Comparison of Gross Primary Productivity Derived from GIMMS NDVI3g, GIMMS, and MODIS in Southeast Asia

Gross primary production (GPP) plays an important role in the net ecosystem exchange of CO2 between the atmosphere and terrestrial ecosystems. It is particularly important to monitor GPP in Southeast Asia because of increasing rates of tropical forest degradation and deforestation in the region in r...
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Autor*in:	Junbang Wang [verfasserIn] Jingwei Dong [verfasserIn] Jiyuan Liu [verfasserIn] Mei Huang [verfasserIn] Guicai Li [verfasserIn] Steven W. Running [verfasserIn] W. Kolby Smith [verfasserIn] Warwick Harris [verfasserIn] Nobuko Saigusa [verfasserIn] Hiroaki Kondo [verfasserIn] Yunfen Liu [verfasserIn] Takashi Hirano [verfasserIn] Xiangming Xiao [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2014

Schlagwörter:	Gross Primary Productivity (GPP) GIMMS NDVI3g GLOPEM-CEVSA GIMMS NDVI1g MOD15A2 MOD17A2 Southeast Asia

Übergeordnetes Werk:	In: Remote Sensing - MDPI AG, 2009, 6(2014), 3, Seite 2108-2133
Übergeordnetes Werk:	volume:6 ; year:2014 ; number:3 ; pages:2108-2133

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/rs6032108

Katalog-ID:	DOAJ046793739

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allfields	10.3390/rs6032108 doi (DE-627)DOAJ046793739 (DE-599)DOAJ13de63ca0e8444abb114c11edb8b09eb DE-627 ger DE-627 rakwb eng Junbang Wang verfasserin aut Comparison of Gross Primary Productivity Derived from GIMMS NDVI3g, GIMMS, and MODIS in Southeast Asia 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Gross primary production (GPP) plays an important role in the net ecosystem exchange of CO2 between the atmosphere and terrestrial ecosystems. It is particularly important to monitor GPP in Southeast Asia because of increasing rates of tropical forest degradation and deforestation in the region in recent decades. The newly available, improved, third generation Normalized Difference Vegetation Index (NDVI3g) from the Global Inventory Modelling and Mapping Studies (GIMMS) group provides a long temporal dataset, from July 1981 to December 2011, for terrestrial carbon cycle and climate response research. However, GIMMS NDVI3g-based GPP estimates are not yet available. We applied the GLOPEM-CEVSA model, which integrates an ecosystem process model and a production efficiency model, to estimate GPP in Southeast Asia based on three independent results of the fraction of photosynthetically active radiation absorbed by vegetation (FPAR) from GIMMS NDVI3g (GPPNDVI3g), GIMMS NDVI1g (GPPNDVI1g), and the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD15A2 FPAR product (GPPMOD15). The GPP results were validated using ground data from eddy flux towers located in different forest biomes, and comparisons were made among the three GPPs as well as the MOD17A2 GPP products (GPPMOD17). Based on validation with flux tower derived GPP estimates the results show that GPPNDVI3g is more accurate than GPPNDVI1g and is comparable in accuracy with GPPMOD15. In addition, GPPNDVI3g and GPPMOD15 have good spatial-temporal consistency. Our results indicate that GIMMS NDVI3g is an effective dataset for regional GPP simulation in Southeast Asia, capable of accurately tracking the variation and trends in long-term terrestrial ecosystem GPP dynamics. Gross Primary Productivity (GPP) GIMMS NDVI3g GLOPEM-CEVSA GIMMS NDVI1g MOD15A2 MOD17A2 Southeast Asia Science Q Jingwei Dong verfasserin aut Jiyuan Liu verfasserin aut Mei Huang verfasserin aut Guicai Li verfasserin aut Steven W. Running verfasserin aut W. Kolby Smith verfasserin aut Warwick Harris verfasserin aut Nobuko Saigusa verfasserin aut Hiroaki Kondo verfasserin aut Yunfen Liu verfasserin aut Takashi Hirano verfasserin aut Xiangming Xiao verfasserin aut In Remote Sensing MDPI AG, 2009 6(2014), 3, Seite 2108-2133 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:6 year:2014 number:3 pages:2108-2133 https://doi.org/10.3390/rs6032108 kostenfrei https://doaj.org/article/13de63ca0e8444abb114c11edb8b09eb kostenfrei http://www.mdpi.com/2072-4292/6/3/2108 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 6 2014 3 2108-2133
