1. Introduction
Wildfires in the U.S. have roughly tripled in size over the past 40 years, with “wildfire season” emerging as a growing threat in California [
1,
2]. The frequent occurrence of fires has prompted adaptive changes in vegetation communities in forest systems to cope with ecological reconstruction [
3,
4,
5]. Nevertheless, the regeneration of post-fire vegetation communities still depends on many external factors, such as short-term and long-term changes in the climate and environment in the fire area, the topography and geomorphological composition of the burned area, burn severity, and species competition in the post-fire vegetation community [
6,
7,
8,
9].
The Earth’s average surface air temperature has risen by 0.07 °C (0.13 °F) per decade since 1880, and the rate of temperature change over time has more than doubled to 0.18 °C (0.32 °F) since 1981 [
10]. According to the State of the Global Climate 2020 report, 2011–2020 was the warmest decade on record, and 2020 was one of the three warmest years ever recorded [
11]. Global warming has emerged as the most prominent risk of our time. Zhuang et al. (2021) found that atmospheric changes can explain up to one-third of the climate trends favoring wildfires, while global warming contributes to at least two-thirds [
12]. This also seems to explain why 2020 was the worst wildfire season in California’s modern history [
13].
Climate factors may alter the regeneration of post-fire vegetation communities. Applestein et al. (2021) evaluated a dataset from the Soda Fire area on the Idaho/Oregon border and found that sagebrush density increased with increasing temperature and rainfall. The annual sagebrush area increase was associated with increased spring rainfall in the year following the fire, but not with fall rainfall in that same year [
14]. This suggests that the climate in the first growing season experienced by vegetation may be very important for the post-fire regeneration of vegetation communities [
15]. Examining satellite imagery of burning subtropical savannas in different seasons, Danielle et al. (2020) found that post-fire vegetation took less time to recover during wet growing seasons than during dry dormant seasons [
16]. However, hot and dry climates were negatively correlated with the recent post-fire seedling abundance [
17]. In addition, a dry climate means a longer regeneration time for forests, and the resulting long-term damage could lead to increased permanent carbon loss in these fragile ecosystems [
18,
19,
20].
Wildfires directly change the original form and composition of forest ecosystems, and different burn severities have different gradient effects on the regeneration of vegetation communities [
21,
22,
23]. Furthermore, wildfires provide a new scenario for species competition. Repeated high-intensity wildfires in the forests of the Klamath Mountains in Northern California catalyzed the invasion of non-native plant species. These invasive species increase fire frequency and compete with native vegetation for early moisture, possibly accelerating the loss of previously dominant species in the region [
24,
25].
Due to its wide coverage, instantaneous imaging, and dynamic monitoring, remote sensing technology has been used as an efficient method to capture wildfires when they occur and to monitor the environment and post-fire vegetation regeneration [
26,
27,
28,
29,
30]. Sentinel-2(MSI), provided by the European Space Agency, is a high-resolution multispectral imaging satellite, and its unique three red-edge band data (B5, B6, B7) cover the 703–783 nm band, making it is very effective for monitoring vegetation growth [
31,
32,
33]. In addition, the revisit period of the A and B satellites is shortened to 5 days, which is very suitable for capturing a time-series observation of post-fire vegetation regeneration [
34,
35].
Most previous research has focused on using remote sensing data to estimate burn severity and map wildfire burn areas [
36,
37]; to measure the impact of burn severity and climate factors on post-disaster vegetation communities [
38,
39,
40,
41]; or to conduct long-term, large-span observations of vegetation categories of interest [
42,
43,
44,
45]. However, few studies have conducted short-term intensive time-series change observations on the integration of multiple environmental factors in post-fire vegetation communities.
This paper verifies the regeneration trend of post-fire forest and shrub communities in the short-term (367 days) period using two topographic factors, three climate factors, and five burn severities. The research includes: (1) the classification of vegetation communities and the division of burn severity; (2) the use of 13 monthly Sentinel-2(MSI) image data to track and analyze changes in vegetation communities and burn severity; (3) the use of a multiple linear regression model to analyze the effects of temperature, rainfall, and daytime on the regeneration of vegetation communities; (4) the use of slope and aspect data, combined with 0.5 m resolution historical images from ESRI World Imagery Wayback, to analyze the impact of sunny slopes and shady slopes on the regeneration of two vegetation communities; and (5) the quantification of the regeneration of forest and shrub communities under different burn severities.
