1. Introduction
Fires can influence the global carbon cycle and act as important disruptors of forest ecosystems by damaging large numbers of trees, changing tree species composition, reducing biomass and changing the surface landscape [
1,
2]. On the other hand, the succession resulting from forest fires plays an important role in adjusting the structure of the plant community and maintaining species diversity, which is an indispensable driving force for the development of the forest ecosystem plant community [
3]. The severity of a forest fire relates to the degree of damage inflicted by the fire on the forest ecosystem, including to vegetation, soil nutrients and physical and chemical characteristics of the soil [
4,
5]. The fire severity refers to the loss or decomposition of organic matter aboveground and belowground. Metrics for this parameter vary with the ecosystem. [
6]. The traditional method of calculating the forest fire severity according to the specific investigation technical standard includes observing and recording forest type, height, diameter at breast height and number of dead and surviving trees. The commonly used methods of evaluating forest fire severity include the following steps: (1) selecting a remote sensing spectral index; (2) combining the spectral index results with field investigation data; (3) conducting a regression analysis and establishing a quantitative equation [
7,
8]. Remote sensing information technology has become an important tool for monitoring vegetation growth as information is obtained rapidly and at large spatial scales [
9]. In addition, remote sensing data are widely used in forest fire monitoring and assessment because of the large spatial areas that can be converted and the high temporal resolution and low cost of the data [
10,
11,
12]. In this way, the technology allows for rapid analysis of damage to vegetation during the early post-fire stage as well as monitoring of vegetation succession over the long term. At present, the most widely used indices are the normalized burn ratio (NBR) and delta NBR (dNBR). The dNBR index is considered more suitable among the spectral indices for describing fire severity in dry Mediterranean climates [
13]. Quantitative evaluation of forest fire severity is helpful for revealing the development and change in various ecological processes and the mechanisms responsible for forest vegetation succession after forest fires [
14,
15]. Quantitative evaluation can also be used to estimate loss in biomass resulting from a forest fire and can provide a reference for the study of vegetation recovery and the global carbon balance [
16].
Research on fire severity has becoming increasingly popular in recent years. The traditional plot survey method is based on the composite burn index (CBI), which is a ground-based measure proposed by Key and Benson [
13] in 2006. CBI remains the standard index used in field investigations and evaluations of forest fire severity within the United States Forest Service. The normalized burn index (NBR) was first proposed by Garcia and Lopez [
17] as an alternative to the Normalized Difference Vegetation Index (NDVI), in which the red (R) band in the NDVI calculation formula is replaced by the short-wave infrared (SWIR) band. Further studies have shown that the dNBR is able to better represent the spatial distribution of forest fire severity compared to the NBR [
7,
18,
19]. Previous studies have shown that the NBR index is more sensitive to changes in chlorophyll and vegetation water content, and have concluded that this index is the most valuable remote sensing method for assessing fire severity [
8,
20,
21]. Most past studies of fire severity using remote sensing data are based on the red (R), near infrared (NIR) and short-wave infrared (SWIR) spectral regions [
22]. However, few studies have to date linked the red-edge spectral domain to fire severity. Filipponi [
23] in 2018 proposed the burned area index for Sentinel-2 (BAIS2) based on the Sentinel-2 red-edge spectral band. A study by Fernández-Manso et al. [
24] of fires in Sierra de Gata, mid-western Spain in 2015 found that the red-edge band of Sentinel-2 data is very helpful for estimating the extent of damage caused by fire and for monitoring post-fire reconstruction.
