| 利用遥感技术建立墨西哥热带地上生物量模型(英文) |
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| 引用本文： | AGUIRRE-SALADO Carlos Arturo,TREVIO-GARZA Eduardo Javier,AGUIRRE-CALDERóN Oscar Alberto,JIMéNEZ-PéREZ Javier,GONZáLEZ-TAGLE Marco Aurelio,VALDEZ-LAZALDE José René,MIRANDA-ARAGóN Liliana,AGUIRRE-SALADO Alejandro Iván.利用遥感技术建立墨西哥热带地上生物量模型(英文)[J].地理学报(英文版),2012,22(4):669-680. |
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| 作者姓名： | AGUIRRE-SALADO Carlos Arturo TREVIO-GARZA Eduardo Javier AGUIRRE-CALDERóN Oscar Alberto JIMéNEZ-PéREZ Javier GONZáLEZ-TAGLE Marco Aurelio VALDEZ-LAZALDE José René MIRANDA-ARAGóN Liliana AGUIRRE-SALADO Alejandro Iván |
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| 作者单位： | 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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| 摘 要： | 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(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:lin-ear(r = 0.87**) and nonlinear(r = 0.86**).
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| 关 键 词： | MODIS MCD43A4 MOD44B forest inventory regression |
Construction of aboveground biomass models with remote sensing technology in the intertropical zone in Mexico |
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| Carlos Arturo Aguirre-Salado,Eduardo Javier Trevi?o-Garza,Oscar Alberto Aguirre-Calderón,Javier Jiménez-Pérez,Marco Aurelio González-Tagle,José René Valdez-Lazalde,Liliana Miranda-Aragón,Alejandro Iván Aguirre-Salado.Construction of aboveground biomass models with remote sensing technology in the intertropical zone in Mexico[J].Journal of Geographical Sciences,2012,22(4):669-680. |
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| Authors: | Carlos Arturo Aguirre-Salado Eduardo Javier Trevi?o-Garza Oscar Alberto Aguirre-Calderón Javier Jiménez-Pérez Marco Aurelio González-Tagle José René Valdez-Lazalde Liliana Miranda-Aragón Alejandro Iván Aguirre-Salado |
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| Institution: | AGUIRRE-SALADO Carlos Arturo1,2,TREVIO-GARZA Eduardo Javier1,AGUIRRE-CALDERóN Oscar Alberto1,JIMéNEZ-PéREZ Javier1,GONZáLEZ-TAGLE Marco Aurelio1,VALDEZ-LAZALDE José René3,MIRANDA-ARAGóN Liliana1,AGUIRRE-SALADO Alejandro Iván3 1.Autonomous University of Nuevo Leon,Linares NL 67700,Mexico;2.Autonomous University of San Luis Potosi,SLP 78290,Mexico;3.The College of Postgraduates,Texcoco MEX 56230,Mexico |
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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 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(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:lin-ear(r = 0.87**) and nonlinear(r = 0.86**). |
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| Keywords: | MODIS MCD43A4 MOD44B forest inventory regression |
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