Construction of aboveground biomass models with remote sensing technology in the intertropical zone in Mexico

Spatially-explicit estimation of aboveground biomass （AGB） plays an important role to generate action policies focused in climate change mitigation, since carbon （C） retained in the biomass is vital for regulating Earth＇s temperature. This work estimates AGB using both chlorophyll （red, near infrare...

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Published in:	Journal of geographical sciences Vol. 22; no. 4; pp. 669 - 680
Main Author:	AGUIRRE-SALADO Carlos Arturo TREVINO-GARZA Eduardo Javier AGUIRRE-CALDERON Oscar Alberto JIMENEZ-PiEREZ Javier GONZALEZ-TAGLE Marco Aurelio VALDEZ-LAZALDE Jose Rene M IRANDA-ARAGON Liliana AGUIRRE-SALADO Alejandro lvan
Format:	Journal Article
Language:	English
Published:	Heidelberg SP Science Press 01-08-2012 Springer Nature B.V Autonomous University of San Luis Potosi, SLP 78290, Mexico%Autonomous University of Nuevo Leon, Linares NL 67700, Mexico%The College of Postgraduates, Texcoco MEX 56230, Mexico Autonomous University of Nuevo Leon, Linares NL 67700, Mexico
Subjects:	Bgi / Prodig Biomass Chlorophyll Climate change Climate change mitigation Correlation coefficient Earth and Environmental Science Energy Geographical Information Systems/Cartography Geography Geography of industry and energy Human geography Moisture stress National forests Nature Conservation Physical Geography Remote sensing Remote Sensing/Photogrammetry Vegetation 中等分辨率成像光谱仪地上生物量墨西哥归一化植被指数热带生物量模型遥感技术非线性相关 Mexico San Luis Potosi forest inventory MCD43A4 regression MODIS MOD44B Satellite imagery Cartography Chlorophyll Vegetation index Moisture Vegetation Forest Biomass Remote sensing
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Abstract	Spatially-explicit estimation of aboveground biomass （AGB） plays an important role to generate action policies focused in climate change mitigation, since carbon （C） retained in the biomass is vital for regulating Earth＇s temperature. This work estimates AGB using both chlorophyll （red, near infrared） and moisture （middle infrared） based normalized vegetation indices constructed with MCD43A4 MODerate-resolution Imaging Spectroradiometer （MODIS） and MOD44B vegetation continuous fields （VCF） data. The study area is located in San Luis Potosi, Mexico, a region that comprises a part of the upper limit of the intertropical zone. AGB estimations were made using both individual tree data from the National Forest Inventory of Mexico and allometric equations reported in scientific literature. Linear and nonlinear （expo- nential） models were fitted to find their predictive potential when using satellite spectral data as explanatory variables. Highly-significant correlations （p = 0.01 ） were found between all the explaining variables tested. NDVI62, linked to chlorophyll content and moisture stress, showed the highest correlation. The best model （nonlinear） showed an index of fit （Pseudo - r2） equal to 0.77 and a root mean square error equal to 26.00 Mg/ha using NDVI62 and VCF as explanatory variables. Validation correlation coefficients were similar for both models： linear （r = 0.87＊＊） and nonlinear （r = 0.86＊＊）.
AbstractList	Spatially-explicit estimation of aboveground biomass (AGB) plays an important role to generate action policies focused in climate change mitigation, since carbon (C) retained in the biomass is vital for regulating Earth's temperature. This work estimates AGB using both chlorophyll (red, near infrared) and moisture (middle infrared) based normalized vegetation indices constructed with MCD43A4 MODerate-resolution Imaging Spectroradiometer (MODIS) and MOD44B vegetation continuous fields (VCF) data. The study area is located in San Luis Potosi, Mexico, a region that comprises a part of the upper limit of the intertropical zone. AGB estimations were made using both individual tree data from the National Forest Inventory of Mexico and allometric equations reported in scientific literature. Linear and nonlinear (exponential) models were fitted to find their predictive potential when using satellite spectral data as explanatory variables. Highly-significant correlations (p = 0.01) were found between all the explaining variables tested. NDVI62, linked to chlorophyll content and moisture stress, showed the highest correlation. The best model (nonlinear) showed an index of fit (Pseudo -- r2) equal to 0.77 and a root mean square error equal to 26.00 Mg/ha using NDVI62 and VCF as explanatory variables. Validation correlation coefficients were similar for both models: linear (r = 0.87) and nonlinear (r = 0.86). Spatially-explicit estimation of aboveground biomass （AGB） plays an important role to generate action policies focused in climate change mitigation, since carbon （C） retained in the biomass is vital for regulating Earth＇s temperature. This work estimates AGB using both chlorophyll （red, near infrared） and moisture （middle infrared） based normalized vegetation indices constructed with MCD43A4 MODerate-resolution Imaging Spectroradiometer （MODIS） and MOD44B vegetation continuous fields （VCF） data. The study area is located in San Luis Potosi, Mexico, a region that comprises a part of the upper limit of the intertropical zone. AGB estimations were made using both individual tree data from the National Forest Inventory of Mexico and allometric equations reported in scientific literature. Linear and nonlinear （expo- nential） models were fitted to find their predictive potential when using satellite spectral data as explanatory variables. Highly-significant correlations （p = 0.01 ） were found between all the explaining variables tested. NDVI62, linked to chlorophyll content and moisture stress, showed the highest correlation. The best model （nonlinear） showed an index of fit （Pseudo - r2） equal to 0.77 and a root mean square error equal to 26.00 Mg/ha using NDVI62 and VCF as explanatory variables. Validation correlation coefficients were similar for both models： linear （r = 0.87＊＊） and nonlinear （r = 0.86＊＊）. Spatially-explicit estimation of aboveground biomass (AGB) plays an important role to generate action policies focused in climate change mitigation, since carbon (C) retained in the biomass is vital for regulating Earth’s temperature. This work estimates AGB using both chlorophyll (red, near infrared) and moisture (middle infrared) based normalized vegetation indices constructed with MCD43A4 MODerate-resolution Imaging Spectroradiometer (MODIS) and MOD44B vegetation continuous fields (VCF) data. The study area is located in San Luis Potosí, Mexico, a region that comprises a part of the upper limit of the intertropical zone. AGB estimations were made using both individual tree data from the National Forest Inventory of Mexico and allometric equations reported in scientific literature. Linear and nonlinear (exponential) models were fitted to find their predictive potential when using satellite spectral data as explanatory variables. Highly-significant correlations (p = 0.01) were found between all the explaining variables tested. NDVI62, linked to chlorophyll content and moisture stress, showed the highest correlation. The best model (nonlinear) showed an index of fit (Pseudo — r 2 ) equal to 0.77 and a root mean square error equal to 26.00 Mg/ha using NDVI62 and VCF as explanatory variables. Validation correlation coefficients were similar for both models: linear (r = 0.87) and nonlinear (r = 0.86). Spatially-explicit estimation of aboveground biomass (AGB) plays an important role to generate action policies focused in climate change mitigation, since carbon (C) retained in the biomass is vital for regulating Earth’s temperature. This work estimates AGB using both chlorophyll (red, near infrared) and moisture (middle infrared) based normalized vegetation indices constructed with MCD43A4 MODerate-resolution Imaging Spectroradiometer (MODIS) and MOD44B vegetation continuous fields (VCF) data. The study area is located in San Luis Potosí, Mexico, a region that comprises a part of the upper limit of the intertropical zone. AGB estimations were made using both individual tree data from the National Forest Inventory of Mexico and allometric equations reported in scientific literature. Linear and nonlinear (exponential) models were fitted to find their predictive potential when using satellite spectral data as explanatory variables. Highly-significant correlations (p = 0.01) were found between all the explaining variables tested. NDVI62, linked to chlorophyll content and moisture stress, showed the highest correlation. The best model (nonlinear) showed an index of fit (Pseudo — r2) equal to 0.77 and a root mean square error equal to 26.00 Mg/ha using NDVI62 and VCF as explanatory variables. Validation correlation coefficients were similar for both models: linear (r = 0.87) and nonlinear (r = 0.86).
