# 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
Specifically, satellite data from the VEGFATION (VGT) sensor aboard the European SPOT-4 satellite were used to assess global BA based on large-scale vegetation features or seasonal burning activity in different ecosystems (boreal, tropical, wooded grassland, savanna, etc). [96; 97; 98]. The GLOBSCAR project was initiated in 2001 as a part of the European Space Agency (ESA) Data User Programme, with the objective of producing global BA maps on the base of the Along Track Scanning Radiometer (ATSR-2) instrument onboard the ESA ERS-2 satellite [99]. The Globcarbon BA product (ESA project), which was developed at a spatial resolution of 1 km, 10 km, 0.250 m, and 0.50 m with monthly temporal resolution to focus on ten complete years (1998-2007), can be flexibly applied to existing archives and readily merged into Earth system models [100]. Two NASA BA products, MODIS MCD45A1 [101] and MCD64A1 [102], which map the spatial extent and approximate date of biomass burning worldwide at a spatial resolution of 500 m, have been validated with the use of Landsat scenes in southern Africa, western United States, and central Siberia. Both products were used to conduct a broad range of studies concerning forest fire estimates and biomass burning emissions [103; 104; 105; 106; 107; 108; 109; 110; 111; 112]. The fire information for a resource management system (FIRMS) is also a useful product developed by NASA, delivering MODIS active fire data to forestry managers in more than 90 countries [113; 114]. A new FireCCI50 BA product was developed within the European Space Agency's (ESA) Climate Change Initiative (CCI) programme [115]. The product was generated from the MODIS (2001-2016 archive) red (R) and near-infrared (NIR) reflectance and thermal anomaly data, providing the highest spatial resolution (250 m) among the existing global BA datasets. In recent years, approaches to planetary-scale geospatial analysis of BA have emerged by using a cloud-based Google Earth Engine (GEE) platform with several satellite data catalogues at different spatial resolutions (MODIS, Landsat, and Sentinel-2). In order to provide higher accuracy and detect small, burned patches, a novel 30 m spatial resolution Global Annual Burned Area Mapping (GABAM 2015) technique was proposed based on a huge catalogue of Landsat time series imagery, as well as the high-performance computing capacity of GEE [116; 117]. Cross-comparison with the Fire_CCI50 BA product showed a similar spatial distribution and a strong coefficient of determination (R\({}^{2}\) = 0.74) between the burned areas of the two products, particularly in coniferous forests [117]. Another version of a global automatic BA algorithm at medium spatial resolution of Landsat or Sentinel-2 reflectance images was also developed in GEE [118]. The proposed automatic algorithm has shown globally spatially and temporally coherent results and higher accuracy in the detection of smaller BA. The growing number of global satellite-based BA products available for the international remote sensing community has led to the development of methods and validation protocols to estimate their accuracy metrics. Although over the last two decades there has been extensive research on the topic [47; 119; 120; 121], the basis of global BA accuracy assessment has been built according to the \"good practice\" guidelines for land cover validation procedure [122]. The validation protocols for these products have not been formally produced. Therefore, the procedure and methodology rely on previous research publications and validation exercises [110; 123; 124]. Several studies compared the accuracy of the above global BA products, proving that there are substantial differences between them in many regions of the world [47; 125; 126; 127; 128; 129; 130; 131]. Six global burned area products were compared using the same protocols and reference data [132].