Angela De Santis

TL;DR: In this paper, the authors applied the inversion of a simulation model to estimate burn severity in terms of the Composite Burn Index (CBI) and compared the performance of the model inversion method with standard empirical techniques.

TL;DR: In this article, a modified Composite Burn Index (CBI) is proposed to improve the retrieval of burn severity from remotely sensed data, which takes into account the fraction of cover (FCOV) of the different vegetation strata used to compute the CBI.

TL;DR: In this paper, an improved simulation model that combines PROSPECT and GeoSail to estimate burn severity from satellite data was tested in three Mediterranean forest fires, and the determination of burn severity was based on a new version of the CBI index (named GeoCBI), that takes into account the vegetation fraction cover (FCOV) to compute burn severity of the total plot.

TL;DR: In this article, a burn severity map of two large fires in California was obtained by inverting a simulation model constrained by post-fire observations from Landsat TM imagery, which was then used to adjust BE reference values per vegetation type found in the area before the fire.

TL;DR: In this paper, the effects of changes in soil background, leaf color and leaf area index as a result of different burn severities can be simulated with two-layer RTM models in the forward mode and also in an inverse mode, and therefore burn severity can be retrieved from remotely sensed data by comparing measured and simulated reflectance.