Evaluación de almacenamiento de carbono azul en el ecosistema de manglar del Pacífico nariñense

dc.contributor.advisorGuzmán Alvis, Ángela Inés
dc.contributor.authorMoreno Muñoz, Angélica Sofía
dc.contributor.educationalvalidatorBenavides Martínez, Iván Felipe
dc.contributor.orcidAngélica Sofía Moreno Muñoz [0000-0002-2482-3832]spa
dc.coverage.regionPacífico Colombiano
dc.date.accessioned2023-02-03T21:40:34Z
dc.date.available2023-02-03T21:40:34Z
dc.date.issued2023-02-02
dc.descriptionIlustraciones, tablas, gráficasspa
dc.description.abstractLos manglares representan grandes reservas de carbono, especialmente en el suelo, sin embargo, a escalas locales se necesita mejorar la precisión de sus estimaciones con muestreos en campo, permitiendo un manejo adecuado del ecosistema. Así, el objetivo fue evaluar el carbono total almacenado en el ecosistema de manglar del Pacífico nariñense. Se utilizó información de inventarios forestales en 10 sitios para determinar el carbono almacenado en la biomasa aérea (AGB) y subterránea (BWG) mediante ecuaciones alométricas y factores de conversión de biomasa a carbono. Para el carbono almacenado en el suelo (COS) se construyó un modelo Random Forest (RF) con 28 perfiles tomados a dos metros de profundidad y 18 variables predictoras. Se halló un buen ajuste del modelo RF (R2 de 0.82). El carbono total almacenado presentó una media de 359.05 ± 71.29 t ha-1 , donde la mayor contribución la tuvo el suelo (75.51%), seguida de la biomasa aérea (17.24%) y la biomasa subterránea (7.25%). Las estimaciones de COS fueron menores a las globales, sugiriendo una posible sobreestimación, debido a que los modelos globales no consideran datos ‘in situ’. Finalmente, las tres cuartas partes del carbono total almacenado en el manglar estudiado se encontraron en el suelo, coincidiendo con otros bosques de manglar y resaltando la importancia de su conservación. (Texto tomado de la fuente)spa
dc.description.abstractMangroves represent large reserves of carbon, especially in the soil. However, it is necessary to improve the precision of their estimates with field sampling at local scales, allowing adequate management of the ecosystem. The aim was to evaluate the total carbon stored in the mangrove ecosystem of the Nariño. Allometric equations and biomass-tocarbon conversion factors used information from forest inventories at 10 sites to determine carbon stored in aboveground (AGB) and belowground (BWG) biomass(R2 of 0.82) was found. For soil carbon stored (SOC), a Random Forest (RF) model was built with 28 profiles to 2 m depth and 18 predictor variables. A good fit of the RF model was found (R2 of 0.82). The total carbon stored presented a mean of 359.05 ± 71.29 t ha-1 , where the greatest contribution was from the soil (75.51%), followed by aboveground (17.24%) and belowground biomass (7.25%). The SOC estimates were lower than the global models, suggesting a possible overestimation because the global models do not consider 'in situ data. Finally, three-quarters of the total carbon stored in the mangrove studied was found in the soil, coinciding with other mangrove forests, and highlighting the importance of its conservation.eng
dc.description.curricularareaIngeniería.Sede Palmiraspa
dc.description.degreelevelMaestríaspa
dc.description.degreenameMagister en Ingeniería Ambientalspa
dc.description.methodsSe utilizó información de inventarios forestales en 10 sitios para determinar el carbono almacenado en la biomasa aérea (AGB) y subterránea (BWG) mediante ecuaciones alométricas y factores de conversión de biomasa a carbono. Para el carbono almacenado en el suelo (COS) se construyó un modelo Random Forest (RF) con 28 perfiles tomados a dos metros de profundidad y 18 variables predictoras.spa
dc.format.extentxii, 64 páginas + anexosspa
dc.format.mimetypeapplication/pdfspa
dc.identifier.instnameUniversidad Nacional de Colombiaspa
dc.identifier.reponameRepositorio Institucional Universidad Nacional de Colombiaspa
dc.identifier.repourlhttps://repositorio.unal.edu.co/spa
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/83304
dc.language.isospaspa
dc.publisherUniversidad Nacional de Colombiaspa
dc.publisher.branchUniversidad Nacional de Colombia - Sede Palmiraspa
dc.publisher.facultyFacultad de Ingeniería y Administraciónspa
dc.publisher.placePalmira, Valle del Cauca, Colombiaspa
dc.publisher.programPalmira - Ingeniería y Administración - Maestría en Ingeniería - Ingeniería Ambientalspa
dc.relation.referencesAbatzoglou, J. T., Dobrowski, S. Z., Parks, S. A., & Hegewisch, K. C. (2018). TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958-2015. Scientific Data, 5, 1–12. https://doi.org/10.1038/sdata.2017.191spa
dc.relation.referencesAdame, M. F., Cherian, S., Reef, R., & Stewart-Koster, B. (2017). Mangrove root biomass and the uncertainty of belowground carbon estimations. Forest Ecology and Management, 403, 52–60. https://doi.org/10.1016/J.FORECO.2017.08.016spa
dc.relation.referencesAdame, M. F., Connolly, R. M., Turschwell, M. P., Lovelock, C. E., Fatoyinbo, T., Lagomasino, D., Goldberg, L. A., Holdorf, J., Friess, D. A., Sasmito, S. D., Sanderman, J., Sievers, M., Buelow, C., Kauffman, J. B., Bryan-Brown, D., & Brown, C. J. (2021). Future carbon emissions from global mangrove forest loss. Global Change Biology, 27(12), 2856–2866. https://doi.org/10.1111/GCB.15571spa
dc.relation.referencesAhmed, R., & Mahmud, K. H. (2022). Potentiality of high-resolution topographic survey using unmanned aerial vehicle in Bangladesh. Remote Sensing Applications: Society and Environment, 26, 100729. https://doi.org/10.1016/J.RSASE.2022.100729spa
