## 1 Introduction  
A forest fire is one of the major disturbances in the ecological diversity, forest succession, the carbon cycle, and hydrological processes of a forest's ecosystem [1; 2; 3; 4]. Habitats are altered [5], carbon is released to the atmosphere [6], and runoff and erosion are increased due to the loss of forest from severe forest fires [7; 8]. After a forest fire, the evaluation of the damage severity, implications, and spatial patterns is important for forest recovery planning, which plays a critical role in the sustainability of the forest ecosystem and carbon cycle [1; 9; 10; 11]. It is also necessary to analyze the growth patterns and responses to forest fire disturbance using time series data [12; 13]. Thus, the process of forest recovery and the ecological and physiological functions of the burned forest area should be continuously monitored. The attributes of forest fires, such as fire severity and total area burned, have been conventionally investigated by field observation. The severity of damage to vegetation and soil is generally classified into three levels of low, moderate, and high severity [1; 14; 15]. However, the field survey has limitations due to frequency of forest fire, assessment difficulty, and the large size of burned areas. For continuous monitoring, field investigators must visit the site multiple times. Remote sensing techniques, such as satellite imaging, can be useful to regularly observe the burned area and damage severity in real time [16]. The burned area can be measured by the combination of the reflectance values of the visible and infrared channels. Many satellite sensors, such as those of Landsat, Aqua, Terra, Envisat, and SNPP, are capable of identifying forest fires and measuring damage severity in burned areas [17; 18; 19; 20]. For example, forest fires can be detected by the brightness temperature of infrared (IR) radiation [21]. In particular, the mid-infrared (MIR) and thermal-infrared (TIR) band are effective to detect forest fire [22]. The burned area and damage severity can be measured using reflectance of near infrared (NIR) and short-wave infrared (SWIR) radiation [23] because the reflectance from living plants and burnt wood are noticeably different. Furthermore, the burned area was extracted from both NIR and the detected forest fire data [19]. The Normalized Burn Ratio (NBR), which uses both NIR and SWIR bands, is widely used to detect burned areas [24]. The Normalized Difference Vegetation Index (NDVI), which uses both red and NIR bands, can identify unhealthy vegetation in burned areas, and has been used to monitor post-fire recovery of forests [25; 26; 27]. Thus, these vegetation and burn indices are suitable not only to detect forest fire regions and measure damage severity, but also to evaluate the forest's recovery progress. [PERSON] [28] showed that forest fire recovery could be evaluated using moderate-resolution satellite imagery to measure the difference in the NDVI between the burned area and the unburned area every year. [PERSON] [29] used the NDVI, Enhanced Vegetation Index (EVI), and Normalized Difference Infrared Index (NDII) to analyze post-fire vegetation recovery. [PERSON] [16] evaluated the sensitivity of vegetation and burn indices to the post-fire recovery of shrubland using seven indices including NBR and NDVI, which were used to evaluate forest recovery. Previous studies that investigated forest fires using remote sensing indices focused on ecological changes, but the physiological states of plants are also important for understanding the newly established forest ecosystem. Generally, the physiological state of a forest can be obtained by examining the carbon dynamics linked to photosynthesis [11]. For example, gross primary production (GPP) can be useful to interpret the physiological state in a forest post-fire because it is strongly related to photosynthetic activities [30; 31]. In this study, we use satellite-based remote sensing data to evaluate forest recovery processes and physiological activity.