Rates of waterfall recession, and major factors that influence the rate, were studied using waterfalls in Boso Peninsula, Japan. The mean rate of waterfall recession was estimated by determining the age and original location. The principal factor in determining the rate of waterfall recession is the ratio of the erosive force of stream to the bedrock resistance. This is expressed in terms of measurable variables, which include the discharge (drainage area and precipitation), the width and height of the waterfall, and the unconfined compressive strength of the bedrock. An empirical equation connecting the force/resistance ratios and the rates of waterfall recession is derived.

Abstract. The number of scientific studies that consider possible applications of remotely piloted aircraft systems (RPASs) for the management of natural hazards effects and the identification of occurred damages strongly increased in the last decade. Nowadays, in the scientific community, the use of these systems is not a novelty, but a deeper analysis of the literature shows a lack of codified complex methodologies that can be used not only for scientific experiments but also for normal codified emergency operations. RPASs can acquire on-demand ultra-high-resolution images that can be used for the identification of active processes such as landslides or volcanic activities but can also define the effects of earthquakes, wildfires and floods. In this paper, we present a review of published literature that describes experimental methodologies developed for the study and monitoring of natural hazards. IntroductionIn the last three decades, the number of natural disasters showed a positive trend with an increase in the number of affected populations. Disasters not only affected the poor and characteristically more vulnerable countries but also those thought to be better protected. The Annual Disaster Statistical Review describes recent impacts of natural disasters on the population and reports 342 naturally triggered disasters in 2016 (Guha-Sapir et al., 2017). This is less than the annual average disaster frequency observed from 2006 to 2015 (376.4 events). However, natural disasters are still responsible for a high number of casualties (8733 death). In the period 2006-2015, the average number of causalities caused annually by natural disasters is 69 827. In 2016, hydrological disasters (177) had the largest share in natural disaster occurrence (51.8 %), followed by meteorological disasters (96; 28.1 %), climatological disasters (38; 11.1 %) and geophysical disasters (31; 9.1 %) (Guha-Sapir et al., 2017). To face these disasters, one of the most important solutions is the use of systems able to provide an adequate level of information for correctly understanding these events and their evolution. In this context, surveying and monitoring natural hazards gained importance. In particular, during the emergency phase it is very important to evaluate and control the phenomenon of evolution, preferably operating in near real time or real time, and consequently, use this information for a better risk assessment scenario. The available acquired data must be processed rapidly to support the emergency services and decision makers.Recently, the use of remote sensing (satellite and airborne platform) in the field of natural hazards and disasters has become common, also supported by the increase in geospatial technologies and the ability to provide and process up-to-date imagery (Joyce et al., 2009;Tarolli, 2014). Remotely sensed data play an integral role in predicting hazard events such as floods and landslides, subsidence events and other ground instabilities. Because of their acquisition mode and capabilPublished by Copern...

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[1] A new global elevation dataset known as G-DEM, based on the ASTER satellite imagery, will be released in late 2008. G-DEM will be the best freely available global digital elevation model (DEM) at a horizontal resolution of 1 arc second. We assess the quality of G-DEM in comparison with 3-arc-second SRTM DEM, the best current global elevation dataset. Basic geomorphometric parameters (elevation, slope and curvature) were examined for a prerelease version of G-DEM and SRTM DEM for western Japan. G-DEM has fewer missing cells than SRTM DEM, particularly in steep terrain. Also, G-DEM gives smoother and more realistic representations of lowlands, valleys, steep slopes, and mountain ridges, whereas, SRTM DEM includes many local spikes and holes, and tends to overestimate valley-floor elevation and underestimate ridge elevation. G-DEM will be commonly used in geoscientific studies, because of its higher resolution, fewer missing data, and better topographic representation than SRTM DEM.