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

• 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
• ISSN: 1009-637X; 1861-9568
• DOI: 10.1007/s11442-012-0955-9

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＊＊）.