Fire temperature retrieval using constrained spectral unmixing and emissivity estimation
Abstract
Accurate retrieval of wildland fire temperature from remote imagery would be useful in improving prediction of
fire propagation and estimates of fire effects such as burn severity and gas and particle production. The feasibility
of estimating temperatures for subpixel fires by spectral unmixing has been established by previous work with
the AVIRIS sensor. However, this unmixing approach can also produce optimizations for temperatures that may
not be physically related to the fraction of flaming combustion in a pixel. Furthermore, previous techniques have
treated fire as a blackbody and have modeled the mixed pixel transmitted radiance as two blackbody sources.
This first order approximation can also affect the temperature retrieval. Knowledge of emissivity and use of
a more complex radiance model should improve the accuracy of the temperature estimation. We therefore,
propose a technique which improves the previous approach by using the potassium emission to pre-determine
pixels that actually contain signal from flaming combustion and a modified mixed pixel radiance model. A
non-linear, constrained multi-dimensional optimization procedure which estimates flame emissivity was applied
to the model to estimate fire temperature and its areal extent. Results are shown for AVIRIS data sets acquired
over Cuiaba, Brazil (1995) and the San Bernardino Mountains (1999).