Vegetation Subtype Classification of Evergreen Broad-Leaved Forests in Mountainous Areas Using a Hierarchy-Based Classifier

Evergreen broad-leaved forests with rich biodiversity play a key role in stabilizing global vegetation productivity and maintaining land carbon sinks. However, quantitative and accurate classification results for humid, evergreen, broad-leaved forests (HEBF) and semi-humid evergreen broad-leaved for...
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Autor*in:	Shiqi Zhang [verfasserIn] Peihao Peng [verfasserIn] Maoyang Bai [verfasserIn] Xiao Wang [verfasserIn] Lifu Zhang [verfasserIn] Jiao Hu [verfasserIn] Meilian Wang [verfasserIn] Xueman Wang [verfasserIn] Juan Wang [verfasserIn] Donghui Zhang [verfasserIn] Xuejian Sun [verfasserIn] Xiaoai Dai [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	vegetation classification semi-humid evergreen broad-leaved humid evergreen broad-leaved forest remote sensing hierarchy-based classifier

Übergeordnetes Werk:	In: Remote Sensing - MDPI AG, 2009, 15(2023), 12, p 3053
Übergeordnetes Werk:	volume:15 ; year:2023 ; number:12, p 3053

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/rs15123053

Katalog-ID:	DOAJ094068658

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520			\|a Evergreen broad-leaved forests with rich biodiversity play a key role in stabilizing global vegetation productivity and maintaining land carbon sinks. However, quantitative and accurate classification results for humid, evergreen, broad-leaved forests (HEBF) and semi-humid evergreen broad-leaved forests (SEBF) with different vegetation productivity and significant differences in species composition are lacking. Remote sensing technology brings the possibility of vegetation subtype classification. Taking the mountainous evergreen broad-leaved forests distributed in Sichuan Province as an example, this study proposed a hierarchy-based classifier combined with environmental variables to quantitatively classify the two vegetation subtypes with different ecological characteristics but similar image features. Additionally, we applied Sun–Canopy–Sensor and C parameter(SCS + C) topographic correction to preprocess the images, effectively correcting the radiometric distortion and enhancing the accuracy of vegetation classification. Finally, achieving an overall accuracy (OA) of 87.91% and a Kappa coefficient of 0.76, which is higher than that of directly using the classifier to classify the two vegetation subtypes. The study revealed the widespread distribution of evergreen broad-leaved forests in Sichuan, with a clear boundary between the distribution areas of HEBF and SEBF. The HEBF in the east is located in the basin and the low marginal mountains; the SEBF is located in the southwest dry valley. The methods employed in this study offer an effective approach to vegetation classification in mountainous areas. The findings can provide guidance for ecological engineering construction, ecological protection, and agricultural and livestock development.
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allfields	10.3390/rs15123053 doi (DE-627)DOAJ094068658 (DE-599)DOAJ63622fdc36454ca290ba4e7199e803ef DE-627 ger DE-627 rakwb eng Shiqi Zhang verfasserin aut Vegetation Subtype Classification of Evergreen Broad-Leaved Forests in Mountainous Areas Using a Hierarchy-Based Classifier 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Evergreen broad-leaved forests with rich biodiversity play a key role in stabilizing global vegetation productivity and maintaining land carbon sinks. However, quantitative and accurate classification results for humid, evergreen, broad-leaved forests (HEBF) and semi-humid evergreen broad-leaved forests (SEBF) with different vegetation productivity and significant differences in species composition are lacking. Remote sensing technology brings the possibility of vegetation subtype classification. Taking the mountainous evergreen broad-leaved forests distributed in Sichuan Province as an example, this study proposed a hierarchy-based classifier combined with environmental variables to quantitatively classify the two vegetation subtypes with different ecological characteristics but similar image features. Additionally, we applied Sun–Canopy–Sensor and C parameter(SCS + C) topographic correction to preprocess the images, effectively correcting the radiometric distortion and enhancing the accuracy of vegetation classification. Finally, achieving an overall accuracy (OA) of 87.91% and a Kappa coefficient of 0.76, which is