Classification of coniferous tree species and age classes using hyperspectral data and geostatistical methods

Classifications of coniferous forest stands regarding tree species and age classes were performed using hyperspectral remote sensing data (HyMap) of a forest in western Germany. Spectral angle mapper (SAM) and maximum likelihood (ML) classifications were used to classify the images. Classification w...

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Bibliographic Details
Published in:International journal of remote sensing Vol. 26; no. 24; pp. 5453 - 5465
Main Authors: Buddenbaum, H., Schlerf, M., Hill, J.
Format: Journal Article
Language:English
Published: Abingdon Taylor & Francis 20.12.2005
Taylor and Francis
Subjects:
Animal, plant and microbial ecology
Applied geophysics
Biological and medical sciences
Earth sciences
Earth, ocean, space
Exact sciences and technology
Fundamental and applied biological sciences. Psychology
General aspects. Techniques
Internal geophysics
Teledetection and vegetation maps
Central Europe
Germany
textures
variograms
accuracy
vegetation
trees
Stem
Hyperspectral imaging sensor
imagery
maximum likelihood
spatial resolution
geostatistics
Introduced species
Forest stand
density
gymnosperms
Europe
Plantae
remote sensing
classification
forests
airborne methods
channels
Gray scale
Spermatophyta
Coniferales
age
Coniferous forest
ISSN:0143-1161, 1366-5901
Online Access:Get full text
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Summary:Classifications of coniferous forest stands regarding tree species and age classes were performed using hyperspectral remote sensing data (HyMap) of a forest in western Germany. Spectral angle mapper (SAM) and maximum likelihood (ML) classifications were used to classify the images. Classification was performed using (i) spectral information alone, (ii) spectral information and stem density, (iii) spectral and textural information, (iv) all data together, and results were compared. Geostatistical and grey level co-occurrence matrix based texture channels were derived from the HyMap data. Variograms, cross variograms, pseudo-cross variograms, madograms, and pseudo-cross madograms were tested as geostatistical texture measures. Pseudo-cross madograms, a newly introduced geostatistical texture measure, performed best. The classification accuracy (kappa) using hyperspectral data alone was 0.66. Application of pseudo-cross madograms increased it to 0.74, a result comparable to that obtained with stem density information derived from high spatial resolution imagery.
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content type line 23
ISSN:0143-1161
1366-5901
DOI:10.1080/01431160500285076