- -

Estimación de emisiones atmosféricas de CO₂, NO₂, CO, NH₃ y Black Carbon vía bottom up, generados por quema de biomasa en el norte de América del Sur

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

Estimación de emisiones atmosféricas de CO₂, NO₂, CO, NH₃ y Black Carbon vía bottom up, generados por quema de biomasa en el norte de América del Sur

Mostrar el registro sencillo del ítem

Ficheros en el ítem

Thumbnail
Nombre: ValenciaAnayaCaro ...
Tamaño: 1.360Mb
Formato: PDF
Descripción: Versión editorial
Abrir
dc.contributor.author Valencia, Germán M. es_ES
dc.contributor.author Anaya, Jesús A. es_ES
dc.contributor.author Caro-Lopera, Francisco J. es_ES
dc.date.accessioned 2022-02-01T11:06:04Z
dc.date.available 2022-02-01T11:06:04Z
dc.date.issued 2022-01-31
dc.identifier.issn 1133-0953
dc.identifier.uri http://hdl.handle.net/10251/180433
dc.description.abstract [EN] Biomass burning is an important source of greenhouse gases (GHG) and air pollutants (AP) in developing countries. In this research, a bottom-up method was implemented for the estimation of emissions, emphasizing the validation process of aerial biomass products (AGB), which it has not been sufficiently approached from the point of view of the quantification of emissions. The most recent results on the validation of burned area (AQ) products and the analysis of uncertainty were also incorporated into the process of estimating the emissions of gases that directly or indirectly promote the greenhouse effect, such as CO₂, NO₂, CO, NH₃, and Black Carbon (BC). In total, 87.60 Mha were burned in the region between 2001 and 2016, represented in a 57% by pasture lands a 23% by savannas, an 8% by savanna woodlands, an 8% by mixed soils with crops and natural vegetation, a 3% by evergreen broadleaf forests, and a 1 % in the region´s remaining types of land cover. With 35480 reference polygons, a model based on the uncertainty of AQ was generated, which served to find the calibration factor of the FireCCI5.0 in all the studied species. The total emissions (minimum and maximum) and the average of the same in the study period were the following: 1760 Tg CO₂ (765.07-2552.88; average 110 Tg), 68.12 Tg of CO (27.11-98.87; average 4.26 Tg), 3.05 Tg of NO₂ (1.27-4.40; average 0.19 Tg), 0.76 Tg of NH₃ (0.33-1.12; average 0.05 Tg), and 0.44 Tg of Black Carbon (0.015-0.64; average 0.03 Tg). es_ES
dc.description.abstract [ES] La quema de biomasa es una fuente importante de gases efecto invernadero (GEI) y contaminantes atmosféricos (CA) en la región norte de Sur América (NHSA). En esta investigación se implementó un método bottom-up para la estimación de emisiones, haciendo énfasis en el proceso de validación de los productos de biomasa aérea (AGB), lo que hasta ahora ha sido poco abordado desde el punto de vista de la cuantificación de las emisiones. También se incorporaron los resultados más recientes sobre la validación de productos de área quemada (AQ) y el análisis de la incertidumbre, dentro del proceso de estimación de las emisiones de gases, que de forma directa o indirecta, promueven el efecto invernadero, como lo son el CO₂, NO₂, CO, NH₃ y Black Carbon (BC). En total se quemaron en la región 87,60 Mha entre 2001 y 2016, representadas en un 57% por pastos; 23%, sabanas; 8%, sabanas arboladas; 8%, suelos mixtos con cultivos y vegetación  natural; 3%, bosques perennes de latifoliadas; 1%, en el resto de  coberturas. Con 35.480 polígonos de referencia se generó un modelo basado en la incertidumbre de AQ, el cual sirvió para encontrar el factor de calibración del FireCCI5.0 en todas las especies estudiadas. Así se obtuvo como resultado que las emisiones totales (mínimas y máximas) y el promedio de las mismas en el periodo de estudio fueron en su orden 1760 Tg CO₂ (765,07-2552,88; promedio 110 Tg), 68,12 Tg de CO (27,11-98,87; promedio 4,26 Tg), 3,05 Tg de NO₂ (1,27-4,40; promedio 0,19 Tg), 0,76 Tg de NH₃ (0,33-1,12; promedio 0,05 Tg), 0,44 Tg de Black Carbon (0,015-0,64; promedio de 0,03 Tg). es_ES
