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], Gross Primary 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. The severely afflicted areas include not only key national areas for ecological function [
7] but also water resource conservation zones for the Chengdu Plain [
7] which is an important habitat for giant pandas and other endangered species [
13]. This region is called the “Green Lung” of the middle and lower reaches of the Yangtze River for its wide range of forest cover [
16], and it performs extremely important ecological functions, including the conservation of soil and water and biological diversity. Therefore, evaluating the ecological damage and recovery of this severely afflicted area is of great importance. This study is an improvement over previous studies, because (1) this is the first study to evaluate vegetation recovery on space and time in severely afflicted area after five years following the Wenchuan earthquake; (2) a new method to quantitatively determine the threshold is proposed to compensate for subjectivity [
14,
15,
16]; and (3) for a disaster event such as an earthquake, six aspects (
i.e., altitude, fault zones, earthquake intensity, soil texture and vegetation types) are normally considered in analyses of vegetation recovery. This approach is synthetic and comprehensive. We tried to include some important aspects for post-disaster assessment in this study, especially for vegetation recovery assessment after the earthquake; (4) we took full advantage of the temporal continuity of the MODIS data to characterise vegetation recovery and changing features between 2009 and 2013 [
14,
16,
18].
4. Discussion
Vegetation damage rate (VDR) is used for describing earthquake-induced vegetation damage. It is assumed that curvature of VDR could reflect abrupt decreases in vegetation related to earthquakes. Based on this assumption, a new method is proposed to quantitatively extract vegetation damage areas. To validate this method, information from 4751 points was collected by field survey, and 2950 of the points (62.09%) were located within the damaged area. The locations of other points outside the damaged area may be due to the following reasons. (1) The landslides were so small (areas less than 250 m × 250 m) that MODIS could not recognize them; (2) The study area was located in mountainous regions covered with dense vegetation and some damaged regions may have been blocked due to the observation angle of the satellite; (3) The study area was covered by clouds that influence the extraction of landslides. Although the most effective monitoring of vegetation damage could have been achieved by using high resolution images such as Landsat TM or Quickbird, low levels of spatial coverage and high costs were the primary limitation. In this mountainous area, another consideration was cloud cover. Because of higher temporal resolution, it is easier for MODIS to remove cloud.
Similarly to Chou [
18] and Lin [
15], vegetation recovery rate (VRR) was used and was divided into four classes for vegetation recovery assessment. However, there were some differences as follows: (1) More aspects were synthesized during the study (
i.e., altitude, fault zones, earthquake intensity, soil texture and vegetation types); (2) Vegetation recovery was analysed over a longer time scale. Vegetation recovery was evaluated for the Wenchuan disaster area from 2009 to 2013, which corresponded to a five-year reconstruction period designated by the Chinese government. This information could provide a more realistic reference for the Chinese government department; (3) The transition matrix for vegetation recovery was applied. During the five-year study period, decreases in vegetation not recovering and recovering slightly promoted the increase of vegetation that largely recovered and vegetation that recovered fully. The most prominent transition was from the vegetation not recovering and vegetation recovering slightly to the vegetation that recovered largely. More interestingly, we also observed that 17.10% of the damaged area suffered degradation. These areas are primarily distributed on ridges or along rivers. This degradation may be caused by two reasons. One reason is that there is an insufficient water supply and poor topsoil on ridges. Another reason is that the Longmenshan Mountains have entered a new period of active geohazards following the Wenchuan [
25]. Geohazards initiated by rainstorm will cause massive landslides and may block rivers and produce barrier lakes in five to 10 years [
25]—an increase in erosion will occur between river banks and toe slopes.
Vegetation recovery observed in our study was primarily distributed in the northeast and southwest regions of the study area. In the northeast regions, such as Qingchuan and Pingwu, vegetation recovered more easily because the relief is much lower than other regions and soil is fertile. However, it is noteworthy that in the southwest regions, such as Lixian, the vegetation recovery rate was also high, even though it is a mountainous region. Another similar finding is that vegetation recovery was stronger with increasing altitude (
Figure 9). This may have been caused by two reasons. One reason is that the vegetation of the high altitude areas was damaged more severely, which provides potential for better recovery [
16]. Another reason is that the vegetation was primarily located on the hillsides where sufficient water could be supplied for herbs. In addition, the proportion of vegetation not recovering at high elevations was also high. This vegetation was located along the ridges where there was a lack of water. Therefore, the vegetation along the ridges did not recover as fast as vegetation on the hillsides. Altitude is an important factor for plant growth, but it is not decisive and it must interact with other factors such as topography and water supply to affect vegetation growth. Lu [
16] suggested that soil moisture is another important factor for plant growth. Nevertheless, according to our study (
Figure 8), there is no obvious relationship between clay content in the topsoil and vegetation recovery. However, a high clay content was beneficial for achieving lower levels of vegetation with no recovery (
Figure 8).
