The red edge position (REP) in the vegetation spectral reflectance is a surrogate measure of vegetation chlorophyll content, and hence can be used to monitor the health and function of vegetation. The Multi-Spectral Instrument (MSI) aboard the future ESA Sentinel-2 (S-2) satellite will provide the opportunity for estimation of the REP at much higher spatial resolution (20m) than has been previously possible with spaceborne sensors such as Medium Resolution Imaging Spectrometer (MERIS) aboard ENVISAT. This study aims to evaluate the potential of S-2 MSI sensor for estimation of canopy chlorophyll content, leaf area index (LAI) and leaf chlorophyll concentration (LCC) using data from multiple field campaigns. Included in the assessed field campaigns are results from SEN3Exp in Barrax, Spain composed of 35 elementary sampling units (ESUs) of LCC and LAI which have been assessed for correlation with simulated MSI data using a CASI airborne imaging spectrometer. Analysis also presents results from SicilyS2EVAL, a campaign consisting of 25 ESUs in Sicily, Italy supported by a simultaneous Specim Aisa-Eagle data acquisition. In addition, these results were compared to outputs from the PROSAIL model for similar values of biophysical variables in the ESUs. The paper in turn assessed the scope of S-2 for retrieval of biophysical variables using these combined datasets through investigating the performance of the relevant Vegetation Indices (VIs) as well as presenting the novel Inverted Red-Edge Chlorophyll Index (IRECI) and Sentinel-2 Red-Edge Position (S2REP). Results indicated significant relationships between both canopy chlorophyll content and LAI for simulated MSI data using IRECI or the Normalised Difference Vegetation Index (NDVI) while S2REP and the MERIS Terrestrial Chlorophyll Index (MTCI) were found to have the strongest correlation for retrieval of LCC

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. Significance Understanding relationships between poverty and environment is crucial for sustainable development and ecological conservation. Annual monitoring of socioeconomic changes using household surveys is prohibitively expensive. Here, we demonstrate that satellite data predicted the poorest households in a landscape in Kenya with 62% accuracy. A multilevel socioecological treatment of satellite data accounting for the complex ways in which households interact with the environment provided better prediction than the standard single-buffer approach. The increasing availability of high-resolution satellite data and volunteered geographic data means this method could be modified and upscaled in the future to help monitor the sustainable development goals.

Coastal communities are some of the most at-risk populations with respect to climate change impacts. It is therefore important to determine the vulnerability of such communities to co-develop viable adaptation options. Global efforts to address this issue include international scientific projects, such as Global Learning for Local Solutions (GULLS), which focuses on five fast warming regions of the southern hemisphere and aims to provide an understanding of the local scale processes influencing community vulnerability that can then be up-scaled to regional, country and global levels. This paper describes the development of a new social and ecological vulnerability framework which integrates exposure, sensitivity and adaptive capacity with the social livelihoods and food security approaches. It also measures community flexibility to understand better the adaptive capacity of different levels of community organization. The translation of the conceptual framework to an implementable method is described and its application in a number of “hotspot” countries, where ocean waters are warming faster than the rest of the world, is presented. Opportunities for cross-cultural comparisons to uncover similarities and differences in vulnerability and adaptation patterns among the study’s coastal communities, which can provide accelerated learning mechanisms to other coastal regions, are highlighted. The social and ecological framework and the associated survey approach allow for future integration of local-level vulnerability data with ecological and oceanographic models.

Physical access to health facilities is an important factor in determining treatment seeking behaviour and has implications for targets within the Sustainable Development Goals, including the right to health. The increased availability of high-resolution land cover and road data from satellite imagery offers opportunities for fine-grained estimations of physical access which can support delivery planning through the provision of more realistic estimates of travel times. The data presented here is of travel time to health facilities in Uganda, Zimbabwe, Tanzania, and Mozambique. Travel times have been calculated for different facility types in each country such as Dispensaries, Health Centres, Clinics and Hospitals. Cost allocation surfaces and travel times are provided for child walking speeds but can be altered easily to account for adult walking speeds and motorised transport. With a focus on Uganda, we describe the data and method and provide the travel maps, software and intermediate datasets for Uganda, Tanzania, Zimbabwe and Mozambique.