3. Methods
Figure 3 displays a flow chart of the spatiotemporal monitoring and analysis of post-fire vegetation regeneration. First, this paper constructs 13 Sentinel-2 time-series datasets that were available in certain months between July 2020 and October 2021. By resampling and preprocessing to improve the images and to calculate the spectral indices, the spatiotemporal features of vegetation pixels and fire pixels were obtained. Second, according to the spectral index and the spectral information of ground objects, the data of time-series vegetation and burn severity for each of these 13 months were classified by Support Vector Machine (SVM) and K-Means. In addition, the changes in the vegetation communities over time were quantitatively analyzed. Third, we analyzed the relationship between the regeneration of vegetation and three climate factors: temperature, rainfall, and daytime. The relationship was verified by establishing a multiple linear regression model. Furthermore, slope and aspect data, combined with ESRI World Imagery Wayback historical images, were used to analyze the effects of sunny and shady slopes on the two vegetation communities. Finally, the regeneration rate of vegetation under different burn severities was quantitatively analyzed by extracting the burn severity mask.
3.1. Pre-Processing
First, all Sentinel-2 image data used in this study were radiometrically calibrated and atmospherically corrected by Sen2Cor, and the image spatial resolution was resampled to 10 m in SNAP (Sentinel Application Platform). Next, mosaicking, clipping, band calculation, and other processing methods were carried out using Envi v5.3. Of these, SNAP is a remote sensing image processing software developed by the European Space Agency, which is mainly used to process sentinel data [
49].
As the study area was located in a high-altitude, mountainous region, the terrain shadow caused by light and shadow considerably affected the classification effect of ground objects. This required the correction of the Sentinel-2(MSI) imagery for terrain and different lighting effects that was finished using Envi v5.3 based on the C correction method. The digital elevation model (DEM) was resampled and used in conjunction with the S2 images to form terrain slope and aspect data. These two variables, along with the solar zenith angle and azimuth at the time of the capture of the images, were used as the C correction algorithm input parameters [
50].
This paper uses the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), the modified soil-adjusted vegetation index (MSAVI), the normalized difference red edge vegetation index (NDRE), the normalized burn rate (NBR) as vegetation classification indicators (
Table 3). We first visually interpret the classification capabilities of these five vegetation indices through the K-Means pre-classification method. Then, use the SVM algorithm to compare the classification accuracy of the vegetation index. Relying on these spectral indices can effectively eliminate the influence of external factors (such as atmosphere and terrain) on the vegetation classification results. They are also suitable for capturing and tracking the calculation of vegetation communities and burn severity in large-scale wildfires, which can effectively improve the accuracy of time-series monitoring. The NDVI is sensitive to chlorophyll and is very useful and reliable in tracking changes in vegetation over time, especially for monitoring forest disturbances, such as wildfires and pests [
51,
52]. The EVI is an improvement on the NDVI and is superior in reducing background and atmospheric effects and saturation. The combination of the two can complement and improve the monitoring of vegetation changes [
53,
54]. The MSAVI can minimize the influence of soil background on vegetation classification [
55,
56]. The NDRE uses the characteristic red edge band of Sentinel-2 combined with the near-infrared band, which has a better monitoring effect on dense vegetation in the overgrowth period [
57,
58]. The dNBR is derived from the NBR and is suitable for burn severity classification calculations [
59,
60,
61].
3.2. Mapping the Classification of Vegetation Communities and Burn Severity
3.2.1. Classification Method
Based on the obtained time-series Sentinel-2(MSI) image dataset, this paper uses SVM and dNBR threshold to classify vegetation and burn severities in 13 images [
62,
63]. The vegetation community and burn severity were classified using image data from 17 July 2020 and 20 October 2020. July is a season of lush vegetation, so it is suitable for collecting data on vegetation distribution pre-fire. Meanwhile, 20 October was the sixth day that the fire stopped expanding, so the fire signs were clear and suitable for collecting burn severity data. This paper defines the vegetation classification in the study area in three categories: forests, shrubs, and sparse/bare land; and burn severity was defined across five gradients: no severity/very low severity, low severity, moderate severity, high severity, and very high severity.
