Post-typhoon forest damage estimation using multiple vegetation indices and machine learning models

The frequency and intensity of typhoons have increased due to climate change. These climate change-induced disasters have caused widespread damage to forests. Evaluation of the effects of typhoons on forest ecosystems is often complex and challenging, mainly because of their sporadic nature. In this...
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Autor*in:	Xinyu Chen [verfasserIn] Ram Avtar [verfasserIn] Deha Agus Umarhadi [verfasserIn] Albertus Stephanus Louw [verfasserIn] Sourabh Shrivastava [verfasserIn] Ali P. Yunus [verfasserIn] Khaled Mohamed Khedher [verfasserIn] Tetsuya Takemi [verfasserIn] Hideaki Shibata [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Forest damage Remote sensing Vegetation indices Multispectral classification CLASlite

Übergeordnetes Werk:	In: Weather and Climate Extremes - Elsevier, 2016, 38(2022), Seite 100494-
Übergeordnetes Werk:	volume:38 ; year:2022 ; pages:100494-

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.wace.2022.100494

Katalog-ID:	DOAJ030505313

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520			\|a The frequency and intensity of typhoons have increased due to climate change. These climate change-induced disasters have caused widespread damage to forests. Evaluation of the effects of typhoons on forest ecosystems is often complex and challenging, mainly because of their sporadic nature. In this paper, we compared existing forest damage estimation techniques with the goal of identifying their respective advantages and suitable use cases. We considered Hokkaido in northern Japan as a case study, where three typhoons successively struck in 2016 and led to forest destruction. Forest damage was estimated from Landsat 8 imagery by three approaches, namely using vegetation damage indices (DVDI, DNDVI and ΔEVI), using supervised classification with Random Forest (RF) and Support Vector Machines (SVM) and finally by using the commercial CLASlite software with built-in methods to detect forest disturbance. Machine learning classifiers obtained the highest damage assessment accuracy, but intensive computation and complex processing steps were required. The RF and SVM classifiers gave the highest accuracies when using Fractional Cover as a predictor variable (Overall Accuracy = 80.36% in both cases, and ROC AUC values of 0.89 and 0.88, respectively.) Among the vegetation damage indices, DNDVI produced the highest accuracy (AUC = 0.85, OA = 77.68%).The most damaged areas were on the windward slopes, where forest patches were exposed to the brunt of the typhoon winds. Forest damage also peaked at the highest elevations in the study area, possibly representing exposed hilltops. Methods and findings presented in this study can help stakeholders to implement more effective forest damage monitoring after typhoons and other extreme weather events in the future.
650		4	\|a Forest damage
650		4	\|a Remote sensing
650		4	\|a Vegetation indices
650		4	\|a Multispectral classification
650		4	\|a CLASlite
653		0	\|a Meteorology. Climatology
700	0		\|a Ram Avtar \|e verfasserin \|4 aut
700	0		\|a Deha Agus Umarhadi \|e verfasserin \|4 aut
700	0		\|a Albertus Stephanus Louw \|e verfasserin \|4 aut
700	0		\|a Sourabh Shrivastava \|e verfasserin \|4 aut
700	0		\|a Ali P. Yunus \|e verfasserin \|4 aut
700	0		\|a Khaled Mohamed Khedher \|e verfasserin \|4 aut
700	0		\|a Tetsuya Takemi \|e verfasserin \|4 aut
700	0		\|a Hideaki Shibata \|e verfasserin \|4 aut
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allfields	10.1016/j.wace.2022.100494 doi (DE-627)DOAJ030505313 (DE-599)DOAJ86bd458c755946cc8feafacebd68c288 DE-627 ger DE-627 rakwb eng QC851-999 Xinyu Chen verfasserin aut Post-typhoon forest damage estimation using multiple vegetation indices and machine learning models 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The frequency and intensity of typhoons have increased due to climate change. These climate change-induced disasters have caused widespread damage to forests. Evaluation of the effects of typhoons on forest ecosystems is often complex and challenging, mainly because of their sporadic nature. In this paper, we compared existing forest damage estimation techniques with the goal of identifying their respective