Author	AGUIRRE-SALADO Carlos Arturo TREVINO-GARZA Eduardo Javier AGUIRRE-CALDERON Oscar Alberto JIMENEZ-PiEREZ Javier GONZALEZ-TAGLE Marco Aurelio VALDEZ-LAZALDE Jose Rene M IRANDA-ARAGON Liliana AGUIRRE-SALADO Alejandro lvan
AuthorAffiliation	Autonomous University of Nuevo Leon, Linares NL 67700, Mexico Autonomous University of San Luis Potosi, SLP 78290, Mexico The College of Postgraduates, Texcoco MEX 56230, Mexico
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Cites_doi	10.2307/1310339 10.1016/j.rse.2007.08.021 10.1016/j.rse.2009.12.018 10.1175/1087-3562(2003)007<0001:GPTCAA>2.0.CO;2 10.1016/j.jaridenv.2008.02.004 10.1051/forest/2009001 10.3390/s90301768 10.1109/TGRS.2003.812910 10.1016/j.rse.2004.08.008 10.1016/j.foreco.2004.03.048 10.1016/j.foreco.2008.11.016 10.1007/s10584-008-9526-6 10.1016/j.rse.2009.07.021 10.1016/j.foreco.2009.10.011 10.1093/jpe/rtm005 10.1146/annurev.environ.33.020107.113339 10.3390/s8042136 10.1016/j.foreco.2005.10.073 10.1016/j.rse.2008.11.013 10.35197/rx.05.03.2009.06.rr 10.5424/srf/2006152-00964 10.21829/myb.2008.1431204 10.1007/978-1-4419-6749-7_8
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Copyright	Science China Press and Springer-Verlag Berlin Heidelberg 2012 Tous droits réservés © Prodig - Bibliographie Géographique Internationale (BGI), 2012 Science China Press and Springer-Verlag Berlin Heidelberg 2012. Copyright © Wanfang Data Co. Ltd. All Rights Reserved.
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Keywords	forest inventory MCD43A4 regression MODIS MOD44B Satellite imagery Cartography Chlorophyll Vegetation index Moisture Vegetation Forest Biomass Remote sensing
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Notes	Spatially-explicit estimation of aboveground biomass （AGB） plays an important role to generate action policies focused in climate change mitigation, since carbon （C） retained in the biomass is vital for regulating Earth＇s temperature. This work estimates AGB using both chlorophyll （red, near infrared） and moisture （middle infrared） based normalized vegetation indices constructed with MCD43A4 MODerate-resolution Imaging Spectroradiometer （MODIS） and MOD44B vegetation continuous fields （VCF） data. The study area is located in San Luis Potosi, Mexico, a region that comprises a part of the upper limit of the intertropical zone. AGB estimations were made using both individual tree data from the National Forest Inventory of Mexico and allometric equations reported in scientific literature. Linear and nonlinear （expo- nential） models were fitted to find their predictive potential when using satellite spectral data as explanatory variables. Highly-significant correlations （p = 0.01 ） were found between all the explaining variables tested. NDVI62, linked to chlorophyll content and moisture stress, showed the highest correlation. The best model （nonlinear） showed an index of fit （Pseudo - r2） equal to 0.77 and a root mean square error equal to 26.00 Mg/ha using NDVI62 and VCF as explanatory variables. Validation correlation coefficients were similar for both models： linear （r = 0.87＊＊） and nonlinear （r = 0.86＊＊）. MODIS; MCD43A4; MOD44B; forest inventory; regression 11-4546/P ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
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PublicationTitle	Journal of geographical sciences
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PublicationYear	2012
Publisher	SP Science Press Springer Nature B.V Autonomous University of San Luis Potosi, SLP 78290, Mexico%Autonomous University of Nuevo Leon, Linares NL 67700, Mexico%The College of Postgraduates, Texcoco MEX 56230, Mexico Autonomous University of Nuevo Leon, Linares NL 67700, Mexico
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Snippet	Spatially-explicit estimation of aboveground biomass （AGB） plays an important role to generate action policies focused in climate change mitigation, since... Spatially-explicit estimation of aboveground biomass (AGB) plays an important role to generate action policies focused in climate change mitigation, since...
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SubjectTerms	Bgi / Prodig Biomass Chlorophyll Climate change Climate change mitigation Correlation coefficient Earth and Environmental Science Energy Geographical Information Systems/Cartography Geography Geography of industry and energy Human geography Moisture stress National forests Nature Conservation Physical Geography Remote sensing Remote Sensing/Photogrammetry Vegetation 中等分辨率成像光谱仪地上生物量墨西哥归一化植被指数热带生物量模型遥感技术非线性相关
Title	Construction of aboveground biomass models with remote sensing technology in the intertropical zone in Mexico
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