Forest fires result in large-scale destruction of surface vegetation in forest ecosystems, which manifests in remote sensing images as a decrease and increase in the reflectance of the NIR and SWIR bands, respectively [
25,
26]. Vegetation recovery of a burned area is a function of various ecosystem factors. At present, medium and low-resolution remote sensing data are widely used for monitoring of vegetation recovery, including Landsat, Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very-High-Resolution Radiometer (AVHRR) data. Caselles et al. [
27] in 1991 used the NIR and SWIR bands of thematic mapper (TM) images to conduct a disaster assessment following forest fires in Valencia, Spain, and to monitor vegetation regeneration within the burned area. Wimberly and Reilly [
26] used TM images and the NBR to study the relationship between forest fire damage and local biodiversity in the southern Appalachian Mountains. Zhu [
28] used multi-spectral and infrared data from satellites to map and classify the extent and severity of fires in fire-prone areas such as An Ning City, Yunnan Province. MODIS and Landsat TM/Enhanced Thematic Mapper (ETM+) (NASA, America) time series data are often used to conduct research on annual vegetation recovery. The NDVI is related to vegetation growth and coverage, and is generally used as an index to monitor dynamic changes in forest vegetation after a fire [
29,
30,
31]. For example, Lanorte et al. [
32] and Pena et al. [
33] used NDVI to study vegetation recovery after fires of different severity. Xiao et al. [
34] studied the impact of forest fires on vegetation and monitored recovery based on leaf area index (LAI), NDVI, enhanced vegetation index (EVI), land surface water index (LSWI) and other vegetation biophysical variables. Most of these studies were based on individual sources of remote sensing data, using one or several vegetation indices. However, there has, to date, not been a comparison between spectral indices and biophysical indices using high-resolution satellite optical data. This comparison is relevant as it can increase understanding of the mechanisms involved in post-fire vegetation recovery. The Sentinel-2 Multi-Spectral imager (MSI) sensor (ESA) provides opportunities to access open data characterized by a trade-off in spatiotemporal resolution (10–60 m pixel size and a 5-day temporal resolution) and to improve research on post-fire vegetation recovery [
35]. Fernández-Manso et al. [
24] and Navarro et al. [
36] successfully evaluated fire severity based on Sentinel-2 MSI data.
In addition, recent studies [
37,
38] have demonstrated the applicability and even superiority of Sentinel-2 MSI data for natural resource applications. Biophysical indices are the variables related to the forest ecosystem, and those extracted from remote sensing data are mainly related to biophysical variables used in the study of the terrestrial ecosystem, including LAI, photosynthetic effective radiation absorption rate (FAPAR), fractional vegetation cover (FVC), canopy water content (CWC) and chlorophyll content in the leaf (CCL). These five variables represent essential climate variables (ECV) recognized by the Global Climate Observation System (GCOS) and the Global Terrestrial Observation System (GTOS) [
39]. The advantages of the newly developed algorithm for Sentinel-2 imagery are two aspects in comparison with other (e.g., look-up table [
40], the empirical relationship between biophysical variables and vegetation indices [
41], etc.) derived biophysical variables methods. First, the algorithm is generic with no need for input of the specific land cover type and could be easily extended to the retrieval of vegetation biophysical variables at the global scale. Second, the algorithm has been integrated into SNAP software as a Simplified Level 2 Product Prototype Processor (SL2P) (ESA) tool that can be used by the public community to produce biophysical products [
42,
43]. That is, we can easily use the SL2P tool to estimate biophysics from a regional level to a global level. Therefore, the Sentinel-2 biophysical estimated by this algorithm will be widely used in ecological environments, even in various fields [
43].
To date, there has not been a comparison between spectral indices and biological indices using high-resolution satellite optical data. The aim of this study was to quantify the short-term dynamic changes of vegetation post-fire. The burned area resulting from a mega forest fire in Bilahe in 2017 was used as a case study, and vegetation short-term dynamic changes after fires of different fire severity was studied using biophysical variables such as LAI and FVC generated from Sentinel-2 satellite data. The specific objectives of the present study were to: (1) extract the burned area and classify the severity of the fire using the BAIS2 index [
23], and to verify these results against the NBR; (2) compare spectral indices with biophysical variables to identify biophysical variables that are more suitable for evaluating fire severity; (3) use biophysical indices to quantitatively measure vegetation changes of various land cover types in response to forest fires.
4. Discussion
The present study used the BAIS2 index to extract the burned area, classify the severity of the fire damage and verified the results using the NBR. NBR has become the established remote sensing spectral index for research into the identification of burned area as it is more suited for reflecting fire severity than other indices [
60,
61]. BAIS2 used the inter-band ratio of the red band spectral domain to detect the burned area and to monitor changes in vegetation after the fire. The BAIS2 index combined with the band ratio to detect radiation response in the SWIR band is considered to be an effective method of determining the burned area [
23]. Morresi et al. [
31] similarly showed that a spectral vegetation index based on the SWIR band is highly sensitive to fire damage to forest cover and subsequent restoration of forest structure.
