Bamboo Classification Using WorldView-2 Imagery of Giant Panda Habitat in a Large Shaded Area in Wolong, Sichuan Province, China

This study explores the ability of WorldView-2 (WV-2) imagery for bamboo mapping in a mountainous region in Sichuan Province, China. A large area of this place is covered by shadows in the image, and only a few sampled points derived were useful. In order to identify bamboos based on sparse training...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Yunwei Tang [verfasserIn] Linhai Jing [verfasserIn] Hui Li [verfasserIn] Qingjie Liu [verfasserIn] Qi Yan [verfasserIn] Xiuxia Li [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2016

Schlagwörter:	classification object-based analysis bamboo mapping WorldView-2 geostatistics

Übergeordnetes Werk:	In: Sensors - MDPI AG, 2003, 16(2016), 11, p 1957
Übergeordnetes Werk:	volume:16 ; year:2016 ; number:11, p 1957

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/s16111957

Katalog-ID:	DOAJ079132065

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allfields	10.3390/s16111957 doi (DE-627)DOAJ079132065 (DE-599)DOAJ5db9c7a8a70a4ee985d57e9b10d33eaa DE-627 ger DE-627 rakwb eng TP1-1185 Yunwei Tang verfasserin aut Bamboo Classification Using WorldView-2 Imagery of Giant Panda Habitat in a Large Shaded Area in Wolong, Sichuan Province, China 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This study explores the ability of WorldView-2 (WV-2) imagery for bamboo mapping in a mountainous region in Sichuan Province, China. A large area of this place is covered by shadows in the image, and only a few sampled points derived were useful. In order to identify bamboos based on sparse training data, the sample size was expanded according to the reflectance of multispectral bands selected using the principal component analysis (PCA). Then, class separability based on the training data was calculated using a feature space optimization method to select the features for classification. Four regular object-based classification methods were applied based on both sets of training data. The results show that the k-nearest neighbor (k-NN) method produced the greatest accuracy. A geostatistically-weighted k-NN classifier, accounting for the spatial correlation between classes, was then applied to further increase the accuracy. It achieved 82.65% and 93.10% of the producer’s and user’s accuracies respectively for the bamboo class. The canopy densities were estimated to explain the result. This study demonstrates that the WV-2 image can be used to identify small patches of understory bamboos given limited known samples, and the resulting bamboo distribution facilitates the assessments of the habitats of giant pandas. classification object-based analysis bamboo mapping WorldView-2 geostatistics Chemical technology Linhai Jing verfasserin aut Hui Li verfasserin aut Qingjie Liu verfasserin aut Qi Yan verfasserin aut Xiuxia Li verfasserin aut In Sensors MDPI AG, 2003 16(2016), 11, p 1957 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:16 year:2016 number:11, p 1957 https://doi.org/10.3390/s16111957 kostenfrei https://doaj.org/article/5db9c7a8a70a4ee985d57e9b10d33eaa kostenfrei http://www.mdpi.com/1424-8220/16/11/1957 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2016 11, p 1957
spelling	10.3390/s16111957 doi (DE-627)DOAJ079132065 (DE-599)DOAJ5db9c7a8a70a4ee985d57e9b10d33eaa DE-627 ger DE-627 rakwb eng TP1-1185 Yunwei Tang verfasserin aut Bamboo Classification Using WorldView-2 Imagery of Giant Panda Habitat in a Large Shaded Area in Wolong, Sichuan Province, China 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This study explores the ability of WorldView-2 (WV-2) imagery for bamboo mapping in a mountainous region in Sichuan Province, China. A large area of this place is covered by shadows in the image, and only a few sampled points derived were useful. In order to identify bamboos based on sparse training data, the sample size was expanded according to the reflectance of multispectral bands selected using the principal component analysis (PCA). Then, class separability based on the training data was calculated using a feature space optimization method to select the features for classification. Four regular object-based classification methods were applied based on both sets of training data. The results show that the k-nearest neighbor (k-NN) method produced the greatest accuracy. A geostatistically-weighted k-NN classifier, accounting for the spatial correlation between classes, was then applied to further increase the accuracy. It achieved 82.65% and 93.10% of the producer’s and user’s accuracies respectively for the bamboo class. The canopy densities were estimated to explain the result. This study demonstrates that the WV-2 image can be used to identify small patches of understory bamboos given limited known samples, and the resulting bamboo distribution facilitates the assessments of the habitats of giant pandas. classification object-based analysis bamboo mapping WorldView-2 geostatistics Chemical technology Linhai Jing verfasserin aut Hui Li verfasserin aut Qingjie Liu verfasserin aut Qi Yan verfasserin aut Xiuxia Li verfasserin aut In Sensors MDPI AG, 2003 16(2016), 11, p 1957 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:16 year:2016 number:11, p 1957 https://doi.org/10.3390/s16111957 kostenfrei https://doaj.org/article/5db9c7a8a70a4ee985d57e9b10d33eaa kostenfrei http://www.mdpi.com/1424-8220/16/11/1957 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2016 11, p 1957
