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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Vydané v:	International journal of remote sensing Ročník 26; číslo 24; s. 5453 - 5465
Hlavní autori:	Buddenbaum, H., Schlerf, M., Hill, J.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Abingdon Taylor & Francis 20.12.2005 Taylor and Francis
Predmet:	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
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Abstract	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.
AbstractList	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.
Author	Hill, J. Schlerf, M. Buddenbaum, H.
Author_xml	– sequence: 1 givenname: H. surname: Buddenbaum fullname: Buddenbaum, H. email: henning@buddenbaum.de organization: Remote Sensing Department , University of Trier – sequence: 2 givenname: M. surname: Schlerf fullname: Schlerf, M. organization: Remote Sensing Department , University of Trier – sequence: 3 givenname: J. surname: Hill fullname: Hill, J. organization: Remote Sensing Department , University of Trier
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Keywords	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
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SubjectTerms	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
Title	Classification of coniferous tree species and age classes using hyperspectral data and geostatistical methods
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