Estimación de biomasa aérea en Colombia a partir de imágenes MODIS

Se propone un método para aumentar el nivel de detalle en estimaciones regionales de biomasa aérea basado en productos MODIS y mediciones de biomasa aérea en campo. El área de estudio se delimita entre 10 grados norte y 3 grados sur con un área de 1,139,012 km2 correspondiente al área continental de...

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Fecha de publicación:
2008
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Universidad de Medellín
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Repositorio UDEM
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spa
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http://hdl.handle.net/11407/3412
Palabra clave:
Biomasa
Trópico
Modis
VCF
EVI
Biomass
Tropics
Modis
VCF
EVI
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oai_identifier_str oai:repository.udem.edu.co:11407/3412
network_acronym_str REPOUDEM2
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repository_id_str
dc.title.spa.fl_str_mv Estimación de biomasa aérea en Colombia a partir de imágenes MODIS
Aerial biomass estimation in Colombia based on MODIS images
title Estimación de biomasa aérea en Colombia a partir de imágenes MODIS
spellingShingle Estimación de biomasa aérea en Colombia a partir de imágenes MODIS
Biomasa
Trópico
Modis
VCF
EVI
Biomass
Tropics
Modis
VCF
EVI
title_short Estimación de biomasa aérea en Colombia a partir de imágenes MODIS
title_full Estimación de biomasa aérea en Colombia a partir de imágenes MODIS
title_fullStr Estimación de biomasa aérea en Colombia a partir de imágenes MODIS
title_full_unstemmed Estimación de biomasa aérea en Colombia a partir de imágenes MODIS
title_sort Estimación de biomasa aérea en Colombia a partir de imágenes MODIS
dc.subject.spa.fl_str_mv Biomasa
Trópico
Modis
VCF
EVI
Biomass
Tropics
Modis
VCF
EVI
topic Biomasa
Trópico
Modis
VCF
EVI
Biomass
Tropics
Modis
VCF
EVI
description Se propone un método para aumentar el nivel de detalle en estimaciones regionales de biomasa aérea basado en productos MODIS y mediciones de biomasa aérea en campo. El área de estudio se delimita entre 10 grados norte y 3 grados sur con un área de 1,139,012 km2 correspondiente al área continental de Colombia. La vegetación se clasificó en pastizales, bosques secundarios y bosques primarios con el fin de mejorar las estimaciones. Se utilizó como variable explicativa de biomasa en bosques primarios y bosques secundarios la proporción de arbolado por píxel de MOD44 (VCF) siguiendo una relación exponencial, mientras que el índice de vegetación EVI (MOD13A1) se utilizó como variable explicativa de biomasa en pastizales siguiendo una relación lineal. La biomasa aérea en pastizales es altamente dinámica en el tiempo y por tanto se estimó su variación con intervalos de 16 días para el año 2004. Por su parte los bosques secundarios tienen una dificultad adicional al no poder separarse de los bosques primarios con el producto MOD44 (VCF) y presentar valores de biomasa muy inferiores, por lo que se utilizaron mapas auxiliares de vegetación. Los intervalos de confianza de la regresión exponencial aumentan al aumentar la biomasa por tanto la incertidumbre es muy alta para la biomasa total: entre 3,473 y 23,693 millones de toneladas con una media de 16,467. Sin embargo la diferencia de los resultados con estudios previos es mínima.
publishDate 2008
dc.date.created.none.fl_str_mv 2008
dc.date.accessioned.none.fl_str_mv 2017-06-15T22:05:18Z
dc.date.available.none.fl_str_mv 2017-06-15T22:05:18Z
dc.type.eng.fl_str_mv Article
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http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.driver.none.fl_str_mv info:eu-repo/semantics/article
dc.identifier.citation.spa.fl_str_mv Anaya, J. A., Chuvieco, E., & Palacios, A. (2008). Estimación de biomasa aérea en Colombia a partir de imágenes MODIS. Revista de Teledetección, 2008 (30), 5-22.
dc.identifier.issn.none.fl_str_mv 19888740
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/11407/3412
identifier_str_mv Anaya, J. A., Chuvieco, E., & Palacios, A. (2008). Estimación de biomasa aérea en Colombia a partir de imágenes MODIS. Revista de Teledetección, 2008 (30), 5-22.
