Classification of vegetation above the tree line in the Krkonoše Mts. National Park using remote sensing multispectral data

This paper compares suitability of multispectral data with different spatial and spectral resolutions for classifications of vegetation above the tree line in the Krkonoše Mts. National Park. Two legends were proposed: the detailed one with twelve classes, and simplified legend with eight classes. A...

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Main Authors: Renáta Suchá, Lucie Jakešová, Lucie Kupková, Lucie Červená
Format: Article
Language:English
Published: Karolinum Press 2016-06-01
Series:Acta Universitatis Carolinae Geographica
Subjects:
Online Access:http://aucgeographica.cz/index.php/aucg/article/view/71
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spelling doaj-2744846fc9ae46d3b864ebac4f0544e52020-11-24T23:08:16ZengKarolinum PressActa Universitatis Carolinae Geographica0300-54022336-19802016-06-0151111312910.14712/23361980.2016.103782Classification of vegetation above the tree line in the Krkonoše Mts. National Park using remote sensing multispectral dataRenáta SucháLucie JakešováLucie KupkováLucie ČervenáThis paper compares suitability of multispectral data with different spatial and spectral resolutions for classifications of vegetation above the tree line in the Krkonoše Mts. National Park. Two legends were proposed: the detailed one with twelve classes, and simplified legend with eight classes. Aerial orthorectified images (orthoimages) with very high spatial resolution (12.5 cm) and four spectral bands have been examined using the object based classification. Satellite data WorldView-2 (WV-2) with high spatial resolution (2 metres) and eight spectral bands have been examined using object based classification and per-pixel classification. Per-pixel classification has been applied also to the freely available Landsat 8 data (spatial resolution 30 metres, seven spectral bands). Of the algorithms for per-pixel classification, the following classifiers were compared: maximum likelihood classification (MLC), support vector machine (SVM), and neural net (NN). The object based classification utilized the example-based approach and SVM algorithm (all available in ENVI 5.2). Both legends (simplified and detailed ones) show best results in the case of orthoimages (overall accuracy 83.56% and 71.96% respectively, Kappa coefficient 0.8 and 0.65 respectively). The WV-2 classification brought best results using the object based approach and simplified legend (68.4%); in the case of per-pixel classification it was the SVM method (RBF) and detailed legend (60.82%). Landsat data were best classified using the MLC (78.31%). Our research confirmed that Landsat data are sufficient to get a general overview of basic land cover classes above the tree line in the Krkonoše Mts. National Park. Based on the comparison of the data with different spectral and spatial resolution we can however conclude that very high spatial resolution is the decisive feature that is essential to reach high overall classification accuracy in the detailed level.http://aucgeographica.cz/index.php/aucg/article/view/71vegetation above the tree lineKrkonoše Mountainsobject based classificationper-pixel classificationmultispectral data
collection DOAJ
language English
format Article
sources DOAJ
author Renáta Suchá
Lucie Jakešová
Lucie Kupková
Lucie Červená
spellingShingle Renáta Suchá
Lucie Jakešová
Lucie Kupková
Lucie Červená
Classification of vegetation above the tree line in the Krkonoše Mts. National Park using remote sensing multispectral data
Acta Universitatis Carolinae Geographica
vegetation above the tree line
Krkonoše Mountains
object based classification
per-pixel classification
multispectral data
author_facet Renáta Suchá
Lucie Jakešová
Lucie Kupková
Lucie Červená
author_sort Renáta Suchá
title Classification of vegetation above the tree line in the Krkonoše Mts. National Park using remote sensing multispectral data
title_short Classification of vegetation above the tree line in the Krkonoše Mts. National Park using remote sensing multispectral data
title_full Classification of vegetation above the tree line in the Krkonoše Mts. National Park using remote sensing multispectral data
title_fullStr Classification of vegetation above the tree line in the Krkonoše Mts. National Park using remote sensing multispectral data
title_full_unstemmed Classification of vegetation above the tree line in the Krkonoše Mts. National Park using remote sensing multispectral data
title_sort classification of vegetation above the tree line in the krkonoše mts. national park using remote sensing multispectral data
publisher Karolinum Press
series Acta Universitatis Carolinae Geographica
issn 0300-5402
2336-1980
publishDate 2016-06-01
description This paper compares suitability of multispectral data with different spatial and spectral resolutions for classifications of vegetation above the tree line in the Krkonoše Mts. National Park. Two legends were proposed: the detailed one with twelve classes, and simplified legend with eight classes. Aerial orthorectified images (orthoimages) with very high spatial resolution (12.5 cm) and four spectral bands have been examined using the object based classification. Satellite data WorldView-2 (WV-2) with high spatial resolution (2 metres) and eight spectral bands have been examined using object based classification and per-pixel classification. Per-pixel classification has been applied also to the freely available Landsat 8 data (spatial resolution 30 metres, seven spectral bands). Of the algorithms for per-pixel classification, the following classifiers were compared: maximum likelihood classification (MLC), support vector machine (SVM), and neural net (NN). The object based classification utilized the example-based approach and SVM algorithm (all available in ENVI 5.2). Both legends (simplified and detailed ones) show best results in the case of orthoimages (overall accuracy 83.56% and 71.96% respectively, Kappa coefficient 0.8 and 0.65 respectively). The WV-2 classification brought best results using the object based approach and simplified legend (68.4%); in the case of per-pixel classification it was the SVM method (RBF) and detailed legend (60.82%). Landsat data were best classified using the MLC (78.31%). Our research confirmed that Landsat data are sufficient to get a general overview of basic land cover classes above the tree line in the Krkonoše Mts. National Park. Based on the comparison of the data with different spectral and spatial resolution we can however conclude that very high spatial resolution is the decisive feature that is essential to reach high overall classification accuracy in the detailed level.
topic vegetation above the tree line
Krkonoše Mountains
object based classification
per-pixel classification
multispectral data
url http://aucgeographica.cz/index.php/aucg/article/view/71
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AT luciekupkova classificationofvegetationabovethetreelineinthekrkonosemtsnationalparkusingremotesensingmultispectraldata
AT luciecervena classificationofvegetationabovethetreelineinthekrkonosemtsnationalparkusingremotesensingmultispectraldata
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