Earth observation and geospatial data can predict the relative distribution of village level poverty in the Sundarban Biosphere Reserve, India

There is increasing interest in leveraging Earth Observation (EO) and geospatial data to predict and map aspects of socioeconomic conditions to support survey and census activities. This is particularly relevant for the frequent monitoring required to assess progress towards the UNs' Sustainable Development Goals (SDGs). The Sundarban Biosphere Reserve (SBR) is a region of international ecological importance, containing the Indian portion of the world's largest mangrove forest. The region is densely populated and home to over 4.4 million people, many living in chronic poverty with a strong dependence on nature-based rural livelihoods. Such livelihoods are vulnerable to frequent natural hazards including cyclone landfall and storm surges. In this study we examine associations between environmental variables derived from EO and geospatial data with a village level multidimensional poverty metric using random forest machine learning, to provide evidence in support of policy formulation in the field of poverty reduction. We find that environmental variables can predict up to 78% of the relative distribution of the poorest villages within the SBR. Exposure to cyclone hazard was the most important variable for prediction of poverty. The poorest villages were associated with relatively small areas of rural settlement (&lt;∼30%), large areas of agricultural land (&gt;∼50%) and moderate to high cyclone hazard. The poorest villages were also associated with less productive agricultural land than the wealthiest. Analysis suggests villages with access to more diverse livelihood options, and a smaller dependence on agriculture may be more resilient to cyclone hazard. This study contributes to the understanding of poverty-environment dynamics within Low-and middle-income countries and the associations found can inform policy linked to socio-environmental scenarios within the SBR and potentially support monitoring of work towards SDG1 (No Poverty) across the region.</p

Recent evolution of 129-I levels in the Nordic Seas and the North Atlantic Ocean

Most of the anthropogenic radionuclide 129I released to the marine environment from the nuclear fuel reprocessing plants (NFRP) at Sellafield (England) and La Hague (France) is transported to the Arctic Ocean via the North Atlantic Current and the Norwegian Coastal Current. 129I concentrations in seawater provides a powerful and well-established radiotracer technique to provide information about the mechanisms which govern water mass transport in the Nordic Seas and the Arctic Ocean and is gaining importance when coupled with other tracers (e.g. CFC, 236U). In this work, 129I concentrations in surface and depth profiles from the Nordic Seas and the North Atlantic (NA) Ocean collected from four different cruises between 2011 and 2012 are presented. This work allowed us to i) update information on 129I concentrations in these areas, required for the accurate use of 129I as a tracer of water masses; and ii) investigate the formation of deep water currents in the eastern part of the Nordic Seas, by the analysis of 129I concentrations and temperature-salinity (T-S) diagrams from locations within the Greenland Sea Gyre. In the Nordic Seas, 129I concentrations in seawater are of the order of 109 at·kg− 1, one or two orders of magnitude higher than those measured at the NA Ocean, not so importantly affected by the releases from the NFRP. 129I concentrations of the order of 108 atoms·kg− 1 at the Ellet Line and the PAP suggest a direct contribution from the NFRP in the NA Ocean. An increase in the concentrations in the Nordic Seas between 2002 and 2012 has been detected, which agrees with the temporal evolution of the 129I liquid discharges from the NFRPs in years prior to this. Finally, 129I profile concentrations, 129I inventories and T-S diagrams suggest that deep water formation occurred in the easternmost area of the Nordic Seas during 2012.Ministerio de Economía y Competitividad FIS2015-69673-

Socioecologically informed use of remote sensing data to predict rural household poverty

Tracking the progress of the Sustainable Development Goals (SDGs) and targeting interventions requires frequent, up-to-date data on social, economic, and ecosystem conditions. Monitoring socioeconomic targets using household survey data would require census enumeration combined with annual sample surveys on consumption and socioeconomic trends. Such surveys could cost up to $253 billion globally during the lifetime of the SDGs, almost double the global development assistance budget for 2013. We examine the role that satellite data could have in monitoring progress toward reducing poverty in rural areas by asking two questions: (i) Can household wealth be predicted from satellite data? (ii) Can a socioecologically informed multilevel treatment of the satellite data increase the ability to explain variance in household wealth? We found that satellite data explained up to 62% of the variation in household level wealth in a rural area of western Kenya when using a multilevel approach. This was a 10% increase compared with previously used single-level methods, which do not consider details of spatial landscape use. The size of buildings within a family compound (homestead), amount of bare agricultural land surrounding a homestead, amount of bare ground inside the homestead, and the length of growing season were important predictor variables. Our results show that a multilevel approach linking satellite and household data allows improved mapping of homestead characteristics, local land uses, and agricultural productivity, illustrating that satellite data can support the data revolution required for monitoring SDGs, especially those related to poverty and leaving no one behind.</p

Submarine Estimates of Arctic Turbulent Spectra (SEATS)

Code in R has been produced to calculate turbulent spectr

Where is mineral ballast important for surface export of particulate organic carbon in the ocean?

Correlations between particulate organic carbon (POC) and mineral fluxes in the deep ocean have inspired the inclusion of ‘ballast effect’ parameterizations in carbon cycle models. A recent study demonstrated regional variability in the effect of ballast minerals on the flux of POC in the deep ocean. We have undertaken a similar analysis of shallow export data from the Arctic, Atlantic and Southern Oceans. Mineral ballasting is of greatest importance in the high-latitude North Atlantic, where 60% of the POC flux is associated with ballast minerals. This fraction drops to around 40% in the Southern Ocean. The remainder of the export flux is not associated with minerals, and this unballasted fraction thus often dominates the export flux. The proportion of mineral-associated POC flux often scales with regional variation in export efficiency (the proportion of primary production that is exported). However, local discrepancies suggest that regional differences in ecology also impact the magnitude of surface export. We propose that POC export will not respond equally across all high-latitude regions to possible future changes in ballast availability

Towards achieving the UNs data revolution: combining earth observation and socioeconomic data for geographic targeting of resources for the sustainable development goals

The UN has called for a ‘data revolution’ to help overcome the low quality and lack of regularly updated statistical data available in developing countries. But how do we achieve this with limited financial resources and insufficient capacity in national statistical offices around the world? Recent studies have demonstrated how information captured by satellite imagery can be combined with social datasets to increase our understanding of socioeconomic systems. Thus, in the future, satellite data may offer a cost-effective way to reliably measure and monitor progress towards development goals. We examine how satellite data can be linked with household and census datasets to provide information on socioeconomic conditions. We suggest that the Sustainable Livelihoods Approach provides an appropriate framework for which to develop remotely sensed earth observation (EO) data proxies for key socioeconomic conditions because it will allow the linking of data in a way that reflects more the way in which populations interact with landscapes. The aim of using EO data for mapping and predicting socioeconomic conditions is not to replace survey data but to provide more frequent information on likely socioeconomic conditions between census and survey enumeration. Timely recalibration of models predicting poverty from EO data would be necessary to reflect often rapid social, economic and political changes. However, if we are to acheive the SDGs more frequent data at finer spatial scales will be required and EO data provides a cos effective solution