spelling	10.3390/rs6032108 doi (DE-627)DOAJ046793739 (DE-599)DOAJ13de63ca0e8444abb114c11edb8b09eb DE-627 ger DE-627 rakwb eng Junbang Wang verfasserin aut Comparison of Gross Primary Productivity Derived from GIMMS NDVI3g, GIMMS, and MODIS in Southeast Asia 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Gross primary production (GPP) plays an important role in the net ecosystem exchange of CO2 between the atmosphere and terrestrial ecosystems. It is particularly important to monitor GPP in Southeast Asia because of increasing rates of tropical forest degradation and deforestation in the region in recent decades. The newly available, improved, third generation Normalized Difference Vegetation Index (NDVI3g) from the Global Inventory Modelling and Mapping Studies (GIMMS) group provides a long temporal dataset, from July 1981 to December 2011, for terrestrial carbon cycle and climate response research. However, GIMMS NDVI3g-based GPP estimates are not yet available. We applied the GLOPEM-CEVSA model, which integrates an ecosystem process model and a production efficiency model, to estimate GPP in Southeast Asia based on three independent results of the fraction of photosynthetically active radiation absorbed by vegetation (FPAR) from GIMMS NDVI3g (GPPNDVI3g), GIMMS NDVI1g (GPPNDVI1g), and the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD15A2 FPAR product (GPPMOD15). The GPP results were validated using ground data from eddy flux towers located in different forest biomes, and comparisons were made among the three GPPs as well as the MOD17A2 GPP products (GPPMOD17). Based on validation with flux tower derived GPP estimates the results show that GPPNDVI3g is more accurate than GPPNDVI1g and is comparable in accuracy with GPPMOD15. In addition, GPPNDVI3g and GPPMOD15 have good spatial-temporal consistency. Our results indicate that GIMMS NDVI3g is an effective dataset for regional GPP simulation in Southeast Asia, capable of accurately tracking the variation and trends in long-term terrestrial ecosystem GPP dynamics. Gross Primary Productivity (GPP) GIMMS NDVI3g GLOPEM-CEVSA GIMMS NDVI1g MOD15A2 MOD17A2 Southeast Asia Science Q Jingwei Dong verfasserin aut Jiyuan Liu verfasserin aut Mei Huang verfasserin aut Guicai Li verfasserin aut Steven W. Running verfasserin aut W. Kolby Smith verfasserin aut Warwick Harris verfasserin aut Nobuko Saigusa verfasserin aut Hiroaki Kondo verfasserin aut Yunfen Liu verfasserin aut Takashi Hirano verfasserin aut Xiangming Xiao verfasserin aut In Remote Sensing MDPI AG, 2009 6(2014), 3, Seite 2108-2133 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:6 year:2014 number:3 pages:2108-2133 https://doi.org/10.3390/rs6032108 kostenfrei https://doaj.org/article/13de63ca0e8444abb114c11edb8b09eb kostenfrei http://www.mdpi.com/2072-4292/6/3/2108 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 6 2014 3 2108-2133
allfields_unstemmed	10.3390/rs6032108 doi (DE-627)DOAJ046793739 (DE-599)DOAJ13de63ca0e8444abb114c11edb8b09eb DE-627 ger DE-627 rakwb eng Junbang Wang verfasserin aut Comparison of Gross Primary Productivity Derived from GIMMS NDVI3g, GIMMS, and MODIS in Southeast Asia 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Gross primary production (GPP) plays an important role in the net ecosystem exchange of CO2 between the atmosphere and terrestrial ecosystems. It is particularly important to monitor GPP in Southeast Asia because of increasing rates of tropical forest degradation and deforestation in the region in recent decades. The newly available, improved, third generation Normalized Difference Vegetation Index (NDVI3g) from the Global Inventory Modelling and Mapping Studies (GIMMS) group provides a long temporal dataset, from July 1981 to December 2011, for terrestrial carbon cycle and climate response research. However, GIMMS NDVI3g-based GPP estimates are not yet available. We applied the