Earthquake-induced landslide hazard and vegetation recovery assessment using remotely sensed data and a neural network-based classifier: a case study in ... View Full Text


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Article Info

DATE

2008-03-07

AUTHORS

Wen-Tzu Lin, Wen-Chieh Chou, Chao-Yuan Lin

ABSTRACT

A catastrophic earthquake with a Richter magnitude of 7.3 occurred in the Chi-Chi area of Nantou County on 21 September 1999. Large-scale landslides were generated in the Chiufenershan area of Nantou County in central Taiwan. This study used a neural network-based classifier and the proposed NDVI-based quantitative index coupled with multitemporal SPOT images and digital elevation models (DEMs) for the assessment of long-term landscape changes and vegetation recovery conditions at the sites of these landslides. The analyzed results indicate that high accuracy of landslide mapping can be extracted using a neural network-based classifier, and the areas affected by these landslides have gradually been restored from 211.52 ha on 27 September 1999 to 113.71 ha on 11 March 2006, a reduction of 46.24%, after six and a half years of assessment. In accordance with topographic analysis at the sites of the landslides, the collapsed and deposited areas of the landslide were 100.54 and 110.98 ha, with corresponding debris volumes of 31,983,800 and 39,339,500 m3. Under natural vegetation succession, average vegetation recovery rate at the sites of the landslides reached 36.68% on 11 March 2006. The vegetation recovery conditions at the collapsed area (29.17%) are shown to be worse than at the deposited area (57.13%) due to topsoil removal and the steep slope, which can be verified based on the field survey. From 1999 to 2006, even though the landslide areas frequently suffered from the interference of typhoon strikes, the vegetation succession process at the sites of the landslides was still ongoing, which indicates that nature, itself, has the capability for strong vegetation recovery for the denudation sites. The analyzed results provide very useful information for decision-making and policy-planning in the landslide area. More... »

PAGES

331-347

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11069-008-9222-x

DOI

http://dx.doi.org/10.1007/s11069-008-9222-x

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1011782961


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