Date of this Version
Portions of this dissertation can be found in the following articles:
Peterson, D., Wang, J., Ichoku, C., Hyer, E., & Ambrosia, V.: A sub-pixel-based calculation of fire radiative power from MODIS observations: 1. Algorithm development and initial assessment, Remote Sensing of Environment, accepted 2012.
Peterson, D., & Wang, J.: A sub-pixel-based calculation of fire radiative power from MODIS observations: 2. Sensitivity analysis and potential fire weather application, Remote Sensing of Environment, accepted 2012.
Peterson, D., Hyer, E., & Wang. J.: A short-term predictor of satellite-observed fire activity in the North American boreal forest: toward improving the prediction of smoke emissions, Atmospheric Environment, in review.
For over two decades, satellite sensors have provided the locations of global fire activity with ever-increasing accuracy. However, the ability to measure fire intensity, know as fire radiative power (FRP), and its potential relationships to meteorology and smoke plume injection heights, are currently limited by the pixel resolution. This dissertation describes the development of a new, sub-pixel-based FRP calculation (FRPf) for fire pixels detected by the MODerate Resolution Imaging Spectroradiometer (MODIS) fire detection algorithm (Collection 5), which is subsequently applied to several large wildfire events in North America. The methodology inherits an earlier bi-spectral algorithm for retrieving sub-pixel fire area and temperature, but also makes a new and important advancement for the derivation of FRPf by accounting for solar and atmospheric effects as a function of Earth-satellite geometry at the MODIS fire detection channels. The retrieved fire (flaming) area is assessed using high-resolution airborne data (3-50 meters), and shows that the FRPf, in combination with retrieved fire area, allows a large fire burning at a low intensity to be separated from a small fire burning at a high intensity. While variations in the atmospheric profile may increase the potential for error, the algorithm is much more sensitive to errors in 11 µm background brightness temperature, where an error of only 1.0 K may alter the retrieved fire area by an order of magnitude or more. These sources of uncertainty can be reduced through the summation of individual pixel-level retrievals for large clusters of fire pixels, which can be defined based on the resolution of a mesoscale model grid. An independent test reveals that unlike the standard MODIS pixel-based FRP, the flux of FRPf per fire pixel cluster, defined as FRPf divided by the retrieved fire area, has a stronger and statistically significant correlation with surface (10-meter) wind speed (R = 0.55) and air temperature (R = 0.77), especially for large fire events. Comparisons between FRPf flux and smoke plume height data, provided by the Multi-angle Imaging SpectroRadiometer (MISR), also produce a much stronger correlation (R = 0.49) compared to the current MODIS FRP (R = 0.16). These strong relationships, combined with additional applications in the North American boreal forest, uniquely demonstrate that FRPf flux not only provides an enhanced characterization of fire weather, but is also an improved quantitative tool for identifying the thermal buoyancy required to estimate smoke plume heights. This information can be used to advance the prediction of smoke emissions and transport, especially when applied to the next generation of satellite sensors.
Adviser: Jun Wang
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