advantages and suitable use cases. We considered Hokkaido in northern Japan as a case study, where three typhoons successively struck in 2016 and led to forest destruction. Forest damage was estimated from Landsat 8 imagery by three approaches, namely using vegetation damage indices (DVDI, DNDVI and ΔEVI), using supervised classification with Random Forest (RF) and Support Vector Machines (SVM) and finally by using the commercial CLASlite software with built-in methods to detect forest disturbance. Machine learning classifiers obtained the highest damage assessment accuracy, but intensive computation and complex processing steps were required. The RF and SVM classifiers gave the highest accuracies when using Fractional Cover as a predictor variable (Overall Accuracy = 80.36% in both cases, and ROC AUC values of 0.89 and 0.88, respectively.) Among the vegetation damage indices, DNDVI produced the highest accuracy (AUC = 0.85, OA = 77.68%).The most damaged areas were on the windward slopes, where forest patches were exposed to the brunt of the typhoon winds. Forest damage also peaked at the highest elevations in the study area, possibly representing exposed hilltops. Methods and findings presented in this study can help stakeholders to implement more effective forest damage monitoring after typhoons and other extreme weather events in the future. Forest damage Remote sensing Vegetation indices Multispectral classification CLASlite Meteorology. Climatology Ram Avtar verfasserin aut Deha Agus Umarhadi verfasserin aut Albertus Stephanus Louw verfasserin aut Sourabh Shrivastava verfasserin aut Ali P. Yunus verfasserin aut Khaled Mohamed Khedher verfasserin aut Tetsuya Takemi verfasserin aut Hideaki Shibata verfasserin aut In Weather and Climate Extremes Elsevier, 2016 38(2022), Seite 100494- (DE-627)767567668 (DE-600)2732464-3 22120947 nnns volume:38 year:2022 pages:100494- https://doi.org/10.1016/j.wace.2022.100494 kostenfrei https://doaj.org/article/86bd458c755946cc8feafacebd68c288 kostenfrei http://www.sciencedirect.com/science/article/pii/S2212094722000731 kostenfrei https://doaj.org/toc/2212-0947 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 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_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 38 2022 100494-
spelling	10.1016/j.wace.2022.100494 doi (DE-627)DOAJ030505313 (DE-599)DOAJ86bd458c755946cc8feafacebd68c288 DE-627 ger DE-627 rakwb eng QC851-999 Xinyu Chen verfasserin aut Post-typhoon forest damage estimation using multiple vegetation indices and machine learning models 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The frequency and intensity of typhoons have increased due to climate change. These climate change-induced disasters have caused widespread damage to forests. Evaluation of the effects of typhoons on forest ecosystems is often complex and challenging, mainly because of their sporadic nature. In this paper, we compared existing forest damage estimation techniques with the goal of identifying their respective advantages and suitable use cases. We considered Hokkaido in northern Japan as a case study, where three typhoons successively struck in 2016 and led to forest destruction. Forest damage was estimated from Landsat 8 imagery by three approaches, namely using vegetation damage indices (DVDI, DNDVI and ΔEVI), using supervised classification with Random Forest (RF) and Support Vector Machines (SVM) and finally by using the commercial CLASlite software with built-in methods to detect forest disturbance. Machine learning classifiers obtained the highest damage assessment accuracy, but intensive computation and complex processing steps were required. The RF and SVM classifiers gave the highest accuracies when using Fractional Cover as a predictor variable (Overall Accuracy = 80.36% in both cases, and ROC AUC values of 0.89 and 0.88, respectively.) Among the vegetation damage indices, DNDVI produced the highest accuracy (AUC = 0.85, OA = 77.68%).The most damaged areas were on the windward slopes, where forest patches were exposed to the brunt of the typhoon winds. Forest damage also peaked at the highest elevations in the study area, possibly representing exposed hilltops. Methods and findings presented in this study can help stakeholders to implement more effective forest damage monitoring