19888740
url http://hdl.handle.net/11407/3412
dc.language.iso.none.fl_str_mv spa
language spa
dc.relation.isversionof.spa.fl_str_mv http://www.aet.org.es/revistas/revista30/numero30_1.pdf
dc.relation.ispartofes.spa.fl_str_mv Revista de Teledetección. 2008. 30: 5-22
dc.relation.references.spa.fl_str_mv BARUCH, Z. 2005. Vegetation-environment relationships and classification of the seasonal savannas in Venezuela. Flora - Morphology, Distribution, Functional Ecology of Plants 200:49-64.
BENITEZ, P.A., y SERNA, J.C. 2004. Deforestación y flujos de carbono en los bosques asociados con ciénagas del Magdalena Medio, Colombia., Universidad Nacional de Colombia, Medellín.
BOLES, S.H., XIAO, X., LIU, J., ZHANG, Q., MUNKHTUYA, S., CHEN, S., y OJIMA, D. 2004. Land cover characterization of Temperate East Asia using multi-temporal VEGETATION sensor data. Remote Sensing of Environment 90:477-489.
BROWN, S. 1997. Estimating biomass and biomass change in tropical forests: a premier. FAO. Forestry Paper 134, Rome.
CIHLAR, J. 2007. Quantification of the regional carbon cycle of the biosphere: Policy, science and land-use decisions. Journal of Environmental Management 85:785-90.
CLARK, D.B., y CLARK, D.A. 2000. Landscapescale variation in forest structure and biomass in a tropical rain forest. Forest Ecology and Management 137:185-198.
CORNARE. 2002. Modelo de financiación alternativo para el manejo sostenible de los bosques de San Nicolás. Universidad Nacional de Colombia - Cornare, Medellín. 2004. LDOPE Tools User's. Release 1.4.
DAAC, O. 2002. NPP Data. Global Change Master Directory.
DEFRIES, R.S., HANSEN, M.C., TOWNSHEND, J.R., JANETOS, A.C., y LOVELAND, T.R. 2000. A new global 1-km dataset of percentage tree cover derived from remote sensing. Global Change Biology 6:247-254. (ed.) 2002. National Acad Sciences.
DONG, J., KAUFMANN, R.K., MYNENI, R.B., TUCKER, C.J., KAUPPI, P.E., LISKI, J., BUERMANN, W., ALEXEYEV, V., y HUGHES, M.K. 2003. Remote sensing estimates of boreal and temperate forest woody biomass: carbon pools, sources, and sinks. Remote Sensing of Environment 84:393-410.
DRAKE, J.B., DUBAYAH, R.O., KNOX, R.G., CLARK, D.B., y BLAIR, J.B. 2002. Sensitivity of large-footprint lidar to canopy structure and biomass in a neotropical rainforest. Remote Sensing of Environment 81:378-392.
FAO. 2005. Global forest resources assessment (FRA) 2005 - main report. Progress towards sustainable forest management. FAO Forestry paper 147:318.
HACIENDA-IGAC, M.D. 1985. Mapa de Bosques. Instituto Geográfico Agustín Codazzi, Bogotá.
HALL, S.A., BURKE, I.C., BOX, D.O., KAUFMANN, M.R., y STOKER, J.M. 2005. Estimating stand structure using discrete-return lidar: an example from low density, fire prone ponderosa pine forests. Forest Ecology and Management 208:189-209.
HANSEN, M.C., DEFRIES, R.S., TOWNSHEND, J.R.G., MARUFU, L., y SOHLBERG, R. 2002. Development of a MODIS tree cover validation data set for Western Province, Zambia. Remote Sensing of Environment 83:320-335.
HANSEN, M.C., DEFRIES, R.S., TOWNSHEND, J.R., CARROLL, M., DIMICELI, C., y SOHLBERG, R. 2003. MOD44B: Vegetation Continuous Fields Collection 3, Version 3.0.0. Earth Interactions:1-20.
HEROLD, M., ACHARD, F., DE FRIES, R.S., SKOLE, D., BROWN, S., y TOWNSHEND, J.R. 2006. Report of he Workshop on Monitoring Tropical Deforestation for Compensated Reductions. Friedrich-Schiller University Jena.