dc.relation.referencesAjonina, G. N., Kairo, J., Grimsditch, G., Sembres, T., Chuyong, G., & Diyouke, E. (2014). Assessment of Mangrove Carbon Stocks in Cameroon, Gabon, the Republic of Congo (RoC) and the Democratic Republic of Congo (DRC) Including their Potential for Reducing Emissions from Deforestation and Forest Degradation (REDD+) (Diop, S., J. Barusseau, & C. Descamps (eds.); pp. 177–189). Springer, Cham. https://doi.org/10.1007/978-3-319-06388-1_15spa
dc.relation.referencesAlongi, D. M. (2014). Carbon cycling and storage in mangrove forests. Annual Review of Marine Science, 6, 195–219. https://doi.org/10.1146/annurev-marine-010213-135020spa
dc.relation.referencesAlongi, D. M. (2018). Mangrove Forests. In Blue Carbon SpringerBriefs in Climate Studies. Springer. https://doi.org/https://doi.org/10.1007/978-3-319-91698-9_3spa
dc.relation.referencesArrouays, D., Grundy, M. G., Hartemink, A. E., Hempel, J. W., Heuvelink, G. B. M., Hong, S. Y., Lagacherie, P., Lelyk, G., McBratney, A. B., McKenzie, N. J., Mendonca-Santos, M. d. L., Minasny, B., Montanarella, L., Odeh, I. O. A., Sanchez, P. A., Thompson, J. A., & Zhang, G. L. (2014). GlobalSoilMap. Toward a Fine-Resolution Global Grid of Soil Properties. In Advances in Agronomy (Vol. 125). https://doi.org/10.1016/B978-0-12-800137-0.00003-0spa
dc.relation.referencesAtwood, T. B., Connolly, R. M., Almahasheer, H., Carnell, P. E., Duarte, C. M., Lewis, C. J. E., Irigoien, X., Kelleway, J. J., Lavery, P. S., Macreadie, P. I., Serrano, O., Sanders, C. J., Santos, I., Steven, A. D. L., & Lovelock, C. E. (2017). Global patterns in mangrove soil carbon stocks and losses. Nature Climate Change, 7(7), 523–528. https://doi.org/10.1038/NCLIMATE3326spa
dc.relation.referencesBejarano M, A., Satizabal C, A., & Zapata, F. A. (1993). Estructura del bosque y granulometría del suelo en un manglar de ribera de la costa Pacífica colombiana. Boletín Científico CCCP, 4, 37–45. https://doi.org/10.26640/01213423.4.37_45spa
dc.relation.referencesBenites, V. M., Machado, P. L. O. A., Fidalgo, E. C. C., Coelho, M. R., & Madari, B. E. (2007). Pedotransfer functions for estimating soil bulk density from existing soil survey reports in Brazil. Geoderma, 139(1–2), 90–97. https://doi.org/10.1016/J.GEODERMA.2007.01.005spa
dc.relation.referencesBermúdez, C., Merly, Á., & Niño, D. (2014). Caracterización de la geomorfología costera y sus coberturas vegetales asociadas, a través de sensores remotos en la bahía de Buenaventura, Valle del Cauca. Boletín Científico CIOH, 34, 49–63. https://doi.org/10.26640/22159045.426spa
dc.relation.referencesBettinger, P., Boston, K., Siry, J. P., & Grebner, D. L. (2017). Valuing and Characterizing Forest Conditions. In P. Bettinger, K. Boston, J. P. Siry, & D. L. Grebner (Eds.), Forest Management and Planning (Segunda edición, pp. 21–63). Academic Press. https://doi.org/10.1016/B978-0-12-809476-1.00002-3spa
dc.relation.referencesBhomia, R. K., Kauffman, J. B., & McFadden, T. N. (2016). Ecosystem carbon stocks of mangrove forests along the Pacific and Caribbean coasts of Honduras. Wetlands Ecology and Management, 24(2), 187–201. https://doi.org/10.1007/S11273-016-9483-1/TABLES/5spa
dc.relation.referencesBishop, T. F. A., McBratney, A. B., & Laslett, G. M. (1999). Modeling soil attribute depth functions with equal-area quadratic smoothing splines. Geoderma, 91(1–2), 27–45. https://doi.org/10.1016/S0016-7061(99)00003-8spa
dc.relation.referencesBlanco-Libreros, J., López-Ródriguez, S., Valencia-Palacios, A., Pérez-Vega, G., & Álvarez-León, R. (2022). HELIO_SP.CO v.2: Hierarchical, Entity-based and Landscape-level Information Observatory for mangrove SPecies in Colombia, version 2. https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/QXQT59spa
dc.relation.referencesBlanco, J. F., Estrada, E. A., Ortiz, L. F., & Urrego, L. E. (2012). Ecosystem-Wide Impacts of Deforestation in Mangroves: The Urabá Gulf (Colombian Caribbean) Case Study. International Scholarly Research Network , 2012, 1–14. https://doi.org/10.5402/2012/958709spa
dc.relation.referencesBolivar, J. M., Gutierrez-Velez, V. H., & Sierra, C. A. (2018). Carbon stocks in aboveground biomass for Colombian mangroves with associated uncertainties. Regional Studies in Marine Science, 18, 145–155. https://doi.org/10.1016/J.RSMA.2017.12.011spa
dc.relation.referencesBreiman, L. (2001). Random Forests. Machine Learning 2001 45:1, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324spa
dc.relation.referencesBreiman, L. (2003). Manual setting up, using, and understanding random forests v4.0. https://www.stat.berkeley.edu/~breiman/Using_random_forests_v4.0.pdfspa
dc.relation.referencesBukoski, J. J., Elwin, A., Mackenzie, R. A., Sharma, S., Purbopuspito, J., Kopania, B., Apwong, M., Poolsiri, R., & Potts, M. D. (2020). The role of predictive model data in designing mangrove forest carbon programs. Environmental Research Letters, 15(8), 084019. https://doi.org/10.1088/1748-9326/AB7E4Espa
dc.relation.referencesBunting, P., Rosenqvist, A., Hilarides, L., Lucas, R. M., Thomas, N., Tadono, T., Worthington, T. A., Spalding, M., Murray, N. J., & Rebelo, L.-M. (2022). Global Mangrove Extent Change 1996–2020: Global Mangrove Watch Version 3.0. Remote Sensing 2022, Vol. 14, Page 3657, 14(15), 3657. https://doi.org/10.3390/RS14153657spa
dc.relation.referencesCentro de Control de Contaminación del Pacífico [CCCP]. (2003). Aportes al entendimiento de la Bahía de Tumaco. Entorno oceanográfico, costero y de riesgos. In Aportes al entendimiento de la Bahía de Tumaco. Entorno oceanográfico, costero y de riesgos. Dirección General Marítima. https://doi.org/10.26640/9583352225.2003spa
dc.relation.referencesChang, K.-T. (2019). Introduction to geographic information systems (9th ed.). McGraw-Hill.spa
