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Sixteen years have passed since the last global volcanic event and more than 25 since a volcanic catastrophe that killed tens of thousands. In this time, volcanology has seen major advances in understanding, modelling and predicting volcanic hazards and, recently, an interest in techniques for reducing and mitigating volcanic risk. This paper provides a synthesis of literature relating to this last aspect, specifically the communication of volcanic risk, with a view to highlighting areas of future research into encouraging risk-reducing behaviour. Evidence suggests that the current 'multidisciplinary' approach within physical science needs a broader scope to include sociological knowledge and techniques. Key areas where this approach might be applied are: (1) the understanding of the incentives that make governments and communities act to reduce volcanic risk; (2) improving the communication of volcanic uncertainties in volcanic emergency management and long-term planning and development. To be successful, volcanic risk reduction programmes will need to be placed within the context of other other risk-related phenomena (e.g. other natural hazards, climate change) and aim to develop an all-risks reduction culture. We suggest that the greatest potential for achieving these two aims comes from deliberative inclusive processes and geographic information systems.

Mapping lava flows using satellite images is an important application of remote sensing in volcanology. Several volcanoes have been mapped through remote sensing using a wide range of data, from optical to thermal infrared and radar images, using techniques such as manual mapping, supervised/unsupervised classification, and elevation subtraction. So far, spectralbased mapping applications mainly focus on the use of traditional pixel-based classifiers, without much investigation into the added value of object-based approaches and into advantages of using machine learning algorithms. In this study, Nyamuragira, characterized by a series of more than 20 overlapping lava flows erupted over the last century, was used as a case study. The random forest classifier was tested to map lava flows based on pixels and objects. Image classification was conducted for the 20 individual flows and for 8 groups of flows of similar age using Landsat 8 imagery and a DEM of the volcano, both at 30-meter spatial resolution. Results show that object-based classification produces maps with continuous and homogeneous lava surfaces, in agreement with the physical characteristics of lava flows, while lava flows mapped through the pixel-based classification are heterogeneous and fragmented including much "salt and pepper 2 noise". In terms of accuracy, both pixel-based and object-based classification performs well but the former results in higher accuracies than the latter except for mapping lava flow age groups without using topographic features. It is concluded that despite spectral similarity, lava flows of contrasting age can be well discriminated and mapped by means of image classification. The classification approach demonstrated in this study only requires easily accessible image data and can be applied to other volcanoes as well if there is sufficient information to calibrate the mapping.

In this study, linear spectral mixture analysis (LSMA) is used to characterize the spectral heterogeneity of lava flows from Nyamuragira volcano, Democratic Republic of Congo, where vegetation and lava are the two main land covers. In order to estimate fractions of vegetation and lava through satellite remote sensing, we made use of 30 m resolution Landsat Enhanced Thematic Mapper Plus (ETM+) and Advanced Land Imager (ALI) imagery. 2 m Pleiades data was used for validation. From the results, we conclude that (1) LSMA is capable of characterizing volcanic fields and discriminating between different types of lava surfaces; (2) three lava endmembers can be identified as lava of old, intermediate and young age, corresponding to different stages in lichen growth and chemical weathering; (3) a strong relationship is observed between vegetation fraction and lava age, where vegetation at Nyamuragira starts to significantly colonize lava flows ~15 years after eruption and occupies over 50% of the lava surfaces ~40 years after eruption. Our study demonstrates the capability of spectral unmixing to characterize lava surfaces and vegetation colonization with time, which is particularly useful for poorly known volcanoes or those not accessible for physical or political reasons.