HOUGHTON, R.A., LAWRENCE, K.T., HACKLER, J.L., y BROWN, S. 2001a. The spatial distribution of forest biomass in the Brazilian Amazon: a comparison of estimates. Global Change Biology 7:731-746. HOUGHTON, R.A., LAWRENCE, K.T., HACKLER, J.L., y BROWN, S. 2001b. The spatial distribution of forest biomass in the Brazilian Amazon: a comparison of estimates. Global Change Biology 7:731-746.
HUETE, A., LIU, H., y LEEUWEN, W. 1997. The Use of Vegetation Indices in Forested Regions: Issues of Linearity and Saturation. IEEE:1966-1968.
HUETE, A., DIDAN, K., MIURA, T., RODRIGUEZ, E.P., GAO, X., y FERREIRA, L.G. 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment 83:195-213.
JIMENEZ, J., MORENO, A.G., LAVELLE, P., y DECAENS, T. 1998. Population dynamics and adaptive strategies of Martiodrilus carimaguensis (Oligochaeta, Glossoscolecidae), a native species from the well-drained savannas of Colombia. Applied Soil Ecology 9:153-160.
KEELING, H.C., y PHILLIPS, O.L. 2007. The global relationship between forest productivity and biomass. Global Ecology and Biogeography 0:1-14.
LOVELOCK, C.E., FELLER, I.C., MCKEE, K.L., y THOMPSON, R. 2005. Variation in Mangrove Forest Structure and Sediment Characteristics in Bocas del Toro, Panama. Caribbean Journal of Science 41:456-464.
MAYAUX, P., BARTHOLOMÉ, E., MASSART, M., VAN CUTSEM, C., CABRAL, A., NONGUIERMA, A., DIALLO, O., PRETORIUS, C., THOMPSON, M., CHERLET, M., PEKEL, J.-F., DEFOURNY, P., VASCONCELOS, M., DI GREGORIO, A., FRITZ, S., DE GRANDI, G., ELVIDGE, C., VOGT, P., y BELWARD, A. 2003. A land cover map of Africa. European Commission, Joint Research Centre.
MEANS, J.E., ACKER, S.A., HARDING, D.J., BLAIR, J.B., LEFSKY, M.A., COHEN, W.B., HARMON, M.E., y MCKEE, W.A. 1999. Use of Large-Footprint Scanning Airborne Lidar To Estimate Forest Stand Characteristics in the Western Cascades of Oregon. Remote Sensing of Environment 67:298-308.
MENAUT, J.C., ABBADIE, L., LAVENU, F., LOUDJANI, P., y PODAIRE, A. 1991. Biomass burning in West African savannas. Global biomass burning - Atmospheric, climatic, and biospheric implications:133-142.
MOUTINHO, P., y SCHWARTZMAN, S., (eds.) 2005. Tropical deforestation and climate change. Amazon Institute for Environmental Reserach, Belem.
PNAS (ed.) 2001. Proceedings of the National Academy of Sciences of the United States of America.
NASCIMENTO, H.E., y LAURANCE, W.F. 2002. Total aboveground biomass in central Amazonian rainforests: a landscape-scale study. Forest Ecology and Management 168:311- 321.
OLSON, J.S., WATTS, J.A., y ALLISON, L.J. 1985. Major World Ecosystems Complexes Ranked by Carbon in Live Vegetation: A Database, pp. 18, In U. S. D. o. Energy, (ed.). R.E. Millemann, T.A Boden, Carbon Dioxide Information Center, Information Resources Organization, Oak Ridge National Lab.
OLSON, J.S., WATTS, J.A., y ALLISON, L.J. 2003. LBA Regional Carbon in Live Vegetation, 0.5-Degree (Olson) [http://www.daac.ornl.gov] Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, U.S.A.
S. A. Orrego yJ. I. Del Valle (ed.) 2001. Simposio Internacional Medición y Monitoreo de la Captura de Carbono en Ecosistemas Forestales, Valdivia, Chile.
PRIVETTE, J.L., MYNENI, R.B., KNYAZIKHIN, Y., MUKELABAI, M., ROBERTS, G., TIAN, Y., WANG, Y., y LEBLANC, S.G. 2002. Early spatial and temporal validation of MODIS LAI product in the Southern Africa Kalahari. Remote Sensing of Environment 83:232-243.
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RIPPSTEIN, G., ESCOBAR, G., y MOTTA, F., (eds.) 2001. Agroecología y Biodiversidad de las Sabanas en los Llanos Orientales de Colombia, Vol. 1, pp. 1-302. CIAT; no. 322.