GLOPEM-CEVSA model, which integrates an ecosystem process model and a production efficiency model, to estimate GPP in Southeast Asia based on three independent results of the fraction of photosynthetically active radiation absorbed by vegetation (FPAR) from GIMMS NDVI3g (GPPNDVI3g), GIMMS NDVI1g (GPPNDVI1g), and the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD15A2 FPAR product (GPPMOD15). The GPP results were validated using ground data from eddy flux towers located in different forest biomes, and comparisons were made among the three GPPs as well as the MOD17A2 GPP products (GPPMOD17). Based on validation with flux tower derived GPP estimates the results show that GPPNDVI3g is more accurate than GPPNDVI1g and is comparable in accuracy with GPPMOD15. In addition, GPPNDVI3g and GPPMOD15 have good spatial-temporal consistency. Our results indicate that GIMMS NDVI3g is an effective dataset for regional GPP simulation in Southeast Asia, capable of accurately tracking the variation and trends in long-term terrestrial ecosystem GPP dynamics. Gross Primary Productivity (GPP) GIMMS NDVI3g GLOPEM-CEVSA GIMMS NDVI1g MOD15A2 MOD17A2 Southeast Asia Science Q Jingwei Dong verfasserin aut Jiyuan Liu verfasserin aut Mei Huang verfasserin aut Guicai Li verfasserin aut Steven W. Running verfasserin aut W. Kolby Smith verfasserin aut Warwick Harris verfasserin aut Nobuko Saigusa verfasserin aut Hiroaki Kondo verfasserin aut Yunfen Liu verfasserin aut Takashi Hirano verfasserin aut Xiangming Xiao verfasserin aut In Remote Sensing MDPI AG, 2009 6(2014), 3, Seite 2108-2133 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:6 year:2014 number:3 pages:2108-2133 https://doi.org/10.3390/rs6032108 kostenfrei https://doaj.org/article/13de63ca0e8444abb114c11edb8b09eb kostenfrei http://www.mdpi.com/2072-4292/6/3/2108 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 6 2014 3 2108-2133
allfieldsGer	10.3390/rs6032108 doi (DE-627)DOAJ046793739 (DE-599)DOAJ13de63ca0e8444abb114c11edb8b09eb DE-627 ger DE-627 rakwb eng Junbang Wang verfasserin aut Comparison of Gross Primary Productivity Derived from GIMMS NDVI3g, GIMMS, and MODIS in Southeast Asia 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Gross primary production (GPP) plays an important role in the net ecosystem exchange of CO2 between the atmosphere and terrestrial ecosystems. It is particularly important to monitor GPP in Southeast Asia because of increasing rates of tropical forest degradation and deforestation in the region in recent decades. The newly available, improved, third generation Normalized Difference Vegetation Index (NDVI3g) from the Global Inventory Modelling and Mapping Studies (GIMMS) group provides a long temporal dataset, from July 1981 to December 2011, for terrestrial carbon cycle and climate response research. However, GIMMS NDVI3g-based GPP estimates are not yet available. We applied the GLOPEM-CEVSA model, which integrates an ecosystem process model and a production efficiency model, to estimate GPP in Southeast Asia based on three independent results of the fraction of photosynthetically active radiation absorbed by vegetation (FPAR) from GIMMS NDVI3g (GPPNDVI3g), GIMMS NDVI1g (GPPNDVI1g), and the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD15A2 FPAR product (GPPMOD15). The GPP results were validated using ground data from eddy flux towers located in different forest biomes, and comparisons were made among the three GPPs as well as the MOD17A2 GPP products (GPPMOD17). Based on validation with flux tower derived GPP estimates the results show that GPPNDVI3g is more accurate than GPPNDVI1g and is comparable in accuracy with GPPMOD15. In addition, GPPNDVI3g and GPPMOD15 have good spatial-temporal consistency. Our results indicate that GIMMS NDVI3g is an effective dataset for regional GPP simulation in Southeast Asia, capable of accurately tracking the variation and trends in long-term terrestrial ecosystem GPP dynamics. Gross Primary Productivity (GPP) GIMMS NDVI3g GLOPEM-CEVSA GIMMS NDVI1g MOD15A2 MOD17A2 