dc.description.sponsorship Queremos agradecer a la Universidades de San Buenaventura a través de sus programas de Especialización en Sistemas de Información Geográfica y Maestría en Geoinformática, a la Universidad de Medellín a través de su Doctorado en Ingenierías y el Doctorado en Modelamiento y Ciencia Computacional. Igualmente damos gracias a Colciencias por su importante aporte para apoyar la formación de investigadores a un alto nivel, con su programa de Becas ColcienciasPrograma Doctoral convocatoria 567, código PDBCNAL71331711. Finalmente, al Doctor Emilio Chuvieco de la Universidad de Alcalá de Henares, y a todo su equipo de investigadores, por el desarrollo del modelo FireCCI, al igual que al programa COPERNICUS por su aporte a la humanidad con el estudio de las variables esenciales de cambio climático. es_ES
dc.language Español es_ES
dc.publisher Universitat Politècnica de València es_ES
dc.relation.ispartof Revista de Teledetección es_ES
dc.rights Reconocimiento - No comercial - Compartir igual (by-nc-sa) es_ES
dc.subject Bottom-up es_ES
dc.subject Área quemada es_ES
dc.subject Emisiones atmosféricas es_ES
dc.subject Validación de biomasa vegetal aérea es_ES
dc.subject Gases efecto invernadero es_ES
dc.subject Contaminantes atmosféricos es_ES
dc.subject Incertidumbre es_ES
dc.subject Burned area es_ES
dc.subject Atmospheric emissions es_ES
dc.subject Aboveground biomass validation es_ES
dc.subject Greenhouse gases es_ES
dc.subject Atmospheric pollutants es_ES
dc.subject Uncertainty es_ES
dc.title Estimación de emisiones atmosféricas de CO₂, NO₂, CO, NH₃ y Black Carbon vía bottom up, generados por quema de biomasa en el norte de América del Sur es_ES
dc.title.alternative Bottom-up estimates of atmospheric emissions of CO₂, NO₂, CO, NH₃, and Black Carbon, generated by biomass burning in the north of South America es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.4995/raet.2022.15594
dc.relation.projectID info:eu-repo/grantAgreement/Minciencias//PDBCNAL71331711/ es_ES
dc.rights.accessRights Abierto es_ES
dc.description.bibliographicCitation Valencia, GM.; Anaya, JA.; Caro-Lopera, FJ. (2022). Estimación de emisiones atmosféricas de CO₂, NO₂, CO, NH₃ y Black Carbon vía bottom up, generados por quema de biomasa en el norte de América del Sur. Revista de Teledetección. 0(59):23-46. https://doi.org/10.4995/raet.2022.15594 es_ES
dc.description.accrualMethod OJS es_ES
dc.relation.publisherversion https://doi.org/10.4995/raet.2022.15594 es_ES
dc.description.upvformatpinicio 23 es_ES
dc.description.upvformatpfin 46 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 0 es_ES
dc.description.issue 59 es_ES
dc.identifier.eissn 1988-8740
dc.relation.pasarela OJS\15594 es_ES
dc.contributor.funder Ministerio de Ciencia, Tecnología e Innovación, Colombia es_ES
dc.description.references Abril-Salcedo, D. S., Melo-Velandia, L. F., ParraAmado, D. 2020. Nonlinear relationship between the weather phenomenon El niño and Colombian food prices. Australian Journal of Agricultural and Resource Economics, 64(4), 1059-1086. https://doi.org/10.1111/1467-8489.12394 es_ES
dc.description.references Akagi, S. K., Yokelson, R. J., Wiedinmyer, C., Alvarado, M. J., Reid, J. S., Karl, T., … Wennberg, P. O. 2011. Emission factors for open and domestic biomass burning for use in atmospheric models. Atmospheric Chemistry and Physics, 11(9), 4039-4072. https://doi.org/10.5194/acp-11-4039-2011 es_ES
dc.description.references Anaya, J. A., Chuvieco, E. 2010. Accuracy assessment of burned area products in the Orinoco basin. American Society for Photogrammetry and Remote Sensing Annual Conference 2010: Opportunities for Emerging Geospatial Technologies, 1(1), 8-17 es_ES
dc.description.references Anaya, J. A., Chuvieco, E., Palacios-Orueta, A. 2009. Aboveground biomass assessment in Colombia: A remote sensing approach. Forest Ecology and Management, 257(4), 1237-1246. https://doi.org/10.1016/j.foreco.2008.11.016 es_ES
dc.description.references Anaya, J. A., Colditz, R. R., Valencia, G. 2015. Land Cover Mapping of a Tropical Region by Integrating Multi-Year Data into an Annual Time Series. Remote Sensing, 7(12), 16274-16292. https://doi.org/10.3390/rs71215833 es_ES
dc.description.references Anderson, B. E., Grant, W. B., Gregory, G. L., Browell, E. V., Collins, J. E., Sachse, G. W., … Blake, N. J. 1996. Aerosols from biomass burning over the tropical South Atlantic region: Distributions and impacts. Journal of Geophysical Research: Atmospheres, 101(D19), 24117-24137. https://doi.org/10.1029/96JD00717 es_ES
