Abstract:
The impact of forest disturbances, especially those caused by hurricanes and
wildland fires, on forest ecosystems and forest carbon sequestration has become
more pronounced in recent years. Remote sensing of these two natural hazards and
their impacts received increasing attention among research communities for the
carbon cycle study and forest management. The research presented in this
dissertation is dedicated to promoting remote sensing of forest disturbances caused
by hurricanes and wildland fires from space.
This study developed an algorithm for rapidly assessing post-hurricane
forest damage using MODIS measurements, without relying on intensive ground
inventory or sampling. The performance of five commonly used vegetation indices
as post-hurricane forest damage indicators was investigated, among which the
Normalized Difference Infrared Index (NDII) was found the optimal vegetation
index. This new algorithm was validated by ground measurements. The validation
showed that the relative change of pre- and post-hurricane NDII was linearly related
to the damage severity estimated in the ground inventory with a coefficient of
determination of 0.79 and p value < 0.0001. This approach was applied to evaluate
forest damage severity and the impacted forest region caused by Hurricane Katrina.
Based on the MODIS enhanced contextual algorithm and a smoke detection
algorithm, an improved algorithm for monitoring low intensity fires in regional
scale was developed. Sources of omission errors in the MODIS active fire product
were diagnosed using a wildland fire database. This database was a collection of
spectral signatures of low intensity fires, including fires missed by the MODIS
enhanced contextual algorithm. This algorithm was applied and evaluated by case
studies, which showed that this improved algorithm was more suitable for regional
low intensity fire detection in the southeastern United States.
These algorithms and findings contribute to studies of the natural hazard
detection, carbon cycle study and forest management. The rapid assessment
algorithm can provide timely information on forest live fuel loading change, impacted
regions and damage severity. The availability of change information on fuel loading
will allow the quantitative study of hurricane impacts on forest fire danger. The
improved fire detection algorithm can provide more accurate information on
wildland fire events and decision support for firefighting activities and forest
management.