higher than that of directly using the classifier to classify the two vegetation subtypes. The study revealed the widespread distribution of evergreen broad-leaved forests in Sichuan, with a clear boundary between the distribution areas of HEBF and SEBF. The HEBF in the east is located in the basin and the low marginal mountains; the SEBF is located in the southwest dry valley. The methods employed in this study offer an effective approach to vegetation classification in mountainous areas. The findings can provide guidance for ecological engineering construction, ecological protection, and agricultural and livestock development. vegetation classification semi-humid evergreen broad-leaved humid evergreen broad-leaved forest remote sensing hierarchy-based classifier Science Q Peihao Peng verfasserin aut Maoyang Bai verfasserin aut Xiao Wang verfasserin aut Lifu Zhang verfasserin aut Jiao Hu verfasserin aut Meilian Wang verfasserin aut Xueman Wang verfasserin aut Juan Wang verfasserin aut Donghui Zhang verfasserin aut Xuejian Sun verfasserin aut Xiaoai Dai verfasserin aut In Remote Sensing MDPI AG, 2009 15(2023), 12, p 3053 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:15 year:2023 number:12, p 3053 https://doi.org/10.3390/rs15123053 kostenfrei https://doaj.org/article/63622fdc36454ca290ba4e7199e803ef kostenfrei https://www.mdpi.com/2072-4292/15/12/3053 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_2108 GBV_ILN_2111 GBV_ILN_2119 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_4392 GBV_ILN_4700 AR 15 2023 12, p 3053
spelling	10.3390/rs15123053 doi (DE-627)DOAJ094068658 (DE-599)DOAJ63622fdc36454ca290ba4e7199e803ef DE-627 ger DE-627 rakwb eng Shiqi Zhang verfasserin aut Vegetation Subtype Classification of Evergreen Broad-Leaved Forests in Mountainous Areas Using a Hierarchy-Based Classifier 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Evergreen broad-leaved forests with rich biodiversity play a key role in stabilizing global vegetation productivity and maintaining land carbon sinks. However, quantitative and accurate classification results for humid, evergreen, broad-leaved forests (HEBF) and semi-humid evergreen broad-leaved forests (SEBF) with different vegetation productivity and significant differences in species composition are lacking. Remote sensing technology brings the possibility of vegetation subtype classification. Taking the mountainous evergreen broad-leaved forests distributed in Sichuan Province as an example, this study proposed a hierarchy-based classifier combined with environmental variables to quantitatively classify the two vegetation subtypes with different ecological characteristics but similar image features. Additionally, we applied Sun–Canopy–Sensor and C parameter(SCS + C) topographic correction to preprocess the images, effectively correcting the radiometric distortion and enhancing the accuracy of vegetation classification. Finally, achieving an overall accuracy (OA) of 87.91% and a Kappa coefficient of 0.76, which is higher than that of directly using the classifier to classify the two vegetation subtypes. The study revealed the widespread distribution of evergreen broad-leaved forests in Sichuan, with a clear boundary between the distribution areas of HEBF and SEBF. The HEBF in the east is located in the basin and the low marginal mountains; the SEBF is located in the southwest dry valley. The methods employed in this study offer an effective approach to vegetation classification in mountainous areas. The findings can provide guidance for ecological engineering construction, ecological protection, and agricultural and livestock development. vegetation classification semi-humid evergreen broad-leaved humid evergreen broad-leaved forest remote sensing hierarchy-based classifier Science Q Peihao Peng verfasserin aut Maoyang Bai verfasserin aut Xiao Wang verfasserin aut Lifu Zhang verfasserin aut Jiao Hu verfasserin aut Meilian Wang verfasserin aut Xueman Wang verfasserin aut Juan Wang verfasserin aut Donghui Zhang verfasserin aut Xuejian Sun verfasserin aut Xiaoai Dai verfasserin aut In Remote Sensing MDPI AG, 2009 15(2023), 12, p 3053 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:15 year:2023 number:12, p 3053 https://doi.org/10.3390/rs15123053 kostenfrei https://doaj.org/article/63622fdc36454ca290ba4e7199e803ef kostenfrei https://www.mdpi.com/2072-4292/15/12/3053 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_2108 GBV_ILN_2111 GBV_ILN_2119 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_4392 GBV_ILN_4700 AR 15 2023 12, p 3053
