Desprats J. F., Raclot Damien, Rousseau M., Cerdan O., Garcin M., Le Bissonnais Y., Ben Slimane A., Fouche J., Monfort-Climent D. (2013). Mapping linear erosion features using high and very high resolution satellite imagery. Land Degradation and Development, 24 (1), p. 22-32. ISSN 1085-3278.
Titre du document
Mapping linear erosion features using high and very high resolution satellite imagery
Desprats J. F., Raclot Damien, Rousseau M., Cerdan O., Garcin M., Le Bissonnais Y., Ben Slimane A., Fouche J., Monfort-Climent D.
Source
Land Degradation and Development, 2013,
24 (1), p. 22-32 ISSN 1085-3278
Mapping and monitoring linear erosion features (LEFs) over large areas is fundamental for a better understanding of the main erosion processes and for planning suitable protection measures. The advent of very high-resolution satellite imagery has expanded the range of satellite LEF identification to moderate-size elements. After determining the relationship between satellite imagery resolution and the ability to detect LEFs, we discuss a highly automated method for extracting such LEFs from a very high spatial resolution image (0.61?m resolution). The method is based on a two-stage strategy: (1) extraction of all linear features visible on the satellite image using filters and photo-interpretation; (2) filtering these linear features according to geometric criteria (e.g. orientation relative to slope, sinuosity, position in landscape, etc.) so as to retain only those relative to linear erosion. A series of three images with increasing spatial resolution (10.5 and 0.61?m) was prepared for an area on the Cap Bon peninsula (Tunisia). This predominantly agricultural area has a high density of LEFs with very varied geometric characteristics. The area's problems are both onsite for the agriculture itself, and offsite with the silting up of hillside reservoirs. Respectively 22 per cent, 37 per cent and 73 per cent of the site's LEFs, with respective average widths of 2.8, 3.0 and 2.2?m, are visible on the 10, 5 and 0.61?m resolution images. Gully identification should help to identify the most threatened areas to help land use planning and management or to validate erosion models whether at regional or local (drainage basin) scale.
