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Abstract:
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A landslide susceptibility map is an essential tool for land-use spatial planning and management in mountain areas. However, a classification system used for readability determines the final appearance of the map and may therefore influence the decision-making tasks adopted. The present paper addresses the spatial comparison and the accuracy assessment of some well-known classification methods applied to a susceptibility map that was based on a discriminant statistical model in an area in the Eastern Pyrenees. A number of statistical approaches (Spearman’s correlation, kappa index, factorial and cluster analyses and landslide density index) for map comparison were performed to quantify the information provided by the usual image analysis. The results showed the reliability and consistency of the kappa index against Spearman’s correlation as accuracy measures to assess the spatial agreement between maps. Inferential tests between unweighted and linear weighted kappa results showed that all the maps were more reliable in classifying areas of highest susceptibility and less reliable in classifying areas of low to moderate susceptibility. The spatial variability detected and quantified by factorial and cluster analyses showed that the maps classified by quantile and natural break methods were the closest whereas those classified by landslide percentage and equal interval methods displayed the greatest differences. The difference image analysis showed that the five classified maps only matched 9 % of the area. This area corresponded to the steeper slopes and the steeper watershed angle with forestless and sunny slopes at low altitudes. This means that the five maps coincide in identifying and classifying the most dangerous areas. The equal interval map overestimated the susceptibility of the study area, and the landslide percentage map was considered to be a very optimistic model. The spatial pattern of the quantile and natural break maps was very similar, but the latter was more consistent and predicted potential landslides more efficiently and reliably in the study area. |
