# Remote Sensing of Forest Burnt Area, Burn Severity, and Post-Fire Recovery: A Review
## 4 Results and Discussions
### BA and BS Estimates
#### 4.1.2 Active RS Sensors  
During the last decades, several techniques have been developed for using synthetic aperture radar (SAR) and LiDAR data for BA mapping and fire damage assessment in forested areas [125; 149; 163; 212; 213]. Unlike optical sensors, the radar data can be obtained around the clock, at any position of the sun and adverse weather conditions, including rain, smoke, and clouds, which is crucial in emergency situations. SAR uses microwave energy for BA mapping, given that the backscatter signal is sensitive to vegetation structure and forest biomass [214; 215]. Radar-based measurements, such as polarimetric decomposition coefficients, interferometric coherence, and backscatter intensities, show satisfactory BAestimation accuracies in wildfire-affected forests [134]. The detection of BA from SAR is based on ecological changes that occur after wildfire, including the removal of tree canopies, exposure of rough ground, and increased water content in the burned surface [216; 217]. These changes are directly proportional to the degree of BA, appearing on SAR images as relatively darker objects compared with non-affected forest areas, particularly when rainfall occurs after the fire [218]. With the launch of ESA's Sentinel (S) satellite, SAR images are now easily accessible with systematic acquisitions at a 5 \(\times\) 20 m spatial resolution, which largely increases the capacity of BA detection [219; 220]. The combined use of the S1 SAR and S2 Multi Spectral Instrument (MSI) sensors made it possible to detect 36,000 ha of BA in Marantaceae forests of the Congo Basin, characterized by open tree canopy and a tall herbaceous layer [27]. A new approach in the use of Sentinel-1A and RADARSAT-2 data from different orbits demonstrated their good agreement and potential of a thresholded, multi-temporal, \(\alpha\)-angle-based index (ND\(\alpha\)l) for mapping BA in a fire-prone region of the Western Cape (SA) [221]. The study also revealed that large omission errors, correlating strongly with areas of high local incidence angle, could be minimised by the combined use of both datasets. Another study of BA detection and mapping in tropical and sub-tropical Africa based on two Sentinel-1 and one Sentinel-2 algorithms suggested that optical-based algorithms provide a significant increase in accuracy compared to radar-based algorithms [131]. Light detection and ranging (LiDAR) has recently emerged as a powerful tool for producing 3D forest structural attributes, mapping BA, and assessing post-fire regeneration [222; 223; 224; 225]. Some studies have combined multispectral/hyperspectral data and LiDAR data to estimate BS patterns using analysis of tree height and forest canopy structure [226; 227; 228; 229; 230; 231]. There has been only a few studies estimating the spatial-temporal match of pre- and post-fire LiDAR acquisitions to quantify BS on forest structure. [PERSON] et al. [232] assessed the accuracy of classifying colour-infrared imagery in combination with post-fire LiDAR, and with differenced (pre- and post-fire) LiDAR, in a second-growth coast redwood forest, CA. The research demonstrated a moderate increase in overall accuracy compared to the use of imagery alone; the model using NDVI and post-fire LiDAR was 85% accurate. [PERSON] et al. [233] developed a methodological approach combining post-fire airborne LiDAR data and field assessed CBI to estimate BA in Spanish forests. Developed regression models confirm that classified maps of BA are acceptable up to 85.5% overall accuracy. [PERSON] et al. [234] investigated the application of photon counting LiDAR data from NASA Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) to classify BA over forests in northern California and western New Mexico. The classification results indicate 83% overall accuracy of BA maps based on spaceborne LiDAR, Sentinel-2, and Landsat 8 derived samples [234].