allfields_unstemmed	10.3390/rs15123053 doi (DE-627)DOAJ094068658 (DE-599)DOAJ63622fdc36454ca290ba4e7199e803ef DE-627 ger DE-627 rakwb eng Shiqi Zhang verfasserin aut Vegetation Subtype Classification of Evergreen Broad-Leaved Forests in Mountainous Areas Using a Hierarchy-Based Classifier 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Evergreen broad-leaved forests with rich biodiversity play a key role in stabilizing global vegetation productivity and maintaining land carbon sinks. However, quantitative and accurate classification results for humid, evergreen, broad-leaved forests (HEBF) and semi-humid evergreen broad-leaved forests (SEBF) with different vegetation productivity and significant differences in species composition are lacking. Remote sensing technology brings the possibility of vegetation subtype classification. Taking the mountainous evergreen broad-leaved forests distributed in Sichuan Province as an example, this study proposed a hierarchy-based classifier combined with environmental variables to quantitatively classify the two vegetation subtypes with different ecological characteristics but similar image features. Additionally, we applied Sun–Canopy–Sensor and C parameter(SCS + C) topographic correction to preprocess the images, effectively correcting the radiometric distortion and enhancing the accuracy of vegetation classification. Finally, achieving an overall accuracy (OA) of 87.91% and a Kappa coefficient of 0.76, which is higher than that of directly using the classifier to classify the two vegetation subtypes. The study revealed the widespread distribution of evergreen broad-leaved forests in Sichuan, with a clear boundary between the distribution areas of HEBF and SEBF. The HEBF in the east is located in the basin and the low marginal mountains; the SEBF is located in the southwest dry valley. The methods employed in this study offer an effective approach to vegetation classification in mountainous areas. The findings can provide guidance for ecological engineering construction, ecological protection, and agricultural and livestock development. vegetation classification semi-humid evergreen broad-leaved humid evergreen broad-leaved forest remote sensing hierarchy-based classifier Science Q Peihao Peng verfasserin aut Maoyang Bai verfasserin aut Xiao Wang verfasserin aut Lifu Zhang verfasserin aut Jiao Hu verfasserin aut Meilian Wang verfasserin aut Xueman Wang verfasserin aut Juan Wang verfasserin aut Donghui Zhang verfasserin aut Xuejian Sun verfasserin aut Xiaoai Dai verfasserin aut In Remote Sensing MDPI AG, 2009 15(2023), 12, p 3053 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:15 year:2023 number:12, p 3053 https://doi.org/10.3390/rs15123053 kostenfrei https://doaj.org/article/63622fdc36454ca290ba4e7199e803ef kostenfrei https://www.mdpi.com/2072-4292/15/12/3053 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_2108 GBV_ILN_2111 GBV_ILN_2119 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_4392 GBV_ILN_4700 AR 15 2023 12, p 3053
allfieldsGer	10.3390/rs15123053 doi (DE-627)DOAJ094068658 (DE-599)DOAJ63622fdc36454ca290ba4e7199e803ef DE-627 ger DE-627 rakwb eng Shiqi Zhang verfasserin aut Vegetation Subtype Classification of Evergreen Broad-Leaved Forests in Mountainous Areas Using a Hierarchy-Based Classifier 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Evergreen broad-leaved forests with rich biodiversity play a key role in stabilizing global vegetation productivity and maintaining land carbon sinks. However, quantitative and accurate classification results for humid, evergreen, broad-leaved forests (HEBF) and semi-humid evergreen broad-leaved forests (SEBF) with different vegetation productivity and significant differences in species composition are lacking. Remote sensing technology brings the possibility of vegetation subtype classification. Taking the mountainous evergreen broad-leaved forests distributed in Sichuan Province as an example, this study proposed a hierarchy-based classifier combined with environmental variables to quantitatively classify the two vegetation subtypes with different ecological characteristics but similar image features. Additionally, we applied Sun–Canopy–Sensor and C parameter(SCS + C) topographic correction to preprocess the images, effectively correcting the radiometric distortion and enhancing the accuracy of vegetation classification. Finally, achieving an overall accuracy (OA) of 87.91% and a Kappa coefficient of 0.76, which is higher than that of directly using the classifier