii Forest in the Qinling Mountains, China

Yuan

Jose

et al. 2018

Abstract:The carbon balance of forests is controlled by many component processes of carbon acquisition and carbon loss and depends on the age of vegetation, soils, species composition, and the local climate. Thus, examining the carbon balance of different forests around the world is necessary to understand the global carbon balance. Nevertheless, the available information on the carbon balance of Larix principis-rupprechtii forests in the Qinling Mountains remains considerably limited. We provide the first set of results (2010-2013) from a long-term project measuring forest-atmosphere exchanges of CO 2 at the Qinling National Forest Ecosystem Research Station (QNFERS), and compare the net ecosystem exchange (NEE) based on biometric measurements with those observed via the eddy covariance method. We also compare the total ecosystem respiration via scaled-up chamber and eddy covariance measurements. The net primary productivity (NPP) was 817.16 ± 81.48 g·C·m −2 ·y −1 , of which ∆B living and D total accounted for 77.7%, and 22.3%, respectively. Total ecosystem respiration was 814.47 ± 64.22 g·C·m −2 ·y −1 , and cumulative annual soil respiration, coarse woody debris respiration, stem respiration, and leaf respiration were 715.47 ± 28.48, 15.41 ± 1.72, 35.28 ± 4.78, and 48.31 ± 5.24 g·C·m −2 ·y −1 , respectively, accounting for 87.85%, 1.89%, 4.33%, and 5.93% of the total ecosystem respiration. A comparison between ecosystem respiration from chamber measurements and that from eddy covariance measurements showed a strong linear correlation between the two methods (R 2 = 0.93). The NEE of CO 2 between forests and the atmosphere measured by eddy covariance was −288.33 ± 25.26 g·C·m −2 ·y −1 , which revealed a carbon sink in the L. principis-rupprechtii forest. This number was 14% higher than the result from the biometric measurements (−336.71 ± 25.15 g·C·m −2 ·y −1 ). The study findings provided a cross-validation of the CO 2 exchange measured via biometric and eddy covariance, which are beneficial for obtaining the true ecosystem fluxes, and more accurately evaluating carbon budgets.

“…The point selection based on a stratified sampling design, described in detail by Cierjacks et al [41] and Rieger et al [44], who also measured and calculated C org content of soil and vegetation for each sample point. In a nutshell, soil samples were extracted from 0-100 cm using an auger, with each soil horizon was sampled separately and carbonate concentration and the concentration of C org were determined.…”

Section: Datamentioning

confidence: 99%

Large-Scale Mapping of Carbon Stocks in Riparian Forests with Self-Organizing Maps and the k-Nearest-Neighbor Algorithm

Suchenwirth

Stümer²,

Schmidt

et al. 2014

Self Cite

Among the machine learning tools being used in recent years for environmental applications such as forestry, self-organizing maps (SOM) and the k-nearest neighbor (kNN) algorithm have been used successfully. We applied both methods for the mapping of organic carbon (C org ) in riparian forests due to their considerably high carbon storage capacity. Despite the importance of floodplains for carbon sequestration, a sufficient scientific foundation for creating large-scale maps showing the spatial C org distribution is still missing. We estimated organic carbon in a test site in the Danube Floodplain based on RapidEye remote sensing data and additional geodata. Accordingly, carbon distribution maps of vegetation, soil, and total C org stocks were derived. Results were compared and statistically evaluated with terrestrial survey data for outcomes with pure remote sensing data and for the combination with additional geodata using bias and the Root Mean Square Error (RMSE). Results show that SOM and kNN approaches enable us to reproduce spatial patterns of riparian forest C org stocks. While vegetation C org has very high RMSEs, outcomes for soil and total C org stocks are less biased with a lower RMSE, especially when remote sensing and additional geodata are conjointly applied. SOMs show similar percentages of RMSE to kNN estimations. OPEN ACCESSForests 2014, 5 1636