Détection d'éléments linéaires des paysages cultivés sur profils altimétriques LiDAR : application à la détection de réseaux hydrographiques artificiels

[Departement_IRSTEA]DS [TR1_IRSTEA]METHODO / SYNERGIEInt. J. Remote Sens. ISI Document Delivery No.: 302WB Times Cited: 14 Cited Reference Count: 41 Bailly, J. S. Lagacherie, P. Millier, C. Puech, C. Kosuth, P. Taylor & francis ltd AbingdonInternational audienceLinear features of agrarian landscapes are the anthropogenic elements such as hedgerows, ditches, and bench terraces that strongly impact agrarian areas' environmental behaviors, especially the ecological and hydrological ones. The need to map these linear elements for environmental impact assessments of agrarian areas is thus crescent since these maps limit the developments of spatial indicators and spatially distributed modelling. Until now, no generic remote sensing methodology has been proposed for such mapping purposes. This research was designed to assess the use of airborne LiDAR data for agrarian landscape linear features mapping. We proposed a methodology that uses LiDAR data in three steps. We first estimated elevation profiles from LiDAR points on a set of pre-located sites. We secondly performed profile shape description with wavelet transform or a watershed algorithm. Finally, we classified the profiles using classification trees with predictors coming from shape analysis. Methodology accuracies were calculated for a ditch network detection problem in a Mediterranean vineyard landscape. LiDAR Toposys data and field survey data for ditch location were collected in June 2002. As ditches are always located on field boundary lattices, elevation profiles were only computed on field boundary sites. Methodologies, using wavelets or the watershed algorithm, gave similar accuracies. Overall accuracy is about 70% with a high ditch omission rate (50 %) but low commission rate (15 %). The omissions conform to the ones obtained when performing visual classification of profiles. This high omission rate in ditch detection is therefore due to LiDAR data, not methods. Dense vegetation over ditches during the summertime and the specific LiDAR points spatial sampling design explain these omissions. However, the proposed methodology, especially using wavelets transform, looks transposable for the automatic detection or characterization of other agrarian linear features