Amalgamation in landslide maps: effects and automatic detection

Abstract

Abstract. Inventories of individually delineated landslides are a key to understanding landslide physics and mitigating their impact. They permit assessment of area–frequency distributions and landslide volumes, and testing of statistical correlations between landslides and physical parameters such as topographic gradient or seismic strong motion. Amalgamation, i.e. the mapping of several adjacent landslides as a single polygon, can lead to potentially severe distortion of the statistics of these inventories. This problem can be especially severe in data sets produced by automated mapping. We present five inventories of earthquake-induced landslides mapped with different materials and techniques and affected by varying degrees of amalgamation. Errors on the total landslide volume and power-law exponent of the area–frequency distribution, resulting from amalgamation, may be up to 200 and 50%, respectively. We present an algorithm based on image and digital elevation model (DEM) analysis, for automatic identification of amalgamated polygons. On a set of about 2000 polygons larger than 1000 m2, tracing landslides triggered by the 1994 Northridge earthquake, the algorithm performs well, with only 2.7–3.6% incorrectly amalgamated landslides missed and 3.9–4.8% correct polygons incorrectly identified as amalgams. This algorithm can be used broadly to check landslide inventories and allow faster correction by automating the identification of amalgamation.

Prediction of the area affected by earthquake-induced landsliding based on seismological parameters

“…A drawback is that this approach requires polygon inventories. Using 95 % of the landslide number as a threshold for point-based inventories would be an adequate solution but this definition would still be quite sensitive to effects affecting the number of identified landslides such as the imagery resolution and/or amalgamation of adjacent smaller landslides (Marc and Hovius, 2015). Another drawback is that R 95 assumes equal rates of decrease of landslide density with distance from the emission line in all directions, which may not always be the case.…”

Factors controlling landslide frequency–area distributions

“…Delineating landslide polygons is subjective and depends on the methodology followed, the skill of the interpreters, and the time invested in the inventory (Soeters and van Westen, 1996). Poor image resolution or contrast between affected and unaffected areas might be another reason for amalgamation (Marc and Hovius, 2015). Poor image resolution or contrast between affected and unaffected areas might be another reason for amalgamation (Marc and Hovius, 2015).…”

“…Adjacent landslides commonly are delineated as a single polygon if their runouts or scars overlap and differentiation is difficult Jibson, 1995, 1996). Marc and Hovius (2015) propose a method for automatic detection and separation of amalgamated polygons. Lack of temporal resolution also can cause amalgamation of landslides.…”

Semiautomated object-based classification of rain-induced landslides with VHR multispectral images on Madeira Island

“…Applying automated methods can contribute to more efficient landslide mapping and updating of existing inventories, and in recent years the number and variety of approaches has rapidly increased (Guzzetti et al, 2012;Holbling et al, 2012;Marc and Hovius, 2015). Landslides can display highly heterogeneous sizes, demanding information with higher spatial resolutions in order to produce complete event inventories.…”