Classification of main forest types integrating GIS and TM imagery by applying supplementary data: A case study in Vetyem, SW-Hungary

Landsat Thematic Mapper (TM) imagery was integrated with information of forest management units including vegetation and terrain parameter (aspect) to evaluate the extent of their influence on the classification accuracy of stands dominated by different tree species. After preprocessing of the image, supervised and unsupervised classifications were performed and integrated with information of tree composition classes as well as with aspect classes to derive cross tabulation results of classified images with these different thematic maps for subsequent statistical analysis. Performing Topographic Normalization using Minnaert constant could not improve the classification accuracy. We intuitively used Correspondence Analysis (CA) of multivariate statistics for evaluation of association between introduced parameters and unsupervised classified images.

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Publication Details

Published
2026-01-08
DOI
https://doi.org/10.1201/9781003761808-49
Primary Topic
Remote Sensing and LiDAR Applications
Type
article
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article

Classification of main forest types integrating GIS and TM imagery by applying supplementary data: A case study in Vetyem, SW-Hungary

Adel Sepehry, Ferenc Horváth, Hamid Ejtehadi
Remote Sensing and LiDAR Applications
article

Classification of main forest types integrating GIS and TM imagery by applying supplementary data: A case study in Vetyem, SW-Hungary

Adel Sepehry, Ferenc Horváth, Hamid Ejtehadi
article en

Abstract

Landsat Thematic Mapper (TM) imagery was integrated with information of forest management units including vegetation and terrain parameter (aspect) to evaluate the extent of their influence on the classification accuracy of stands dominated by different tree species. After preprocessing of the image, supervised and unsupervised classifications were performed and integrated with information of tree composition classes as well as with aspect classes to derive cross tabulation results of classified images with these different thematic maps for subsequent statistical analysis. Performing Topographic Normalization using Minnaert constant could not improve the classification accuracy. We intuitively used Correspondence Analysis (CA) of multivariate statistics for evaluation of association between introduced parameters and unsupervised classified images.

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