to classify the two vegetation subtypes. The study revealed the widespread distribution of evergreen broad-leaved forests in Sichuan, with a clear boundary between the distribution areas of HEBF and SEBF. The HEBF in the east is located in the basin and the low marginal mountains; the SEBF is located in the southwest dry valley. The methods employed in this study offer an effective approach to vegetation classification in mountainous areas. The findings can provide guidance for ecological engineering construction, ecological protection, and agricultural and livestock development. vegetation classification semi-humid evergreen broad-leaved humid evergreen broad-leaved forest remote sensing hierarchy-based classifier Science Q Peihao Peng verfasserin aut Maoyang Bai verfasserin aut Xiao Wang verfasserin aut Lifu Zhang verfasserin aut Jiao Hu verfasserin aut Meilian Wang verfasserin aut Xueman Wang verfasserin aut Juan Wang verfasserin aut Donghui Zhang verfasserin aut Xuejian Sun verfasserin aut Xiaoai Dai verfasserin aut In Remote Sensing MDPI AG, 2009 15(2023), 12, p 3053 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:15 year:2023 number:12, p 3053 https://doi.org/10.3390/rs15123053 kostenfrei https://doaj.org/article/63622fdc36454ca290ba4e7199e803ef kostenfrei https://www.mdpi.com/2072-4292/15/12/3053 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_2108 GBV_ILN_2111 GBV_ILN_2119 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_4392 GBV_ILN_4700 AR 15 2023 12, p 3053
allfieldsSound	10.3390/rs15123053 doi (DE-627)DOAJ094068658 (DE-599)DOAJ63622fdc36454ca290ba4e7199e803ef DE-627 ger DE-627 rakwb eng Shiqi Zhang verfasserin aut Vegetation Subtype Classification of Evergreen Broad-Leaved Forests in Mountainous Areas Using a Hierarchy-Based Classifier 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Evergreen broad-leaved forests with rich biodiversity play a key role in stabilizing global vegetation productivity and maintaining land carbon sinks. However, quantitative and accurate classification results for humid, evergreen, broad-leaved forests (HEBF) and semi-humid evergreen broad-leaved forests (SEBF) with different vegetation productivity and significant differences in species composition are lacking. Remote sensing technology brings the possibility of vegetation subtype classification. Taking the mountainous evergreen broad-leaved forests distributed in Sichuan Province as an example, this study proposed a hierarchy-based classifier combined with environmental variables to quantitatively classify the two vegetation subtypes with different ecological characteristics but similar image features. Additionally, we applied Sun–Canopy–Sensor and C parameter(SCS + C) topographic correction to preprocess the images, effectively correcting the radiometric distortion and enhancing the accuracy of vegetation classification. Finally, achieving an overall accuracy (OA) of 87.91% and a Kappa coefficient of 0.76, which is higher than that of directly using the classifier to classify the two vegetation subtypes. The study revealed the widespread distribution of evergreen broad-leaved forests in Sichuan, with a clear boundary between the distribution areas of HEBF and SEBF. The HEBF in the east is located in the basin and the low marginal mountains; the SEBF is located in the southwest dry valley. The methods employed in this study offer an effective approach to vegetation classification in mountainous areas. The findings can provide guidance for ecological engineering construction, ecological protection, and agricultural and livestock development. vegetation classification semi-humid evergreen broad-leaved humid evergreen broad-leaved forest remote sensing hierarchy-based classifier Science Q Peihao Peng verfasserin aut Maoyang Bai verfasserin aut Xiao Wang verfasserin aut Lifu Zhang verfasserin aut Jiao Hu verfasserin aut Meilian Wang verfasserin aut Xueman Wang verfasserin aut Juan Wang verfasserin aut Donghui Zhang verfasserin aut Xuejian Sun verfasserin aut Xiaoai Dai verfasserin aut In Remote Sensing MDPI AG, 2009 15(2023), 12, p 3053 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:15 year:2023 number:12, p 3053 https://doi.org/10.3390/rs15123053 kostenfrei https://doaj.org/article/63622fdc36454ca290ba4e7199e803ef kostenfrei https://www.mdpi.com/2072-4292/15/12/3053 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_2108 GBV_ILN_2111 GBV_ILN_2119 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_4392 GBV_ILN_4700 AR 15 2023 12, p 3053
language	English