Vegetation type is another significant consideration. According to our study, herb areas such as meadows indicated the best vegetation recovery and they recovered better than woody plants. This confirms Liu’s conclusions [
19]. After Wenchuan earthquake, there has been sufficient precipitation for undersized vegetation (such as shrubs and herbs) under damaged trees which then became healthier [
19]. However, recovery of woody plants will take more time. Therefore, herbs and scrub recovered better than woody plants. The proportion of full recovered grass was slightly lower (only approximately 31.58%). This proportion of recovery was low because the area of grass was small (accounting for 0.08% of the study area) and was influenced by random factors.
There are some uncertainties in vegetation recovery assessment. One uncertainty is due to cloud cover. Some data include levels of cloud cover that restrict the NDVI of vegetation and influences estimates of vegetation recovery rate. Although maximum value composite (MVC) and average value were used to limit cloud effects in our study, bias may also have been created if there was so much cloud cover in the image that we could not obtain enough clear-sky data. Another uncertainty was the VRR. Because VRR is not a normalized variable with a theoretical range from −∞ to +∞, some abnormal high or low values will influence the results, which is the primary reason why VRR is classified in this paper. A third uncertainty is that the MODIS data had low space resolution and could not recognize the smaller damaged area.
5. Conclusion
This is the first study to evaluate the vegetation recovery on a spatial and temporal basis in the five years following the Wenchuan earthquake. For a disaster event such as an earthquake, six aspects (i.e., altitude, fault zones, earthquake intensity, soil texture and vegetation types) are normally considered in analyses of vegetation recovery. Compared with previous studies, our study was comprehensive. We tried to include some important aspects for post-disaster assessment in this study, especially for vegetation recovery assessment after the earthquake. The Chinese government usually requires five years as a period for post-disaster reconstruction. This paper could be regarded as guidance for Chinese government departments whereby additional investment is needed for vegetation recovery.
Even though Zhang [
13], Liu [
19] and Cui [
23] studied vegetation damage and recovery following the Wenchuan earthquake, they did not provide methods for extracting vegetation damage areas. Zhang [
13] used field survey data that required a long period of time and thereby affected the timeliness of disaster relief. This paper uses a mathematical method to rapidly extract vegetation damaged areas. In terms of the techniques and methods, our method is similar to those described in [
14,
15,
16,
17,
18,
21,
22]. We all use image difference technology. However, the referenced studies used experimental thresholds to extract vegetation damage areas. This approach is strongly subjective. For that reason, we have proposed a new method to numerically determine the threshold to extract vegetation damage areas with the goal of reducing the effect of human error and providing more objective and stable results. Using this method resulted in the recognition of 62.09% of the geological hazard points with MODIS data.
The total area of vegetation damage from the Wenchuan earthquake was 475,688 ha, which accounted for 14.34% of our study area and was primarily distributed in the central fault zone, the southwest mountainous area and along rivers in the mid-west region of the study area. Vegetation recovery in damage areas was better in the northeast region of the study area, and in the western portion of the Wenchuan-Maoxian fracture; vegetation recovery was better with increasing altitude. The relationship between vegetation recovery and clay content in the topsoil is not obvious. The best vegetation recovery was for meadows and the worst recovery occurred in mixed coniferous broad leaved forests.
The best vegetation recovery occurred in the northeast and southwest regions of the study area and was better in the mid-west region, but the worst recovery was in the central and eastern regions. After five years of recovery in the severely afflicted area, recovering vegetation accounted for 40% of the damaged areas. The vegetation will continue to primarily transform from vegetation with no recovery to vegetation that has largely recovered, but will never fully recover. Some areas such as southeast proportion of the study area and the mountains have a high proportion of vegetation with no recovery. Since the earthquake, there have been many landslides, debris flows and other geological hazards in the disaster area. A total of 81,338 ha of vegetation is undergoing degradation. This area of degradation accounts for 17.10% of the damaged area. These areas are mainly distributed on ridges or along rivers, and the main vegetation types are coniferous forest (31.39%) and scrub (34.17%). These areas needs to be studied as part of further research. In summary, approximately 41% of the vegetation in the damaged area recovered fully during the study period and it will take years to reach the vegetation level before the earthquake.