Recovery of Forest Vegetation in a Burnt Area in the Republic of Korea: A Perspective Based on Sentinel-2 Data  
[PERSON]  
1 Safety Inspection for Infrastructure Laboratory (SIIL), Advanced Institute of Convergence Technology (AICT), 145 Gwanggyro-ro, Yeongong-gu, Suwon-Si 16229, Yeongonggi-do, Korea; [EMAIL_ADDRESS] (Y.K. ); [EMAIL_ADDRESS] (M.Y. ); [EMAIL_ADDRESS] (J.K.)  
[PERSON]  
2 Department of Civil Engineering, Chosun University, 309, Pilnum-daero, Dong-gu, Gwangju 61452, Korea 2* 2* 2* 2* 3  
Detecting the vegetation regeneration area's exact boundary after a forest fire is a significant point in selecting a forest restoration target area. The Republic of Korean government categorizes forest fires as a major disaster and establishes comprehensive forest fire prevention and recovery measures nationwide [2]. When establishing a countermeasure against forest fires, it is essential to accurately divide regional boundaries for budget distribution. This study is meaningful because it accurately identifies the renewable area's spatial boundary after a forest fire. Moreover, by providing a scientific and accurate methodology for detecting regeneration areas, it is possible to establish a forest fire recovery plan. Following the forest fires in Yangyang county, Gangwon province, in 2005, it took more than three years for the trees to be restored; the environment is currently similar to that before the fires, but complete restoration of the ecosystem remains underway. Thus, an extensive period is required to restore forests after a fire. According to the National Institute of Forest Science, it takes an estimated two to thirty years to restore a forest and more than 100 years to restore acidified soil after a fire [3]. In the aftermath of a forest fire, forest restoration is conducted using various methods; however, removing all of the damaged trees from the fire site and planting new trees is the most common method, which is the most straightforward technique for assisting vegetation regeneration [4]. However, this method is costly because it requires significant time, labor, and financial resources; healthy seedlings that were not damaged in the fire were also removed in this process, which is a more significant problem. Satellite images are used for disaster management globally [5]. These images can be used to analyze large areas, such as forest fires [6; 7]. Due to their broad-spectrum, multispectral images are the most widely used type of images for observing disaster-prone areas [8; 9]. To explore fire-prone areas with multispectral images, studies have mainly been conducted using indices, such as the Vegetation Index (VI) and burn index, that are based on the band characteristics of different wavelengths [10]. Among these, Advanced Very-High-Resolution Radiometer (AVHRR) data and the Normalized Difference Vegetation Index (NDVI) were used to define a new hazard index in which the NDVI value is modulated to the fire probability map [11]. Other biological indices have been developed to compare various vegetation indices, such as the Soil-adjusted Vegetation Index (SAVI), Enhanced Vegetation Index (EVI), and Burn Area Index (BAI) [12; 13; 14]. In a follow-up study on an affected area after a forest fire outbreak, the vegetation's regenerative capacity was evaluated according to various vegetation indices and fire severity indices via the spectra of multispectral satellite images [15]. The correlation between damage and regenerative capacity was measured by monitoring vegetation regeneration by comparing the NDVI before and after a fire [16]. The regenerative vegetation capacity is mainly assessed by the burn index, which considers the long-term effects of fires on plants [17; 18]. Normalized Burn Ratio (NBR) is generally used with NDVI. Furthermore, studies have also been conducted to derive correlations by analyzing the recovery period according to plant moisture conditions using the temporal patterns of NBR and NDVI [19; 20; 21; 22].