after typhoons and other extreme weather events in the future. Forest damage Remote sensing Vegetation indices Multispectral classification CLASlite Meteorology. Climatology Ram Avtar verfasserin aut Deha Agus Umarhadi verfasserin aut Albertus Stephanus Louw verfasserin aut Sourabh Shrivastava verfasserin aut Ali P. Yunus verfasserin aut Khaled Mohamed Khedher verfasserin aut Tetsuya Takemi verfasserin aut Hideaki Shibata verfasserin aut In Weather and Climate Extremes Elsevier, 2016 38(2022), Seite 100494- (DE-627)767567668 (DE-600)2732464-3 22120947 nnns volume:38 year:2022 pages:100494- https://doi.org/10.1016/j.wace.2022.100494 kostenfrei https://doaj.org/article/86bd458c755946cc8feafacebd68c288 kostenfrei http://www.sciencedirect.com/science/article/pii/S2212094722000731 kostenfrei https://doaj.org/toc/2212-0947 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 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_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 38 2022 100494-
allfields_unstemmed	10.1016/j.wace.2022.100494 doi (DE-627)DOAJ030505313 (DE-599)DOAJ86bd458c755946cc8feafacebd68c288 DE-627 ger DE-627 rakwb eng QC851-999 Xinyu Chen verfasserin aut Post-typhoon forest damage estimation using multiple vegetation indices and machine learning models 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The frequency and intensity of typhoons have increased due to climate change. These climate change-induced disasters have caused widespread damage to forests. Evaluation of the effects of typhoons on forest ecosystems is often complex and challenging, mainly because of their sporadic nature. In this paper, we compared existing forest damage estimation techniques with the goal of identifying their respective advantages and suitable use cases. We considered Hokkaido in northern Japan as a case study, where three typhoons successively struck in 2016 and led to forest destruction. Forest damage was estimated from Landsat 8 imagery by three approaches, namely using vegetation damage indices (DVDI, DNDVI and ΔEVI), using supervised classification with Random Forest (RF) and Support Vector Machines (SVM) and finally by using the commercial CLASlite software with built-in methods to detect forest disturbance. Machine learning classifiers obtained the highest damage assessment accuracy, but intensive computation and complex processing steps were required. The RF and SVM classifiers gave the highest accuracies when using Fractional Cover as a predictor variable (Overall Accuracy = 80.36% in both cases, and ROC AUC values of 0.89 and 0.88, respectively.) Among the vegetation damage indices, DNDVI produced the highest accuracy (AUC = 0.85, OA = 77.68%).The most damaged areas were on the windward slopes, where forest patches were exposed to the brunt of the typhoon winds. Forest damage also peaked at the highest elevations in the study area, possibly representing exposed hilltops. Methods and findings presented in this study can help stakeholders to implement more effective forest damage monitoring after typhoons and other extreme weather events in the future. Forest damage Remote sensing Vegetation indices Multispectral classification CLASlite Meteorology. Climatology Ram Avtar verfasserin aut Deha Agus Umarhadi verfasserin aut Albertus Stephanus Louw verfasserin aut Sourabh Shrivastava verfasserin aut Ali P. Yunus verfasserin aut Khaled Mohamed Khedher verfasserin aut Tetsuya Takemi verfasserin aut Hideaki Shibata verfasserin aut In Weather and Climate Extremes Elsevier, 2016 38(2022), Seite 100494- (DE-627)767567668 (DE-600)2732464-3 22120947 nnns volume:38 year:2022 pages:100494- https://doi.org/10.1016/j.wace.2022.100494 kostenfrei https://doaj.org/article/86bd458c755946cc8feafacebd68c288 kostenfrei http://www.sciencedirect.com/science/article/pii/S2212094722000731 kostenfrei https://doaj.org/toc/2212-0947 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 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_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 38 2022 100494-