SALDARRIAGA, J.C., DARRELL, C.W., THARP, M.L., y UHL, C. 1998. Long-Term chronosequence of forest succession in the upper Rio Negro of Colombia and Venezuela. Journal of Ecology 76:938-958.
SAN JOSE, J., y MONTES, R.A. 1998. NPP Grassland: Calabozo, Venezuela, 1969-1987. Data set. [http://www.daac.ornl.gov] Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, U.S.A.
SAN JOSE, J.J., y MONTES, R.A. 2007. Resource apportionment and net primary production across the Orinoco savanna-woodland continuum, Venezuela. Acta Oecológica In Press, Corrected Proof.
SCANLON, T.M., CAYLOR, K.K., MANFREDA, S., LEVIN, S.A., y RODRIGUES, I. 2005. Dynamic response of grass cover to rainfall variability: implications for the function and persistence of savanna ecosystems. Advances in Water Resources 28:291-302.
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spelling 2017-06-15T22:05:18Z2017-06-15T22:05:18Z2008Anaya, J. A., Chuvieco, E., & Palacios, A. (2008). Estimación de biomasa aérea en Colombia a partir de imágenes MODIS. Revista de Teledetección, 2008 (30), 5-22.19888740http://hdl.handle.net/11407/3412Se propone un método para aumentar el nivel de detalle en estimaciones regionales de biomasa aérea basado en productos MODIS y mediciones de biomasa aérea en campo. El área de estudio se delimita entre 10 grados norte y 3 grados sur con un área de 1,139,012 km2 correspondiente al área continental de Colombia. La vegetación se clasificó en pastizales, bosques secundarios y bosques primarios con el fin de mejorar las estimaciones. Se utilizó como variable explicativa de biomasa en bosques primarios y bosques secundarios la proporción de arbolado por píxel de MOD44 (VCF) siguiendo una relación exponencial, mientras que el índice de vegetación EVI (MOD13A1) se utilizó como variable explicativa de biomasa en pastizales siguiendo una relación lineal. La biomasa aérea en pastizales es altamente dinámica en el tiempo y por tanto se estimó su variación con intervalos de 16 días para el año 2004. Por su parte los bosques secundarios tienen una dificultad adicional al no poder separarse de los bosques primarios con el producto MOD44 (VCF) y presentar valores de biomasa muy inferiores, por lo que se utilizaron mapas auxiliares de vegetación. Los intervalos de confianza de la regresión exponencial aumentan al aumentar la biomasa por tanto la incertidumbre es muy alta para la biomasa total: entre 3,473 y 23,693 millones de toneladas con una media de 16,467. Sin embargo la diferencia de los resultados con estudios previos es mínima.This paper presents a method to increase level of detail for above ground biomass estimates at a regional scale. The methodology and materials are based on MODIS products and field measurements corresponding to the continental area of Colombia, covering from 4 degrees south up to 12 degrees north of the Equator with a total of 1,139,012 km2 . Vegetation was classified in three broad classes: grasslands, secondary forests and primary forests which have been proved to enhance biomass estimates. MOD44 (VCF) was used as explanatory variable for primary and secondary forests following an exponential relationship, while EVI (MOD13A1) was used as explanatory variable for grasslands following a linear relationship; biomass for this vegetation class was estimated every 16 days given its large variation throughout the year. Vegetation maps where used to separate primary forests from secondary forest, since the latter shown lower biomass levels. Despite the uncertainty our biomass results are within the estimates of previous studies. Confidence intervals of the exponential regression are larger as the biomass values increases, for this reason the uncertainty is quite high ranging from 3,473 to 23,693 millions of tons with a mean of 16,467.spaAsociación Española De TeledetecciónIngeniería AmbientalFacultad de Ingenieríashttp://www.aet.org.es/revistas/revista30/numero30_1.pdfRevista de Teledetección. 