Key rules of life and the fading cryosphere: Impacts in alpine lakes and streams.

Alpine regions are changing rapidly due to loss of snow and ice in response to ongoing climate change. While studies have documented ecological responses in alpine lakes and streams to these changes, our ability to predict such outcomes is limited. We propose that the application of fundamental rules of life can help develop necessary predictive frameworks. We focus on four key rules of life and their interactions: the temperature dependence of biotic processes from enzymes to evolution; the wavelength dependence of the effects of solar radiation on biological and ecological processes; the ramifications of the non-arbitrary elemental stoichiometry of life; and maximization of limiting resource use efficiency across scales. As the cryosphere melts and thaws, alpine lakes and streams will experience major changes in temperature regimes, absolute and relative inputs of solar radiation in ultraviolet and photosynthetically active radiation, and relative supplies of resources (e.g., carbon, nitrogen, and phosphorus), leading to nonlinear and interactive effects on particular biota, as well as on community and ecosystem properties. We propose that applying these key rules of life to cryosphere-influenced ecosystems will reduce uncertainties about the impacts of global change and help develop an integrated global view of rapidly changing alpine environments. However, doing so will require intensive interdisciplinary collaboration and international cooperation. More broadly, the alpine cryosphere is an example of a system where improving our understanding of mechanistic underpinnings of living systems might transform our ability to predict and mitigate the impacts of ongoing global change across the daunting scope of diversity in Earth's biota and environments.

Carbon Dioxide Emissions and Methane Flux from Forested Wetland Soils of the Great Dismal Swamp, USA.

The Great Dismal Swamp, a freshwater forested peatland, has accumulated massive amounts of soil carbon since the postglacial period. Logging and draining have severely altered the hydrology and forest composition, leading to drier soils, accelerated oxidation, and vulnerability to disturbance. The once dominant Atlantic white cedar, cypress, and pocosin forest types are now fragmented, resulting in maple-gum forest communities replacing over half the remaining area. In order to determine the effect of environmental variabes on carbon emissions, this study observes 2 years of CO2 and CH4 soil flux, which will also help inform future management decisions. Soil emissions were measured using opaque, non-permanent chambers set into the soil. As soil moisture increased by 1 unit of soil moisture content, CH4 flux increased by 457 µg CH4-C/m2/h. As soil temperature increased by 1 °C, CO2 emissions increased by 5109 µg CO2-C/m2/h. The area of Atlantic white cedar in the study boundary has an average yearly flux of 8.6 metric tons (t) of carbon from CH4 and 3270 t of carbon from CO2; maple-gum has an average yearly flux of 923 t of carbon from CH4 and 59,843 t of carbon from CO2; pocosin has an average yearly flux of 431 t of carbon from CH4 and 15,899 t of carbon from CO2. Total Cha-1year-1 ranged from 1845 kg of Cha-1year-1 in maple-gum to 2024 kg Cha-1year-1 for Atlantic white cedar. These results show that soil carbon gas flux depends on soil moisture, temperature and forest type, which are affected by anthropogenic activities.

Saharan dust detection using multi-sensor satellite measurements.

Contemporary scientists have vested interest in trying to understand the climatology of the North Atlantic Basin since this region is considered as the genesis for hurricane formation that eventually get shipped to the tropical Atlantic region and the Caribbean. The effects of atmospheric water cycle and the climate of West Africa and the Atlantic basin are hugely impacted by the radiative forcing of Saharan dust. The focus area in this paper would be to improve the dust detection schemes by employing the use of multi sensor measurements in the thermal emissive wavelengths using legacy sensors such as Terra (T) and Aqua (A) MODerate-resolution Imaging Spectroradiometer (MODIS), fusing with Ozone Monitoring Instrument (OMI). Previous work by Hao and Qu (2007) had considered a limited number of thermal infrared channels which led to a correlation coefficient R2 value of 0.765 between the Aerosol Optical Thickness (AOT) at 550 nm and the modeled dust index. In this work, we extend the thermal infrared based dust detection by employing additional channels: the 8.55 µm which has shown high sensitivity to the Saharan dust, along with water vapor channel of 7.1 µm and cloud top channel of 13.1 µm. Also, the dust pixels were clearly identified using the OMI based aerosol types. The dust pixels were cleanly segregated from the other aerosol types such as sulfates, biomass, and other carbonaceous aerosols. These improvements led to a much higher correlation coefficient R2 value of 0.85 between the modified dust index and the AOT in comparison to the previous work. The key limitations from the current AOT products based on MODIS and were put to test by validating the improved dust detection algorithm. Two improvements were noted. First, the dust measurement radiometry using MODIS is significantly improved by at least an order of 2. Second the spatial measurements are enhanced by a factor of at least 10.

Oil spill detection with fully polarimetric UAVSAR data.

In this study, two ocean oil spill detection approaches based on four scattering matrices measured by fully polarimetric synthetic aperture radar (SAR) are presented and compared. The first algorithm is based on the co-polar correlation coefficient, ρ, and the scattering matrix decomposition parameters, Cloud entropy (H), mean scattering angle (α) and anisotropy (A). While each of these parameters has oil spill signature in it, we find that combining these parameters into a new parameter, F, is a more effective way for oil slick detection. The second algorithm uses the total power of four polarimetric channels image (SPAN) to find the optimal representation of the oil spill signature. Otsu image segmentation method can then be applied to the F and SPAN images to extract the oil slick features. Using the L-band fully polarimetric Uninhabited Aerial Vehicle - synthetic aperture radar (UAVSAR) data acquired during the 2010 Deepwater Horizon oil spill disaster event in the Gulf of Mexico, we are able to successfully extract the oil slick information in the contaminated ocean area. Our result shows that both algorithms perform well in identifying oil slicks in this case.