Simulation approaches for burn severity estimation using remotely sensed images

Traditional field-based methods for estimating burn severity are time-consuming, labour intensive and normally limited in spatial extent. Remotely sensed data provide a means to estimate severity levels across large areas, but it is critical to understand the causes of variability in spectral response with variations in burn severity. Since experimental measurements over a range of burn severities are difficult to obtain, the simulation tools provided by radiative transfer models (RTM) offer a promising alternative to better understand factors affecting burn severity refl ectances. Two-layer RTM, such as the combined leaf (Prospect) and canopy (Kuusk) model can be used to simulate a wide range of burn severity conditions. Specifically, 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. This approach can provide a deeper understanding of the effects of each factor in satellite-sensed reflectance, as well as their relative importance. Additionally, RTMs can also be used in an inverse mode, and therefore burn severities can be retrieved from remotely sensed data by comparing measured and simulated reflectance. Examples of these two-way modes of RTMs are presented in this paper. Burn severity was measured using the Composite Burn Index (CBI). The Kuusk model was used to simulate scenarios of different combinations of changes in the substrate, upper vegetation, and lower vegetation strata. This paper shows some results of inverting the simulated reflectances to estimate CBI from calibrated reflectance derived from different satellite sensors. The case study is based on a large forest fire that affected central Spain in July, 2005. Landsat-TM, SPOT-HRV, IRS-AWIFS, Envisat-MERIS and Terra-MODIS data were used for this retrieval. Determination coefficients (r2) values range between 0.436 (MODIS) to 0.629 (Landsat-TM), with lower precision for the intermediate range of CBI values.