dc.relation.referencesChatting, M., Al-Maslamani, I., Walton, M., Skov, M. W., Kennedy, H., Husrevoglu, Y. S., & Le Vay, L. (2022). Future Mangrove Carbon Storage Under Climate Change and Deforestation. Frontiers in Marine Science, 9, 58. https://doi.org/10.3389/FMARS.2022.781876/BIBTEXspa
dc.relation.referencesChave, J., Coomes, D., Jansen, S., Lewis, S. L., Swenson, N. G., & Zanne, A. E. (2009). Towards a worldwide wood economics spectrum. Ecology Letters, 12(4), 351–366. https://doi.org/10.1111/J.1461-0248.2009.01285.Xspa
dc.relation.referencesClough, B. F. (1992). Primary Productivity and Growth of Mangrove Forests. In A. I. , Robertson & D. M. Alongi (Eds.), Coastal and Estuarine Studies Tropical Mangrove Ecosystems (pp. 225–249). American Geophysical Union (AGU). https://doi.org/10.1029/CE041P0225spa
dc.relation.referencesCordero-Llach, L. (1971). Report on a Wood Testing Programme Carried Out for Undp/sf Project 234, Inventory and Forest Demostrations. Panama. Physical and Mechanical Properties of 113 Species. IICA. Instituto interamericano de ciencias agricolas, Turrialba - Costa Rica.spa
dc.relation.referencesCorponariño. (2010). Caracterización, Diagnóstico y Zonificación de los manglares en el departamento de Nariño | WWF. https://www.wwf.org.co/?201159/CaracterizacionDiagnosticoZonificacionmanglaresdepartamentoNarinospa
dc.relation.referencesCostanza, R., de Groot, R., Braat, L., Kubiszewski, I., Fioramonti, L., Sutton, P., Farber, S., & Grasso, M. (2017). Twenty years of ecosystem services: How far have we come and how far do we still need to go? Ecosystem Services, 28, 1–16. https://doi.org/10.1016/J.ECOSER.2017.09.008spa
dc.relation.referencesDe La Peña, A., Rojas, C. A., De La Peña, M., Grande, C., Marta, S., De, A., Cesar, P., & Rojas, A. (2010). Valoración económica del manglar por el almacenamiento de carbono, en la Ciénaga Grande de Santa Marta. Clío América, 4(7), 133–150. https://doi.org/10.21676/23897848.400spa
dc.relation.referencesde Souza, J. J. L. L., Souza, B. I., Xavier, R. A., Cardoso, E. C. M., de Medeiros, J. R., da Fonseca, C. F., & Schaefer, C. E. G. R. (2022). Organic carbon rich-soils in the brazilian semiarid region and paleoenvironmental implications. CATENA, 212, 106101. https://doi.org/10.1016/J.CATENA.2022.106101spa
dc.relation.referencesDelVecchia, A. G., Bruno, J. F., Benninger, L., Alperin, M., Banerje, O., & De Dios Morales, J. (2014). Organic carbon inventories in natural and restored Ecuadorian mangrove forests. PeerJ, 2014(1), e388. https://doi.org/10.7717/PEERJ.388/SUPP-1spa
dc.relation.referencesDey, A., Ghosh, A., Das, S., Bhattacharyya, R., Tigga, P., Dey, A., Das, S., Bhattacharyya, · R, Tigga, · P, & Ghosh, A. (2021). Belowground Carbon Storage and Dynamics. In Soil Science: Fundamentals to Recent Advances (pp. 49–67). Springer, Singapore. https://doi.org/10.1007/978-981-16-0917-6_4spa
dc.relation.referencesDonato, D. C., Kauffman, J. B., Murdiyarso, D., Kurnianto, S., Stidham, M., & Kanninen, M. (2011). Mangroves among the most carbon-rich forests in the tropics. Nature Geoscience 2011 4:5, 4(5), 293–297. https://doi.org/10.1038/ngeo1123spa
dc.relation.referencesEllison, J. C. (2015). Vulnerability assessment of mangroves to climate change and sea-level rise impacts. Wetlands Ecology and Management, 23(2), 115–137. https://doi.org/10.1007/S11273-014-9397-8/FIGURES/6spa
dc.relation.referencesEmadi, M., Taghizadeh-Mehrjardi, R., Cherati, A., Danesh, M., Mosavi, A., & Scholten, T. (2020). Predicting and mapping of soil organic carbon using machine learning algorithms in Northern Iran. Remote Sensing, 12(14). https://doi.org/10.3390/rs12142234spa
dc.relation.referencesFathizad, H., Taghizadeh-Mehrjardi, R., Hakimzadeh Ardakani, M. A., Zeraatpisheh, M., Heung, B., & Scholten, T. (2022). Spatiotemporal Assessment of Soil Organic Carbon Change Using Machine-Learning in Arid Regions. Agronomy, 12(3). https://doi.org/10.3390/agronomy12030628spa
dc.relation.referencesFélix-Pico, E., Holguín-Quiñones, O., Hernández-Herrera, A., & Flores-Verdugo, F. (2006). Producción primaria de los mangles del Estero El Conchalito en Bahía de La Paz (Baja California Sur, México). Ciencias Marinas, 32(1A), 53–63. http://www.scielo.org.mx/scielo.php?script=sci_arttext&pid=S0185-38802006000200006&lng=es&nrm=iso&tlng=esspa
dc.relation.referencesFick, S. E., & Hijmans, R. J. (2017). WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37(12), 4302–4315. https://doi.org/10.1002/JOC.5086spa
dc.relation.referencesFigueroa, L., & Álvarez, R. (2011). Evaluación de suelos de manglar en dos localidades de la ensenada de Tumaco, Pacífico colombiano. Arquivos de Ciências Do Mar, 44(1), 12–20.spa
dc.relation.referencesFricke, K. (2014). Water Balance Model. In Analysis and Modelling of Water Supply and Demand Under Climate Change, Land Use Transformation and Socio-Economic Development (pp. 39–120). Springer, Cham. https://doi.org/10.1007/978-3-319-01610-8_3spa
dc.relation.referencesFriess, D. A., Rogers, K., Lovelock, C. E., Krauss, K. W., Hamilton, S. E., Lee, S. Y., Lucas, R., Primavera, J., Rajkaran, A., & Shi, S. (2019). The State of the World’s Mangrove Forests: Past, Present, and Future. Annual Review of Environment and Resources, 44(April 2021), 89–115. https://doi.org/10.1146/annurev-environ-101718-033302spa
dc.relation.referencesFu, Y., Liao, H., & Lv, L. (2021). A comparative study of various methods of handling missing data in unsoda. Agriculture (Switzerland), 11(8). https://doi.org/10.3390/agriculture11080727spa
dc.relation.referencesGao, Y., Zhou, J., Wang, L., Guo, J., Feng, J., Wu, H., & Lin, G. (2019). Distribution patterns and controlling factors for the soil organic carbon in four mangrove forests of China. Global Ecology and Conservation, 17, e00575. https://doi.org/10.1016/J.GECCO.2019.E00575spa
dc.relation.referencesGarcés, O., & Espinosa, L. (2019). Contaminación por Hidrocarburos en Sedimentos de Manglar del Estuario del Río Mira, Pacífico Colombiano, Afectados por Derrames de Petróleo Crudo. Bulletin of Marine and Coastal Research, 48(1), 159–168.spa
dc.relation.referencesGuevara, M., Arroyo, C., Brunsell, N., Cruz, C. O., Domke, G., Equihua, J., Etchevers, J., Hayes, D., Hengl, T., Ibelles, A., Johnson, K., de Jong, B., Libohova, Z., Llamas, R., Nave, L., Ornelas, J. L., Paz, F., Ressl, R., Schwartz, A., … Vargas, R. (2020). Soil Organic Carbon Across Mexico and the Conterminous United States (1991–2010). Global Biogeochemical Cycles, 34(3), no. https://doi.org/10.1029/2019GB006219spa
dc.relation.referencesGutíerrez, J., Ordoñez, N., Bolívar, S., Bunning, S., Guevara, M., Medina, E., Olivera, C., Olmedo, G., Sevilla, V., & Vargas, R. (2020). Estimación del carbono orgánico en los suelos de ecosistema de páramo en Colombia. Ecosistemas, 29(1), 1–10. https://doi.org/https://doi.org/10.7818/ECOS.1855spa
dc.relation.referencesHamilton, S. E., & Casey, D. (2016). Creation of a high spatio-temporal resolution global database of continuous mangrove forest cover for the 21st century (CGMFC-21). Global Ecology and Biogeography, 25(6), 729–738. https://doi.org/10.1111/geb.12449spa
dc.relation.referencesHamilton, S. E., & Friess, D. A. (2018). Global carbon stocks and potential emissions due to mangrove deforestation from 2000 to 2012. Nature Climate Change, 8(3), 240–244. https://doi.org/10.1038/s41558-018-0090-4spa
dc.relation.referencesHamilton, S. E., Lovette, J. P., Borbor-Cordova, M. J., & Millones, M. (2017). The Carbon Holdings of Northern Ecuador’s Mangrove Forests. Annals of the American Association of Geographers, 107(1), 54–71. https://doi.org/10.1080/24694452.2016.1226160spa
dc.relation.referencesHengl, T., & MacMillan, R. . (2019). Predictive Soil Mapping with R. https://soilmapper.org/spa
dc.relation.referencesHengl, T., Nussbaum, M., Wright, M. N., Heuvelink, G. B. M., & Gräler, B. (2018). Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables. PeerJ, 2018(8). https://doi.org/10.7717/peerj.5518spa
dc.relation.referencesHernández-Blanco, M., Costanza, R., & Cifuentes-Jara, M. (2018). Valoración económica de los servicios ecosistémicos provistos por los manglares del Golfo de Nicoya. Conservación Internacional.spa
dc.relation.referencesHoward, J., Hoyt, S., Isensee, K., Pidgeon, E., & Telszewski, M. (2018). Coastal Blue Carbon: Methods for assessing carbon stocks and emissions factors in mangroves, tidal salt marshes, and seagrass meadows. Conservation International, Intergovernmental Oceanographic Commission of UNESCO, International Union for Conservation of Nature.spa
dc.relation.referencesHu, Y., Fest, B. J., Swearer, S. E., & Arndt, S. K. (2021). Fine-scale spatial variability in organic carbon in a temperate mangrove forest: Implications for estimating carbon stocks in blue carbon ecosystems. Estuarine, Coastal and Shelf Science, 259, 107469. https://doi.org/10.1016/J.ECSS.2021.107469spa
dc.relation.referencesHutchison, J., Manica, A., Swetnam, R., Balmford, A., & Spalding, M. (2014). Predicting Global Patterns in Mangrove Forest Biomass. Conservation Letters, 7(3), 233–240. https://doi.org/10.1111/CONL.12060spa
dc.relation.referencesIdeam. (2014). Atlas Interactivo - Climatológico - IDEAM. http://atlas.ideam.gov.co/visorAtlasClimatologico.htmlspa
dc.relation.referencesIGAC. (2004). Estudio General de suelos y Zonificación de tierras departamento de Nariño. Instituto Geográfico Agustín Codazzi.spa
dc.relation.referencesInvemar. (2010). Informe del Estado de los Ambientes y Recursos Marinos y Costeros en Colombia: Año 2009. Serie de Publicaciones Periódicas.spa
dc.relation.referencesInvemar. (2018). Manglares . https://hub.arcgis.com/datasets/4a14d0ccabf540e9a8934a56a2b55442/explore?layer=5&location=8.448780%2C-78.105500%2C6.00spa
dc.relation.referencesInvemar. (2019). Servicios Ecosistémicos Marinos y Costeros de Colombia: Énfasis en Manglares Y Pastos Marinos. INVEMAR. https://aquadocs.org/handle/1834/15783spa
dc.relation.referencesInvemar. (2020). Sistema de información para la gestión de los manglares en Colombia (SIGMA). http://sigma.invemar.org.co/iniciospa
dc.relation.referencesInvemar, Univalle, & Corponariño. (2017). Implementación de acciones que contribuyan a la rehabilitación ecológica de áreas afectadas por hidrocarburos en zona costera y piedemonte del departamento de Nariño. http://www.invemar.org.co/inicio?p_p_id=101&p_p_lifecycle=0&p_p_state=maximized&p_p_mode=view&_101_struts_action=%2Fasset_publisher%2Fview_content&_101_returnToFullPageURL=%2F&_101_assetEntryId=192196&_101_type=content&_101_urlTitle=implementacion-de-acciones-que-contribuyan-a-la-rehabilitacion-ecologica-de-areas-afectadas-por-hidrocarburos-en-zona-costera-y-piedemonte-del-departa&inheritRedirect=falsespa
dc.relation.referencesJardine, S. L., & Siikamäki, J. V. (2014). A global predictive model of carbon in mangrove soils. Environmental Research Letters, 9(10). https://doi.org/10.1088/1748-9326/9/10/104013spa
dc.relation.referencesJennerjahn, T. C., Gilman, E., Krauss, K. W., Lacerda, L. D., Nordhaus, I., & Wolanski, E. (2017). Mangrove ecosystems under climate change. In V. Rivera-Monroy, S. Lee, E. Kristensen, & R. Twilley (Eds.), Mangrove Ecosystems: A Global Biogeographic Perspective: Structure, Function, and Services (pp. 211–244). Springer International Publishing. https://doi.org/10.1007/978-3-319-62206-4_7/COVERspa
dc.relation.referencesKauffman, J. B., Adame, M. F., Arifanti, V. B., Schile-Beers, L. M., Bernardino, A. F., Bhomia, R. K., Donato, D. C., Feller, I. C., Ferreira, T. O., Jesus Garcia, M. del C., MacKenzie, R. A., Megonigal, J. P., Murdiyarso, D., Simpson, L., & Hernández Trejo, H. (2020). Total ecosystem carbon stocks of mangroves across broad global environmental and physical gradients. Ecological Monographs, 90(2), 1–18. https://doi.org/10.1002/ecm.1405spa
dc.relation.referencesKauffman, J. B., & Donato, D. (2012). Protocols for the measurement, monitoring and reporting of structure, biomass and carbon stocks in mangrove forests. Center for International Forestry Research (CIFOR). https://doi.org/10.17528/CIFOR/003749spa
dc.relation.referencesKauffman, J. B., Heider, C., Norfolk, J., & Payton, F. (2014). Carbon stocks of intact mangroves and carbon emissions arising from their conversion in the Dominican Republic. Ecological Applications, 24(3), 518–527. https://doi.org/10.1890/13-0640.1spa
dc.relation.referencesKauffman, J., Donato, D., & Adame, M. F. (2013). Protocolo para la medición, monitoreo y reporte de la estructura, biomasa y reservas de carbono de los manglares. In Protocolo para la medición, monitoreo y reporte de la estructura, biomasa y reservas de carbono de los manglares. CIFOR. http://creativecommons.org/licenses/by-nc-nd/3.0/spa
dc.relation.referencesKida, M., Watanabe, I., Kinjo, K., Kondo, M., Yoshitake, S., Tomotsune, M., Iimura, Y., Umnouysin, S., Suchewaboripont, V., Poungparn, S., Ohtsuka, T., & Fujitake, N. (2021). Organic carbon stock and composition in 3.5-m core mangrove soils (Trat, Thailand). Science of The Total Environment, 801, 149682. https://doi.org/10.1016/J.SCITOTENV.2021.149682spa
dc.relation.referencesKomiyama, A., Ong, J. E., & Poungparn, S. (2008). Allometry, biomass, and productivity of mangrove forests: A review. Aquatic Botany, 89(2), 128–137. https://doi.org/10.1016/J.AQUABOT.2007.12.006spa
dc.relation.referencesKusumaningtyas, M. A., Hutahaean, A. A., Fischer, H. W., Pérez-Mayo, M., Ransby, D., & Jennerjahn, T. C. (2019). Variability in the organic carbon stocks, sources, and accumulation rates of Indonesian mangrove ecosystems. Estuarine, Coastal and Shelf Science, 218, 310–323. https://doi.org/10.1016/J.ECSS.2018.12.007spa
dc.relation.referencesLang, A. P., Madi, M., Torres Boeger, M. R., Reissmann, C. B., Geronazzo Martins, K., E D E B O G O T Á, S., De, F., Departamento, C., & Biología, D. E. (2016). Soil-plant nutrient interactions in two mangrove areas at Southern Brazil. Acta Biológica Colombiana, 21(1), 39–50. https://doi.org/10.15446/ABC.V21N1.42894spa
dc.relation.referencesLey 2243, Ley de protección de ecosistemas de manglar y otras disposiciones (08 de Julio de 2022). Ministerio de Ambiente y Desarrollo Sostenible.spa
dc.relation.referencesLittle, E., & Wadsworth, F. (1964). Trees of Puerto Rico and the Virgin Islands. US Department of Agriculture. https://doi.org/10.2307/2484965spa
dc.relation.referencesLópez-Angarita, J., Roberts, C. M., Tilley, A., Hawkins, J. P., & Cooke, R. G. (2016). Mangroves and people: Lessons from a history of use and abuse in four Latin American countries. Forest Ecology and Management, 368, 151–162. https://doi.org/10.1016/J.FORECO.2016.03.020spa
dc.relation.referencesLyard, F. H., Allain, D. J., Cancet, M., Carrère, L., & Picot, N. (2021). FES2014 global ocean tide atlas: Design and performance. Ocean Science, 17(3), 615–649. https://doi.org/10.5194/OS-17-615-2021spa
dc.relation.referencesMakonyo, M., & Msabi, M. M. (2021). Identification of groundwater potential recharge zones using GIS-based multi-criteria decision analysis: A case study of semi-arid midlands Manyara fractured aquifer, North-Eastern Tanzania. Remote Sensing Applications: Society and Environment, 23, 100544. https://doi.org/10.1016/J.RSASE.2021.100544spa
dc.relation.referencesMalone, B., McBratney, A. B., Minasny, B., & Laslett, G. M. (2009). Mapping continuous depth functions of soil carbon storage and available water capacity. Geoderma, 154(1–2), 138–152. https://doi.org/10.1016/j.geoderma.2009.10.007spa
dc.relation.referencesMalone, B., Minasny, B., & Mcbratney, A. B. (2017). Using R for Digital Soil Mapping. Springer. http://www.springer.com/series/8746spa
dc.relation.referencesMcNally, A., & NASA/GSFC/HSL. (2018). FLDAS Noah Land Surface Model L4 Global Monthly 0.1 x 0.1 degree (MERRA-2 and CHIRPS) (FLDAS_NOAH01_C_GL_M 001). https://disc.gsfc.nasa.gov/datasets/FLDAS_NOAH01_C_GL_M_001/summaryspa
dc.relation.referencesMejía-Rentería, J. C., Castellanos-Galindo, G. A., Cantera-Kintz, J. R., & Hamilton, S. E. (2018). A comparison of Colombian Pacific mangrove extent estimations: Implications for the conservation of a unique Neotropical tidal forest. Estuarine, Coastal and Shelf Science, 212, 233–240. https://doi.org/10.1016/j.ecss.2018.07.020spa
dc.relation.referencesMonsalve, A., & Ramírez, G. (2015). Caracterización de la estructura y contenido de carbono de los bosques de manglar en el área de jurisdicción del Consejo comunitario La Plata, Bahía Málaga, Valle del Cauca. http://climares.invemar.org.co/proyectos/-/asset_publisher/3W2QxkeIz5Ex/content/id/41676spa
dc.relation.referencesMurillo-Sandoval, P. J., Fatoyinbo, L., & Simard, M. (2022). Mangroves Cover Change Trajectories 1984-2020: The Gradual Decrease of Mangroves in Colombia. Frontiers in Marine Science, 9, 1277. https://doi.org/10.3389/FMARS.2022.892946/BIBTEXspa
dc.relation.referencesNASA Goddard Space Flight Center, & Ocean Ecology Laboratory. (2014). MODIS-Aqua Ocean Color Data; NASA Goddard Space Flight Center, Ocean Ecology Laboratory, Ocean Biology Processing Group. https://oceancolor.gsfc.nasa.gov/data/aqua/spa
dc.relation.referencesOlmedo, G. ., & Baritz, R. (2018). Preparation of local soil data. In Y. Yigini, G. . Olmedo, S. Reiter, R. Baritz, K. Viatkin, & R. . Vargas (Eds.), Soil Organic Carbon Mapping Cookbook (2nd ed., pp. 19–35). FAO. https://fao-gsp.github.io/SOC-Mapping-Cookbook/index.htmlspa
dc.relation.referencesPalacios, M., Vargas, E., & de la Pava, M. (1990). Determinación del aporte de materia orgánica del Manglar en la zona de Bocagrande. Boletín Científico CCCP, 1(1), 55–72. https://doi.org/10.26640/01213423.1.55_72spa
dc.relation.referencesPalacios Peñaranda, M. L., Cantera Kintz, J. R., & Peña Salamanca, E. J. (2019). Carbon stocks in mangrove forests of the Colombian Pacific. Estuarine, Coastal and Shelf Science, 227(May), 106299. https://doi.org/10.1016/j.ecss.2019.106299spa
dc.relation.referencesParques Nacionales Naturales de Colombia [PNNC]. (2017). Actualización plan de manejo Parque Nacional Natural Sanquianga 2018-2023. https://www.parquesnacionales.gov.co/portal/wp-content/uploads/2020/10/plan-de-manejo-pnn-sanquianga.pdfspa
dc.relation.referencesParra, A. S., & Restrepo Ángel, J. D. (2014). The environmental collapse in the Patía river, Colombia: Morphological variations and impacts on mangrove ecosystems. Latin American Journal of Aquatic Research, 42(1), 40–60. https://doi.org/10.3856/vol42-issue1-fulltext-4spa
dc.relation.referencesPerdomo-Trujillo, L. V., Mancera-Pineda, J. E., Medina-Calderón, J. H., Sánchez-Núñez, D. A., & Schnetter, M. L. (2021). Effect of Restoration Actions on Organic Carbon Pools in the Lagoon—Delta Ciénaga Grande de Santa Marta, Colombian Caribbean. Water 2021, Vol. 13, Page 1297, 13(9), 1297. https://doi.org/10.3390/W13091297spa
dc.relation.referencesPerdomo, L. (2020). Biomasa y producción radicular en manglares de cuenca neotropicales a lo largo de una trayectoria de restauración y su contribución a las reservas de carbono en el ecosistema. Universidad Nacional de Colombia.spa
dc.relation.referencesPham, T. D., Yokoya, N., Nguyen, T. T. T., Le, N. N., Ha, N. T., Xia, J., Takeuchi, W., & Pham, T. D. (2020). Improvement of Mangrove Soil Carbon Stocks Estimation in North Vietnam Using Sentinel-2 Data and Machine Learning Approach. Https://Doi.Org/10.1080/15481603.2020.1857623, 58(1), 68–87. https://doi.org/10.1080/15481603.2020.1857623spa
dc.relation.referencesPhillips, S., & US National Oceanographic Data Center. (2011). AVHRR Pathfinder version 5.0 global 4km sea surface temperature (SST) day-night monthly and yearly averages for 1985-2009 (NCEI Accession 0077816). https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.nodc:0077816spa
dc.relation.referencesPoggio, L., De Sousa, L. M., Batjes, N. H., Heuvelink, G. B. M., Kempen, B., Ribeiro, E., & Rossiter, D. (2021). SoilGrids 2.0: Producing soil information for the globe with quantified spatial uncertainty. Soil, 7(1), 217–240. https://doi.org/10.5194/soil-7-217-2021spa
dc.relation.referencesR Core Team. (2021). R: The R Stats Package. https://stat.ethz.ch/R-manual/R-devel/library/stats/html/stats-package.htmlspa
dc.relation.referencesR Core Team. (2022). Package ‘caret.’ CRAN. https://cran.r-project.org/web/packages/caret/caret.pdfspa
dc.relation.referencesResolución 1447, se reglamenta el Sistema de monitoreo, reporte y verificación de las acciones de mitigación a nivel nacional (01 de Agosto de 2018). Ministerio de Ambiente y Desarrollo Sostenible.spa
dc.relation.referencesRodríguez-Rodríguez, J. A., Sierra-Correa, P. C., Gómez-Cubillos, M. C., & Villanueva, L. V. L. (2016). Mangroves of Colombia. In C. . Finlayson, G. . Milton, R. . Prentice, & N. Davidson (Eds.), The Wetland Book (pp. 1–10). Springer, Dordrecht. https://doi.org/10.1007/978-94-007-6173-5_280-2spa
dc.relation.referencesRomero-Berny, E. I., Acosta-Velázquez, J., Tovilla-Hernández, C., Schmook, B., & Gómez-Ortega, R. (2016). Cambios de cobertura y fragmentación de manglares en la región del Soconusco, Chiapas, México, 1994-2011 . Revista Geográfica de América Central, 1(54), 153–169. https://doi.org/10.15359/RGAC.1-54.7spa
dc.relation.referencesRovai, A. S., Riul, P., Twilley, R. R., Castañeda-Moya, E., Rivera-Monroy, V. H., Williams, A. A., Simard, M., Cifuentes-Jara, M., Lewis, R. R., Crooks, S., Horta, P. A., Schaeffer-Novelli, Y., Cintrón, G., Pozo-Cajas, M., & Pagliosa, P. R. (2016). Scaling mangrove aboveground biomass from site-level to continental-scale. Global Ecology and Biogeography, 25(3), 286–298. https://doi.org/10.1111/GEB.12409spa
dc.relation.referencesRovai, A. S., Twilley, R. R., Castañeda-Moya, E., Riul, P., Cifuentes-Jara, M., Manrow-Villalobos, M., Horta, P. A., Simonassi, J. C., Fonseca, A. L., & Pagliosa, P. R. (2018). Global controls on carbon storage in mangrove soils. Nature Climate Change, 8(6), 534–538. https://doi.org/10.1038/s41558-018-0162-5spa
dc.relation.referencesRowley, M. C., Grand, S., & Verrecchia, É. P. (2018). Calcium-mediated stabilisation of soil organic carbon. Biogeochemistry, 137(1–2), 27–49. https://doi.org/10.1007/S10533-017-0410-1/FIGURES/3spa
dc.relation.referencesRozainah, M. Z., Nazri, M. N., Sofawi, A. B., Hemati, Z., & Juliana, W. A. (2018). Estimation of carbon pool in soil, above and below ground vegetation at different types of mangrove forests in Peninsular Malaysia. Marine Pollution Bulletin, 137, 237–245. https://doi.org/10.1016/J.MARPOLBUL.2018.10.023spa
dc.relation.referencesSaavedra-Hortua, D. A., Friess, D. A., Zimmer, M., & Gillis, L. G. (2020). Sources of Particulate Organic Matter across Mangrove Forests and Adjacent Ecosystems in Different Geomorphic Settings. Wetlands, 40(5), 1047–1059. https://doi.org/10.1007/S13157-019-01261-9/FIGURES/7spa
dc.relation.referencesSahu, B., Ghosh, A. K., & Seema. (2021). Deterministic and geostatistical models for predicting soil organic carbon in a 60 ha farm on Inceptisol in Varanasi, India. Geoderma Regional, 26, e00413. https://doi.org/10.1016/J.GEODRS.2021.E00413spa
dc.relation.referencesSánchez, H., Álvarez, R., Guevara, O., Zamora, A., Rodríguez, H., & Bravo, H. (1997). Diagnóstico y zonificación preliminar de los manglares del Pacífico de Colombia. Ministerio del Medio Ambiente.spa
dc.relation.referencesSanderman, J., Hengl, T., Fiske, G., Solvik, K., Adame, M. F., Benson, L., Bukoski, J. J., Carnell, P., Cifuentes-Jara, M., Donato, D., Duncan, C., Eid, E. M., Ermgassen, P. Z., Lewis, C. J. E., Macreadie, P. I., Glass, L., Gress, S., Jardine, S. L., Jones, T. G., … Landis, E. (2018). A global map of mangrove forest soil carbon at 30 m spatial resolution. Environmental Research Letters, 13(5). https://doi.org/10.1088/1748-9326/aabe1cspa
dc.relation.referencesSasmito, S. D., Kuzyakov, Y., Lubis, A. A., Murdiyarso, D., Hutley, L. B., Bachri, S., Friess, D. A., Martius, C., & Borchard, N. (2020). Organic carbon burial and sources in soils of coastal mudflat and mangrove ecosystems. Catena, 187(December 2019), 104414. https://doi.org/10.1016/j.catena.2019.104414spa
dc.relation.referencesSimard, M., Fatoyinbo, L., Smetanka, C., Rivera-Monroy, V. H., Castañeda-Moya, E., Thomas, N., & Van der Stocken, T. (2019). Mangrove canopy height globally related to precipitation, temperature and cyclone frequency. Nature Geoscience, 12(1), 40–45. https://doi.org/10.1038/s41561-018-0279-1spa
dc.relation.referencesSouthwell, C. R., & Bultman, J. D. (1971). Marine Borer Resistance of Untreated Woods Over Long Periods of Immersion in Tropical Waters. Biotropica, 3(1), 81. https://doi.org/10.2307/2989709spa
dc.relation.referencesStekhoven, D. J., & Bühlmann, P. (2012). MissForest—non-parametric missing value imputation for mixed-type data. Bioinformatics, 28(1), 112–118. https://doi.org/10.1093/BIOINFORMATICS/BTR597spa
dc.relation.referencesStockmann, U., Adams, M. A., Crawford, J. W., Field, D. J., Henakaarchchi, N., Jenkins, M., Minasny, B., McBratney, A. B., Courcelles, V. de R. de, Singh, K., Wheeler, I., Abbott, L., Angers, D. A., Baldock, J., Bird, M., Brookes, P. C., Chenu, C., Jastrow, J. D., Lal, R., … Zimmermann, M. (2013). The knowns, known unknowns and unknowns of sequestration of soil organic carbon. Agriculture, Ecosystems & Environment, 164, 80–99. https://doi.org/10.1016/J.AGEE.2012.10.001spa
dc.relation.referencesSuhaili, N. S., Fei, J. L. J., Sha’ari, F. W., Idris, M. I., Hatta, S. M., Kodoh, J., & Besar, N. A. (2020). Carbon stock estimation of mangrove forest in Sulaman Lake Forest Reserve, Sabah, Malaysia. Biodiversitas Journal of Biological Diversity, 21(12), 5657–5664. https://doi.org/10.13057/BIODIV/D211223spa
dc.relation.referencesTaillardat, P., Friess, D. A., & Lupascu, M. (2018). Mangrove blue carbon strategies for climate change mitigation are most effective at the national scale. Biology Letters, 14(10). https://doi.org/10.1098/rsbl.2018.0251spa
dc.relation.referencesTanner, M. K., Moity, N., Costa, M. T., Marin Jarrin, J. R., Aburto-Oropeza, O., & Salinas-de-León, P. (2019). Mangroves in the Galapagos: Ecosystem services and their valuation. Ecological Economics, 160(June 2018), 12–24. https://doi.org/10.1016/j.ecolecon.2019.01.024spa
dc.relation.referencesTrettin, C. C., Dai, Z., Tang, W., Lagomasino, D., Thomas, N., Lee, S. K., Simard, M., Ebanega, M. O., Stoval, A., & Fatoyinbo, T. E. (2021). Mangrove carbon stocks in Pongara National Park, Gabon. Estuarine, Coastal and Shelf Science, 259(April 2020). https://doi.org/10.1016/j.ecss.2021.107432spa
dc.relation.referencesTue, N. T., Dung, L. V., Nhuan, M. T., & Omori, K. (2014). Carbon storage of a tropical mangrove forest in Mui Ca Mau National Park, Vietnam. CATENA, 121, 119–126. https://doi.org/10.1016/J.CATENA.2014.05.008spa
dc.relation.referencesTwilley, R. R., Castañeda-Moya, E., Rivera-Monroy, V. H., & Rovai, A. (2017). Productivity and carbon dynamics in mangrove wetlands. In V. . Rivera-Monroy, S. . Lee, E. . Kristensen, & R. Twilley (Eds.), Mangrove Ecosystems: A Global Biogeographic Perspective: Structure, Function, and Services (pp. 113–162). Springer International Publishing. https://doi.org/10.1007/978-3-319-62206-4_5/FIGURES/14spa
dc.relation.referencesTwilley, R. R., & Rivera-Monroy, V. H. (2009). Ecogeomorphic Models of Nutrient Biogeochemistry for Mangrove Wetlands. In Coastal Wetlands: An Integrated Ecosystem Approach (First edit, Issue June, pp. 641–684). Elsevier. https://doi.org/10.1016/B978-0-444-53103-2.00023-5spa
dc.relation.referencesTwilley, R. R., Rivera-Monroy, V. H., Rovai, A. S., Castañeda-Moya, E., & Davis, S. (2019). Mangrove Biogeochemistry at Local to Global Scales Using Ecogeomorphic Approaches. In G. . Perillo, E. . Wolanski, D. . Cahoon, & Hopkinson C. (Eds.), Coastal Wetlands: An Integrated Ecosystem Approach (pp. 717–785). Elsevier. https://doi.org/10.1016/B978-0-444-63893-9.00021-6spa
dc.relation.referencesTwilley, R. R., Rovai, A. S., & Riul, P. (2018). Coastal morphology explains global blue carbon distributions. Frontiers in Ecology and the Environment, 16(9), 503–508. https://doi.org/10.1002/FEE.1937spa
dc.relation.referencesWadoux, A. M. J.-C., Odeh, I. O. A., & McBratney, A. B. (2021). Overview of Pedometrics. Reference Module in Earth Systems and Environmental Sciences. https://doi.org/10.1016/B978-0-12-822974-3.00001-Xspa
dc.relation.referencesWang, G., Singh, M., Wang, J., Xiao, L., & Guan, D. (2021). Effects of marine pollution, climate, and tidal range on biomass and sediment organic carbon in Chinese mangrove forests. CATENA, 202, 105270. https://doi.org/10.1016/J.CATENA.2021.105270spa
dc.relation.referencesWang, N., Xue, J., Peng, J., Biswas, A., He, Y., & Shi, Z. (2020). Integrating remote sensing and landscape characteristics to estimate soil salinity using machine learning methods: A case study from southern xinjiang, china. Remote Sensing, 12(24), 1–21. https://doi.org/10.3390/rs12244118spa