allfieldsGer	10.1016/j.wace.2022.100494 doi (DE-627)DOAJ030505313 (DE-599)DOAJ86bd458c755946cc8feafacebd68c288 DE-627 ger DE-627 rakwb eng QC851-999 Xinyu Chen verfasserin aut Post-typhoon forest damage estimation using multiple vegetation indices and machine learning models 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The frequency and intensity of typhoons have increased due to climate change. These climate change-induced disasters have caused widespread damage to forests. Evaluation of the effects of typhoons on forest ecosystems is often complex and challenging, mainly because of their sporadic nature. In this paper, we compared existing forest damage estimation techniques with the goal of identifying their respective advantages and suitable use cases. We considered Hokkaido in northern Japan as a case study, where three typhoons successively struck in 2016 and led to forest destruction. Forest damage was estimated from Landsat 8 imagery by three approaches, namely using vegetation damage indices (DVDI, DNDVI and ΔEVI), using supervised classification with Random Forest (RF) and Support Vector Machines (SVM) and finally by using the commercial CLASlite software with built-in methods to detect forest disturbance. Machine learning classifiers obtained the highest damage assessment accuracy, but intensive computation and complex processing steps were required. The RF and SVM classifiers gave the highest accuracies when using Fractional Cover as a predictor variable (Overall Accuracy = 80.36% in both cases, and ROC AUC values of 0.89 and 0.88, respectively.) Among the vegetation damage indices, DNDVI produced the highest accuracy (AUC = 0.85, OA = 77.68%).The most damaged areas were on the windward slopes, where forest patches were exposed to the brunt of the typhoon winds. Forest damage also peaked at the highest elevations in the study area, possibly representing exposed hilltops. Methods and findings presented in this study can help stakeholders to implement more effective forest damage monitoring after typhoons and other extreme weather events in the future. Forest damage Remote sensing Vegetation indices Multispectral classification CLASlite Meteorology. Climatology Ram Avtar verfasserin aut Deha Agus Umarhadi verfasserin aut Albertus Stephanus Louw verfasserin aut Sourabh Shrivastava verfasserin aut Ali P. Yunus verfasserin aut Khaled Mohamed Khedher verfasserin aut Tetsuya Takemi verfasserin aut Hideaki Shibata verfasserin aut In Weather and Climate Extremes Elsevier, 2016 38(2022), Seite 100494- (DE-627)767567668 (DE-600)2732464-3 22120947 nnns volume:38 year:2022 pages:100494- https://doi.org/10.1016/j.wace.2022.100494 kostenfrei https://doaj.org/article/86bd458c755946cc8feafacebd68c288 kostenfrei http://www.sciencedirect.com/science/article/pii/S2212094722000731 kostenfrei https://doaj.org/toc/2212-0947 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 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_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 38 2022 100494-
allfieldsSound	10.1016/j.wace.2022.100494 doi (DE-627)DOAJ030505313 (DE-599)DOAJ86bd458c755946cc8feafacebd68c288 DE-627 ger DE-627 rakwb eng QC851-999 Xinyu Chen verfasserin aut Post-typhoon forest damage estimation using multiple vegetation indices and machine learning models 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The frequency and intensity of typhoons have increased due to climate change. These climate change-induced disasters have caused widespread damage to forests. Evaluation of the effects of typhoons on forest ecosystems is often complex and challenging, mainly because of their sporadic nature. In this paper, we compared existing forest damage estimation techniques with the goal of identifying their respective advantages and suitable use cases. We considered Hokkaido in northern Japan as a case study, where three typhoons successively struck in 2016 and led to forest destruction. Forest damage was estimated from Landsat 8 imagery by three approaches, namely using vegetation damage indices (DVDI, DNDVI and ΔEVI), using supervised classification with Random Forest (RF) and Support Vector Machines (SVM) and finally by using the commercial CLASlite software with built-in methods to detect forest disturbance. Machine learning classifiers obtained the highest damage assessment accuracy, but intensive computation and complex processing steps were required. The RF and SVM classifiers gave the highest accuracies when using Fractional Cover as a predictor variable (Overall Accuracy = 80.36% in both cases, and ROC AUC values of 0.89 and 0.88, respectively.) Among