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Small island developing States, such as those in the Pacific, are often prone to multiple hazards that have potential to result in disaster and / or restrict development. Hazard data can be limited in resolution or omitted in or near SIDS' coasts, but a growing and improved range of datasets are becoming available. Through an analysis of approximately 100 policy documents on hazards and disaster risk management in Pacific island nations, we found: limited information on hazards and how they manifest to disasters at local levels, thus not fully connecting drivers and subsequent risk; at times a non-specific multi-hazard approach prompting the need to address more specific hazards; and restricted temporal and spatial scales of analysis that potentially limit continuity of actions where mitigation methods evolve. These limitations suggest that appropriate and timely high resolution hazard data is needed from the top-down to underpin the design and development of local disaster risk management plans, simultaneous to local, bottom-up knowledge and interpretation to bring the realities of such hazard data to life. Developing and ensuring openly available hazard data will enable island States to develop more robust, inclusive disaster risk management plans and mitigation policies, plus aid inter-island comparison for communal learning. [ABSTRACT FROM AUTHOR]

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 (<∼30%), large areas of agricultural land (>∼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. • Earth Observation and geospatial data could support monitoring of development. • We examine association between poverty and the environment with machine learning. • Cyclone hazard was the most important variable for prediction of poverty in the SBR. • The poorest villages were predicted with up to 78% accuracy. • Results can inform policy linked to socio-environmental scenarios in the SBR. [ABSTRACT FROM AUTHOR]