Toward a High-Resolution Monitoring of Continental Surface Water Extent and Dynamics, at Global Scale: from GIEMS (Global Inundation Extent from Multi-Satellites) to SWOT (Surface Water Ocean Topography)

Up to now, high-resolution mapping of surface water extent from satellites has only been available for a few regions, over limited time periods. The extension of the temporal and spatial coverage was difficult, due to the limitation of the remote sensing technique e.g., the interaction of the radiation with vegetation or cloud for visible observations or the temporal sampling with the synthetic aperture radar (SAR)]. The advantages and the limitations of the various satellite techniques are reviewed. The need to have a global and consistent estimate of the water surfaces over long time periods triggered the development of a multi-satellite methodology to obtain consistent surface water all over the globe, regardless of the environments. The Global Inundation Extent from Multi-satellites (GIEMS) combines the complementary strengths of satellite observations from the visible to the microwave, to produce a low-resolution monthly dataset () of surface water extent and dynamics. Downscaling algorithms are now developed and applied to GIEMS, using high-spatial-resolution information from visible, near-infrared, and synthetic aperture radar (SAR) satellite images, or from digital elevation models. Preliminary products are available down to 500-m spatial resolution. This work bridges the gaps and prepares for the future NASA/CNES Surface Water Ocean Topography (SWOT) mission to be launched in 2020. SWOT will delineate surface water extent estimates and their water storage with an unprecedented spatial resolution and accuracy, thanks to a SAR in an interferometry mode. When available, the SWOT data will be adopted to downscale GIEMS, to produce a long time series of water surfaces at global scale, consistent with the SWOT observations.

Engineering a multi-biofunctional composite using poly(ethylenimine) decorated graphene oxide for bone tissue regeneration

Toward preparing strong multi-biofunctional materials, poly(ethylenimine) (PEI) conjugated graphene oxide (GO_PEI) was synthesized using poly(acrylic acid) (PAA) as a spacer and incorporated in poly( e-caprolactone) (PCL) at different fractions. GO_PEI significantly promoted the proliferation and formation of focal adhesions in human mesenchymal stem cells (hMSCs) on PCL. GO_PEI was highly potent in inducing stem cell osteogenesis leading to near doubling of alkaline phosphatase expression and mineralization over neat PCL with 5% filler content and was approximate to 50% better than GO. Remarkably, 5% GO_ PEI was as potent as soluble osteoinductive factors. Increased adsorption of osteogenic factors due to the amine and oxygen containing functional groups on GO_ PEI augment stem cell differentiation. GO_ PEI was also highly efficient in imparting bactericidal activity with 85% reduction in counts of E. coli colonies compared to neat PCL at 5% filler content and was more than twice as efficient as GO. This may be attributed to the synergistic effect of the sharp edges of the particles along with the presence of the different chemical moieties. Thus, GO_ PEI based polymer composites can be utilized to prepare bioactive resorbable biomaterials as an alternative to using labile biomolecules for fabricating orthopedic devices for fracture fixation and tissue engineering.

Multi-scale vulnerability assessment for adaptation planning

Vulnerability of communities and natural ecosystems, to potential impacts of climate change in developing countries like India, and the need for adaptation are rapidly emerging as central issues in the debate around policy responses to climate change. The present study presents an approach to identify and prioritize the most vulnerable districts, villages and households in Karnataka State, through a multi-scale assessment of inherent vulnerability to current climate variability. It also identifies the drivers of inherent vulnerability, thereby providing a tool for developing and mainstreaming adaptation strategies, in ongoing developmental or dedicated adaptation programmes. The multi-scale assessment was made for all 30 districts at the state level in Karnataka, about 1220 villages in Chikballapur district, and at the household level for two villages - Gundlapalli and Saddapalli - in Bagepalli taluk of Chikballapur district. At the district, village and household levels, low levels of education and skills are the dominant factors contributing to vulnerability. At the village and household level, the lack of income diversification and livelihood support institutions are key drivers of vulnerability. The approach of multi-scale vulnerability assessment facilitates identification and prioritization of the drivers of vulnerability at different scales, to focus adaptation interventions to address these drivers.

ON THE UTILITY OF CANONICAL CORRELATION ANALYSIS FOR DOMAIN ADAPTATION IN MULTI-VIEW HEADPOSE ESTIMATION

The utility of canonical correlation analysis (CCA) for domain adaptation (DA) in the context of multi-view head pose estimation is examined in this work. We consider the three problems studied in 1], where different DA approaches are explored to transfer head pose-related knowledge from an extensively labeled source dataset to a sparsely labeled target set, whose attributes are vastly different from the source. CCA is found to benefit DA for all the three problems, and the use of a covariance profile-based diagonality score (DS) also improves classification performance with respect to a nearest neighbor (NN) classifier.