3.2.2. Accuracy Assessment
Specifically, this paper compares the classification results of five effective vegetation indices, selecting the classification combination with the highest accuracy. The accuracy was verified by selecting high-resolution image data with similar imaging dates from Google Earth Pro historical images, and 389 sample points were randomly collected and nested in the classified vegetation images for interpretation. The results were interpreted using confusion matrix mapping, with producer’s accuracy (PA), user accuracy (UA), overall accuracy (OA), and KAPPA coefficients (Equations (1)–(4)) as the accuracy verification standards [
64]. The burn severity was classified according to the classification proposed by the European Forest Fire Information Service (EFFIS) using the dNBR threshold as the calculation index. The classification results were compared with the fire area information published by the US Forest Service to verify the accuracy.
where
Pii is the number of correctly classified samples;
N denotes the total number of samples; and
Ri and
Ci represent the total number of vegetation community
i in the correct data and the total number from the classification results, respectively.
3.3. Relationships between Post-Fire Vegetation Community Regeneration and Environmental Factors
In this paper, three climate factors (temperature, rainfall, and daytime) and two topographical factors (slope and aspect) were examined within one post-fire year as environmental factors that may be related to the regeneration of vegetation communities. By using the unary Linear Regression and multiple linear regression model, a relationship analysis was carried out between the three climate factors and the regeneration evolution of the two vegetation communities. In addition, through World Imagery Wayback high-resolution historical image data provided by ESRI, combined with slope and slope aspects, the forest and shrub communities were examined in three periods (pre-fire, post-fire, and short-term regeneration). Growth images were used to analyze and compare the topographic effects of sunny slopes, shady slopes, and slopes on vegetation community regeneration.
3.4. Vegetation Community Regeneration under Different Burn Severity
To further quantify the relationship between burn severity and vegetation regeneration (see
Section 4.3), we used Envi v5.3 software to create masks for the burn severity of four gradients, respectively, and nested the mask samples within the time-series vegetation area, obtaining a vegetation area map at different burn severities. We then used statistical analysis to obtain the regeneration rates of forest and shrub communities under five burn severities.
5. Discussion
Ascertaining the effects of burn severity and environmental factors on the early resto-ration of forest ecosystems is the first important step in ecosystem regeneration and species protection [
81,
82,
83,
84,
85]. In this study, we used Sentinel-2(MSI) intensive time-series imagery to explore the regeneration changes in post-fire forest and shrub communities in the year on a wide spatial scale. The independent and combined effects of climate factors on vegetation regeneration is emphasized in our work.
Using the dNBR threshold as a criterion for the classification of the burn severity has a high accuracy. The differential normalized burning index (dNBR), which includes NIR and SWIR spectral bands, is commonly used for monitoring burn areas [
86,
87,
88]. The European Forest Fire Information System (EFFIS) provides a threshold classification standard based on the dNBR index, which is very useful for determining burning area and degree [
89,
90]. In addition, previous studies have shown that dNBR is more accurate with higher resolution than other indices [
91,
92,
93]. For example, Llorens et al. (2021) found that the burning estimation results of Sentinel-2 in the Spanish forest fire in 2017 were 10% higher than those of MODIS. In remote sensing image processing, the K-Means clustering algorithm can also attain good analytical results by controlling the threshold of dNBR through the cluster centers of different clusters [
94,
95]. Therefore, this study used Sentinel-2 images to compare the classification results based on the dNBR threshold index with the K-Means clustering algorithm. The results show that the overall accuracy based on the dNBR threshold was improved by approximately 4.67%. The K-Means clustering method overestimates the area of the undamaged region, which may be limited due to the spatial resolution of the image.
The NBR is the best index for vegetation classification in this study (OA: 92.3%–99.5%), but this does not deny the classification effect of other indexes [
96,
97,
98]. In addition, previous research has shown that ground feature classification combined with spectral index, spatial features, and other indicators is significantly effective [
99,
100,
101]. In this study, a new classification method was applied: (1) five vegetation indexes were pre-classified with the K-Means method. This step visually filtered some indexes that are difficult to cluster (such as the EVI index in this study); (2) the pre-classified image was superimposed with the spectral information of ground objects. By using the SVM classification algorithm, these mixed pixels were further filtered, and the classification accuracy was improved. According to the results, the classification accuracy based on the SVM is approximately 6.6% higher than that of K-Means, and the main confusion part is between shrub communities and sparse bare land.
