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...

Full description

Saved in:
Bibliographic Details
Published in:Journal of geographical sciences 2012-08, Vol.22 (4), p.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: Article
Language:English
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
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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＊＊）.
ISSN:1009-637X
1861-9568
DOI:10.1007/s11442-012-0955-9