Southeast Asia Science Q Jingwei Dong verfasserin aut Jiyuan Liu verfasserin aut Mei Huang verfasserin aut Guicai Li verfasserin aut Steven W. Running verfasserin aut W. Kolby Smith verfasserin aut Warwick Harris verfasserin aut Nobuko Saigusa verfasserin aut Hiroaki Kondo verfasserin aut Yunfen Liu verfasserin aut Takashi Hirano verfasserin aut Xiangming Xiao verfasserin aut In Remote Sensing MDPI AG, 2009 6(2014), 3, Seite 2108-2133 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:6 year:2014 number:3 pages:2108-2133 https://doi.org/10.3390/rs6032108 kostenfrei https://doaj.org/article/13de63ca0e8444abb114c11edb8b09eb kostenfrei http://www.mdpi.com/2072-4292/6/3/2108 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 6 2014 3 2108-2133
allfieldsSound	10.3390/rs6032108 doi (DE-627)DOAJ046793739 (DE-599)DOAJ13de63ca0e8444abb114c11edb8b09eb DE-627 ger DE-627 rakwb eng Junbang Wang verfasserin aut Comparison of Gross Primary Productivity Derived from GIMMS NDVI3g, GIMMS, and MODIS in Southeast Asia 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Gross primary production (GPP) plays an important role in the net ecosystem exchange of CO2 between the atmosphere and terrestrial ecosystems. It is particularly important to monitor GPP in Southeast Asia because of increasing rates of tropical forest degradation and deforestation in the region in recent decades. The newly available, improved, third generation Normalized Difference Vegetation Index (NDVI3g) from the Global Inventory Modelling and Mapping Studies (GIMMS) group provides a long temporal dataset, from July 1981 to December 2011, for terrestrial carbon cycle and climate response research. However, GIMMS NDVI3g-based GPP estimates are not yet available. We applied the GLOPEM-CEVSA model, which integrates an ecosystem process model and a production efficiency model, to estimate GPP in Southeast Asia based on three independent results of the fraction of photosynthetically active radiation absorbed by vegetation (FPAR) from GIMMS NDVI3g (GPPNDVI3g), GIMMS NDVI1g (GPPNDVI1g), and the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD15A2 FPAR product (GPPMOD15). The GPP results were validated using ground data from eddy flux towers located in different forest biomes, and comparisons were made among the three GPPs as well as the MOD17A2 GPP products (GPPMOD17). Based on validation with flux tower derived GPP estimates the results show that GPPNDVI3g is more accurate than GPPNDVI1g and is comparable in accuracy with GPPMOD15. In addition, GPPNDVI3g and GPPMOD15 have good spatial-temporal consistency. Our results indicate that GIMMS NDVI3g is an effective dataset for regional GPP simulation in Southeast Asia, capable of accurately tracking the variation and trends in long-term terrestrial ecosystem GPP dynamics. Gross Primary Productivity (GPP) GIMMS NDVI3g GLOPEM-CEVSA GIMMS NDVI1g MOD15A2 MOD17A2 Southeast Asia Science Q Jingwei Dong verfasserin aut Jiyuan Liu verfasserin aut Mei Huang verfasserin aut Guicai Li verfasserin aut Steven W. Running verfasserin aut W. Kolby Smith verfasserin aut Warwick Harris verfasserin aut Nobuko Saigusa verfasserin aut Hiroaki Kondo verfasserin aut Yunfen Liu verfasserin aut Takashi Hirano verfasserin aut Xiangming Xiao verfasserin aut In Remote Sensing MDPI AG, 2009 6(2014), 3, Seite 2108-2133 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:6 year:2014 number:3 pages:2108-2133 https://doi.org/10.3390/rs6032108 kostenfrei https://doaj.org/article/13de63ca0e8444abb114c11edb8b09eb kostenfrei http://www.mdpi.com/2072-4292/6/3/2108 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 6 2014 3 2108-2133
language	English
source	In Remote Sensing 6(2014), 3, Seite 2108-2133 volume:6 year:2014 number:3 pages:2108-2133
sourceStr	In Remote Sensing 6(2014), 3, Seite 2108-2133 volume:6 year:2014 number:3 pages:2108-2133
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Gross Primary Productivity (GPP) GIMMS NDVI3g GLOPEM-CEVSA GIMMS NDVI1g MOD15A2 MOD17A2 Southeast Asia Science Q
isfreeaccess_bool	true
