Recent advances in marine acoustic survey and land-based topographic monitoring technologies have resulted in increasingly cost-effective data acquisition in coastal areas. The DEFRA-funded National Network of Regional Coastal Monitoring Programmes of England are, for example, utilising swath bathymetry and airborne light detection and ranging (LiDAR) technology more routinely to survey the coastal zone around the coastline of England. The demand for data processing, visualisation and interpretation techniques to keep pace with such advances in data acquisition is clear. This study discusses collection and processing techniques for such data on the south coast of Dorset, England, which have enabled the production of a seamless, high spatial resolution digital elevation model across the coastal zone. Case studies demonstrate how this elevation model can be viewed and analysed using state-of-the-art digital techniques to allow geological mapping to be extended from onshore to offshore in unprecedented detail, effectively eliminating what is known as the 'White Ribbon' for coastal geological mapping. The potential for rolling out such techniques for wider surveying programmes across many environmental disciplines is significant, which could contribute towards improving the multi-disciplinary scientific evidence base in the complex coastal zone.

Effective disaster risk management (DRM) and disaster risk reduction (DRR) require modeling potential and post-event impacts using building exposure data. The data used to develop building exposure databases will influence the accuracy of risk assessments and the appropriateness of subsequent decisions. This article proposes a framework for classifying approaches of developing building exposure databases into levels. To examine the uncertainty introduced through using various approaches to exposure development, a probabilistic seismic risk assessment was run with the exposure data corresponding to each proposed level using the County of Los Angeles as the study area. A factor of ∼2.5 was observed in the final loss estimates. The variance was less dependent on the spatial scale of data than on key values, most notably estimates of building size and replacement cost.

I. Background A. Some environmental effects of mineral deposits and mineral-resource development. B. Environmental-geology models of mineral deposits; their uses in predicting mine-drainage chemistry. II. Mineral-Resource and Mineral-Environmental Assessment of Colorado A. Streams affected by metals. B. Regional environmental studies; lead concentrations in stream sediments of Colorado. C. "Summitville-Type" deposits: their environmental characteristics and occurrences in Colorado. D."Leadvilie-Type" deposits: their environmental characteristics and occurrences in Colorado. E. Applications of regional geoscience surveys and environmental-geology models of mineral deposits to land management and resource planning. III. Mineral-Resource and Mineral-Environmental Assessment of the San Juan National Forest A. Location of mining districts and streams affected by metals. B. Mineral-resource assessment map. C. Environmental geology map. D. Sources of acidity in streams. E. Geology-based environmental risk assessment. F. Applications of integrated mineral-resource and mineral-environmental assessments to land management and resource planning.

<p>The METEOR project (Modelling Exposure Through Earth Observation Routines) is a three year project ending in March 2021, co-funded by the UK Space Agency International Partnership Programme. The aim of this project was to develop innovative methods to understand multi-hazard and exposure, and to deliver robust data for Disaster Risk Management (DRM) in Nepal and Tanzania.</p><p>In developing economies there is a pressing need to characterise hazard, exposure and vulnerability to allow for comprehensive DRM plans and pre-positioning. In the METEOR project these exposure protocols and standards were co-developed and validated in Nepal and Tanzania to ensure that they are fit-for-purpose.  Many multi-hazard mapping approaches focus on the frequency of events and use historical financial losses as a proxy for infrastructure impact or exposure (Bell and Glade, 2004; Tate et al., 2010; Schmidt et al., 2011; Kappes et al., 2012). Whilst such approaches may be appropriate for hazards with historic inventories detailing the distribution and scale of events, for others estimation of key factors such as historic frequency, or probability of occurrence or losses, is much more complex.</p><p>Here we will present a new methodology for assessing the national impact of multi-hazards on exposure, grounded in earth observation data, in the context of data paucity and high levels of inherent uncertainty. We explore a subset of the METEOR data for Nepal to discuss the main controls on the uncertainty of the final outputs of our model.  We also show how our model can be tied to existing vulnerability curves to link hazard assessments with expected damage.</p>

No abstract