Remote Sensing Algorithm Development For Wildland Fire Detection And Mapping PDF Download

"This thesis deals with fire monitoring using remote sensing images. The first part of this thesis is devoted to novel global wildland fire detection algorithms, while the remainder concerns mapping of fire regions in high resolution remote sensing images. As a major natural disturbance that has tremendous impact on the environment, humans and the climate, fire is drawing increased attention from both scientists and the general public alike. The most practical and economically feasible manner of monitoring wild land fires is through remote sensing. To date, remote fire detection has been an active research field for many years and a number of algorithms have been proposed for processing satellite and airborne image data. Different from the current algorithms used in forest fire detection, this thesis proposes new approaches based on statistics and image models. These approaches are brought forward using different statistical image models. They incorporate the statistical properties of multi-spectral images and essentially treat fire pixels as anomalies in images. These algorithms discriminate fire pixels based on the Mahalanobis distance in multi-spectral feature space. The use of the Mahalanobis distance naturally incorporates the covariance of the multi-spectral image into the decision and requires the setting of a single detection threshold. ... We demonstrate the flexibility of our algorithm with images taken by two different types of sensors, the MODIS sensors and the Wildfire Airborne Sensor Project (WASP). ... To estimate the extent and spread of wild-land fires in high spatial resolution multi-spectral images, we propose fuzzy clustering algorithms to map fire extent, the active fire front, hot burn scar and smoke region based on a statistical model"--Abstract.