Preliminary report on methane emissions from the Three Gorges Reservoir in the summer drainage period

Recently reported summertime methane （CH4） emissions （6.7 ± 13.3 mg CH4/（m2·hr）） from newly created marshes in the drawdown area of the Three Gorges Reservoir （TGR）, China have triggered broad concern in academic circles and among the public. The CH4 emissions from TGR water surfaces and drawdown areas were monitored from 3rd June to 16th October 2010 with floating and static chambers and gas chromatography. The average CH4 emission flux from permanently flooded areas in Zigui, Wushan and Yunyang Counties was （0.33 ± 0.09） mg CH4/（m2·hr）. In half of these hottest months of the year, the wilderness, cropland and deforested drawdown sites were aerobic and located above water level, and the CH4 emissions were very small, ranging from a sink at 0.12 mg CH4/（m2·hr） to a source at 0.08 mg CH4/（m2·hr） except for one mud-covered site after flood. Mean CH4 emission in flooded drawdown sites was 0.34 mg CH4/（m2·hr）. The emissions from the rice paddy sites in the drawdown area were averaged at （4.86 ± 2.31） mg CH4/（m2·hr）. Excepting the rice-paddy sites, these results show much lower emission levels than previously reported. Our results indicated considerable spatial and temporal variation in CH4 emissions from the TGR. Human activities and occasional events, such as flood, may also affect emission levels. Long-term CH4 measurements and modeling in a large region are necessary to accurately estimate greenhouse gas emissions from the TGR.

Soil warming effect on net ecosystem exchange of carbon dioxide during the transition from winter carbon source to spring carbon sink in a temperate urban lawn

The significant warming in urban environment caused by the combined effects of global warming and heat island has stimulated widely development of urban vegetations. However, it is less known of the climate feedback of urban lawn in warmed environment. Soil warming effect on net ecosystem exchange （NEE） of carbon dioxide during the transition period from winter to spring was investigated in a temperate urban lawn in Beijing, China. The NEE （negative for uptake） under soil warming treatment （temperature was about 5~C higher than the ambient treatment as a control） was -0.71 ~mol/（m2.sec）, the ecosytem was a CO2 sink under soil warming treatment, the lawn ecosystem under the control was a CO2 source （0.13 Ixmol/（mE.sec））, indicating that the lawn ecosystem would provide a negative feedback to global warming. There was no significant effect of soil warming on nocturnal NEE （i.e., ecosystem respiration）, although the soil temperature sensitivity （Q10） of ecosystem respiration under soil warming treatment was 3.86, much lower than that in the control （7.03）. The CO2 uptake was significantly increased by soil warming treatment that was attributed to about 100% increase of ct （apparent quantum yield） and Amax （maximum rate of photosynthesis）. Our results indicated that the response of photosynthesis in urban lawn is much more sensitive to global warming than respiration in the transition period.

A segmentation method for processing greenhouse vegetable foliar disease symptom images

Uneven illumination and clutter background were the most challenging problems to segmentation of disease symptom images.In order to achieve robust segmentation,a method for processing greenhouse vegetable foliar disease symptom images was proposed in this paper.The segmentation method was based on a decision tree which was constructed by a two-step coarse-to-fine procedure.Firstly,a coarse decision tree was built by the CART(Classification and Regression Tree)algorithm with a feature subset.The feature subset consisted of color features that was selected by Pearson’s Rank correlations.Then,the coarse decision tree was optimized by pruning.Using the optimized decision tree,segmentation of disease symptom images was achieved by conducting pixel-wise classification.In order to evaluate the robustness and accuracy of the proposed method,an experiment was performed using greenhouse cucumber downy mildew images.Results showed that the proposed method achieved an overall accuracy of 90.67%,indicating that the method was able to obtain robust segmentation of disease symptom images.