the vegetation damage indices, DNDVI produced the highest accuracy (AUC = 0.85, OA = 77.68%).The most damaged areas were on the windward slopes, where forest patches were exposed to the brunt of the typhoon winds. Forest damage also peaked at the highest elevations in the study area, possibly representing exposed hilltops. Methods and findings presented in this study can help stakeholders to implement more effective forest damage monitoring after typhoons and other extreme weather events in the future. Forest damage Remote sensing Vegetation indices Multispectral classification CLASlite Meteorology. Climatology Ram Avtar verfasserin aut Deha Agus Umarhadi verfasserin aut Albertus Stephanus Louw verfasserin aut Sourabh Shrivastava verfasserin aut Ali P. Yunus verfasserin aut Khaled Mohamed Khedher verfasserin aut Tetsuya Takemi verfasserin aut Hideaki Shibata verfasserin aut In Weather and Climate Extremes Elsevier, 2016 38(2022), Seite 100494- (DE-627)767567668 (DE-600)2732464-3 22120947 nnns volume:38 year:2022 pages:100494- https://doi.org/10.1016/j.wace.2022.100494 kostenfrei https://doaj.org/article/86bd458c755946cc8feafacebd68c288 kostenfrei http://www.sciencedirect.com/science/article/pii/S2212094722000731 kostenfrei https://doaj.org/toc/2212-0947 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 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_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 38 2022 100494-
language	English
source	In Weather and Climate Extremes 38(2022), Seite 100494- volume:38 year:2022 pages:100494-
sourceStr	In Weather and Climate Extremes 38(2022), Seite 100494- volume:38 year:2022 pages:100494-
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Forest damage Remote sensing Vegetation indices Multispectral classification CLASlite Meteorology. Climatology
isfreeaccess_bool	true
container_title	Weather and Climate Extremes
authorswithroles_txt_mv	Xinyu Chen @@aut@@ Ram Avtar @@aut@@ Deha Agus Umarhadi @@aut@@ Albertus Stephanus Louw @@aut@@ Sourabh Shrivastava @@aut@@ Ali P. Yunus @@aut@@ Khaled Mohamed Khedher @@aut@@ Tetsuya Takemi @@aut@@ Hideaki Shibata @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	767567668
id	DOAJ030505313
language_de	englisch
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These climate change-induced disasters have caused widespread damage to forests. Evaluation of the effects of typhoons on forest ecosystems is often complex and challenging, mainly because of their sporadic nature. In this paper, we compared existing forest damage estimation techniques with the goal of identifying their respective advantages and suitable use cases. We considered Hokkaido in northern Japan as a case study, where three typhoons successively struck in 2016 and led to forest destruction. Forest damage was estimated from Landsat 8 imagery by three approaches, namely using vegetation damage indices (DVDI, DNDVI and ΔEVI), using supervised classification with Random Forest (RF) and Support Vector Machines (SVM) and finally by using the commercial CLASlite software with built-in methods to detect forest disturbance. Machine learning classifiers obtained the highest damage assessment accuracy, but intensive computation and complex processing steps were required. 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callnumber-first	Q - Science
author	Xinyu Chen
spellingShingle	Xinyu Chen misc QC851-999 misc Forest damage misc Remote sensing misc Vegetation indices misc Multispectral classification misc CLASlite misc Meteorology. Climatology Post-typhoon forest damage estimation using multiple vegetation indices and machine learning models
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topic_title	QC851-999 Post-typhoon forest damage estimation using multiple vegetation indices and machine learning models Forest damage Remote sensing Vegetation indices Multispectral classification CLASlite
topic	misc QC851-999 misc Forest damage misc Remote sensing misc Vegetation indices misc Multispectral classification misc CLASlite misc Meteorology. Climatology
topic_unstemmed	misc QC851-999 misc Forest damage misc Remote sensing misc Vegetation indices misc Multispectral classification misc CLASlite misc Meteorology. Climatology
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title	Post-typhoon forest damage estimation using multiple vegetation indices and machine learning models