dc.description.references Andreae, M. 1991. Biomass burning: its history, use, and distribution and its impact on environmental quality and global climate. In J. Levine (Ed.), MIT Press (pp. 3-21). Cambridge. es_ES
dc.description.references Avitabile, V, Herold, M., Heuvelink, G. B. M., Lewis, S. L., Phillips, O. L., Asner, G. P., … Willcock, S. 2015. An integrated pan-tropical biomass map using multiple reference datasets. Global Change Biology, n/a-n/a. https://doi.org/10.1111/gcb.13139 es_ES
dc.description.references Avitabile, Valerio, Camia, A. 2018. An assessment of forest biomass maps in Europe using harmonized national statistics and inventory plots. Forest Ecology and Management, 409(November 2017), 489-498. https://doi.org/10.1016/j.foreco.2017.11.047 es_ES
dc.description.references Baccini, a., Goetz, S. J., Walker, W. S., Laporte, N. T., Sun, M., Sulla-Menashe, D., … Houghton, R. a. 2012. Estimated carbon dioxide emissions from tropical deforestation improved by carbondensity maps. Nature Clim. Change, 2(3), 182-185. https://doi.org/10.1038/nclimate1354 es_ES
dc.description.references Bastarrika, A., Alvarado, M., Artano, K., Martinez, M. P., Mesanza, A., Torre, L., … Chuvieco, E. 2014. BAMS: A Tool for Supervised Burned Area Mapping Using Landsat Data. Remote Sensing, 6, 12360-12380. https://doi.org/10.3390/rs61212360 es_ES
dc.description.references Bauduin, S., Clarisse, L., Theunissen, M., George, M., Hurtmans, D., Clerbaux, C., Coheur, P. F. 2017. IASI's sensitivity to near-surface carbon monoxide (CO): Theoretical analyses and retrievals on test cases. Journal of Quantitative Spectroscopy and Radiative Transfer, 189, 428-440. https://doi.org/10.1016/j.jqsrt.2016.12.022 es_ES
dc.description.references BBC. 2019. Amazon fires increase by 84% in one year - space agency - BBC News. BBC. Retrieved from https://www.bbc.com/news/world-latinamerica-49415973 es_ES
dc.description.references Boschetti, L., Roy, D. P., Giglio, L., Huang, H., Zubkova, M., Humber, M. L. 2019. Global validation of the collection 6 MODIS burned area product. Remote Sensing of Environment, 235(October), 111490. https://doi.org/10.1016/j.rse.2019.111490 es_ES
dc.description.references Brown, K. 2017. NASA Pinpoints Cause of Earth's Recent Record Carbon Dioxide Spike. National Aeronotics and Space Administration (NASA). Retrieved from http://www.nasa.gov/press-release/ nasa-pinpoints-cause-of-earth-s-recent-recordcarbon-dioxide-spike es_ES
dc.description.references Buis, A. 2019. The Atmosphere: Getting a Handle on Carbon Dioxide - Climate Change: Vital Signs of the Planet. Retrieved December 6, 2020, from https://climate.nasa.gov/news/2915/the-atmospheregetting-a-handle-on-carbon-dioxide/ es_ES
dc.description.references Chave, J., Davies, S. J., Phillips, O. L., Lewis, S. L., Sist, P., Schepaschenko, D., … Saatchi, S. 2019. Ground Data are Essential for Biomass Remote Sensing Missions. Surveys in Geophysics, 40(4), 863-880. https://doi.org/10.1007/s10712-019-09528-w es_ES
dc.description.references Chuvieco, E., Mouillot, F., van der Werf, G. R., San Miguel, J., Tanasse, M., Koutsias, N., … Giglio, L. 2019. Historical background and current developments for mapping burned area from satellite Earth observation. Remote Sensing of Environment, 225(November 2018), 45-64. https://doi.org/10.1016/j.rse.2019.02.013 es_ES
dc.description.references Chuvieco, E., Opazo, S., Sione, W., Del Valle, H., Anaya, J., Di Bella, C., … Libonati, R. 2008. Global burned-land estimation in Latin America using MODIS composite data. Ecological Applications, 18(1), 64-79. https://doi.org/10.1890/06-2148.1 es_ES
dc.description.references Clerbaux, C., Hadji-Lazaro, J., Turquety, S., George, M., Boynard, A., Pommier, M., … Van Damme, M. 2015. Tracking pollutants from space: Eight years of IASI satellite observation. Comptes Rendus - Geoscience, 347(3), 134-144. https://doi.org/10.1016/j.crte.2015.06.001 es_ES
dc.description.references Crutzen, P. J., Andreae, M. O. 1990. Biomass Burning in the Tropics: Impact on Atmospheric Chemistry and Biogeochemical Cycles. Science, 250(4988), 1669-1678. https://doi.org/10.1126/science.250.4988.1669 es_ES
