Abstract
Abstract. Landslide mapping benefits from the ever increasing availability of Earth Observation (EO) data resulting from programmes like the Copernicus Sentinel missions and improved infrastructure for data access. However, there arises the need for improved automated landslide information extraction processes from EO data while the dominant method is still manual delineation. Object-based image analysis (OBIA) provides the means for the fast and efficient extraction of landslide information. To prove its quality, automated results are often compared to manually delineated landslide maps. Although there is awareness of the uncertainties inherent in manual delineations, there is a lack of understanding how they affect the levels of agreement in a direct comparison of OBIA-derived landslide maps and manually derived landslide maps. In order to provide an improved reference, we present a fuzzy approach for the manual delineation of landslides on optical satellite images, thereby making the inherent uncertainties of the delineation explicit. The fuzzy manual delineation and the OBIA classification are compared by accuracy metrics accepted in the remote sensing community. We have tested this approach for high resolution (HR) satellite images of three large landslides in Austria and Italy. We were able to show that the deviation of the OBIA result from the manual delineation can mainly be attributed to the uncertainty inherent in the manual delineation process, a relevant issue for the design of validation processes for OBIA-derived landslide maps.
Highlights
Landslide mapping benefits from the ever increasing availability of Earth Observation (EO) data resulting from programmes like the Landsat programme of NASA and the Sentinel missions of the European Copernicus programme
On the manually delineated lowest likelihood level, the landslide polygon near Badia (Figure 4) includes a vegetated area as part of the landslide that has not been indicated by the object-based result, since there is no spectral indication of a landslide
The results would lead to a better estimation of acceptable levels of disagreement between results from semi-automated landslide mapping approaches and results from manual delineation of landslides
Summary
Landslide mapping benefits from the ever increasing availability of Earth Observation (EO) data resulting from programmes like the Landsat programme of NASA and the Sentinel missions of the European Copernicus programme. In addition to the high resolution (HR) optical satellite imagery mentioned above (Landsat, Sentinel-2), very high resolution (VHR) optical satellite imagery, e.g. WorldView, QuickBird, Pléiades, are used for landslide detection and inventory preparation (Scaioni et al, 2014; Singhroy, 2005, van Westen et al, 2008). Manual visual image interpretation is useful for mapping shallow landslides causing significant changes on the surface that are well perceivable in satellite images by their colour contrast, shape, size, pattern and texture (Morgan, 2010), or in case of stereographic imagery elements of the third dimension (Scaioni et al, 2014). The lack of repeatability renders manual interpretations somewhat subjective
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