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title_full	Post-typhoon forest damage estimation using multiple vegetation indices and machine learning models
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author_browse	Xinyu Chen Ram Avtar Deha Agus Umarhadi Albertus Stephanus Louw Sourabh Shrivastava Ali P. Yunus Khaled Mohamed Khedher Tetsuya Takemi Hideaki Shibata
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title_sort	post-typhoon forest damage estimation using multiple vegetation indices and machine learning models
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title_auth	Post-typhoon forest damage estimation using multiple vegetation indices and machine learning models
abstract	The frequency and intensity of typhoons have increased due to climate change. These climate change-induced disasters have caused widespread damage to forests. Evaluation of the effects of typhoons on forest ecosystems is often complex and challenging, mainly because of their sporadic nature. In this paper, we compared existing forest damage estimation techniques with the goal of identifying their respective advantages and suitable use cases. We considered Hokkaido in northern Japan as a case study, where three typhoons successively struck in 2016 and led to forest destruction. Forest damage was estimated from Landsat 8 imagery by three approaches, namely using vegetation damage indices (DVDI, DNDVI and ΔEVI), using supervised classification with Random Forest (RF) and Support Vector Machines (SVM) and finally by using the commercial CLASlite software with built-in methods to detect forest disturbance. Machine learning classifiers obtained the highest damage assessment accuracy, but intensive computation and complex processing steps were required. The RF and SVM classifiers gave the highest accuracies when using Fractional Cover as a predictor variable (Overall Accuracy = 80.36% in both cases, and ROC AUC values of 0.89 and 0.88, respectively.) Among the vegetation damage indices, DNDVI produced the highest accuracy (AUC = 0.85, OA = 77.68%).The most damaged areas were on the windward slopes, where forest patches were exposed to the brunt of the typhoon winds. Forest damage also peaked at the highest elevations in the study area, possibly representing exposed hilltops. Methods and findings presented in this study can help stakeholders to implement more effective forest damage monitoring after typhoons and other extreme weather events in the future.
abstractGer	The frequency and intensity of typhoons have increased due to climate change. These climate change-induced disasters have caused widespread damage to forests. Evaluation of the effects of typhoons on forest ecosystems is often complex and challenging, mainly because of their sporadic nature. In this paper, we compared existing forest damage estimation techniques with the goal of identifying their respective advantages and suitable use cases. We considered Hokkaido in northern Japan as a case study, where three typhoons successively struck in 2016 and led to forest destruction. Forest damage was estimated from Landsat 8 imagery by three approaches, namely using vegetation damage indices (DVDI, DNDVI and ΔEVI), using supervised classification with Random Forest (RF) and Support Vector Machines (SVM) and finally by using the commercial CLASlite software with built-in methods to detect forest disturbance. Machine learning classifiers obtained the highest damage assessment accuracy, but intensive computation and complex processing steps were required. The RF and SVM classifiers gave the highest accuracies when using Fractional Cover as a predictor variable (Overall Accuracy = 80.36% in both cases, and ROC AUC values of 0.89 and 0.88, respectively.) Among the vegetation damage indices, DNDVI produced the highest accuracy (AUC = 0.85, OA = 77.68%).The most damaged areas were on the windward slopes, where forest patches were exposed to the brunt of the typhoon winds. Forest damage also peaked at the highest elevations in the study area, possibly representing exposed hilltops. Methods and findings presented in this study can help stakeholders to implement more effective forest damage monitoring after typhoons and other extreme weather events in the future.
