The comparison of macroseismic intensity scales

The number of different macroseismic scales that have been used to express earthquake shaking in the course of the last 200 years is not known; it may reach three figures. The number of important scales that have been widely adopted is much smaller, perhaps about eight, not counting minor variants. Where data sets exist that are expressed in different scales, it is often necessary to establish some sort of equivalence between them, although best practice would be to reassign intensity values rather than convert them. This is particularly true because difference between workers in assigning intensity is often greater than differences between the scales themselves, particularly in cases where one scale may not be very well defined. The extent to which a scale guides the user to arrive at a correct assessment of the intensity is a measure of the quality of the scale. There are a number of reasons why one should prefer one scale to another for routine use, and some of these tend in different directions. If a scale has many tests (diagnostics) for each degree, it is more likely that the scale can be applied in any case that comes to hand, but if the diagnostics are so numerous that they include ones that do not accurately indicate any one intensity level, then the use of the scale will tend to produce false values. The purpose of this paper is chiefly to discuss in a general way the principles involved in the analysis of intensity scales. Conversions from different scales to the European Macroseismic Scale are discussed

Fine-Scale Monitoring of Long-term Wetland Loss Using LiDAR Data and Historical Aerial Photographs the Example of the Couesnon Floodplain, France

International audienceWetland area has decreased in most parts of the world and remains threatened by human pressures. However, wetland loss is difficult to accurately detect, delineate and quantify. While wetland distribution is influenced mainly by landform, LiDAR data provide accurate digital elevation models that can be used to delineate wetlands. Our objective was to map wetland loss at a fine-scale using LiDAR data and historical aerial photographs based on a functional typology that identifies potential, existing and efficient wetlands. The study focused on a 132 km(2) site with valley bottom wetlands located in western France. Boundaries of potential wetlands were extracted from a LiDAR-derived Digital Terrain Model that was standardized according to channel network elevation. We identified existing wetlands using interpretation of aerial photographs acquired in 1952, 1978 and 2012. We used multiple correspondence analysis to identify different types of wetland loss. Results show that potential wetlands were successfully delineated at 15000 (88-90% overall accuracy) and that 14% of existing wetland area was lost. This highlights the importance of identifying "negotiation areas" where wetland restoration is a priority. The results also reveal two main types of wetland loss based on area, geomorphic context, land cover and period of loss

Botanical composition, production and nutrient status of an originally Lolium perenne-dominant cut grass sward receiving long-term manure applications

Effects of long-term applications (50, 100 and 200 m(3) ha(-1)y(-1)) of pig and cow slurries on yield, botanical composition and nutrient content of herbage of an original perennial ryegrass sward were assessed in a three-cut silage system and compared with unamended and fertilized controls in the 36th year of the experiment. Cow slurry at 50 m(3) ha(-1) produced similar annual herbage DM yield to 200 kg ha(-1) fertilizer N in 2006, whereas about 100 m(3) ha(-1) pig slurry were required to produce a similar amount of DM. The highest slurry application rate significantly influenced sward botanical composition without depressing DM yield. The principal invading species were creeping bent and meadow grasses (similar to findings at a previous assessment in 1981) except in the unamended control (which were common bent and Yorkshire fog). Perennial ryegrass remained a main species in plots receiving fertilizer (31 % annual DM yield) and low slurry rates (38 %) but declined to 3 % annual DM yield at the highest slurry rate where the ability of ryegrass to utilize slurry N and P may have been affected by chemically or physically induced deficiencies of other nutrients (e.g. Ca) or direct physical effects such as smothering