dc.description.references Dammers, E., Palm, M., Van Damme, M., Vigouroux, C., Smale, D., Conway, S., … Erisman, J. W. 2016. An evaluation of IASI-NH3 with ground-based Fourier transform infrared spectroscopy measurements. Atmospheric Chemistry and Physics, 16(16), 10351-10368. https://doi.org/10.5194/acp-16-10351-2016 es_ES
dc.description.references Edwards, D. P., Emmons, L. K., Hauglustaine, D. a., Chu, D. a., Gille, J. C., Kaufman, Y. J., … Drummond, J. R. 2004. Observations of carbon monoxide and aerosols from the Terra satellite: Northern Hemisphere variability. Journal of Geophysical Research D: Atmospheres, 109(24), 1-17. https://doi.org/10.1029/2004JD004727 es_ES
dc.description.references EPA. 2019a. Basic Information of Air Emissions Factors and Quantification. es_ES
dc.description.references EPA. 2019b. Basic Information of Air EmissionsFactors and Quantification, 2017-2019. Retrieved from https://www.epa.gov/air-emissions-factorsand-quantification/basic-information-air-emissionsfactors-and-quantification es_ES
dc.description.references Evangeliou, N., Balkanski, Y., Eckhardt, S., Cozic, A., Van Damme, M., Coheur, P. F., … Hauglustaine, Di. 2021. 10-Year Satellite-Constrained Fluxes of Ammonia Improve Performance of Chemistry Transport Models. Atmospheric Chemistry and Physics, 21(6), 4431-4451. https://doi.org/10.5194/acp-21-4431-2021 es_ES
dc.description.references Freitas, S. R., Longo, K. M., Alonso, M. F., Pirre, M., Marecal, V., Grell, G., … Sánchez Gácita, M. 2011. PREP-CHEM-SRC - 1.0: a preprocessor of trace gas and aerosol emission fields for regional and global atmospheric chemistry models. Geoscientific Model Development, 4(2), 419-433. https://doi.org/10.5194/gmd-4-419-2011 es_ES
dc.description.references Fry, M. M., Naik, V., West, J. J., Schwarzkopf, M. D., Fiore, A. M., Collins, W. J., … Zeng, G. 2012. The influence of ozone precursor emissions from four world regions on tropospheric composition and radiative climate forcing. Journal of Geophysical Research Atmospheres, 117(7), 1-16. https://doi.org/10.1029/2011JD017134 es_ES
dc.description.references Galloway, J. N., Aber, J. D., Erisman, J. W., Seitzinger, S. P., Howarth, R. W., Cowling, E. B., Cosby, B. J. 2003. The Nitrogen Cascade. BioScience, 53(4), 341. https://doi.org/10.1641/0006-3568(2003)053[0341:TNC]2.0.CO;2 es_ES
dc.description.references Ghasemi, A., Zahediasl, S. 2012. Normality tests for statistical analysis: A guide for non-statisticians. International Journal of Endocrinology and Metabolism, 10(2), 486-489. https://doi.org/10.5812/ijem.3505 es_ES
dc.description.references Giglio, L., Csiszar, I., Justice, C. O. 2006. Global distribution and seasonality of active fires as observed with the Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. Journal of Geophysical Research, 111(July 1996), 1-12. https://doi.org/10.1029/2005JG000142 es_ES
dc.description.references Giglio, L., Randerson, J. T., Van Der Werf, G. R. 2013. Analysis of daily, monthly, and annual burned area using the fourth-generation global fire emissions database (GFED4). Journal of Geophysical Research: Biogeosciences, 118(1), 317-328. https://doi.org/10.1002/jgrg.20042 es_ES
dc.description.references Gray, E. 2019. Satellite Data Record Shows Climate Change's Impact on Fires. Retrieved December 6, 2020, from https://climate.nasa.gov/news/2912/satellite-data-record-shows-climate-changesimpact-on-fires/ es_ES
dc.description.references Hampel, F. R., Ronchetti, E. M., Rousseeuw, P. J., Stahel, W. A. 1986. Robust Statistics: The Approach Based on Influence Functions. (J. W. & Sons, Ed.). New York. es_ES
dc.description.references Huber, P. J., Ronchetti, E. M. 2009. Robust Statistics. (Wiley, Ed.) (2nd ed.). https://doi.org/10.1002/9780470434697 es_ES
dc.description.references IPCC. 2018. IPCC Special Report on the impacts of global warming of 1.5°C. Ipcc - Sr15. Retrieved from http://www.ipcc.ch/report/sr15/ es_ES
dc.description.references Jaffe, L. S. 1968. Ambient carbon monoxide and its fate in the atmosphere. Journal of the Air Pollution Control Association, 18(8), 534-540. https://doi.org/10.1080/00022470.1968.10469168 es_ES
dc.description.references Janssens-Maenhout, G., Dentener, F., Aardenne, J. Van, Monni, S., Pagliari, V., Orlandini, L., … Keating, T. 2012. EDGAR-HTAP: a harmonized gridded air pollution emission dataset based on national inventories. … Office, Ispra (Italy). https://doi.org/10.2788/14102 es_ES
