Human-triggered magnification of erosion rates in European Alps since the Bronze Age.

A major feature of the Anthropocene is the drastic increase in global soil erosion. Soil erosion is threatening Earth habitability not only as soils are an essential component of the Earth system but also because societies depend on soils. However, proper quantification of the impact of human activities on erosion over thousands of years is still lacking. This is particularly crucial in mountainous areas, where the highest erosion rates are recorded. Here we use the Lake Bourget catchment, one of the largest in the European Alps, to estimate quantitatively the impact of human activities on erosion. Based on a multi-proxy, source-to-sink approach relying on isotopic geochemistry, we discriminate the effects of climate fluctuations from those of human activities on erosion over the last 10,000 years. We demonstrate that until 3800 years ago, climate is the only driver of erosion. From that time on, climate alone cannot explain the measured rates of erosion. Thanks to an unprecedented regional paleoenvironmental reconstruction, we highlight that the development of pastoralism at high altitudes from the Bronze Age onwards and the extension of agriculture starting in the Middle Ages were key factors in the drastic increase in erosion observed in the Alps.

Sedimentary structure discrimination with hyperspectral imaging in sediment cores.

Hyperspectral imaging (HSI) is a non-destructive, high-resolution imaging technique that is currently under significant development for analyzing geological areas with remote devices or natural samples in a laboratory. In both cases, the hyperspectral image provides several sedimentary structures that must be separated to temporally and spatially describe the sample. Sediment sequences are composed of successive deposits (strata, homogenite, flood) that are visible depending on sample properties. The classical methods to identify them are time-consuming, have a low spatial resolution (millimeters) and are generally based on naked-eye counting. In this study, we compare several supervised classification algorithms to discriminate sedimentological structures in lake sediments. Instantaneous events in lake sediments are generally linked to extreme geodynamical events (e.g., floods, earthquakes), so their identification and counting are essential to understand long-term fluctuations and improve hazard assessments. Identification and counting are done by reconstructing a chronicle of event layer occurrence, including estimation of deposit thicknesses. Here, we applied two hyperspectral imaging sensors (Visible Near-Infrared, VNIR, 60 µm, 400-1000 nm; Short Wave Infrared, SWIR, 200 µm, 1000-2500 nm) on three sediment cores from different lake systems. We highlight that the SWIR sensor is the optimal one for creating robust classification models with discriminant analyses (prediction accuracies of 0.87-0.98). Indeed, the VNIR sensor is impacted by the surface reliefs and structures that are not in the learning set, which causes mis-classification. These observations are also valid for the combined sensor (VNIR-SWIR) and the RGB images. Several spatial and spectral pre-processing were also compared and enabled one to highlight discriminant information specific to a sample and a sensor. These works show that the combined use of hyperspectral imaging and machine learning improves the characterization of sedimentary structures compared to conventional methods.