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Article Dans Une Revue Land Degradation and Development Année : 2013

Mapping linear erosion features using high and very high resolution satellite imagery

Résumé

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 (0.61m resolution). The method is based on a two-stage strategy: (1) extraction of all linear features visible on the satellite image using filters and photointerpretation; (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 0.61m 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.

Domaines

Géomorphologie

Dates et versions

hal-00780345 , version 1 (23-01-2013)

Identifiants

Citer

Jean-François Desprats, Damien Raclot, Marie Rousseau, Olivier Cerdan, Manuel Garcin, et al.. Mapping linear erosion features using high and very high resolution satellite imagery. Land Degradation and Development, 2013, 24 (1), pp.22-32. ⟨10.1002/ldr.1094⟩. ⟨hal-00780345⟩
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