dc.description.references Janssens-Maenhout, G., Petrescu, A. M. R., Muntean, M., Blujdea, V. 2011. Verifying Greenhouse Gas Emissions: Methods to Support International Climate Agreements. Greenhouse Gas Measurement and Management, 1(2), 132-133. https://doi.org/10.1080/20430779.2011.579358 es_ES
dc.description.references Jones, M. W., Smith, A., Betts, R., Canadell, J. G., Prentice, I. C., Le Quéré, C. 2020. Climate change increases the risk of wildfires. Rapid Response Review, (March 2013), 2013-2015. Retrieved from https://sciencebrief.org/briefs/wildfires es_ES
dc.description.references Kaiser, J. W., Heil, a., Andreae, M. O., Benedetti, a., Chubarova, N., Jones, L., … Van Der Werf, G. R. 2012. Biomass burning emissions estimated with a global fire assimilation system based on observed fire radiative power. Biogeosciences, 9(1), 527-554. https://doi.org/10.5194/bg-9-527-2012 es_ES
dc.description.references Koenker, R. 1994. Confidence Intervals for Regression Quantiles. In P. Mandl & M. Hušková (Eds.), Asymptotic Statistics (pp. 349-359). https://doi.org/10.1007/978-3-642-57984-4_29 es_ES
dc.description.references Koenker, R. W. 2005. Quantile Regression. (Cambridge University Press, Ed.). https://doi.org/10.1017/CBO9780511754098 es_ES
dc.description.references Kumar, S. S., Hult, J., Picotte, J., Peterson, B. 2020. Potential underestimation of satellite fire radiative power retrievals over gas flares and wildland fires. Remote Sensing, 12(2), 10-14. https://doi.org/10.3390/rs12020238 es_ES
dc.description.references Lamarque, J. F., Bond, T. C., Eyring, V., Granier, C., Heil, a., Klimont, Z., … Van Vuuren, D. P. 2010. Historical (1850-2000) gridded anthropogenic and biomass burning emissions of reactive gases and aerosols: Methodology and application. Atmospheric Chemistry and Physics, 10(15), 7017-7039. https://doi.org/10.5194/acp-10-7017-2010 es_ES
dc.description.references Langmann, B., Duncan, B., Textor, C., Trentmann, J., van der Werf, G. R. 2009. Vegetation fire emissions and their impact on air pollution and climate. Atmospheric Environment, 43(1), 107-116. https://doi.org/10.1016/j.atmosenv.2008.09.047 es_ES
dc.description.references Lees, K. J., Quaife, T., Artz, R. R. E., Khomik, M., Clark, J. M. 2018. Potential for using remote sensing to estimate carbon fluxes across northern peatlands - A review. Science of the Total Environment, 615, 857-874. https://doi.org/10.1016/j.scitotenv.2017.09.103 es_ES
dc.description.references Levine, J. S., Cofer III, W. R., Pinto, J. P. 2001. Chapter 14. Biomass Burning. In Atmospheric methane: source, sinks, and role in Global Change (Vol. 113, pp. 299-313). NATO ASI series. Retrieved from http://earthobservatory.nasa.gov/Features/BiomassBurning/ https://doi.org/10.1007/978-3-642-84605-2_14 es_ES
dc.description.references Libonati, R., DaCamara, C., Setzer, A. W., Morelli, F., Melchiori, A. E., Cândido, P. de A., Jesús, S. C. de. 2015. Validating MODIS burned area products over Cerrado region. In XVII Simpósio Brasileiro de Sensoriamento Remoto - SBSR (pp. 6381-6388). es_ES
dc.description.references Limpert, E., Stahel, W. A. 2011. Problems with using the normal distribution - and ways to improve quality and efficiency of data analysis. PLoS ONE, 6(7). https://doi.org/10.1371/journal.pone.0021403 es_ES
dc.description.references Liu, Y. Y., van Dijk, A. I. J. M., de Jeu, R. a M., Canadell, J. G., McCabe, M. F., Evans, J. P., Wang, G. 2015. Recent reversal in loss of global terrestrial biomass. Nature Climate Change, 5(May), 1-5. https://doi.org/10.1038/nclimate2581 es_ES
dc.description.references Löndahl, J., Swietlicki, E., Lindgren, E., Loft, S. 2010. Aerosol exposure versus aerosol cooling of climate: What is the optimal emission reduction strategy for human health? Atmospheric Chemistry and Physics, 10(19), 9441-9449. https://doi.org/10.5194/acp-10-9441-2010 es_ES
dc.description.references Longo, K. M., Freitas, S. R., Andreae, M. O., Setzer, a., Prins, E., Artaxo, P. 2010. The Coupled Aerosol and Tracer Transport model to the Brazilian developments on the Regional Atmospheric Modeling System (CATT-BRAMS) - Part 2: Model sensitivity to the biomass burning inventories. Atmospheric Chemistry and Physics, 10(13), 5785-5795. https://doi.org/10.5194/acp-10-5785-2010 es_ES