container_title	Remote Sensing
authorswithroles_txt_mv	Junbang Wang @@aut@@ Jingwei Dong @@aut@@ Jiyuan Liu @@aut@@ Mei Huang @@aut@@ Guicai Li @@aut@@ Steven W. Running @@aut@@ W. Kolby Smith @@aut@@ Warwick Harris @@aut@@ Nobuko Saigusa @@aut@@ Hiroaki Kondo @@aut@@ Yunfen Liu @@aut@@ Takashi Hirano @@aut@@ Xiangming Xiao @@aut@@
publishDateDaySort_date	2014-01-01T00:00:00Z
hierarchy_top_id	608937916
id	DOAJ046793739
language_de	englisch
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author	Junbang Wang
spellingShingle	Junbang Wang misc Gross Primary Productivity (GPP) misc GIMMS NDVI3g misc GLOPEM-CEVSA misc GIMMS NDVI1g misc MOD15A2 misc MOD17A2 misc Southeast Asia misc Science misc Q Comparison of Gross Primary Productivity Derived from GIMMS NDVI3g, GIMMS, and MODIS in Southeast Asia
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topic_title	Comparison of Gross Primary Productivity Derived from GIMMS NDVI3g, GIMMS, and MODIS in Southeast Asia Gross Primary Productivity (GPP) GIMMS NDVI3g GLOPEM-CEVSA GIMMS NDVI1g MOD15A2 MOD17A2 Southeast Asia
topic	misc Gross Primary Productivity (GPP) misc GIMMS NDVI3g misc GLOPEM-CEVSA misc GIMMS NDVI1g misc MOD15A2 misc MOD17A2 misc Southeast Asia misc Science misc Q
topic_unstemmed	misc Gross Primary Productivity (GPP) misc GIMMS NDVI3g misc GLOPEM-CEVSA misc GIMMS NDVI1g misc MOD15A2 misc MOD17A2 misc Southeast Asia misc Science misc Q
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title	Comparison of Gross Primary Productivity Derived from GIMMS NDVI3g, GIMMS, and MODIS in Southeast Asia
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title_full	Comparison of Gross Primary Productivity Derived from GIMMS NDVI3g, GIMMS, and MODIS in Southeast Asia
author_sort	Junbang Wang
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author_browse	Junbang Wang Jingwei Dong Jiyuan Liu Mei Huang Guicai Li Steven W. Running W. Kolby Smith Warwick Harris Nobuko Saigusa Hiroaki Kondo Yunfen Liu Takashi Hirano Xiangming Xiao
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title_sort	comparison of gross primary productivity derived from gimms ndvi3g, gimms, and modis in southeast asia
title_auth	Comparison of Gross Primary Productivity Derived from GIMMS NDVI3g, GIMMS, and MODIS in Southeast Asia
abstract	Gross primary production (GPP) plays an important role in the net ecosystem exchange of CO2 between the atmosphere and terrestrial ecosystems. It is particularly important to monitor GPP in Southeast Asia because of increasing rates of tropical forest degradation and deforestation in the region in recent decades. The newly available, improved, third generation Normalized Difference Vegetation Index (NDVI3g) from the Global Inventory Modelling and Mapping Studies (GIMMS) group provides a long temporal dataset, from July 1981 to December 2011, for terrestrial carbon cycle and climate response research. However, GIMMS NDVI3g-based GPP estimates are not yet available. We applied the GLOPEM-CEVSA model, which integrates an ecosystem process model and a production efficiency model, to estimate GPP in Southeast Asia based on three independent results of the fraction of photosynthetically active radiation absorbed by vegetation (FPAR) from GIMMS NDVI3g (GPPNDVI3g), GIMMS NDVI1g (GPPNDVI1g), and the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD15A2 FPAR product (GPPMOD15). The GPP results were validated using ground data from eddy flux towers located in different forest biomes, and comparisons were made among the three GPPs as well as the MOD17A2 GPP products (GPPMOD17). Based on validation with flux tower derived GPP estimates the results show that GPPNDVI3g is more accurate than GPPNDVI1g and is comparable in accuracy with GPPMOD15. In addition, GPPNDVI3g and GPPMOD15 have good spatial-temporal consistency. Our results indicate that GIMMS NDVI3g is an effective dataset for regional GPP simulation in Southeast Asia, capable of accurately tracking the variation and trends in long-term terrestrial ecosystem GPP dynamics.
