Forest structure classification using airborne multispectral image texture and kriging analysis

Author

Zhang, C. ; Franklin, S.E.

Author_Institution

Dept. of Geogr., Calgary Univ., Alta., Canada

Volume

4

fYear

2002

fDate

24-28 June 2002

Firstpage

2522

Abstract

A study in southwestern Alberta conifer stands was designed based on the premise that obtaining image texture from a kriging surface could improve the accuracy of forest structure classification because the resulting textures would be less sensitive to random variations in spectral response and more related to structural features, such as crown size and shape. We extracted image spectral data, textural derivatives and a kriging surface from multispectral CASI imagery (2 m spatial resolution); 83% classification accuracy (K_hat=0.79) was obtained in nine species composition classes.

Keywords

image classification; image texture; vegetation mapping; CASI imagery; Canada; Kananaskis Valley; Rocky Mountains; airborne multispectral image texture; classification accuracy; crown shape; crown size; forest structure classification; image spectral data; kriging analysis; kriging surface; southwestern Alberta conifer stands; species composition classes; spectral response; structural features; Data mining; Geography; Image analysis; Image resolution; Image texture analysis; Interpolation; Multispectral imaging; Spatial resolution; Surface texture; Vegetation mapping;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing Symposium, 2002. IGARSS '02. 2002 IEEE International

Print_ISBN

0-7803-7536-X

Type

conf

DOI

10.1109/IGARSS.2002.1026598

Filename

1026598