PoplarV1, Main, Exploration, bibRecord, 001F48

[Mahalanobis distance based hyperspectral characteristic discrimination of leaves of different desert tree species].

Identifieur interne : 001F48 ( Main/Exploration ); précédent : 001F47; suivant : 001F49

[Mahalanobis distance based hyperspectral characteristic discrimination of leaves of different desert tree species].

Auteurs : Hai-Jun Lin ; Hui-Fang Zhang ; Ya-Qi Gao ; Xia Li ; Fan Yang ; Yan-Fei Zhou

Source :

Guang pu xue yu guang pu fen xi = Guang pu [ 1000-0593 ] ; 2014.

RBID : pubmed:25881439

Descripteurs français

KwdFr :
- Analyse spectrale (MeSH), Arbres (classification), Biodiversité (MeSH), Climat désertique (MeSH), Environnement (MeSH), Feuilles de plante (MeSH), Populus (MeSH), Rivières (MeSH), Technologie de télédétection (MeSH).
MESH :
- classification : Analyse spectrale, Arbres, Biodiversité, Climat désertique, Environnement, Feuilles de plante, Populus, Rivières, Technologie de télédétection.

English descriptors

KwdEn :
- Biodiversity (MeSH), Desert Climate (MeSH), Environment (MeSH), Plant Leaves (MeSH), Populus (MeSH), Remote Sensing Technology (MeSH), Rivers (MeSH), Spectrum Analysis (MeSH), Trees (classification).
MESH :
- classification : Trees.
- Biodiversity, Desert Climate, Environment, Plant Leaves, Populus, Remote Sensing Technology, Rivers, Spectrum Analysis.

Abstract

The hyperspectral reflectance of Populus euphratica, Tamarix hispida, Haloxylon ammodendron and Calligonum mongolicum in the lower reaches of Tarim River and Turpan Desert Botanical Garden was measured by using the HR-768 field-portable spectroradiometer. The method of continuum removal, first derivative reflectance and second derivative reflectance were used to deal with the original spectral data of four tree species. The method of Mahalanobis Distance was used to select the bands with significant differences in the original spectral data and transform spectral data to identify the different tree species. The progressive discrimination analyses were used to test the selective bands used to identify different tree species. The results showed that The Mahalanobis Distance method was an effective method in feature band extraction. The bands for identifying different tree species were most near-infrared bands. The recognition accuracy of four methods was 85%, 93.8%, 92.4% and 95.5% respectively. Spectrum transform could improve the recognition accuracy. The recognition accuracy of different research objects and different spectrum transform methods were different. The research provided evidence for desert tree species classification, monitoring biodiversity and the analysis of area in desert by using large scale remote sensing method.

PubMed: 25881439

Affiliations:

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Le document en format XML

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<author><name sortKey="Lin, Hai Jun" sort="Lin, Hai Jun" uniqKey="Lin H" first="Hai-Jun" last="Lin">Hai-Jun Lin</name>
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<author><name sortKey="Zhang, Hui Fang" sort="Zhang, Hui Fang" uniqKey="Zhang H" first="Hui-Fang" last="Zhang">Hui-Fang Zhang</name>
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<author><name sortKey="Gao, Ya Qi" sort="Gao, Ya Qi" uniqKey="Gao Y" first="Ya-Qi" last="Gao">Ya-Qi Gao</name>
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<author><name sortKey="Li, Xia" sort="Li, Xia" uniqKey="Li X" first="Xia" last="Li">Xia Li</name>
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<author><name sortKey="Yang, Fan" sort="Yang, Fan" uniqKey="Yang F" first="Fan" last="Yang">Fan Yang</name>
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<author><name sortKey="Zhou, Yan Fei" sort="Zhou, Yan Fei" uniqKey="Zhou Y" first="Yan-Fei" last="Zhou">Yan-Fei Zhou</name>
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<term>Desert Climate (MeSH)</term>
<term>Environment (MeSH)</term>
<term>Plant Leaves (MeSH)</term>
<term>Populus (MeSH)</term>
<term>Remote Sensing Technology (MeSH)</term>
<term>Rivers (MeSH)</term>
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<term>Arbres (classification)</term>
<term>Biodiversité (MeSH)</term>
<term>Climat désertique (MeSH)</term>
<term>Environnement (MeSH)</term>
<term>Feuilles de plante (MeSH)</term>
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<term>Rivières (MeSH)</term>
<term>Technologie de télédétection (MeSH)</term>
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<keywords scheme="MESH" qualifier="classification" xml:lang="en"><term>Trees</term>
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<term>Environment</term>
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<term>Populus</term>
<term>Remote Sensing Technology</term>
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<term>Climat désertique</term>
<term>Environnement</term>
<term>Feuilles de plante</term>
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<front><div type="abstract" xml:lang="en">The hyperspectral reflectance of Populus euphratica, Tamarix hispida, Haloxylon ammodendron and Calligonum mongolicum in the lower reaches of Tarim River and Turpan Desert Botanical Garden was measured by using the HR-768 field-portable spectroradiometer. The method of continuum removal, first derivative reflectance and second derivative reflectance were used to deal with the original spectral data of four tree species. The method of Mahalanobis Distance was used to select the bands with significant differences in the original spectral data and transform spectral data to identify the different tree species. The progressive discrimination analyses were used to test the selective bands used to identify different tree species. The results showed that The Mahalanobis Distance method was an effective method in feature band extraction. The bands for identifying different tree species were most near-infrared bands. The recognition accuracy of four methods was 85%, 93.8%, 92.4% and 95.5% respectively. Spectrum transform could improve the recognition accuracy. The recognition accuracy of different research objects and different spectrum transform methods were different. The research provided evidence for desert tree species classification, monitoring biodiversity and the analysis of area in desert by using large scale remote sensing method.</div>
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<Abstract><AbstractText>The hyperspectral reflectance of Populus euphratica, Tamarix hispida, Haloxylon ammodendron and Calligonum mongolicum in the lower reaches of Tarim River and Turpan Desert Botanical Garden was measured by using the HR-768 field-portable spectroradiometer. The method of continuum removal, first derivative reflectance and second derivative reflectance were used to deal with the original spectral data of four tree species. The method of Mahalanobis Distance was used to select the bands with significant differences in the original spectral data and transform spectral data to identify the different tree species. The progressive discrimination analyses were used to test the selective bands used to identify different tree species. The results showed that The Mahalanobis Distance method was an effective method in feature band extraction. The bands for identifying different tree species were most near-infrared bands. The recognition accuracy of four methods was 85%, 93.8%, 92.4% and 95.5% respectively. Spectrum transform could improve the recognition accuracy. The recognition accuracy of different research objects and different spectrum transform methods were different. The research provided evidence for desert tree species classification, monitoring biodiversity and the analysis of area in desert by using large scale remote sensing method.</AbstractText>
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[Mahalanobis distance based hyperspectral characteristic discrimination of leaves of different desert tree species].

[Mahalanobis distance based hyperspectral characteristic discrimination of leaves of different desert tree species].

Source :

Descripteurs français

English descriptors

Abstract

Links toward previous steps (curation, corpus...)

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri

Pour générer des pages wiki