2008. 30: 5-22BARUCH, Z. 2005. Vegetation-environment relationships and classification of the seasonal savannas in Venezuela. Flora - Morphology, Distribution, Functional Ecology of Plants 200:49-64.BENITEZ, P.A., y SERNA, J.C. 2004. Deforestación y flujos de carbono en los bosques asociados con ciénagas del Magdalena Medio, Colombia., Universidad Nacional de Colombia, Medellín.BOLES, S.H., XIAO, X., LIU, J., ZHANG, Q., MUNKHTUYA, S., CHEN, S., y OJIMA, D. 2004. Land cover characterization of Temperate East Asia using multi-temporal VEGETATION sensor data. Remote Sensing of Environment 90:477-489.BROWN, S. 1997. Estimating biomass and biomass change in tropical forests: a premier. FAO. Forestry Paper 134, Rome.CIHLAR, J. 2007. Quantification of the regional carbon cycle of the biosphere: Policy, science and land-use decisions. Journal of Environmental Management 85:785-90.CLARK, D.B., y CLARK, D.A. 2000. Landscapescale variation in forest structure and biomass in a tropical rain forest. Forest Ecology and Management 137:185-198.CORNARE. 2002. Modelo de financiación alternativo para el manejo sostenible de los bosques de San Nicolás. Universidad Nacional de Colombia - Cornare, Medellín. 2004. LDOPE Tools User's. Release 1.4.DAAC, O. 2002. NPP Data. Global Change Master Directory.DEFRIES, R.S., HANSEN, M.C., TOWNSHEND, J.R., JANETOS, A.C., y LOVELAND, T.R. 2000. A new global 1-km dataset of percentage tree cover derived from remote sensing. Global Change Biology 6:247-254. (ed.) 2002. National Acad Sciences.DONG, J., KAUFMANN, R.K., MYNENI, R.B., TUCKER, C.J., KAUPPI, P.E., LISKI, J., BUERMANN, W., ALEXEYEV, V., y HUGHES, M.K. 2003. Remote sensing estimates of boreal and temperate forest woody biomass: carbon pools, sources, and sinks. Remote Sensing of Environment 84:393-410.DRAKE, J.B., DUBAYAH, R.O., KNOX, R.G., CLARK, D.B., y BLAIR, J.B. 2002. Sensitivity of large-footprint lidar to canopy structure and biomass in a neotropical rainforest. Remote Sensing of Environment 81:378-392.FAO. 2005. Global forest resources assessment (FRA) 2005 - main report. Progress towards sustainable forest management. FAO Forestry paper 147:318.HACIENDA-IGAC, M.D. 1985. Mapa de Bosques. Instituto Geográfico Agustín Codazzi, Bogotá.HALL, S.A., BURKE, I.C., BOX, D.O., KAUFMANN, M.R., y STOKER, J.M. 2005. Estimating stand structure using discrete-return lidar: an example from low density, fire prone ponderosa pine forests. Forest Ecology and Management 208:189-209.HANSEN, M.C., DEFRIES, R.S., TOWNSHEND, J.R.G., MARUFU, L., y SOHLBERG, R. 2002. Development of a MODIS tree cover validation data set for Western Province, Zambia. Remote Sensing of Environment 83:320-335.HANSEN, M.C., DEFRIES, R.S., TOWNSHEND, J.R., CARROLL, M., DIMICELI, C., y SOHLBERG, R. 2003. MOD44B: Vegetation Continuous Fields Collection 3, Version 3.0.0. Earth Interactions:1-20.HEROLD, M., ACHARD, F., DE FRIES, R.S., SKOLE, D., BROWN, S., y TOWNSHEND, J.R. 2006. Report of he Workshop on Monitoring Tropical Deforestation for Compensated Reductions. Friedrich-Schiller University Jena.HOUGHTON, R.A., LAWRENCE, K.T., HACKLER, J.L., y BROWN, S. 2001a. The spatial distribution of forest biomass in the Brazilian Amazon: a comparison of estimates. Global Change Biology 7:731-746. HOUGHTON, R.A., LAWRENCE, K.T., HACKLER, J.L., y BROWN, S. 2001b. The spatial distribution of forest biomass in the Brazilian Amazon: a comparison of estimates. Global Change Biology 7:731-746.HUETE, A., LIU, H., y LEEUWEN, W. 1997. The Use of Vegetation Indices in Forested Regions: Issues of Linearity and Saturation. IEEE:1966-1968.HUETE, A., DIDAN, K., MIURA, T., RODRIGUEZ, E.P., GAO, X., y FERREIRA, L.G. 