The purpose of this study was to develop a method for detecting dead and defoliated spruces and defoliated stands in remote-sensing material using a semi-automatic pattern-recognition technique, spectral properties of trees, and different degrees of defoliation. The study material included two mapped defoliation stands in the municipality of Juupajoki (61°50′N, 24°18′E) in southern Finland. The ground truth data were collected during 1996-1997. The aerial color infrared (CIR) photographs, scaled to 1:5000, were taken on 28 June 1995 and on 19 June 1997. The degree of defoliation was visually estimated for every conifer in the defoliation stands. Individual trees in the digital aerial photographs were segmented using a robust segmentation method based on the recognition of tree crown patterns at a sub-pixel accuracy. The images were filtered with a Gaussian N x N smoothing operator, and local maxima above a threshold level were segmented using a directional derivate with some constraints. The segments were placed into defoliation classes using linear Fisher classification models, the parameters of which were estimated by cross-validation. Discriminant analysis was used to find variables for the segment classification. Defoliated tree segments and stands were classified satisfactorily. The accuracy of the pattern-recognition method proved adequate for detecting dead or heavily defoliated trees and heavily defoliated stands. The method described provides an interesting approach to using digital aerial data for automatically detecting severely defoliated spruce stands or individual trees. © 2002 Elsevier Science B.V. All rights reserved.
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