source	In Remote Sensing 15(2023), 12, p 3053 volume:15 year:2023 number:12, p 3053
sourceStr	In Remote Sensing 15(2023), 12, p 3053 volume:15 year:2023 number:12, p 3053
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	vegetation classification semi-humid evergreen broad-leaved humid evergreen broad-leaved forest remote sensing hierarchy-based classifier Science Q
isfreeaccess_bool	true
container_title	Remote Sensing
authorswithroles_txt_mv	Shiqi Zhang @@aut@@ Peihao Peng @@aut@@ Maoyang Bai @@aut@@ Xiao Wang @@aut@@ Lifu Zhang @@aut@@ Jiao Hu @@aut@@ Meilian Wang @@aut@@ Xueman Wang @@aut@@ Juan Wang @@aut@@ Donghui Zhang @@aut@@ Xuejian Sun @@aut@@ Xiaoai Dai @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	608937916
id	DOAJ094068658
language_de	englisch
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author	Shiqi Zhang
spellingShingle	Shiqi Zhang misc vegetation classification misc semi-humid evergreen broad-leaved misc humid evergreen broad-leaved forest misc remote sensing misc hierarchy-based classifier misc Science misc Q Vegetation Subtype Classification of Evergreen Broad-Leaved Forests in Mountainous Areas Using a Hierarchy-Based Classifier
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topic_title	Vegetation Subtype Classification of Evergreen Broad-Leaved Forests in Mountainous Areas Using a Hierarchy-Based Classifier vegetation classification semi-humid evergreen broad-leaved humid evergreen broad-leaved forest remote sensing hierarchy-based classifier
topic	misc vegetation classification misc semi-humid evergreen broad-leaved misc humid evergreen broad-leaved forest misc remote sensing misc hierarchy-based classifier misc Science misc Q
topic_unstemmed	misc vegetation classification misc semi-humid evergreen broad-leaved misc humid evergreen broad-leaved forest misc remote sensing misc hierarchy-based classifier misc Science misc Q
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title	Vegetation Subtype Classification of Evergreen Broad-Leaved Forests in Mountainous Areas Using a Hierarchy-Based Classifier
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title_full	Vegetation Subtype Classification of Evergreen Broad-Leaved Forests in Mountainous Areas Using a Hierarchy-Based Classifier
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author_browse	Shiqi Zhang Peihao Peng Maoyang Bai Xiao Wang Lifu Zhang Jiao Hu Meilian Wang Xueman Wang Juan Wang Donghui Zhang Xuejian Sun Xiaoai Dai
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title_sort	vegetation subtype classification of evergreen broad-leaved forests in mountainous areas using a hierarchy-based classifier
title_auth	Vegetation Subtype Classification of Evergreen Broad-Leaved Forests in Mountainous Areas Using a Hierarchy-Based Classifier
abstract	Evergreen broad-leaved forests with rich biodiversity play a key role in stabilizing global vegetation productivity and maintaining land carbon sinks. However, quantitative and accurate classification results for humid, evergreen, broad-leaved forests (HEBF) and semi-humid evergreen broad-leaved forests (SEBF) with different vegetation productivity and significant differences in species composition are lacking. Remote sensing technology brings the possibility of vegetation subtype classification. Taking the mountainous evergreen broad-leaved forests distributed in Sichuan Province as an example, this study proposed a hierarchy-based classifier combined with environmental variables to quantitatively classify the two vegetation subtypes with different ecological characteristics but similar image features. Additionally, we applied Sun–Canopy–Sensor and C parameter(SCS + C) topographic correction to preprocess the images, effectively correcting the radiometric distortion and enhancing the accuracy of vegetation classification. Finally, achieving an overall accuracy (OA) of 87.91% and a Kappa coefficient of 0.76, which is higher than that of directly using the classifier to classify the two vegetation subtypes. The study revealed the widespread distribution of evergreen broad-leaved forests in Sichuan, with a clear boundary between the distribution areas of HEBF and SEBF. The HEBF in the east is located in the basin and the low marginal mountains; the SEBF is located in the southwest dry valley. The methods employed in this study offer an effective approach to vegetation classification in mountainous areas. The findings can provide guidance for ecological engineering construction, ecological protection, and agricultural and livestock development.
abstractGer	Evergreen broad-leaved forests with rich biodiversity play a key role in stabilizing global vegetation productivity and maintaining land carbon sinks. However, quantitative and accurate classification results for humid, evergreen, broad-leaved forests (HEBF) and semi-humid evergreen broad-leaved forests (SEBF) with different vegetation productivity and significant differences in species composition are lacking. Remote sensing technology brings the possibility of vegetation subtype classification. Taking the mountainous evergreen broad-leaved forests distributed in Sichuan Province as an example, this study proposed a hierarchy-based classifier combined with environmental variables to quantitatively classify the two vegetation subtypes with different ecological characteristics but similar image features. Additionally, we applied Sun–Canopy–Sensor and C parameter(SCS + C) topographic correction to preprocess the images, effectively correcting the radiometric distortion and enhancing the accuracy of vegetation classification. Finally, achieving an overall accuracy (OA) of 87.91% and a Kappa coefficient of 0.76, which is higher than that of directly using the classifier to classify the two vegetation subtypes. The study revealed the widespread distribution of evergreen broad-leaved forests in Sichuan, with a clear boundary between the distribution areas of HEBF and SEBF. The HEBF in the east is located in the basin and the low marginal mountains; the SEBF is located in the southwest dry valley. The methods employed in this study offer an effective approach to vegetation classification in mountainous areas. The findings can provide guidance for ecological engineering construction, ecological protection, and agricultural and livestock development.
abstract_unstemmed	Evergreen broad-leaved forests with rich biodiversity play a key role in stabilizing global vegetation productivity and maintaining land carbon sinks. However, quantitative and accurate classification results for humid, evergreen, broad-leaved forests (HEBF) and semi-humid evergreen broad-leaved forests (SEBF) with different vegetation productivity and significant differences in species composition are lacking. Remote sensing technology brings the possibility of vegetation subtype classification. Taking the mountainous evergreen broad-leaved forests distributed in Sichuan Province as an example, this study proposed a hierarchy-based classifier combined with environmental variables to quantitatively classify the two vegetation subtypes with different ecological characteristics but similar image features. Additionally, we applied Sun–Canopy–Sensor and C parameter(SCS + C) topographic correction to preprocess the images, effectively correcting the radiometric distortion and enhancing the accuracy of vegetation classification. Finally, achieving an overall accuracy (OA) of 87.91% and a Kappa coefficient of 0.76, which is higher than that of directly using the classifier to classify the two vegetation subtypes. The study revealed the widespread distribution of evergreen broad-leaved forests in Sichuan, with a clear boundary between the distribution areas of HEBF and SEBF. The HEBF in the east is located in the basin and the low marginal mountains; the SEBF is located in the southwest dry valley. The methods employed in this study offer an effective approach to vegetation classification in mountainous areas. The findings can provide guidance for ecological engineering construction, ecological protection, and agricultural and livestock development.
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title_short	Vegetation Subtype Classification of Evergreen Broad-Leaved Forests in Mountainous Areas Using a Hierarchy-Based Classifier
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