Forest Remote Sensing Models

Satellite sensors observe upwelling radiant flux from the Earth’s surface. Classification of forest structures from these measurements is a statistical inference problem. A hierarchical model has been developed by linking several sub-models which represent the image acquisition process and the spatial interaction of the classes.

The model for blur assumes the underlying, unobserved image is degraded according to the system point spread function.

The model for topographic effects assumes the unblurred pixel values are determined by the corresponding bidirectional reflectance distribution function (BRDF) and the mean spectral reflectance of each class. A discrete Markov random field (MRF) model provides information about the spatial contiguity of the classes. Prior distributions are specified for the mean and covariance parameters. Bayes theorem is used to construct a posterior probability distribution for the classification given the data.