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Title
Detection of gully-affected areas by applying object-based image analysis (OBIA) in the region of taroudannt, Morocco
AuthorD'Oleire-Oltmanns, Sebastian ; Marzolff, Irene ; Tiede, Dirk ; Blaschke, Thomas
Published in
Remote sensing, Basel, 2014, Vol. 2014, Issue 6, page 8287-83309
PublishedMDPI, 2014
LanguageEnglish
Document typeJournal Article
Keywords (EN)object-based_image_analysis / gully / landform_mapping / gully-affected_areas / accuracy / manual_assessment / optical_satellite_data
ISSN2072-4292
URNurn:nbn:at:at-ubs:3-1174 Persistent Identifier (URN)
DOI10.3390/rs6098287 
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 The work is publicly available
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Detection of gully-affected areas by applying object-based image analysis (OBIA) in the region of taroudannt, Morocco [7.23 mb]
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Abstract (English)

Abstract: This study aims at the detection of gully-affected areas by applying object-based image analysis in the region of Taroudannt, Morocco, which is highly affected by gully erosion while simultaneously representing a major region of agro-industry with a high demand of arable land. As high-resolution optical satellite data are readily available from various sensors and with a much better temporal resolution than 3D terrain data, an area-wide mapping approach to extract gully-affected areas using only optical satellite imagery was developed. The methodology additionally incorporates expert knowledge and freely-available vector data in a cyclic object-based image analysis approach. This connects the two fields of geomorphology and remote sensing. The classification results show the successful implementation of the developed approach and allow conclusions on the current distribution of gullies. The results of the classification were checked against manually delineated reference data incorporating expert knowledge based on several field campaigns in the area, resulting in an Overall classification accuracy of 62%. The error of omission accounts for 38% and the error of commission for 16%, respectively. Additionally, a manual assessment was carried out to assess the quality of the applied classification algorithm. The limited error of omission contributes with 23% to the overall error of omission and the limited error of commission contributes with 98% to the overall error of commission. This assessment improves the results and confirms the high quality of the developed Approach for area-wide mapping of gully-affected areas in larger regions. In the field of landform Remote Sens. 2014, 6 8288 mapping, the overall quality of the classification results is often assessed with more than one method to incorporate all aspects adequately.

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