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

doi:10.1007/s11442-012-0955-9

Abstract

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 (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^**).

Key words: MODIS, MCD43A4, MOD44B, forest inventory, regression

AGUIRRE-SALADO Carlos Arturo, TREVIŇO-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. 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.

References

Acosta M, Carrillo F, Díaz M, 2009. Determination of total carbon unmixed pine forests (Pinus patula Schl. et Cham.). Terra Latinoamericana, 27(2): 105-114.
Aguirre C, Valdez R, ángeles, G et al., 2009. Mapping aboveground tree carbon in managed Patula pine forests in Hidalgo, México. Agrociencia, 43: 209-220.
Aguirre C, Valdez R, ángeles, G et al., 2011. Mapping leaf area index and canopy cover using hemisphericalphotography and SPOT 5 HRG data: Regression and k-NN. Agrociencia, 45: 105-119.
Amiri R, Weng Q, Alimohamadi A et al., 2009. Spatial-temporal dynamics of land surface temperature in relationto fractional vegetation cover and land use/cover in the Tabriz urban area, Iran. Remote Sensing of Environment,113: 2606-2617.
Anaya J, Chuvieco E, Palacios-Orueta, 2009. Aboveground biomass assessment in Colombia: A remote sensingapproach. Forest Ecology and Management 257: 1237-1246.
Andersson K, Evans T, Richards K, 2009. National forest carbon inventories: Policy needs and assessment capacity. Climatic Change. 93: 69-101.
Antonio X, Trevi?o E, Jurado E, 2008. Forest fragmentation in the subwatershed of the Pilon River: Diagnosticand priorities. Madera y Bosques, 14(3): 5-23.
Barrio M, Balboa M, Castedo F et al., 2006. An ecoregional model for estimating volume, biomass and carbonpools in maritime pine stands in Galicia (northwestern Spain). Forest Ecology and Management, 223: 24-34.
Bhaduri K, Das K, Votava P, 2010. Distributed anomaly detection using satellite data from multiple modalities.Proceedings of The 2010 Conference on Intelligent Data Understanding (CIDU-NASA). San Francisco Bay Area. October 5-7, 109-123.
Blackard J, Finco M, Helmer E et al., 2008. Mapping U.S. forest biomass using nation wide forest inventory dataand moderate resolution information. Remote Sensing of Environment, 112: 1658-1677.
CONAFOR, 2010. Manual de Procedimientos para el muestreo de campo. Remuestreo-2010. Inventario Nacional Forestal y de Suelos. Comisión Nacional Forestal. Secretaría del Medio Ambiente y Recursos Naturales. Zapopan, Jalisco, México. 140 p.
Coops N, Ferster C, Waring R et al., 2009. Comparison of three models for predicting gross primary production acrossand within forested ecoregions in the contiguous United States. Remote Sensing of Environment, 113: 680-690.
Daucsavage J, Kaminski M, Ramachandran B et al, 2010. ASTER and MODIS land data management at the Land Processes, and National Snow and Ice Data Centers. In: Ramachandran B, Justice C, Abrams M. Land Remote Sensing and Global Environmental Change. NASA Earth Observing System and the Science of ASTER and MODIS. Springer, 167-182.
DeFries R, 2008. Terrestrial vegetation in the coupled human-earth system: Contributions of remote sensing.Annual Review on Environmental Resources, 33: 369-390.
Espinoza R, Návar J, 2005. Producción de biomasa, diversidad y ecología de especies en un gradiente deproductividad en el matorral espinoso tamaulipeco del nordeste de México. Revista Chapingo. Serie Ciencias Forestales y del Ambiente, 11(1): 25-31.
Gallaun H, Zanchi G, Nabuurs G et al., 2009. EU-wide maps of growing stock and above-ground biomass in forestsbased on remote sensing and field measurements. Forest Ecology and Management, 260(3): 252-261.
Gao J, 2009. Digital Analysis of Remotely Sensed Imagery. McGraw Hill, 18.
Glenn E, Huete A, Nagler P et al., 2008. Relationship between remotely-sensed vegetation indices, canopy attributesand plant physiological processes: what vegetation indices can and cannot tell us about the landscape.Sensors, 8: 2136-2160.
