Elsevier

Ecological Modelling

Volume 222, Issue 3, 10 February 2011, Pages 835-845
Ecological Modelling

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

https://doi.org/10.1016/j.ecolmodel.2010.11.007Get rights and content

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.

Introduction

The catastrophic Chi–Chi earthquake occurred on September 21, 1999 in Central Taiwan. It caused massive landslides – 2415 people died and 11,306 people were injured. Due to massive debris piled on the slope land after the Chi–Chi earthquake, severe disasters were caused during the typhoon season, such as debris flow, floods, sediment disasters, etc. Effectively monitoring landslides and deriving vegetation restoration information is a vital issue. Ten years have passed since the earthquake occurred. Vegetation restoration at landslide areas has been studied by many researchers (Singh et al., 2000, Lin et al., 2004, Lin et al., 2006, Lin et al., 2008). Integrating remote sensing (RS), geographic information system (GIS), and image classification technique for landslide hazard monitoring and vegetation restoration assessment is very important in the future.

There are many computer models for simulating the dynamics of ecological succession. The Markov chain is mathematically and conceptually the most straightforward succession model presently in use (Usher, 1979, Lepš, 1988). Anderson and Goodman (1957) developed methods for estimating the transitive matrix from observing the states of a system through time. Their methods have been widely developed and applied (Cox, 1972, Kalbfleisch and Lawless, 1985). This has led to its application to vegetation succession (Balzer, 2000, Logofet and Lesnaya, 2000, Benabdellah et al., 2003, White, 2005). Aaviksoo (1993) integrated aerial photograph, GIS, and the Markov chain model to simulate the changes in plant cover and land use types in Estonia (corresponding to 1950s, 1960s, and 1980s). Muller and Middleton (1994) used a first order Markov chain as a stochastic model to make quantitative comparisons of land cover changes between discrete time periods from 1935 to 1981 in the Niagara Region, Ontario, Canada. The result pointed out that the urbanization of agricultural land was the predominant land use change and a continuous exchange of land area occurs between wooded and agricultural land use categories. Boerner (1996) applied the Markov Inertia and Dynamism models on two contiguous Ohio landscapes. Jenerette and Wu (2001) developed the Markov cellular automata model to simulate land use changes in Central Arizona.

From the reasons mentioned above, this paper integrates the RS, GIS, image classification and vegetation succession model (Markov chain) to assess vegetation restoration at landslide areas. It is necessary to understand the reason for non-vegetation restoration at a site because such large mass movements of rocks and soil have already preconditioned the area for failure and may ultimately trigger the next potential hazard.

Section snippets

Chiufenershan

The Chiufenershan landslide is located about 15 km NNE of the epicenter (23.85°N and 120.81°E). The peak ground acceleration (PGA) recorded by a seismic monitoring station from Taiwan's Central Weather Bureau about 6 km north of this landslide was 465.3 gal in the east–west component, 370.5 gal in the north–south component, and 274.7 gal in the vertical component during the Chi–Chi earthquake. The landslide area lies between 23°58′08″N and 23°56′52″N latitudes and between 120°49′36″E and

Materials

Before vegetation restoration assessment, landslide elimination is the most important and first work. NDVI (Normalized difference vegetation index) can be used to quantify vegetation on the surface and is suitable for understanding land cover changes (Hsien, 1996). However, actual landslides may include the collapse of bare lands. If landslide elimination was considered only using image differencing, the bare land collapse areas would not be extracted. Chuang and Lin (2010) used pre- and

Training sample analysis

Training samples, whether good or bad, influence the image classification accuracy. Selecting good training samples for image classification is very important. The catastrophic earthquake caused massive landslides on the mountain slopes, making the growth conditions for plants much worse.

Generally, the growth conditions for grass are easier with a much faster growth rate than trees. Grass is considered as a pioneer species (or an important reference) for early vegetation succession. That is why

Conclusion

Grass can be considered a pioneer species at landslide areas and suitable for a reference index for vegetation restoration. Grass must be extracted individually. This study presented original wavebands plus vegetation index coupled with the back-propagation neural network to produce image classification at landslide areas. Kappa coefficients of image classification were more than 0.7 and thus could be applied for vegetation restoration assessment. Land cover from each stage was prepared using a

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