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Open AccessArticle

Automated Extraction of Shallow Erosion Areas Based on Multi-Temporal Ortho-Imagery

Institute of Geography, University of Innsbruck, Innrain 52, 6020 Innsbruck, Austria
Institute for Interdisciplinary Mountain Research, Austrian Academy of Sciences, Technikerstr. 21a, Otto Hittmair-Platz 1, 6020 Innsbruck, Austria
Author to whom correspondence should be addressed.
Remote Sens. 2013, 5(5), 2292-2307;
Received: 30 March 2013 / Revised: 2 May 2013 / Accepted: 2 May 2013 / Published: 13 May 2013
In several areas of the Alps, steep grassland is characterized by shallow erosions. These erosions represent a hazard through the increased availability of unconsolidated material in steep locations, loss of soil and impaired landscape aesthetics. Generally, the erosions concern only small areas but sometimes occur in large numbers. Remote sensing technologies have emerged as suitable tools to study the spatio-temporal changes of these eroded areas. The detection of such eroded areas is often done by manual digitalization of aerial photographs, which is labour-intensive and includes a certain risk of subjectivity. In this study we present a methodological tool that allows the automatic classification of shallow erosions on the basis of orthophoto series. The approach was carried out within a test site in the inner Schmirn Valley, Austria. The study covers both the detection of erosion areas and a multi-temporal analysis of the geomorphological changes. The presented approach is an appropriate tool for detecting shallow erosions and for analysing them in multi-temporal terms. The multi-temporal analysis revealed one period of higher increases in eroded areas compared to shrinking during the other periods. However, the analysis of the change of all single erosions indicates that in each study period there was both increase and decrease of erosion areas. The differences in the rates of increase between the observation years are most likely due to the irregular occurrence of events that encourage erosion. In contrast, the rates of decrease are almost constant and suggest a continuous rate of recovery. View Full-Text
Keywords: shallow erosion; segmentation; change detection; Alps shallow erosion; segmentation; change detection; Alps
MDPI and ACS Style

Wiegand, C.; Rutzinger, M.; Heinrich, K.; Geitner, C. Automated Extraction of Shallow Erosion Areas Based on Multi-Temporal Ortho-Imagery. Remote Sens. 2013, 5, 2292-2307.

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