# 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
While the overall accuracy (OA) in this study exceeded 99% for all the products, the BA commission error ratio was above 40% for all products and the omission error ratio was above 65% for all products. A Bayesian algorithm (BY-MODIS) to detect BA, originally developed for the North American boreal forests, was compared with MODIS database BA products MCD64A1 Version 6 and MCD45A1 Version 5.1 for the 2002-2012 period in forest ecoregions of Spain [87]. The total BA distinguished by the BY-MODIS classifier was 78.6% according to the data obtained from the Landsat images, with the lowest average commission error (11%) out of the three products and a correlation (R\({}^{2}\)) of 0.82 [87]. Meanwhile, the commission and omission errors of the GABAM-2015 machine learning classification were 13.17% and 30.13%, respectively, and the overall accuracy reached 93.92% according to preliminary global validation [116]. The GABAM product also detected the largest forest BA in Amazonia, followed by TREES (a regional BA product, developed particularly for the Amazon region), MCD64A1, and Fire_cci [133]. The above studies show that there is a need for global and regional products with higher spatial resolution to provide the most accurate BA estimation. Among the analysed publications, there has been considerable interest regarding satellite-based assessments of BS pattern heterogeneity within the border of a forest fire. For the purposes of the present study, we defined BS as the degree to which an ecosystem has changed due to both short- and long-term wildfire effects [134; 135]. Relative magnitudes of BS are often expressed in terms of specific forest ecosystem characteristics (vegetation, litter, and soil) that are visually ranked after fire occurrence [136; 137]. In the context of remote sensing, BS usually refers to measurements made directly after the wildfire, including metrics such as canopy consumption and tree mortality [134] or consumption and charring of organic soil profiles [137; 138]. The assessment of burn severity in the field is often based on direct measures of the composite burn index (CBI), which was designed to estimate severity in situ by rating the average burn condition on a field plot [134; 139]. During the last two decades, CBI has been frequently used in many studies (Table 2) for validating different remotely sensed products and for comprehensive assessment of BS in a wide range of various forest ecosystems [140; 141; 142; 143; 144; 145; 32]. A drawback of the CBI is that it requires knowledge of the pre-fire conditions of the assessed area [136; 141], and it does not account for differences in the fractional cover of each ecosystem stratum [155]. [PERSON] and [PERSON] [156] proposed a modified geometrically structured composite burn index (GeoCBI) to improve the retrieval of burn severity from remotely sensed data. It is an operational tool to visually assess the burn severity by fire on the ground in five forest strata (soils, understory vegetation, mid-canopy, overstory, and dominant trees). Several studies [157; 158; 159; 160] reported that GeoCBI is more strongly correlated with spectral reflectance of vegetation on burned areas. Other studies have suggested that burned area and burn severity patterns can also be quantified using remotely sensed change in LAI and empirical models [161; 162; 163; 164; 165; 166]. While NDVI is still used in current research, during 2005-2020 (see Table 2), the Landsat normalized burn ratio (NBR) has emerged to be meant for BS assessment of forestry wildfires using remote sensing technology [155; 156; 157; 158; 159; 160]. The NBR (and difference NBR) incorporates information about the spectral changes at the surface using the differing responses of near-infrared (NIR, 0.76-0.90 mm) and mid-infrared (SWIR, 2.08-2.35 mm) bands to generate a scaled index of BS [167; 168; 169; 170; 134].