We describe a new method to map intra-plot soil surface heterogeneities at a 5 cm spatial resolution. Our approach unites aerial image classification acquired at very high spatial resolution (VHSR) with local soil sampling. VHSR aerial image processing, based on image classification, allows precise mapping of the spatial distribution of soil surfaces; soil sampling defines soil typology by physical and chemical characteristics. This method has been applied to a plot area located on the hillslopes of Burgundy vineyards (Monthelie, France), where decennial erosion data were already available, in order to assess the effect of soil surface characteristics and slope angle on erosion intensity and localisation. From this method, four classes of radiance were distinguished and interpreted as four soil surface state classes (SSC), defining specific areas within the studied plot. These SSCs have been characterised by their grain-size distribution, their organic carbon, calcium carbonate, and total nitrogen contents. By allowing soil surface states to be mapped at five centimetre resolution, this approach provides novel insights into the characterisation of soil patterns and into erosion analysis on cultivated hillslopes. Our work shows that the spatial distribution of soil erosion is related to the local slope steepness but also to the spatial distribution of stoniness that results from water and tillage erosion processes.
International audienceIn vineyards, soil erosion is controlled by complex interactions between geomorphological and anthropogenic factors, leading to intra-plot spatial topsoil heterogeneities that are observed at a 1-m scale. This study explores the relative impacts of slope, lithology, historical landscape structure and present-day management practices on soil erosion on vineyard hillslopes. The selected plot is located in the Monthelie vineyard hillslopes (Côte de Beaune, France), where intensive erosion occurs during high-intensity rainfall events. Soil erosion quantification was performed at a square metre scale using dendrogeomorphology. For the same plot, planted in 1972, an initial erosion map was drawn in 2004, with a second map being produced in 2012. These two maps, combined with lithology and slope data, the evolution of landscape structure and the evolution of management practices allow the driving factors of water erosion to be assessed. From the 2004 erosion map, we observed that the spatial distribution of erosion, for the thirty-year period after planting, was mainly controlled by lithology and historical landscape structure, whatever the slope. By subtracting 2004 data from the 2012 data, and thus evaluating erosion over the last decade, we discovered that the erosion rate had increased significantly, that spatial distribution of erosion had changed and is now basically controlled by slope steepness and present-day vineyard management practices. Erosion patterns for the last decade show that the impact of historical landscape structure is gradually declining. This study shows that it is crucial to take into account the pre-plantation history of vineyard plots and management practices to further increase our understanding of the spatial distribution of erosion on vineyard hillslopes
Depuis plus de dix ans, des recherches sur les ensembles de structures en pierre sous forêt sont menées dans la forêt du Châtillonnais (Côte-d'Or, France). Les prospections réalisées jusqu'à ce jour consistaient en un relevé GNSS systématique des structures. Ces prospections ont été complétées en 2012 par une acquisition LiDAR sur 400 km² réalisée par les Parcs Nationaux de France (PNF). Cet article a pour objectif de présenter la démarche développée au sein du projet, pour traiter et enregistrer les données archéologiques détectées par le LiDAR. L'enjeu de celle-ci est d'harmoniser la saisie et les modalités d'enregistrement des structures identifiées (à partir des jeux d'images dérivées des données LiDAR) par les différents chercheurs. Pour la mise en place de cette méthode, un test de vectorisation a été réalisé sur un même secteur par huit chercheurs afin d'identifier les biais de vectorisation puis de proposer un processus méthodologique associant les indices de visualisation utilisés, la nature des vestiges à détecter et leur implantation topographique et des techniques d'enregistrement (base de données spatiales, outils de vectorisation, etc.) connectées aux méthodes de relevés de terrain.
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