We analysed the late-Holocene pedo-sedimentary archives of La Narse de la Sauvetat, a hydromorphic depression in the southern Limagne plain (central France), where chronologically accurate studies are scarce. The multi-proxy geoarchaeological and palaeoenvironmental analysis of two cores from different areas of the basin was carried out through sedimentological, geochemical, micromorphological and malacological investigations. Integration of these datasets supported by a robust radiocarbon-based chronology allowed discussion of socio-environmental interactions and anthropogenic impacts from Late Neolithic to Early Middle Ages. Until the Middle Bronze Age, there was no clear evidence of anthropogenic impact on soils and hydro-sedimentary dynamics of the catchment, but two peaks of high alluvial activity probably related to the 4.2 and 3.5 kyr. BP climate events were first recorded in Limagne. Significant anthropogenic impacts started in the Late Bronze Age with increased erosion of the surrounding volcanic slopes. However, a major threshold was reached c. 2600 cal BP with a sharp increase in the catchment erosion interpreted as resulting from strong anthropogenic environmental changes related to agricultural activities and drainage. This implies an anthropogenic forcing on soils and hydro-sedimentary systems much earlier than was usually considered in Limagne. These impacts then gradually increased during Late Iron Age and Roman periods, but environmental effects were certainly contained by progress in agricultural management. Late Antiquity environmental changes are consistent with regional trend to drainage deterioration in lowlands, but marked asynchrony in this landscape change suggests that societal factors implying differential land management were certainly predominant here.

The use of Light Detection and Ranging (LiDAR) is becoming more and more common in different landscape exploration domains such as archaeology or geomorphology. In order to allow the detection of features of interest, visualization filters have to be applied to the raw Digital Elevation Model (DEM), to enhance small relief variations. Several filters have been proposed for this purpose, such as Sky View Factor, Slope, negative and positive Openness, or Local Relief Model (LRM). The efficiency of each of these methods is strongly dependent on the input parameters chosen in regard of the topography of the investigated area. The LRM has proved to be one of the most efficient, but it has to be parameterized in order to be adapted to the natural slopes characterizing the investigated area. Generally, this setting has a single value, chosen as the best compromise between optimal values for each relief configuration. As LiDAR is mainly used in wide areas, a large distribution of natural slopes is often encountered. The aim of this paper is to propose a Self AdaptIve LOcal Relief Enhancer (SAILORE) based on the Local Relief Model approach. The filtering effect is adapted to the local slope, allowing the detection at the same time of low-frequency relief variation on flat areas, as well as the identification of high-frequency relief variation in the presence of steep slopes. First, the interest of this self-adaptive approach is presented, and the principle of the method, compared to the classical LRM method, is described. This new tool is then applied to a LiDAR dataset characterized by various terrain configurations in order to test its performance and compare it with the classical LRM. The results of this test show that SAILORE significantly increases the detection capability while simplifying it.