The airborne hyperspectral remote sensing is used as an approach to monitor actual state of environmental components. This thesis covers priority treatment to analyse of hyperspectral data with the aim of a tree crowns delineation. Specific algorithm applying adaptive equalization and the Voronoi diagrams is designed to subdivide a forest area into individual trees. A computer program executes the algorithm and allows testing it on real data, checking and analyzing the results.

Segmentation of an image into individual tree crowns is a key step in the processing of remotely sensed data for forestry practice. The doctoral thesis gives a broad overview of this topic. It comprehends theoretical context from mathematical point of view and defines basic terms from airborne imaging and laser scanning. Mathematical methods of tree detection are focused on a robust adaptation to the actual conditions in a region of interest. A novel approach of crown area delineation is introduced, it combines a seeded region growing technique with an active contour as a crown boundary representation. The parametrisation of all algorithms is analysed in a practical half of the thesis and more application-oriented issues are mentioned. Executable computer programs are attached.