# Remote Sensing of Forest Burnt Area, Burn Severity, and Post-Fire Recovery: A Review
## 4 Results and Discussions
### Monitoring of Post-Fire Forest Recovery
The study results show that the pine (_Pinus densiflora_) ecosystem conditions improved to the initial state 8 years after the fire event, while vegetation biomass required more time to recover. [PERSON] et al. [274] also used changes in NDVI following thirty-five fires in mixed conifer forest in the Sierra Nevada Mountains of California between 1999 and 2006. They reported that post-fire drought has a significant effect on the recovery of burned mixed-conifer forest, while burned red fir forest was sensitive to post-fire temperature. [PERSON] and [PERSON] [275] found that vegetation rapidly recovered in the first three years (NDVI increased substantially) following the wildfire in Bastrop State Park (USA), but that recovery was still significantly delayed compared to the control plot of the field data. On the other hand, the NDVI curves performed differently when estimated by BS level, showing a net decline in heavy and moderate BS areas [275]. Post-fire recovery dynamics were assessed in Mediterranean pine (_Pinus halepensis_ and _Pinus pinaster_) forests (Spain) using temporal segmentation of the Landsat time series (1994-2018) and trajectory metrics from Tasseled Cap Wetness (TCW) and Tasseled Cap Angle (TCA) [276]. The research suggests that pine forest recovery rates were highly correlated to post-fire climate in the mid- and long-term, and with BS in the short-term [276]. Several studies in modelling of forest recovery and regeneration rates adopted other significant factors such as vegetation type, underlying topography, soils, slope-aspect, rainfall, and drought indicators [277; 278; 279]. [PERSON] et al. [280] estimated post-fire forest spectral recovery in the Greater Yellowstone Ecosystem (GYE) using the Landsat Time Series Stack-Vegetation Change Tracker (LTSS-VCT) algorithm and high spatial resolution images from Google Earth. They found that forest regeneration is highly correlated with forest type, elevation, and soil type of the GYE. Another study in Mediterranean forest ecosystems [281] demonstrated evidence that the number of ongoing wildfires within a short period decreased the forest recovery negatively measured as EVI, which was also influenced by micro-climatic effects (soil and environmental humidity). Such studies also suggest that results are more meaningful when other important factors are considered, modelled, and compared with the forest recovery on BA. In addition to optical sensors, the value of using unmanned aerial vehicles (UAV), SAR, and LiDAR imagery for estimation and monitoring of post-fire forest recovery has also been demonstrated [282; 283; 284; 285]. [PERSON] et al. [286] reported that the efficiency of Sentinel-2 satellite data for post-fire forest regeneration monitoring in the north-eastern region of Portugal is more cost-effective than UAV data. Several studies confirmed that the combination of SAR and Landsat bands was essential for estimation of regeneration stages in different forest ecosystems [282; 287; 288]. [PERSON] et al. [259] estimated post-fire vegetation recovery on the Mediterranean island of Thasos using multi-temporal SPOT and European Remote-Sensing (ERS) (C-band VV) images covering the period from 1993 to 2007. Results showed that the use of the ERS backscatter coefficient reduced the commission errors almost by half when mapping the forested areas recovery in Thasos [259]. LiDAR stands out as an important technology capable of being directly associated with ecosystem biomass and post-fire changes on the local level, since it measures the three-dimensional structures of forest cover. [PERSON] et al. [289] estimated post-fire changes in forest canopy height and biomass using airborne LiDAR in western Amazonia. Their results indicated that even ten years after the occurrence of an understory fire event, burned forests had significantly lower biomass and height than control sites. [PERSON] et al. [290] investigated the effect of mixed severity wildfire on the regrowth of open forests in Australia on the base of LiDAR data and field surveys.