Physics

Geology

Aerospace engineering

Subpixel rendering

Nadir

Reflectivity

Optics

Civil engineering

Engineering

Pixel

Random forest

Bidirectional reflectance distribution function

Artificial intelligence

Satellite

Land cover

Computer science

Land use

Environmental science

Remote sensing

Remote Sensing of Environment

The free-availability of global coverage Landsat-8 and Sentinel-2 data provides the opportunity for systematic generation of medium spatial resolution land products. This paper presents a combined Landsat-8 Sentinel-2 burned area mapping algorithm. The data handling integrates recent research on Landsat-8 and Sentinel-2 pre-processing to generate registered, surface nadir BRDF-adjusted reflectance (NBAR) sensor time series that are used as an input. The different sensor data are combined through a random forest change regression, trained with synthetic data built from laboratory and field spectra and using a spectral model of fire effects on reflectance. The random forest regression is applied independently at each gridded 30 m pixel location on a temporally rolling basis considering three months of sensor data to map the central month. Temporal consistency checks are used to reduce commission errors due to non-fire related spectral changes, and a region growing algorithm is used to reduce omission errors due to temporally sparse observations. In the resulting product, each 30 m pixel is labelled as burned, unburned or unmapped. At burned pixels the estimated day of burning, and a single value that provides an estimate of the product of the subpixel fraction burned (f) and the combustion completeness (cc), henceforth termed "f.cc", and an associated quality measure, are defined. The algorithm is demonstrated using six months of every available Landsat-8 and Sentinel-2A acquisition over 10° × 10° of Southern Africa. Experiments comparing the mapped burned areas considering Sentinel-2A only and both Landsat-8 and Sentinel-2A data indicate a greater area burned and a smaller number of unmapped pixels when both sensors are used. The results are compared with contemporaneous NASA MODIS fire products to gain insights into their temporal and spatial reporting differences. Temporally, the Sentinel-2A and Landsat-8 30 m product reports the day of burning on average three days later than the MODIS 500 m burned area product, because of the lower revisit frequency of the Sentinel-2A and Landsat-8 observations. Spatially, the Sentinel-2A and Landsat-8 30 m product captures more detail than the MODIS 500 m burned area product, with systematically higher burned area estimates. Despite the areal differences, the spatial pattern of the two products is similar, as reflected by the correlation (r2 ~ 0.7) and slope (>0.8) of regressions of the proportions of area burned defined in coarse resolution cells between the two products, taking into account the 3 day temporal bias. A comparison with multi-date PLANET 3 m data, shows high visual agreement in the location and approximate day of burning and higher f.cc values in the interiors of distinct burns than at the edges, and lower values in areas containing mixes of burned and unburned 3 m pixels. The Landsat-8/Sentinel-2A burned area mapping results are validated by comparison with burned areas interpreted visually from two date Sentinel-2A data, over 30 km × 30 km boxes selected by systematic sampling. The results mapped less area burned than the interpreted maps, as reflected by a 0.24 omission error and a negative relative bias (−0.19), with a small 0.06 commission error. Regression between the proportions of area burned defined in 1.5 km cells provide a high correlation (r2 = 0.89) and slope close to unity (0.82) and indicate that the Landsat-8/Sentinel-2A results identified similar burned area spatial patterns as the interpreted maps.

Landsat-8 and Sentinel-2 burned area mapping - A combined sensor multi-temporal change detection approach