Application of Markov-chain model for vegetation restoration assessment at landslide areas caused by a catastrophic earthquake in Central Taiwan

Abstract

The 921 earthquake caused a catastrophic disaster in Central Taiwan. Ten years have passed since the earthquake occurred. Vegetation succession is the basis for establishing a restoration reference which plays an important role in vegetation restoration at landslide sites. Generally, growth conditions for grass are easier and the growth rate is faster than that for trees. Therefore, grass can be considered a pioneer species or an important reference for the early vegetation succession stage. This is the reason why grass is required to be extracted from other land covers. Integrating remote sensing, geographic information system and image classification into vegetation succession models is very important. In this study, the Markov chain model was applied for vegetation restoration assessment and discussion. Chiufenershan and Ninety-nine peaks were selected as the study areas. Five SPOT satellite images are used for land cover mapping and vegetation restoration simulations. Four categories of land covers were extracted, including forest, grass, bare land and water, respectively. From the transitive probability matrix (derived from any two land covers), the results show that vegetation restoration at the Chiufenershan and Ninety-nine peaks landslide areas is ongoing, but that has been disturbed by natural disasters.