dc.description.references Malhi, Y., Rowland, L., Aragão, L. E. O. C., Fisher, R. A. 2018. New insights into the variability of the tropical land carbon cycle from the El Niño of 2015/2016. Philosophical Transactions of the Royal Society B: Biological Sciences, 373(1760). https://doi.org/10.1098/rstb.2017.0298 es_ES
dc.description.references Masek, J.., Vermote, E. F., Saleous, N. E., Wolfe, R., Hall, F. G., Huemmrich, K. F., … Lim, T. 2006. A Landsat Surface Reflectance Dataset for North America, 1990-2000. IEEE Geoscience and Remote Sensing Letters, 3(1), 68-72. https://doi.org/10.1109/LGRS.2005.857030 es_ES
dc.description.references Masek, J.., Vermote, E. F., Saleous, N., Wolfe, R., Hall, F. G., Huemmrich, F., … Lim, T. K. 2013. LEDAPS Calibration, Reflectance, Atmospheric Correction Preprocessing Code. Oak Ridge National Laboratory Distributed Active Archive Center. Tennessee, U.S.A. https://doi.org/10.3334/ORNLDAAC/1146 es_ES
dc.description.references Mavroidis, I., Chaloulakou, a. 2011. Long-term trends of primary and secondary NO2 production in the Athens area. Variation of the NO2/NOx ratio. Atmospheric Environment, 45(38), 6872-6879. https://doi.org/10.1016/j.atmosenv.2010.11.006 es_ES
dc.description.references Mieville, a., Granier, C., Liousse, C., Guillaume, B., Mouillot, F., Lamarque, J.-F., … Pétron, G. 2010. Emissions of gases and particles from biomass burning during the 20th century using satellite data and an historical reconstruction. Atmospheric Environment, 44(11), 1469-1477. https://doi.org/10.1016/j.atmosenv.2010.01.011 es_ES
dc.description.references Monks, P. S., Granier, C., Fuzzi, S., Stohl, A., Williams, M. L., Akimoto, H., … von Glasow, R. 2009. Atmospheric composition change - global and regional air quality. Atmospheric Environment, 43(33), 5268-5350. https://doi.org/10.1016/j.atmosenv.2009.08.021 es_ES
dc.description.references Moreira, D. S., Freitas, S. R., Bonatti, J. P., Mercado, L. M., Rosário, N. M. É., Longo, K. M., …Gatti, L. V. 2013. Coupling between the JULES land-surface scheme and the CCATT-BRAMS atmospheric chemistry model (JULES-CCATTBRAMS1.0): applications to numerical weather forecasting and the CO2 budget in South America. Geoscientific Model Development, 6(4), 1243-1259. https://doi.org/10.5194/gmd-6-1243-2013 es_ES
dc.description.references Mouillot, F., Schultz, M. G., Yue, C., Cadule, P., Tansey, K., Ciais, P., Chuvieco, E. 2014. Ten years of global burned area products from spaceborne remote sensing-A review: Analysis of user needs and recommendations for future developments. International Journal of Applied Earth Observation and Geoinformation, 26(1), 64-79. https://doi.org/10.1016/j.jag.2013.05.014 es_ES
dc.description.references Opazo, S., Chuvieco, E. 2013. Análisis geográfico de áreas quemadas en Sudamérica. Geofocus, 13(2), 1-24. https://doi.org/10.1104/pp.104.051110.3582 es_ES
dc.description.references Padilla, M., Olofsson, P., Stehman, S. V, Tansey, K., Chuvieco, E. 2017. Stratification and sample allocation for reference burned area data. Remote Sensing of Environment, 203, 240-255. https://doi.org/10.1016/j.rse.2017.06.041 es_ES
dc.description.references Padilla, M., Stehman, S. V., Chuvieco, E. 2014. Validation of the 2008 MODIS-MCD45 global burned area product using stratified random sampling. Remote Sensing of Environment, 144, 187-196. https://doi.org/10.1016/j.rse.2014.01.008 es_ES
dc.description.references Padilla, M., Stehman, S. V., Ramo, R., Corti, D., Hantson, S., Oliva, P., … Chuvieco, E. 2015. Comparing the accuracies of remote sensing global burned area products using stratified random sampling and estimation. Remote Sensing of Environment, 160(April), 114-121. https://doi.org/10.1016/j.rse.2015.01.005 es_ES
dc.description.references Palmer, P. I., Feng, L., Baker, D., Chevallier, F., Bösch, H., Somkuti, P. 2019. Net carbon emissions from African biosphere dominate pan-tropical atmospheric CO2 signal. Nature Communications, 10(1), 1-9. https://doi.org/10.1038/s41467-019-11097-w es_ES
dc.description.references Palomino, S., Anaya, J. A. 2012. Evaluation of the Causes of Error in the Mcd45 Burned-Area Product for the Savannas of Northern South America. DynaColombia, 79(176), 35-44. es_ES