abstract_unstemmed	The frequency and intensity of typhoons have increased due to climate change. These climate change-induced disasters have caused widespread damage to forests. Evaluation of the effects of typhoons on forest ecosystems is often complex and challenging, mainly because of their sporadic nature. In this paper, we compared existing forest damage estimation techniques with the goal of identifying their respective advantages and suitable use cases. We considered Hokkaido in northern Japan as a case study, where three typhoons successively struck in 2016 and led to forest destruction. Forest damage was estimated from Landsat 8 imagery by three approaches, namely using vegetation damage indices (DVDI, DNDVI and ΔEVI), using supervised classification with Random Forest (RF) and Support Vector Machines (SVM) and finally by using the commercial CLASlite software with built-in methods to detect forest disturbance. Machine learning classifiers obtained the highest damage assessment accuracy, but intensive computation and complex processing steps were required. The RF and SVM classifiers gave the highest accuracies when using Fractional Cover as a predictor variable (Overall Accuracy = 80.36% in both cases, and ROC AUC values of 0.89 and 0.88, respectively.) Among the vegetation damage indices, DNDVI produced the highest accuracy (AUC = 0.85, OA = 77.68%).The most damaged areas were on the windward slopes, where forest patches were exposed to the brunt of the typhoon winds. Forest damage also peaked at the highest elevations in the study area, possibly representing exposed hilltops. Methods and findings presented in this study can help stakeholders to implement more effective forest damage monitoring after typhoons and other extreme weather events in the future.
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title_short	Post-typhoon forest damage estimation using multiple vegetation indices and machine learning models
url	https://doi.org/10.1016/j.wace.2022.100494 https://doaj.org/article/86bd458c755946cc8feafacebd68c288 http://www.sciencedirect.com/science/article/pii/S2212094722000731 https://doaj.org/toc/2212-0947
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up_date	2024-07-03T15:23:58.295Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ030505313</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230307150849.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230226s2022 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.wace.2022.100494</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ030505313</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ86bd458c755946cc8feafacebd68c288</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">QC851-999</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Xinyu Chen</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Post-typhoon forest damage estimation using multiple vegetation indices and machine learning models</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">The frequency and intensity of typhoons have increased due to climate change. These climate change-induced disasters have caused widespread damage to forests. Evaluation of the effects of typhoons on forest ecosystems is often complex and challenging, mainly because of their sporadic nature. In this paper, we compared existing forest damage estimation techniques with the goal of identifying their respective advantages and suitable use cases. We considered Hokkaido in northern Japan as a case study, where three typhoons successively struck in 2016 and led to forest destruction. Forest damage was estimated from Landsat 8 imagery by three approaches, namely using vegetation damage indices (DVDI, DNDVI and ΔEVI), using supervised classification with Random Forest (RF) and Support Vector Machines (SVM) and finally by using the commercial CLASlite software with built-in methods to detect forest disturbance. Machine learning classifiers obtained the highest damage assessment accuracy, but intensive computation and complex processing steps were required. The RF and SVM classifiers gave the highest accuracies when using Fractional Cover as a predictor variable (Overall Accuracy = 80.36% in both cases, and ROC AUC values of 0.89 and 0.88, respectively.) Among the vegetation damage indices, DNDVI produced the highest accuracy (AUC = 0.85, OA = 77.68%).The most damaged areas were on the windward slopes, where forest patches were exposed to the brunt of the typhoon winds. Forest damage also peaked at the highest elevations in the study area, possibly representing exposed hilltops. Methods and findings presented in this study can help stakeholders to implement more effective forest damage monitoring after typhoons and other extreme weather events in the future.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Forest damage</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Remote sensing</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Vegetation indices</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Multispectral classification</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">CLASlite</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Meteorology. 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Post-typhoon forest damage estimation using multiple vegetation indices and machine learning models

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