GOFC-GOLD, 2010. A sourcebook of methods and procedures for monitoring and reporting anthropogenicgreenhouse gas emissions and removals caused by deforestation, gains and losses of carbon stocks in forestsremaining forests, and forestation. GOFC-GOLD Report version COP16-1. GOFC-GOLD Project Office,Natural Resources Canada, Alberta, Canada. 203 p.
Gong P, Pu R, Biging G et al., 2003. Estimation of forest leaf area index using vegetation indices derived from Hyperion Hyperspectral Data. IEEE Transactions on Geoscience and Remote Sensing, 41(6): 1355-1362.
Hansen M, DeFries R, Townshend J et al., 2003. Global percent tree cover at a spatial resolution of 500 meters: First results of the MODIS vegetation continuous fields algorithm. Earth Interactions, 7: 1-15.
INEGI, 2002. Síntesis Geográfica del Estado de San Luis Potosí. Instituto Nacional de Estadística y Geografía e Informática. México.
INEGI, 2009. Mapa de Vegetación y Uso de Suelo Serie IV. Instituto Nacional de Estadística y Geografía e Informática. México.
IPCC, 2007. Climate change 2007: The physical science basis. In: Contribution of Working Group I to the Fourth Assessment Report of the IPCC (Cambridge University Press, Cambridge, UK).
Lu D, Mausel P, Brondizio E et al., 2004. Relationships between forest stand parameters and Landsat TM spectralresponses in the Brazilian Amazon Basin. Forest Ecology and Management, 198: 149-167.
Márquez M, Trevi?o, E, Jurado E, 2005. Replacement of forested areas by chaparral and herbaceous communitiesduring 1970-2000 at a microbasin in Durango, Mexico. Investigaciones Geográficas, 58: 54-65.
Návar J, 2009. Biomass component equations for Latin American species and groups of species. Annals of Forest Science, 66: 208.
Návar J, 2011. The spatial distribution of aboveground biomass in tropical forests of Mexico. Tropical and Subtropical Agroecosystems, 13: 149-158.
Navarro R, Blanco P, 2006. Estimation of above-ground biomass in shrubland ecosystems of southern Spain. Investigación Agraria. Sistemas y Recursos Forestales, 15(2): 197-207.
Powell S, Cohen W, Healey S et al., 2010. Quantification of live aboveground forest biomass dynamics with Landsat time-series and field inventory data: A comparison of empirical modeling approaches. Remote Sensingof Environment, 114: 1053-1068.
Reich R, Aguirre C, Bravo V, 2008. New approach for modeling climatic data with applications in modeling treespecies distributions in the states of Jalisco and Colima. Journal of Arid Environments 72(7): 1343-1357.
Robles A, Espa?a J, Robles H, 2008. Biomasa y forraje, distribución espacial y abundancia de la planta de sotol(Dasylirion spp) en el ejido el Jazmín, Mazapil, Zacatecas, México. Revista Investigación Científica, 4(2): 1-9.
Rock B, Vogelmann J, Williams D et al., 1986. Remote detection of forest damage. Bioscience, 36: 439-445.
Rodríguez R, Jiménez J, Aguirre O et al., 2006. Estimación del carbono almacenado en un bosque de niebla en Tamaulipas, México. Ciencia UANL, 9(2): 179-188.
Rodríguez R, Jiménez J, Aguirre O et al., 2009. Estimación de carbono almacenado en el bosque de pino-encinoen la Reserva de la Biosfera “El cielo”, Tamaulipas, México. Ra Ximhai, 5(3): 317-327.
SAS Institute Inc., 2004. SAS/STAT 9.1 User’s Guide. SAS Institute Inc., Cary, NC, USA. 5121 p.
Wu H, Zhao L, 2009. Scale issues in remote sensing: A review on analysis processing and modeling. Sensors, 9:1768-1793.
Xie Y, Sha Z, Yu M, 2008. Remote sensing imagery in vegetation mapping: A review. Journal of Plant Ecology,1(1): 9-23.
Zheng D, Rademacher J, Chen J et al., 2004. Estimating aboveground biomass using Landsat 7 ETM+ data acrossa managed landscape in northern Wisconsin, USA. Remote Sensing of Environment, 93: 402-411.

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