dc.description.references Pierre-Louis, K. 2019. The Amazon, Siberia, Indonesia: A World of Fire. The New York Times. Retrieved from https://www.nytimes.com/2019/08/28/climate/fire-amazon-africa-siberia-worldwide.html es_ES
dc.description.references Portnoy, S., Koenker, R. 1997. The Gaussian hare and the Laplacian tortoise: computability of squarederror versus absolute-error estimators, 279-300. https://doi.org/10.1214/ss/1030037960 es_ES
dc.description.references Prosperi, P., Bloise, M., Tubiello, F. N., Conchedda, G., Rossi, S., Boschetti, L., … Bernoux, M. 2020. New estimates of greenhouse gas emissions from biomass burning and peat fires using MODIS Collection 6 burned areas. Climatic Change, 161(3), 415-432. https://doi.org/10.1007/s10584-020-02654-0 es_ES
dc.description.references Rodriguez-Montellano, A., Libonati, R., Melchiori, E. 2015. Sensibilidad en la detección de áreas quemadas en tres ecosistemas vegetales de Bolivia, utilizando tres productos regionales. In XVII Simpósio Brasileiro de Sensoriamento Remoto - SBSR (Vol. 1, pp. 1663-1670). es_ES
dc.description.references Rodríguez-Veiga, P., Wheeler, J., Louis, V., Tansey, K., Balzter, H. 2017. Quantifying Forest Biomass Carbon Stocks From Space. Current Forestry Reports, 3, 1-18. https://doi.org/10.1007/s40725-017-0052-5 es_ES
dc.description.references Rousseeuw, P. J., Huber, M. 1997. Recent developments in PROGRESS. In L1-Statistical Procedures and Related Topics. Dodge, IMS Lecture Notes, 31, 201-214. https://doi.org/10.1214/lnms/1215454138 es_ES
dc.description.references Rousseeuw, P. J., Leroy, A. M. 2005. Robust Regression and Outlier Detection. (John Wiley & Sons, Ed.). Wiley. Retrieved from https://books.google.com.co/books?id=woaH_73s-MwC es_ES
dc.description.references Saatchi, S. S., Harris, N. L., Brown, S., Lefsky, M., Mitchard, E. T. A., Salas, W., … Morel, A. 2011. Benchmark map of forest carbon stocks in tropical regions across three continents. Proceedings of the National Academy of Sciences of the United States of America, 108(24), 9899-904. https://doi.org/10.1073/pnas.1019576108 es_ES
dc.description.references Santoro, M., Beaudoin, A., Beer, C., Cartus, O., Fransson, J. E. S., Hall, R. J., … Wegmüller, U. 2015. Forest growing stock volume of the northern hemisphere: Spatially explicit estimates for 2010 derived from Envisat ASAR. Remote Sensing of Environment, 168, 316-334. https://doi.org/10.1016/j.rse.2015.07.005 es_ES
dc.description.references Seiler, W., Crutzen, P. J. 1980. Estimates of gross and net fluxes of carbon between the biosphere and the atmosphere from biomass burning. Climatic Change, 2(3), 207-247. https://doi.org/10.1007/BF00137988 es_ES
dc.description.references Shi, Y., Matsunaga, T., Saito, M., Yamaguchi, Y., Chen, X. 2015. Comparison of global inventories of CO2 emissions from biomass burning during 2002-2011 derived from multiple satellite products. Environmental Pollution, 206, 479-487. https://doi.org/10.1016/j.envpol.2015.08.009 es_ES
dc.description.references Shi, Y., Matsunaga, T., Yamaguchi, Y. 2015. HighResolution Mapping of Biomass Burning Emissions in Three Tropical Regions. Environmental Science and Technology, 49(18), 10806-10814. https://doi.org/10.1021/acs.est.5b01598 es_ES
dc.description.references Simões Amaral, S., Andrade de Carvalho, J., Martins Costa, M., Pinheiro, C. 2016. Particulate Matter Emission Factors for Biomass Combustion. Atmosphere, 7(11), 141. https://doi.org/10.3390/atmos7110141 es_ES
dc.description.references Solaun, K., Sopelana, A., Arraibi, E., Pérez, M. 2014. Series CO2: Black Carbon y sus efectos en el clima. Factor CO2, 52. Retrieved from https://www.factorco2.com/comun/docs/131-Series%20CO2_Black%20Carbon_Factor%20CO2_20140613.pdf es_ES
dc.description.references Stahl, S. 2014. Evolution of the Normal Distribution. In Mathematics magazine (pp. 96-113). Retrieved from https://www.maa.org/sites/default/files/pdf/upload_library/22/Allendoerfer/stahl96.pdf https://doi.org/10.1080/0025570X.2006.11953386 es_ES
dc.description.references Tie, X., Chandra, S., Ziemke, J. R., Granier, C., Brasseur, G. P. 2007. Satellite measurements of tropospheric column O3 and NO 2 in eastern and southeastern asia: Comparison with a global model (MOZART-2). Journal of Atmospheric Chemistry, 56(2), 105-125. https://doi.org/10.1007/s10874-006-9045-7 es_ES