LAI and FVC were selected as the key biophysical variables to examine in the present study. FVC is an important variable for the study of the ecological effects of vegetation at large spatial scales [
62,
63]. Ecological effect can be defined as the degree of impact on the quality of an ecological environment [
64]. The increase of FVC in the study area was found to have a significant ecological effect. Nowadays, LAI has become an important variable to measure urban ecological benefits and has been widely used in urban green space planning.
Changes in green vegetation cover can be said to be one of the clearest and most ecologically significant effects of fires [
65]. Vegetation cover and leaf state/color change are key inputs to CBI [
5] and have made significant contributions to dNBR of remote sensing [
38,
66,
67], such as the recent research results of Chuvieco et al. [
22]. From a spectral point of view, the use of LAI to quantify burn severity is reasonable. LAI is more directly related to other key aspects of forest management compared to NBR. By measuring combustion severity as the change in LAI, fire impact data and other ecological information can be more effectively integrated, and forest landscapes at risk to fires can be managed scientifically [
19]. FVC as an index is a continuous scale of the proportion of green vegetation in the landscape [
56] and is a promising index for the evaluation of vegetation recovery after a fire [
68]. A recent study [
69] used an FVC Landsat time series to successfully demonstrate different approaches to forest recovery after a fire in a Siberian Larch forest. However, at the pixel level, BAIS2 is highly correlated with LAI and FVC, and part of the scattering may reflect the ability of BAIS2 to detect fire effects rather than the loss of canopy leaves, such as changes in plant water content and carbonization of soil surfaces. In general, LAI and FVC have high correlations with BAIS2 as well as to other biophysical variables. The use of satellite time-series NDVI data and derived pheno-metric indicators show the potential for tracking the dynamics of vegetation cover and continuous changes to wildfire interference and forest restoration processes [
70]. By using quantitative inversion, the health trajectory of the ecosystem can be rapidly determined, and therefore this method can play an irreplaceable role in the realization of sustainable development in the study area. Therefore, it is of great scientific significance to quantitatively retrieve vegetation variables by remote sensing [
71].
The spatial distributions of the LAI and FVC area were extracted for various land cover types of burned area of different fire severity within the study area during the period 2016–2018 for the years before and after the fire during the month of the fire. Although no field survey data were used to verify the threshold of the burn severity level in BAIS2 in the present study, the difference in the short-term dynamic changes of biophysical variables post-fire is obvious when the burn severity increases. Since it is great that changes have taken place so that forest fires can result in a change in vegetation, the increase or decrease of biophysical variables can reflect the severity of a forest fire. Vegetation type and fire severity have a significant influence on the recovery of the burned area, and the impact of fire severity on vegetation recovery after a fire is critical; however, the interaction between fire severity and vegetation type is not significant [
32]. It was found that the rates of growth of LAI and FVC were fastest and most significant in the severely burned area in the year after the fire. During the early stage after a severe fire, the canopy density of the forest was low, resulting in sufficient light falling to the floor of the forest to allow prolific growth of the shrub and grass layer [
3]. Shi et al. [
72] found that severe fire can promote an increase in biomass of undergrowth vegetation during the early stage of recovery. The LAI and FVC values of the low fire severity area were higher than those of the moderate and high fire severity areas in May and September of the fire year. This may have been due increased opportunities for seeds to establish in soil, which is conducive to regeneration of coniferous forests [
73], and good natural regeneration and vegetation recovery was observed in the low fire severity area [
74]. Carter et al. [
75] found that fires of moderate severity are beneficial for the development of the shrub and herb layers and can promote carbon storage of vegetation. The LAI of forest was larger than that of grassland in the year before and after the fire, whereas during the fire year, the LAI of forest was smaller than that of grassland. The effect of fire severity on forest recovery after a fire was greater than that on shrub, grassland and swamp vegetation types, and fire severity also has a significant effect on the vertical structure of the forest community.
Research into understanding the relationship between the burned area and vegetation’s short-term dynamic change characteristics is of great importance for estimating hydrogeological risks, such as triggered debris flow due to heavy rain. Abbate et al. [
76] attempted to quantitatively explain the influence of wildfire on terrain characteristics by simulating the key variables, although they did not address the dynamics of the two debris flow events in a more quantitative and targeted approach. The use of Sentinel-2 remote sensing data provides a large amount of data for rather isolated zones that cannot be studied by conventional on-site monitoring. Therefore, the present work can be of great importance for the quantification of wildfire on terrain characteristics, and particularly on the influence of wildfire in hydrogeological modeling.