## 1 Introduction  
A disturbance is a relatively discrete event that disrupts the structure of an ecosystem, community or population [1; 2]. Currently, human activities and natural hazards are the primary disturbance factors for vegetation and ecosystems. Research on vegetation and ecosystem disturbance primarily centres on human activities [3; 4; 5], such as resource exploitation, construction [6] and urban expansion [7]. In addition, natural hazards also disturb ecosystem and vegetation groups, such as climate change [8; 9], fire [10], flood [11], earthquake and draught [1]. Short-term earthquake damage and subsequent impact on the local ecosystem were analysed with respects to vegetation damage and recovery. Based on various studies, there are two ways to study vegetation damage and recovery. The first method is to calculate vegetation density, species diversity, basal area, _etc._ from field data and use statistical analysis to study vegetation damage and recovery [12; 13]. This method is very precise but requires a large amount of manpower and requires more resources such as money and equipment. Because this method requires a long time for collection of observed data, it is not conducive to large-scale monitoring of vegetation recovery. The second method involves the use of indices, such as vegetation recovery rate [14; 15; 16], collapse rate [15] and landscape pattern metrics [17]. Statistical methods are used to obtain the regional spatial distribution patterns of vegetation damage and vegetation recovery change over time. This approach is suitable for rapidly and efficiently extracting damaged areas and evaluating vegetation damage and recovery in a disaster area. The data are available through a wide range of disaster surveys and long-term assessments. However, there are some problems: the threshold for extraction of vegetation damage area is experimental and strongly subject to randomness [14; 15; 16]; previous scholars have studied vegetation recovery over one or two years and did not have enough images to describe the yearly change of vegetation recovery [14; 16; 18]. Relative variables are often used to detect vegetation change and express vegetation information in remote sensing analysis and are categorized as the Normalized Differential Vegetation Index (NDVI) [15; 16; 18], Fractional Vegetation Coverage (FVC) [17], vegetation cover index [14; 18], GrossPrimary Productivity (GPP) [19], _etc._ There are two types of methods for extracting vegetation damage: one is to use a change detection threshold [15, 16], and the other is to employ image classification [18, 20]. Of these, the former is more subjective, and the latter requires repeated image classification, the reliability of which seriously affects the precision of the extraction of the damaged area. There are also two ways to validate the extraction accuracy of the damaged area: one is to use images with higher spatial resolution to validate the image with lower resolution [17], and the other is to examine the extraction using field data [21]. The vegetation recovery can be characterised by the vegetation recovery rate [14, 15, 16, 17], landscape pattern metrics [17, 21], land cover variation [21], _etc._ At present, there are two main study events to examine earthquakes as a major disturbance factor; namely, the Jou-Jou earthquake that occurred in Central Taiwan in 1999 [14, 15, 18, 20, 21, 22] and the Wenchuan earthquake that occurred on the Chinese Mainland in 2008 [7, 16, 17, 23]. This paper utilizes previous variables such as Vegetation Damage Rate (VDR) [17] and Vegetation Recovery Rate (VRR) [14, 15, 16, 17] to extract vegetation damage area and evaluate vegetation recovery. However, this paper emphasizes the vegetation recovery after five years and a synthetic and comprehensive analysis (considering more influence factors on vegetation recovery) is used. The Wenchuan earthquake, which occurred on 12 May 2008, resulted in substantial damage to the local ecological environment and human infrastructure.