dc.description.references Urbanski, S. P., Hao, W. M., Nordgren, B. 2011. The wildland fire emission inventory: Western United States emission estimates and an evaluation of uncertainty. Atmospheric Chemistry and Physics, 11(24), 12973-13000. https://doi.org/10.5194/acp-11-12973-2011 es_ES
dc.description.references Valencia, G. M., Anaya, J. A., Caro-Lopera, F. J. 2016. Implementación y evaluación del modelo Landsat Ecosystem Disturbance Adaptive Processing System ( LEDAPS ): estudio de caso en los Andes colombianos. Revista de Teledetección, 46(46), 83-101. https://doi.org/10.4995/raet.2016.3582 es_ES
dc.description.references Valencia, G. M., Anaya, J. A., Ramo, R., Velásquez, É. A., Francisco, J. 2020a. About ValidationComparison of Burned Area Products. Remote Sensing, 12(2018), 1-39. https://doi.org/10.3390/rs12233972 es_ES
dc.description.references van der Werf, G. R., Randerson, J. T., Giglio, L., Leeuwen, T. T. Van, Chen, Y., Collatz, G. J., … Kasibhatla, P. S. 2017. Global fire emissions estimates during 1997 - 2016. Earth System Science Data, 9, 697-720. https://doi.org/10.5194/essd-9-697-2017 es_ES
dc.description.references Vasconcelos, S. S. De, Fearnside, P. M., Graça, P. M. L. D. A., Nogueira, E. M., Oliveira, L. C. De, Figueiredo, E. O. 2013. Forest fires in southwestern Brazilian Amazonia: Estimates of area and potential carbon emissions. Forest Ecology and Management, 291, 199-208. https://doi.org/10.1016/j.foreco.2012.11.044 es_ES
dc.description.references Voiland, A. 2015. Fourteen years of carbon monoxide from MOPITT - Climate Change: Vital Signs of the Planet. Retrieved December 6, 2020, from https://climate.nasa.gov/news/2291/fourteen-years-ofcarbon-monoxide-from-mopitt/ es_ES
dc.description.references von Bobrutzki, K., Braban, C., Famulari, D., Jones, S., Blackall, T., Smith, T. E. L., … Nemitz, E. 2010. Field inter-comparison of eleven atmospheric ammonia measurement techniques, 91-112. https://doi.org/10.5194/amtd-2-1783-2009 es_ES
dc.description.references Whitburn, S., Van Damme, M., Kaiser, J. W. W., Van Der Werf, G. R. R., Turquety, S., Hurtmans, D., … Coheur, P.-F. F. 2014. Ammonia emissions in tropical biomass burning regions: Comparison between satellite-derived emissions and bottom-up fire inventories. Atmospheric Environment, 121, 42-54. https://doi.org/10.1016/j.atmosenv.2015.03.015 es_ES
dc.description.references Whitburn, Simon, Van Damme, M., Clarisse, L., Hurtmans, D., Clerbaux, C., Coheur, P. F. 2017. IASIderived NH3 enhancement ratios relative to CO for the tropical biomass burning regions. Atmospheric Chemistry and Physics, 17(19), 12239-12252. https://doi.org/10.5194/acp-17-12239-2017 es_ES
dc.description.references Wiedinmyer, C., Akagi, S. K., Yokelson, R. J., Emmons, L. K., Al-Saadi, J. A., Orlando, J. J., Soja, A. J. 2011. The Fire INventory from NCAR (FINN) - a high resolution global model to estimate the emissions from open burning. Geoscientific Model Development Discussions, 3(4), 2439-2476. https://doi.org/10.5194/gmdd-3-2439-2010 es_ES
dc.description.references Williams, A. P., Abatzoglou, J. T., Gershunov, A., Guzman-Morales, J., Bishop, D. A., Balch, J. K., Lettenmaier, D. P. 2019. Observed Impacts of Anthropogenic Climate Change on Wildfire in California. Earth's Future, 7(8), 892-910. https://doi.org/10.1029/2019EF001210 es_ES
dc.description.references Yang, J., Gong, P., Fu, R., Zhang, M., Chen, J., Liang, S., … Dickinson, R. 2013. The role of satellite remote sensing in climate change studies. Nature Climate Change, 3(10), 875-883. https://doi.org/10.1038/nclimate1908 es_ES
dc.description.references YuSheng, S., Matsunaga, T., Yamaguchi, Y. 2015. High-resolution mapping of biomass burning emissions in three tropical regions. Environmental Science & Technology, 49(18), 10806-10814. https://doi.org/10.1021/acs.est.5b01598 es_ES
dc.description.references Zuluaga, O., Patiño, J. E., Valencia, G. M. 2021. Modelos implementados en el análisis de series de tiempo de temperatura superficial e índices de vegetación: una propuesta taxonómica en el contexto de cambio climático global. Revista de Geografía Norte Grande, 78, 323-344. https://doi.org/10.4067/S0718-34022021000100323 es_ES


Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem