# Remote Sensing of Forest Burnt Area, Burn Severity, and Post-Fire Recovery: A Review
## 4 Results and Discussions
### BA and BS Estimates
#### 4.1.1 Spectral Indices and Mapping Techniques
Calculated by subtracting the post-fire NBR from the pre-fire NBR, the dNBR approach produces a more accurate assessment of burn severity than the single NBR because it is based on the measurement of per pixel changes in reflectance, making the overall approach more transferable across ecosystems [172; 173; 174; 175; 176; 177; 178]. Delineating the calculated dNBR Landsat images and thresholding them into severity classes has formed the basis for the Monitoring Trends in Burn Severity (MTBS) program, whereby all wildfires larger than 200 ha are mapped at the national level [181; 182; 183]. Both the dNBR and dNDVI indices were accurate in detecting high severity in north Patagonian forests, but to a lesser degree for detecting moderate and low severity burns [184]. [PERSON] et al. [185] evaluated 13 remotely sensed indices (single bands, band ratios, vegetation indices, multivariate components, normalized differences) for mapping burn severity in interior Alaska, and concluded that post-fire NBR gave the best correlations with the field-based CBI in closed needleleaf, mixed, and broadleaf forest classes, followed by the dNBR. However, they warned that the relationship between the NBR and CBI was poorer in areas with sparse tree cover (open woods, scrub, herbs, and pastures). dNBR provides a measure of absolute change, whereas RdNBR was developed as a relative measure to account for spatial variation in the pre-fire leaf area index and to improve estimations in areas with heterogeneous and non-productive vegetation cover [186; 187; 188]. There is no common consent among researchers regarding the use of each BS index in terms of forest ecosystems, terrain, and estimated scales. Some studies have concluded that RdNBR provides a more consistent measure of burn severity with higher classification accuracies than dNBR [189; 190]. Other researchers have reported that the relationships between the NBR and its derivatives (dNBR and RdNBR) to burn severity are highly variable and inconsistent across the burned areas and ecosystem types (savanna, boreal and tropical forests) [191; 192; 193; 194]. The study in southeast China showed that the dNBR is a more reliable index for wildfire burn severity mapping than the RdNBR, which resulted in higher R\({}^{2}\) and smaller RMSE values for the forest area [195]. Based on field data from six fires, a study in the Canadian National Parks revealed that dNBR had higher overall classification accuracy than RdNBR, and that fires with the lowest forest canopy closure and heterogeneity ranking showed no improvement with RdNBR [167]. The results of this research show that the RdNBR-derived model did not estimate burn severity more accurately than dNBR (65.2% versus 70.2% classification accuracy). In addition, the use of NBR or dNBR to assess burn severity may be difficult because of variations in topography and solar elevation angle over the course of the growing season [31; 194]. The relativized burn ratio (RBR), which provides an alternative to dNBR and RdNBR, was proposed as a Landsat-based metric and evaluated for the wildfires in the conterminous western USA [196]. Their results show that the RBR outperformed the dNBR and RdNBR in overall accuracy and in correspondence with field-measured CBI. However, it has also been found out that in heterogeneous areas, with a high presence of shrubs and grass, there were minor differences among dNBR, RdNBR or RBR in estimating BS [197; 198]. While all these standard spectral indices remain the most appropriate for estimating burn severity, there is no strong agreement among researchers regarding the strengths and weaknesses of each NBR-derived index [200; 201; 202]. Several studies have proposed the use of changes in land surface temperature (LST) and land surface albedo (LSA), as land surface biophysical parameters describing the balance of water and energy, for estimating the BA and BS of forest ecosystems [203; 204; 205; 206; 207; 208; 209; 144; 203; 204; 205; 206; 207; 208; 209].