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment 83:195-213.JIMENEZ, J., MORENO, A.G., LAVELLE, P., y DECAENS, T. 1998. Population dynamics and adaptive strategies of Martiodrilus carimaguensis (Oligochaeta, Glossoscolecidae), a native species from the well-drained savannas of Colombia. Applied Soil Ecology 9:153-160.KEELING, H.C., y PHILLIPS, O.L. 2007. The global relationship between forest productivity and biomass. Global Ecology and Biogeography 0:1-14.LOVELOCK, C.E., FELLER, I.C., MCKEE, K.L., y THOMPSON, R. 2005. Variation in Mangrove Forest Structure and Sediment Characteristics in Bocas del Toro, Panama. Caribbean Journal of Science 41:456-464.MAYAUX, P., BARTHOLOMÉ, E., MASSART, M., VAN CUTSEM, C., CABRAL, A., NONGUIERMA, A., DIALLO, O., PRETORIUS, C., THOMPSON, M., CHERLET, M., PEKEL, J.-F., DEFOURNY, P., VASCONCELOS, M., DI GREGORIO, A., FRITZ, S., DE GRANDI, G., ELVIDGE, C., VOGT, P., y BELWARD, A. 2003. A land cover map of Africa. European Commission, Joint Research Centre.MEANS, J.E., ACKER, S.A., HARDING, D.J., BLAIR, J.B., LEFSKY, M.A., COHEN, W.B., HARMON, M.E., y MCKEE, W.A. 1999. Use of Large-Footprint Scanning Airborne Lidar To Estimate Forest Stand Characteristics in the Western Cascades of Oregon. Remote Sensing of Environment 67:298-308.MENAUT, J.C., ABBADIE, L., LAVENU, F., LOUDJANI, P., y PODAIRE, A. 1991. Biomass burning in West African savannas. Global biomass burning - Atmospheric, climatic, and biospheric implications:133-142.MOUTINHO, P., y SCHWARTZMAN, S., (eds.) 2005. Tropical deforestation and climate change. Amazon Institute for Environmental Reserach, Belem.PNAS (ed.) 2001. Proceedings of the National Academy of Sciences of the United States of America.NASCIMENTO, H.E., y LAURANCE, W.F. 2002. Total aboveground biomass in central Amazonian rainforests: a landscape-scale study. Forest Ecology and Management 168:311- 321.OLSON, J.S., WATTS, J.A., y ALLISON, L.J. 1985. Major World Ecosystems Complexes Ranked by Carbon in Live Vegetation: A Database, pp. 18, In U. S. D. o. Energy, (ed.). R.E. Millemann, T.A Boden, Carbon Dioxide Information Center, Information Resources Organization, Oak Ridge National Lab.OLSON, J.S., WATTS, J.A., y ALLISON, L.J. 2003. 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Relationship between herbaceous biomass and 1-km2 Advanced Very High Resolution Radiometer (AVHRR) NDVI in Kruger National Park, South Africa. International Journal of Remote Sensing 27:951-973.ZHANG, B., SONG, K., ZHANG, Y., LI, F., y WANG, Z. 2005. Study on the Relationship between Hyperspectral Reflectance and Soybean LAI, aboveground Biomass. IEEE:3583-3586.Revista de TeledetecciónBiomasaTrópicoModisVCFEVIBiomassTropicsModisVCFEVIEstimación de biomasa aérea en Colombia a partir de imágenes MODISAerial biomass estimation in Colombia based on MODIS imagesArticleinfo:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Anaya Acevedo, J. A.Chuvieco Salinero, EmilioPalacios Orueta, AliciaAnaya Acevedo, J. A.; Universidad de MedellínChuvieco Salinero, Emilio; Universidad de AlcaláPalacios Orueta, Alicia; Universidad Politécnica de Madrid, EspañaTHUMBNAILportada.JPGportada.JPGimage/jpeg14047http://repository.udem.edu.co/bitstream/11407/3412/2/portada.JPGabe23f9bf7cb3dbd59947ad80f13e19eMD52ORIGINALArticulo.htmltext/html473http://repository.udem.edu.co/bitstream/11407/3412/1/Articulo.html1ddc57cd0c3075a5f7a06d00b69c526eMD5111407/3412oai:repository.udem.edu.co:11407/34122020-05-27 18:17:06.489Repositorio Institucional Universidad de Medellinrepositorio@udem.edu.co

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