The severity of burns and climate change affected the early forest regeneration process [
102,
103]. The remote sensing characterization of burn severity and climate factors shows that the first post-fire growing season may be a key element in vegetation regeneration. Temperature, rainfall, and daytime have significant effects on vegetation regeneration (R
2 is 0.42–0.88) [
104,
105,
106]. The results also show that there was a significant relationship between burn severity and forest regeneration rate in the whole study area [
107,
108,
109]. Forest and shrub communities in the study area have high adaptability to post-fire ecology and the species competition started immediately [
110,
111,
112]. For example,
Figure 13 shows that tolerable low-severity burns can positively promote the regeneration of forest communities (the regeneration rate increases by 6.37 times). However, the high-severity burn causes almost no regeneration of forest communities, while the shrub communities show rapid growth (the rate increases by 19.37 times). This may be because forest communities can maintain growth dominance in low-severity areas. However, with the gradual increase in burning degrees, this advantage eventually weakens to the lowest in the high-severity area, resulting in the high-speed growth of shrub communities. The study area has a Mediterranean climate, which is mild and rainy in winter, and hot and dry in summer. Through multivariate regression analysis, it was found that the two vegetation communities had a significant need for rainfall in the regeneration period (February 2021 to October 2021). Rainfall was the only significant factor for shrub community growth, and both daytime and rainfall acted on the regeneration of forest communities. Furthermore, terrain and other factors also have an important impact on vegetation regeneration [
113,
114]. By randomly sampling on a spatial scale, we found that the best slope for vegetation regeneration was 15–35°. Slope direction has no obvious effect on forest communities, while shrub communities tend to grow on the shady slopes. This is probably because shady slopes are conducive to maintaining humidity for shrub communities.
There are some uncertainties in this study. First, we only used the S2 satellite, which is limited in terms of spatial resolution and spectral precision. In addition, the January 2021 image data were not included in this study due to quality issues, such as clouds and shadows. However, from another point of view, this limitation was also an advantage. Our method shows that vegetation regeneration research conducted over a wide range of space can still be completed using medium-resolution S2 images, despite the lack of high-resolution sensors [
115,
116]. The high timeliness return visit cycle of the S2 satellite also provided an early intensive time-series monitoring mode with strong timeliness in this study. Second, as the division map of burn severity was not officially released, the accuracy verification could only rely on the overall accuracy and the accuracy of the dNBR threshold classification (OA: 94.87%).
There are two important insights to be drawn from our research. First, time-series monitoring with strong timeliness in the short term can better capture the early dynamics and trends of vegetation regeneration [
117,
118] and is conducive to the development of long-term strategies for forest adaptive management and vegetation protection. Second, the combination of vegetation index, pre-classification by K-Means, and the SVM classification algorithm with spectral information can effectively improve the classification accuracy, providing a new concept for the accurate classification of medium-resolution sensors. This work can provide valuable analysis and verification of the early regeneration of post-fire forests and can be used for adaptive management and the oriented cultivation of native dominant species.
6. Conclusions
In this study, we used the NBR as a spectral indicator to monitor vegetation communities in a time series, and used the dNBR index and SVM classification methods to smoothly map the distribution of forest and shrub communities across five burn severities combined with spectral features. First, the classification based on the dNBR threshold can effectively express 94.87% of the burning area. Furthermore, the vegetation classification overall accuracy based on the SVM algorithm is 6.6% higher than that of K-Means. Together, these studies provide insights into the interaction mechanisms between climate factors, topographic factors, burn severity, and the regeneration of the two vegetation communities. The results show that this method can effectively reflect the regeneration of the two vegetation communities. In total, 367 days of post-fire vegetation regeneration, the cumulative area of bare soil decreased by approximately 6511.9 hectares (around 12% for forests and 88% for shrubs). For forests, according to the analysis and quantification results, it was found that due to the increase in temperature and daytime duration in the first half of the year, the regeneration rate of forests in spring and summer was approximately 40 hectares/month more than in autumn and winter. In addition, the regenerated forests were mainly concentrated in the low-severity area (about 88%), indicating that the regeneration of forest communities not only has strong seasonality but that it could also be promoted by tolerable fire stimulation. For shrubs, it was found that they can grow at a high rate of 298.6 hectares/month on average due to overcoming and alleviating external restrictions; that is, the competitive inhibition of forests to shrubs that occurred in the low-severity areas was overcome in the medium- and high-severity areas. Furthermore, among the climate factors, both daytime and rainfall had a significant impact on forests’ regeneration, while rainfall was the only factor that had an effect on shrubs’ regeneration. In addition, according to the slope aspect data, the shrub communities mainly grew on shady slopes, which greatly alleviated the issue of insufficient humidity caused by drought, indicating that shrubs maintain high adaptability to fire ecology. Taken together, our findings contribute to a better understanding of the mechanisms and methods between environmental factors and burn severity. In addition, they provide ideas for adaptive management in forests with different vegetation communities according to climate and seasonal changes and burn severity in order to protect these precious forest resources.