Knowledge of peatland initiation, accumulation, and decline or cessation is critical in understanding peatland development and the related carbon source/sink effect. In this study, we investigated the development of three peat profiles along the eastern margin of the Tibetan Plateau (ETP) and compared the results with those of our previous work along this transect. Our work showed that the initiation over the northern ETP is later and the slowdown/cessation earlier than in the middle to southern ETP. The timing of optimum peatland formation over the northern ETP lags the Holocene climatic optimum. These spatio-temporal differences are likely to be related to the intensity of Asian summer monsoon. Our work suggests that some peatlands along the ETP transect have returned or are now returning their previously captured carbon to the atmosphere and thus act as carbon sources. Some peatlands still have net accumulation at present, but the rates have been reduced concomitant with the decreasing summer monsoon intensity. We speculate that more of the previously stored carbon in the ETPpeatlands will be re-emitted to the atmosphere if the aridity continues, as might occur under a continuous global-warming scenario.
To estimate the synergistic emission reduction effect resulting from carbon emissions trading scheme (ETS) pilots launched in 2013, this study estimated the synergistic emission reduction relationship between carbon dioxide (CO2) and atmospheric pollutants, consisting of sulfur dioxide (SO2), nitrogen oxides (NOX), dust pollutants (Dust) and particulate matter 2.5 (PM2.5). Using the extended logarithmic mean Divisia index (LMDI) method and the IPAT equation, the synergistic emission reduction effect was decomposed into direct and indirect categories driven by energy efficiency, economic development and industrial structure. Moreover, the synergistic emission reduction effect of ETS pilots was quantified with the difference-in-differences method (DID) and propensity score matching difference-in-differences method (PSM-DID). The results show that, from 2013 to 2016, CO2 and atmospheric pollutants achieved emission reduction synergistically through ETS, among which the synergistic emission reduction effect between CO2 and SO2 was most significant. Compared with the direct category, the indirect category accounted for smaller proportion of the synergistic emission reduction effect. The combined action of energy efficiency and industrial structure has a potential positive influence on synergistic emission reduction effect of ETS. Consequently, this suggests that the government needs to develop the domestic carbon market further, improve energy efficiency and optimize industrial structure to promote synergistic emission reduction.
Background
Pyropia is an economically advantageous genus of red macroalgae, which has been cultivated in the coastal areas of East Asia for over 300 years. Realizing estimation of macroalgae biomass in a high-throughput way would great benefit their cultivation management and research on breeding and phenomics. However, the conventional method is labour-intensive, time-consuming, manually destructive, and prone to human error. Nowadays, high-throughput phenotyping using unmanned aerial vehicle (UAV)-based spectral imaging is widely used for terrestrial crops, grassland, and forest, but no such application in marine aquaculture has been reported.
Results
In this study, multispectral images of cultivated Pyropia yezoensis were taken using a UAV system in the north of Haizhou Bay in the midwestern coast of Yellow Sea. The exposure period of P. yezoensis was utilized to prevent the significant shielding effect of seawater on the reflectance spectrum. The vegetation indices of normalized difference vegetation index (NDVI), ratio vegetation index (RVI), difference vegetation index (DVI) and normalized difference of red edge (NDRE) were derived and indicated no significant difference between the time that P. yezoensis was completely exposed to the air and 1 h later. The regression models of the vegetation indices and P. yezoensis biomass per unit area were established and validated. The quadratic model of DVI (Biomass = − 5.550DVI2 + 105.410DVI + 7.530) showed more accuracy than the other index or indices combination, with the highest coefficient of determination (R2), root mean square error (RMSE), and relative estimated accuracy (Ac) values of 0.925, 8.06, and 74.93%, respectively. The regression model was further validated by consistently predicting the biomass with a high R2 value of 0.918, RMSE of 8.80, and Ac of 82.25%.
Conclusions
This study suggests that the biomass of Pyropia can be effectively estimated using UAV-based spectral imaging with high accuracy and consistency. It also implied that multispectral aerial imaging is potential to assist digital management and phenomics research on cultivated macroalgae in a high-throughput way.
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