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allfieldsGer	10.3390/s16111957 doi (DE-627)DOAJ079132065 (DE-599)DOAJ5db9c7a8a70a4ee985d57e9b10d33eaa DE-627 ger DE-627 rakwb eng TP1-1185 Yunwei Tang verfasserin aut Bamboo Classification Using WorldView-2 Imagery of Giant Panda Habitat in a Large Shaded Area in Wolong, Sichuan Province, China 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This study explores the ability of WorldView-2 (WV-2) imagery for bamboo mapping in a mountainous region in Sichuan Province, China. A large area of this place is covered by shadows in the image, and only a few sampled points derived were useful. In order to identify bamboos based on sparse training data, the sample size was expanded according to the reflectance of multispectral bands selected using the principal component analysis (PCA). Then, class separability based on the training data was calculated using a feature space optimization method to select the features for classification. Four regular object-based classification methods were applied based on both sets of training data. The results show that the k-nearest neighbor (k-NN) method produced the greatest accuracy. A geostatistically-weighted k-NN classifier, accounting for the spatial correlation between classes, was then applied to further increase the accuracy. It achieved 82.65% and 93.10% of the producer’s and user’s accuracies respectively for the bamboo class. The canopy densities were estimated to explain the result. This study demonstrates that the WV-2 image can be used to identify small patches of understory bamboos given limited known samples, and the resulting bamboo distribution facilitates the assessments of the habitats of giant pandas. classification object-based analysis bamboo mapping WorldView-2 geostatistics Chemical technology Linhai Jing verfasserin aut Hui Li verfasserin aut Qingjie Liu verfasserin aut Qi Yan verfasserin aut Xiuxia Li verfasserin aut In Sensors MDPI AG, 2003 16(2016), 11, p 1957 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:16 year:2016 number:11, p 1957 https://doi.org/10.3390/s16111957 kostenfrei https://doaj.org/article/5db9c7a8a70a4ee985d57e9b10d33eaa kostenfrei http://www.mdpi.com/1424-8220/16/11/1957 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2016 11, p 1957
allfieldsSound	10.3390/s16111957 doi (DE-627)DOAJ079132065 (DE-599)DOAJ5db9c7a8a70a4ee985d57e9b10d33eaa DE-627 ger DE-627 rakwb eng TP1-1185 Yunwei Tang verfasserin aut Bamboo Classification Using WorldView-2 Imagery of Giant Panda Habitat in a Large Shaded Area in Wolong, Sichuan Province, China 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This study explores the ability of WorldView-2 (WV-2) imagery for bamboo mapping in a mountainous region in Sichuan Province, China. A large area of this place is covered by shadows in the image, and only a few sampled points derived were useful. In order to identify bamboos based on sparse training data, the sample size was expanded according to the reflectance of multispectral bands selected using the principal component analysis (PCA). Then, class separability based on the training data was calculated using a feature space optimization method to select the features for classification. Four regular object-based classification methods were applied based on both sets of training data. The results show that the k-nearest neighbor (k-NN) method produced the greatest accuracy. A geostatistically-weighted k-NN classifier, accounting for the spatial correlation between classes, was then applied to further increase the accuracy. It achieved 82.65% and 93.10% of the producer’s and user’s accuracies respectively for the bamboo class. The canopy densities were estimated to explain the result. This study demonstrates that the WV-2 image can be used to identify small patches of understory bamboos given limited known samples, and the resulting bamboo distribution facilitates the assessments of the habitats of giant pandas. classification object-based analysis bamboo mapping WorldView-2 geostatistics Chemical technology Linhai Jing verfasserin aut Hui Li verfasserin aut Qingjie Liu verfasserin aut Qi Yan verfasserin aut Xiuxia Li verfasserin aut In Sensors MDPI AG, 2003 16(2016), 11, p 1957 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:16 year:2016 number:11, p 1957 https://doi.org/10.3390/s16111957 kostenfrei https://doaj.org/article/5db9c7a8a70a4ee985d57e9b10d33eaa kostenfrei http://www.mdpi.com/1424-8220/16/11/1957 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2016 11, p 1957
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callnumber-first	T - Technology
author	Yunwei Tang
spellingShingle	Yunwei Tang misc TP1-1185 misc classification misc object-based analysis misc bamboo mapping misc WorldView-2 misc geostatistics misc Chemical technology Bamboo Classification Using WorldView-2 Imagery of Giant Panda Habitat in a Large Shaded Area in Wolong, Sichuan Province, China
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topic_title	TP1-1185 Bamboo Classification Using WorldView-2 Imagery of Giant Panda Habitat in a Large Shaded Area in Wolong, Sichuan Province, China classification object-based analysis bamboo mapping WorldView-2 geostatistics