The age of past lava flows is crucial information for evaluating the hazards and risks posed by effusive volcanoes, but traditional dating methods are expensive and time‐consuming. This study proposes an alternative statistical dating method based on remote sensing observations of tropical volcanoes by exploiting the relationship between lava flow age and vegetation cover. First, the factors controlling vegetation density on lava flows, represented by the normalized difference vegetation index (NDVI), were investigated. These factors were then integrated into pixel‐based multi‐variable regression models of lava flow age to derive lava flow age maps. The method was tested at a pixel scale on three tropical African volcanoes with considerable recent effusive activity: Nyamuragira (Democratic Republic of Congo), Mt Cameroon (Cameroon) and Karthala (the Comoros). Due to different climatic and topographic conditions, the parameters of the spatial modeling are volcano‐specific. Validation suggests that the obtained statistical models are robust and can thus be applied for estimating the age of unmodified undated lava flow surfaces for these volcanoes. When the models are applied to fully vegetated lava flows, the results should be interpreted with caution due to the saturation of NDVI. In order to improve the accuracy of the models, when available, spatial data on temperature and precipitation should be included to directly represent climatic variation. Copyright © 2017 John Wiley & Sons, Ltd.

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Abstract:We report on spectral reflectance measurements of basaltic lava flows on Tenerife Island, Spain. Lava flow surfaces of different ages, surface roughness and elevations were systematically measured using a field spectroradiometer operating in the range of 350-2500 nm. Surface roughness, oxidation and lichen coverage were documented at each measured site. Spectral properties vary with age and morphology of lava. Pre-historical lavas with no biological coverage show a prominent increase in spectral reflectance in the 400-760 nm range and a decrease in the 2140-2210 nm range. Pāhoehoe surfaces have higher reflectance values than 'a'ā ones and attain a maximum reflectance at wavelengths < 760 nm. Lichen-covered lavas are characterized by multiple lichen-related absorption and reflection features. We demonstrate that oxidation and lichen growth are two major factors controlling spectra of Tenerife lava surfaces and, therefore, propose an oxidation index and a lichen index to quantify surface alterations of lava flows: (1) the oxidation index is based on the increase of the slope of the spectral profile from blue to red as the field-observed oxidation level strengthens; and (2) the lichen index is based on the spectral reflectance in the 1660-1725 nm range, which proves to be highly correlated with lichen coverage documented in the field. The two spectral indices are applied to Landsat ETM+ and Hyperion imagery of the study area for mapping oxidation and lichen coverage on lava surfaces, respectively. Hyperion is shown to be capable of discriminating different volcanic surfaces, i.e., tephra vs. lava and oxidized lava vs. lichen-covered lava. Our study highlights the value of field spectroscopic measurements to aid interpretation of lava flow characterization using satellite images and of the effects of environmental factors on lava surface evolution over time, and, therefore, has the potential to contribute to the mapping as well as dating of lava surfaces.

Dominica, along with several other Caribbean islands, was severely damaged by category-5 Hurricane Maria in September 2017. The hurricane left 68 people dead or missing, marking Maria as the worst natural catastrophe to hit this small island nation. Here, we report the results of our coastal runup field survey in February 2018 and of tide gauge sea-level data analysis. Analysis of tide gauge records shows that the duration of Maria's surge varied between 2.1 and 2.6 days in the Caribbean region and was 2.1 days at Marigot, Dominica. The surge amplitude was 75 cm in Marigot, which indicates that the size of the surge was small for a category-5 hurricane. The measured field survey runups were from 1.0 to 3.7 m, with the maximum runup at Scotts Head on the southern tip of Dominica. The largest measured runups were concentrated along the west coast of the southern half of the island and consistently decreased northwards. We attribute the observed damage to coastal structures to four mechanisms: surge/wave erosion; surge/wave forces/impacts; debris impacts to coastal structures involving in particular floating tree debris brought to the sea by river floods associated with Hurricane Maria; and intense coastal sedimentation, involving sediment brought to the sea by river floods. A flowchart of the hurricane-driven damage mechanisms is presented which provides the propagating sequence, or cascade, of events that contributed to damage and emphasizes the interactions between different processes in the hurricane. ARTICLE HISTORY