abstractGer	Gross primary production (GPP) plays an important role in the net ecosystem exchange of CO2 between the atmosphere and terrestrial ecosystems. It is particularly important to monitor GPP in Southeast Asia because of increasing rates of tropical forest degradation and deforestation in the region in recent decades. The newly available, improved, third generation Normalized Difference Vegetation Index (NDVI3g) from the Global Inventory Modelling and Mapping Studies (GIMMS) group provides a long temporal dataset, from July 1981 to December 2011, for terrestrial carbon cycle and climate response research. However, GIMMS NDVI3g-based GPP estimates are not yet available. We applied the GLOPEM-CEVSA model, which integrates an ecosystem process model and a production efficiency model, to estimate GPP in Southeast Asia based on three independent results of the fraction of photosynthetically active radiation absorbed by vegetation (FPAR) from GIMMS NDVI3g (GPPNDVI3g), GIMMS NDVI1g (GPPNDVI1g), and the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD15A2 FPAR product (GPPMOD15). The GPP results were validated using ground data from eddy flux towers located in different forest biomes, and comparisons were made among the three GPPs as well as the MOD17A2 GPP products (GPPMOD17). Based on validation with flux tower derived GPP estimates the results show that GPPNDVI3g is more accurate than GPPNDVI1g and is comparable in accuracy with GPPMOD15. In addition, GPPNDVI3g and GPPMOD15 have good spatial-temporal consistency. Our results indicate that GIMMS NDVI3g is an effective dataset for regional GPP simulation in Southeast Asia, capable of accurately tracking the variation and trends in long-term terrestrial ecosystem GPP dynamics.
abstract_unstemmed	Gross primary production (GPP) plays an important role in the net ecosystem exchange of CO2 between the atmosphere and terrestrial ecosystems. It is particularly important to monitor GPP in Southeast Asia because of increasing rates of tropical forest degradation and deforestation in the region in recent decades. The newly available, improved, third generation Normalized Difference Vegetation Index (NDVI3g) from the Global Inventory Modelling and Mapping Studies (GIMMS) group provides a long temporal dataset, from July 1981 to December 2011, for terrestrial carbon cycle and climate response research. However, GIMMS NDVI3g-based GPP estimates are not yet available. We applied the GLOPEM-CEVSA model, which integrates an ecosystem process model and a production efficiency model, to estimate GPP in Southeast Asia based on three independent results of the fraction of photosynthetically active radiation absorbed by vegetation (FPAR) from GIMMS NDVI3g (GPPNDVI3g), GIMMS NDVI1g (GPPNDVI1g), and the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD15A2 FPAR product (GPPMOD15). The GPP results were validated using ground data from eddy flux towers located in different forest biomes, and comparisons were made among the three GPPs as well as the MOD17A2 GPP products (GPPMOD17). Based on validation with flux tower derived GPP estimates the results show that GPPNDVI3g is more accurate than GPPNDVI1g and is comparable in accuracy with GPPMOD15. In addition, GPPNDVI3g and GPPMOD15 have good spatial-temporal consistency. Our results indicate that GIMMS NDVI3g is an effective dataset for regional GPP simulation in Southeast Asia, capable of accurately tracking the variation and trends in long-term terrestrial ecosystem GPP dynamics.
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title_short	Comparison of Gross Primary Productivity Derived from GIMMS NDVI3g, GIMMS, and MODIS in Southeast Asia
url	https://doi.org/10.3390/rs6032108 https://doaj.org/article/13de63ca0e8444abb114c11edb8b09eb http://www.mdpi.com/2072-4292/6/3/2108 https://doaj.org/toc/2072-4292
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Comparison of Gross Primary Productivity Derived from GIMMS NDVI3g, GIMMS, and MODIS in Southeast Asia

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