topic	misc TP1-1185 misc classification misc object-based analysis misc bamboo mapping misc WorldView-2 misc geostatistics misc Chemical technology
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title	Bamboo Classification Using WorldView-2 Imagery of Giant Panda Habitat in a Large Shaded Area in Wolong, Sichuan Province, China
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title_sort	bamboo classification using worldview-2 imagery of giant panda habitat in a large shaded area in wolong, sichuan province, china
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title_auth	Bamboo Classification Using WorldView-2 Imagery of Giant Panda Habitat in a Large Shaded Area in Wolong, Sichuan Province, China
abstract	This study explores the ability of WorldView-2 (WV-2) imagery for bamboo mapping in a mountainous region in Sichuan Province, China. A large area of this place is covered by shadows in the image, and only a few sampled points derived were useful. In order to identify bamboos based on sparse training data, the sample size was expanded according to the reflectance of multispectral bands selected using the principal component analysis (PCA). Then, class separability based on the training data was calculated using a feature space optimization method to select the features for classification. Four regular object-based classification methods were applied based on both sets of training data. The results show that the k-nearest neighbor (k-NN) method produced the greatest accuracy. A geostatistically-weighted k-NN classifier, accounting for the spatial correlation between classes, was then applied to further increase the accuracy. It achieved 82.65% and 93.10% of the producer’s and user’s accuracies respectively for the bamboo class. The canopy densities were estimated to explain the result. This study demonstrates that the WV-2 image can be used to identify small patches of understory bamboos given limited known samples, and the resulting bamboo distribution facilitates the assessments of the habitats of giant pandas.
abstractGer	This study explores the ability of WorldView-2 (WV-2) imagery for bamboo mapping in a mountainous region in Sichuan Province, China. A large area of this place is covered by shadows in the image, and only a few sampled points derived were useful. In order to identify bamboos based on sparse training data, the sample size was expanded according to the reflectance of multispectral bands selected using the principal component analysis (PCA). Then, class separability based on the training data was calculated using a feature space optimization method to select the features for classification. Four regular object-based classification methods were applied based on both sets of training data. The results show that the k-nearest neighbor (k-NN) method produced the greatest accuracy. A geostatistically-weighted k-NN classifier, accounting for the spatial correlation between classes, was then applied to further increase the accuracy. It achieved 82.65% and 93.10% of the producer’s and user’s accuracies respectively for the bamboo class. The canopy densities were estimated to explain the result. This study demonstrates that the WV-2 image can be used to identify small patches of understory bamboos given limited known samples, and the resulting bamboo distribution facilitates the assessments of the habitats of giant pandas.
abstract_unstemmed	This study explores the ability of WorldView-2 (WV-2) imagery for bamboo mapping in a mountainous region in Sichuan Province, China. A large area of this place is covered by shadows in the image, and only a few sampled points derived were useful. In order to identify bamboos based on sparse training data, the sample size was expanded according to the reflectance of multispectral bands selected using the principal component analysis (PCA). Then, class separability based on the training data was calculated using a feature space optimization method to select the features for classification. Four regular object-based classification methods were applied based on both sets of training data. The results show that the k-nearest neighbor (k-NN) method produced the greatest accuracy. A geostatistically-weighted k-NN classifier, accounting for the spatial correlation between classes, was then applied to further increase the accuracy. It achieved 82.65% and 93.10% of the producer’s and user’s accuracies respectively for the bamboo class. The canopy densities were estimated to explain the result. This study demonstrates that the WV-2 image can be used to identify small patches of understory bamboos given limited known samples, and the resulting bamboo distribution facilitates the assessments of the habitats of giant pandas.
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title_short	Bamboo Classification Using WorldView-2 Imagery of Giant Panda Habitat in a Large Shaded Area in Wolong, Sichuan Province, China
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up_date	2024-07-03T21:53:04.400Z
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Bamboo Classification Using WorldView-2 Imagery of Giant Panda Habitat in a Large Shaded Area in Wolong, Sichuan Province, China

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