dc.relation.referencesWang, S., Adhikari, K., Wang, Q., Jin, X., & Li, H. (2018). Role of environmental variables in the spatial distribution of soil carbon (C), nitrogen (N), and C:N ratio from the northeastern coastal agroecosystems in China. Ecological Indicators, 84, 263–272. https://doi.org/10.1016/J.ECOLIND.2017.08.046spa
dc.relation.referencesWicaksono, P., Danoedoro, P., Hartono, & Nehren, U. (2015). Mangrove biomass carbon stock mapping of the Karimunjawa Islands using multispectral remote sensing. 37(1), 26–52. https://doi.org/10.1080/01431161.2015.1117679spa
dc.relation.referencesWiesmeier, M., Urbanski, L., Hobley, E., Lang, B., von Lützow, M., Marin-Spiotta, E., van Wesemael, B., Rabot, E., Ließ, M., Garcia-Franco, N., Wollschläger, U., Vogel, H. J., & Kögel-Knabner, I. (2019). Soil organic carbon storage as a key function of soils - A review of drivers and indicators at various scales. Geoderma, 333, 149–162. https://doi.org/10.1016/J.GEODERMA.2018.07.026spa
dc.relation.referencesWoodroffe, C. D., Rogers, K., McKee, K. L., Lovelock, C. E., Mendelssohn, I. A., & Saintilan, N. (2016). Mangrove Sedimentation and Response to Relative Sea-Level Rise. Http://Dx.Doi.Org/10.1146/Annurev-Marine-122414-034025, 8, 243–266. https://doi.org/10.1146/ANNUREV-MARINE-122414-034025spa
dc.relation.referencesWorthington, T. A., zu Ermgassen, P. S. E., Friess, D. A., Krauss, K. W., Lovelock, C. E., Thorley, J., Tingey, R., Woodroffe, C. D., Bunting, P., Cormier, N., Lagomasino, D., Lucas, R., Murray, N. J., Sutherland, W. J., & Spalding, M. (2020). A global biophysical typology of mangroves and its relevance for ecosystem structure and deforestation. Scientific Reports 2020 10:1, 10(1), 1–11. https://doi.org/10.1038/s41598-020-71194-5spa
dc.relation.referencesYang, R. M., Zhang, G. L., Liu, F., Lu, Y. Y., Yang, F., Yang, F., Yang, M., Zhao, Y. G., & Li, D. C. (2016). Comparison of boosted regression tree and random forest models for mapping topsoil organic carbon concentration in an alpine ecosystem. Ecological Indicators, 60, 870–878. https://doi.org/10.1016/j.ecolind.2015.08.036spa
dc.relation.referencesZakaria, R. M., Chen, G., Chew, L. L., Sofawi, A. B., Moh, H. H., Chen, S., Teoh, H. W., & Adibah, S. Y. S. N. (2021). Carbon stock of disturbed and undisturbed mangrove ecosystems in Klang Straits, Malaysia. Journal of Sea Research, 176, 102113. https://doi.org/10.1016/J.SEARES.2021.102113spa
dc.relation.referencesZarate-Barrera, T. G., & Maldonado, J. H. (2015). Valuing blue carbon: Carbon sequestration benefits provided by the marine protected areas in Colombia. PLoS ONE, 10(5), 1–22. https://doi.org/10.1371/journal.pone.0126627spa
dc.relation.referencesZeraatpisheh, M., Garosi, Y., Reza Owliaie, H., Ayoubi, S., Taghizadeh-Mehrjardi, R., Scholten, T., & Xu, M. (2022). Improving the spatial prediction of soil organic carbon using environmental covariates selection: A comparison of a group of environmental covariates. Catena, 208(September 2021), 105723. https://doi.org/10.1016/j.catena.2021.105723spa
dc.relation.referencesZhang, Z., Li, J., Tsui, C. C., & Chen, Z. S. (2020). The Study of Gaining More Detailed Variability Information of Soil Organic Carbon in Surface Soils and Its Significance to Enriching the Existing Soil Database. Sustainability 2020, Vol. 12, Page 4866, 12(12), 4866. https://doi.org/10.3390/SU12124866spa
dc.relation.referencesZhou, Y., Xue, J., Chen, S., Zhou, Y., Liang, Z., Wang, N., & Shi, Z. (2020). Fine-resolution mapping of soil total nitrogen across china based on weighted model averaging. Remote Sensing, 12(1), 1–18. https://doi.org/10.3390/RS12010085spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.licenseAtribución-NoComercial-SinDerivadas 4.0 Internacionalspa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/spa
dc.subject.agrovocCarbono azul
dc.subject.agrovocBlue carbon
dc.subject.agrovocBiomasa arbórea por encima del suelo
dc.subject.agrovocAbove ground tree biomass
dc.subject.agrovocBiomasa sobre el suelo
dc.subject.agrovocBiomasa por debajo del suelo
dc.subject.agrovocBelow ground biomass
dc.subject.agrovocSecuestro de carbono
dc.subject.armarcCarbon sequestration
dc.subject.ddc620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingenieríaspa
dc.subject.proposalBiomasa aéreaspa
dc.subject.proposalBiomasa subterráneaspa
dc.subject.proposalCarbono orgánico del suelospa
dc.subject.proposalManglaresspa
dc.subject.proposalPacífico Colombianospa
dc.subject.proposalAboveground biomasseng
dc.subject.proposalBelowground biomasseng
dc.subject.proposalSoil organic carboneng
dc.subject.proposalMangroveseng
dc.subject.proposalColombian Pacificeng
dc.subject.unescoMangrove areas
dc.subject.unescoEcosistema marino
dc.subject.unescoMarine ecosystems
dc.titleEvaluación de almacenamiento de carbono azul en el ecosistema de manglar del Pacífico nariñensespa
dc.title.translatedEvaluation of blue carbon storage in the mangrove ecosystem of the Nariño Pacificeng
dc.typeTrabajo de grado - Maestríaspa
dc.type.coarhttp://purl.org/coar/resource_type/c_bdccspa
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa
dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/masterThesisspa
dc.type.redcolhttp://purl.org/redcol/resource_type/TMspa
dc.type.versioninfo:eu-repo/semantics/acceptedVersionspa
dcterms.audience.professionaldevelopmentEstudiantesspa
dcterms.audience.professionaldevelopmentPúblico generalspa
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2spa

Archivos

Bloque original

Mostrando 1 - 1 de 1
Cargando...
Miniatura
Nombre:
1085936056_2023.pdf
Tamaño:
2.11 MB
Formato:
Adobe Portable Document Format
Descripción:
Tesis de Maestría en Ingeniería Ambiental

Bloque de licencias

Mostrando 1 - 1 de 1
No hay miniatura disponible
Nombre:
license.txt
Tamaño:
5.74 KB
Formato:
Item-specific license agreed upon to submission
Descripción: