Método para estimar la distribución espacial del contenido de carbono orgánico en el suelo de páramo con base en datos de sensores remotos

Vista sencilla de ítem
dc.contributor.advisorMartínez Martínez, Luis Joelspa
dc.contributor.authorSerrano Agudelo, Pablo Cesarspa
dc.contributor.researchgroupGIPSOspa
dc.date.accessioned2024-03-12T20:54:59Z
dc.date.available2024-03-12T20:54:59Z
dc.date.issued2023
dc.descriptionilustraciones, diagramas, mapasspa
dc.description.abstractEn esta investigación se desarrolló un método para estimar la distribución espacial del carbono orgánico en el suelo de páramo basado en datos derivados de sensores remotos y utilizando algoritmo de aprendizaje automatizado. Para este fin se efectuaron análisis de correlaciones entre en contenido del carbono orgánico de 169 muestras a dos profundidades (0-15cm, 15-30cm) con la covariables derivadas de los sensores Sentinel 1, Sentinel 2, modelos digitales de elevación de Alos Palsar y datos de clima obtenidos de la plataforma WorldClim. Las covariables que mayor correlación tuvieron con el contenido de carbono orgánico del suelo (COS), fueron la temperatura, la altura, los índices derivados del modelo de elevación digital, los índices espectrales en especial el NDVI, y el índice VH de Sentinel 1. El mejor desempeño fue para los modelos desarrollados con random forest. Por último, se validó y documentó el método, permitiendo hacer una estimación de la distribución espacial del COS en los suelos de páramo, de gran utilidad para apoyar la toma de decisiones sobre uso y manejo de conservación de estos ecosistemas. (Texto tomado de la fuente).spa
dc.description.abstractIn this research, a method was developed to estimate the spatial distribution of organic carbon in páramo soil based on remotely sensed data and using an automated learning algorithm. For this purpose, correlation analyses were performed between the organic carbon content of 169 samples at two depths (0-15cm, 15-30cm) with covariates derived from Sentinel 1, Sentinel 2 sensors, Alos Palsar digital elevation models and climate data obtained from the WorldClim platform. The covariates that had the highest correlation with soil organic carbon content (COS) were temperature, altitude, indices derived from the digital elevation model, spectral indices, especially NDVI, and the VH index from Sentinel 1. The best performance was for the models developed with random forest. Finally, the method was validated and documented, allowing an estimation of the spatial distribution of COS in páramo soils, which is very useful to support decision making on the use and conservation management of these ecosystems.eng
dc.description.degreelevelMaestríaspa
dc.description.degreenameMagíster en Geomáticaspa
dc.description.researchareaGeoinformación para el uso sostenible de recursos naturalesspa
dc.format.extentix, 74 páginasspa
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/85803
dc.language.isospaspa
dc.publisherUniversidad Nacional de Colombiaspa
dc.publisher.branchUniversidad Nacional de Colombia - Sede Bogotáspa
dc.publisher.facultyFacultad de Ciencias Agrariasspa
dc.publisher.placeBogotá, Colombiaspa
dc.publisher.programBogotá - Ciencias Agrarias - Maestría en Geomáticaspa
dc.relation.indexedAgrosaviaspa
dc.relation.indexedAgrovocspa
dc.relation.referencesAbuín, J. R. (2007). Regresión lineal múltiple. IdEyGdM-Ld Estadística, Editor, 32.spa
dc.relation.referencesAdhikari, K., Hartemink, A. E., Minasny, B., Bou Kheir, R., Greve, M. B., & Greve, M. H. (2014). Digital mapping of soil organic carbon contents and stocks in Denmark. PLoS ONE, 9(8). https://doi.org/10.1371/journal.pone.0105519spa
dc.relation.referencesAfricano, K., Cely, G., & Serrano, P. (2016). Potential CO2 Capture Associated with Edaphic Component in Moorlands Guantiva - La Rusia, Department of Boyacá, Colombia.spa
dc.relation.referencesAkpa, S. I. C., Odeh, I. O. A., Bishop, T. F. A., Hartemink, A. E., & Amapu, I. Y. (2016). Total soil organic carbon and carbon sequestration potential in Nigeria. Geoderma, 271, 202–215. https://doi.org/10.1016/j.geoderma.2016.02.021spa
dc.relation.referencesAlberto Quesada, C., Paz, C., Oblitas Mendoza, E., Lawrence Phillips, O., Saiz, G., & Lloyd, J. (2020). Variations in soil chemical and physical properties explain basin-wide Amazon forest soil carbon concentrations. SOIL, 6(1), 53–88. https://doi.org/10.5194/soil-6-53-2020spa
dc.relation.referencesArmas, D., Guevara, M., Alcaraz-Segura, D., Vargas, R., Soriano-Luna, M. A., Durante, P., & Oyonarte, C. (2017). Mapa digital del perfil del carbono orgánico en los suelos de Andalucía, España. Ecosistemas, 26(3), 80–88. https://doi.org/10.7818/ECOS.2017.26-3.10spa
dc.relation.referencesAyala, J. (2019). Mapeo digital de carbono orgánico del suelo mediante imágenes satelitales y algoritmos de autoaprendizaje en el ecosistema Herbazal del Páramo, provincia de Chimborazo, Ecuador (Doctoral dissertation, Universidad Nacional de La Plata).spa
dc.relation.referencesAyala, L., Villa, M., Aguirre, Z., & Aguirre, N. (2014). Quantification of carbon in the moors of the Yacuri National Park, provinces of Loja and Zamora Chinchipe, Ecuador. In CEDAMAZ 2014 · (Vol. 4, Issue 1).spa
dc.relation.referencesBannari, A., Morin, D., Bonn, F., & Huete, A. R. (1995). A review of vegetation indices. Remote Sensing Reviews, 13(1–2), 95–120. https://doi.org/10.1080/02757259509532298spa
dc.relation.referencesBernardeau, A., Sarría, F., & Castillo, F. J. (2014). Elaboración de un mapa de carbono orgánico del suelo en la Región de Murcia. XVI Congreso Nacional de Tecnologías de la Información Geográfica, Universidad de Alicante.spa
dc.relation.referencesBehrens, T., Zhu, A. X., Schmidt, K., & Scholten, T. (2010). Multi-scale digital terrain analysis and feature selection for digital soil mapping. Geoderma, 155(3–4), 175–185. https://doi.org/10.1016/j.geoderma.2009.07.010spa
dc.relation.referencesBlanco, A., Sarría, F., & Castillo, F. (2014). Elaboración de un mapa de carbono orgánico del suelo en la Región de Murcia.spa
dc.relation.referencesBreiman, L. (2001). Random forests. Machine learning, 45 (1), 5-32.spa
dc.relation.referencesBrus, D., Kempen, B., & Heuvelink, G. (2011). Sampling for validation of digital soil maps. European Journal of Soil Science, 62(3), 394-407.spa
dc.relation.referencesBou Kheir, R., Greve, M. H., Bøcher, P. K., Greve, M. B., Larsen, R., & McCloy, K. (2010). Predictive mapping of soil organic carbon in wet cultivated lands using classification-tree based models: The case study of Denmark. Journal of Environmental Management, 91(5), 1150–1160. https://doi.org/10.1016/j.jenvman.2010.01.001spa
dc.relation.referencesBuol, S. W., Southard, R. J., Graham, R. C., & McDaniel, P. A. (2011). Soil genesis and classification. John Wiley & Sons.spa
dc.relation.referencesBurbano, H. (2018). El carbono orgánico del suelo y su papel frente al cambio climático. Revista de Ciencias Agrícolas, 35(1), 82–86. https://doi.org/10.22267/rcia.183501.85spa
dc.relation.referencesCampbell, J.B., 2002. Introduction to Remote Sensing. Taylor & Francis, London.spa
dc.relation.referencesChang, K. T., & Tsai, B. W. (1991). The effect of dem resolution on slope and aspect mapping. Cartography and Geographic Information Systems, 18(1), 69–77. https://doi.org/10.1559/152304091783805626spa
dc.relation.referencesChen, S., Arrouays, D., Leatitia Mulder, V., Poggio, L., Minasny, B., Roudier, P., Libohova, Z., Lagacherie, P., Shi, Z., Hannam, J., Meersmans, J., Richer-de-Forges, A. C., & Walter, C. (2022). Digital mapping of GlobalSoilMap soil properties at a broad scale: A review. Geoderma, 409. https://doi.org/10.1016/j.geoderma.2021.115567spa
dc.relation.referencesChenu, C., Angers, D. A., Barré, P., Derrien, D., Arrouays, D., & Balesdent, J. (2019). Increasing organic stocks in agricultural soils: Knowledge gaps and potential innovations. Soil and Tillage Research, 188, 41–52. https://doi.org/10.1016/j.still.2018.04.011spa
dc.relation.referencesConrad, O., Bechtel, B., Bock, M., Dietrich, H., Fischer, E., Gerlitz, L., Wehberg, J., Wichmann, V., & Böhner, J. (2015). System for Automated Geoscientific Analyses (SAGA) v. 2.1.4. Geoscientific Model Development, 8(7), 1991–2007. https://doi.org/10.5194/gmd-8-1991-2015spa
dc.relation.referencesCroft, H., Kuhn, N. J., & Anderson, K. (2012). On the use of remote sensing techniques for monitoring spatio-temporal soil organic carbon dynamics in agricultural systems. In Catena (Vol. 94, pp. 64–74). https://doi.org/10.1016/j.catena.2012.01.001spa
dc.relation.referencesConrad, O. (2006). SAGA—program structure and current state of implementation. SAGA–Analysis and Modelling Applications, edited by: Böhner, J., McCloy, KR, and Strobl, J., Göttinger Geographische Abhandlungen, Göttingen, 39-52.spa
dc.relation.referencesConforti, M., Pascale, S., Robustelli, G., Sdao, F., 2014. Evaluation of prediction capability of the artificial neural networks for mapping landslide susceptibility in theTurbolo River catchment (northern Calabria, Italy). Catena 113, 236–250.spa
dc.relation.referencesCuervo, E., Cely, G., & Moreno, D. (2016). Determinación de las fracciones de carbono orgánico en el suelo del páramo La Cortadera, Boyacá. 139–149.spa
dc.relation.referencesDavy, M. C., & Koen, T. B. (2013). Variations in soil organic carbon for two soil types and six land uses in the Murray Catchment, New South Wales, Australia. Soil Research, 51(7–8), 631–644. https://doi.org/10.1071/SR12353spa
dc.relation.referencesDe Paul Obade, V., & Lal, R. (2013). Assessing land cover and soil quality by remote sensing and geographical information systems (GIS). In Catena (Vol. 104, pp. 77–92). https://doi.org/10.1016/j.catena.2012.10.014spa
dc.relation.referencesDong, W., Wu, T., Luo, J., Sun, Y., & Xia, L. (2019). Land parcel-based digital soil mapping of soil nutrient properties in an alluvial-diluvia plain agricultural area in China. Geoderma, 340, 234-248.spa
dc.relation.referencesElachi, C., & Van Zyl, J. J. (2006). Introduction to the physics and techniques of remote sensing (Vol. 28). John Wiley & Sons.spa
dc.relation.referencesFAO 2017.Carbono orgánico del suelo: el potencial oculto. Organización para las Naciones Unidas para la alimentación. Roma Italia. 90Pspa
dc.relation.referencesFAO - Organización de las Naciones Unidas para la Alimentación. (2002). Captura de carbono en los suelos para un mejor manejo de la tierra. Basado en el trabajo de Michel Robert. Informes sobre recursos mundiales de suelos. FAO. 61p.spa
dc.relation.referencesFilippi, N., Gallego, F., Montanarella, L., & Panagos, P. (2005). Soil Sampling Protocol to Certify the Changes of Organic Carbon Stock in Mineral Soils of European Union. https://www.researchgate.net/publication/237440368spa
dc.relation.referencesForkuor, G., Hounkpatin, O., Welp, G., & Thiel, M. (2017). High resolution mapping of soil properties using remote sensing variables in south-western Burkina Faso: a comparison of machine learning and multiple linear regression models. PloS one, 12(1), e0170478.spa
dc.relation.referencesGardi, C., Angelini, M., Barceló, S., Comerma, J., Cruz Gaistardo, C., Encina Rojas, A., Jones A., Krasilnikov, P., Mendonça Santos Brefin, M. L., Montanarella, L., Muñiz Ugarte, O., Schad, P., Vara Rodríguez, M. I. y Vargas, R. (Eds.), (2014). Atlas de suelos de América Latina y el Caribe. Luxembourg: Comisión Europea - Oficina de Publicaciones de la Unión Europea, L-2995.spa
dc.relation.referencesGallant, J., & Dowling, I. (2003). A multiresolution index of valley bottom flatness for mapping depositional areas. Water Resources Research, 39(12). https://doi.org/10.1029/2002WR001426spa
dc.relation.referencesGholizadeh, A., Žižala, D., Saberioon, M., & Borůvka, L. (2018). Soil organic carbon and texture retrieving and mapping using proximal, airborne and Sentinel-2 spectral imaging. Remote Sensing of Environment, 218, 89–103. https://doi.org/10.1016/j.rse.2018.09.015spa
dc.relation.referencesGomez, C., Viscarra Rossel, A., & McBratney, A. (2008). Soil organic carbon prediction by hyperspectral remote sensing and field vis-NIR spectroscopy: An Australian case study. Geoderma, 146(3–4), 403–411. https://doi.org/10.1016/j.geoderma.2008.06.011spa
dc.relation.referencesGomes, L., Faria, R., de Souza, E., Veloso, V., Schaefer, C., & Fernandes Filho, I. (2019). Modelling and mapping soil organic carbon stocks in Brazil. Geoderma, 340, 337-350. Gray, J. M., Bishop, T. F. A., & Yang, X. (2015). Pragmatic models for the prediction and digital mapping of soil properties in eastern Australia. Soil Research, 53(1), 24–42. https://doi.org/10.1071/SR13306spa
dc.relation.referencesGriffiths, R. P., Madritch, M. D., & Swanson, A. K. (2009). The effects of topography on forest soil characteristics in the Oregon Cascade Mountains (USA): Implications for the effects of climate change on soil properties. Forest Ecology and Management, 257(1), 1–7. https://doi.org/10.1016/j.foreco.2008.08.010spa
dc.relation.referencesGuo, P., Li, F., Luo, W., Tang, Q., Liu, W., & Lin, M. (2015). Digital mapping of soil organic matter for rubber plantation at regional scale: An application of random forest plus residuals kriging approach. Geoderma, 237, 49-59.spa
dc.relation.referencesGutiérrez, J., Ordoñez, N., Bolívar, A., Bunning, S., Guevara, M., Medina, E., Olivera, C., Olmedo, G., Rodríguez, L., Sevilla, V., & Vargas, R. (2020). Estimation of organic carbon in paramo ecosystem soils in Colombia. Ecosistemas, 29(1). https://doi.org/10.7818/ECOS.1855spa
dc.relation.referencesHinge, G., Surampalli, R., & Goyal, M. (2018). Prediction of soil organic carbon stock using digital mapping approach in humid India. Environmental Earth Sciences, 77(5). https://doi.org/10.1007/s12665-018-7374-xspa
dc.relation.referencesHengl, T., Heuvelink, G. B. M., Kempen, B., Leenaars, J. G. B., Walsh, M. G., Shepherd, K. D., Sila, A., MacMillan, R. A., De Jesus, J. M., Tamene, L., & Tondoh, J. E. (2015). Mapping soil properties of Africa at 250 m resolution: Random forests significantly improve current predictions. PLoS ONE, 10(6). https://doi.org/10.1371/journal.pone.0125814spa
dc.relation.referencesHobley, E., Wilson, B., Wilkie, A., Gray, J., & Koen, T. (2015). Drivers of soil organic carbon storage and vertical distribution in Eastern Australia. Plant and Soil, 390(1–2), 111–127. https://doi.org/10.1007/s11104-015-2380-1spa
dc.relation.referencesHosseini, M., & McNairn, H. (2017). Using multi-polarization C-and L-band synthetic aperture radar to estimate biomass and soil moisture of wheat fields. International Journal of Applied Earth Observation and Geoinformation, 58, 50-64.spa
dc.relation.referencesHuamán-Carrión, M. L., Espinoza-Montes, F., Barrial-Lujan, A. I., & Ponce-Atencio, Y. (2021). Influence of altitude and soil characteristics on organic carbon storage capacity of high Andean natural pastures. Scientia Agropecuaria, 12(1), 83–90. https://doi.org/10.17268/SCI.AGROPECU.2021.010spa
dc.relation.referencesHunt, J. R., Celestina, C., & Kirkegaard, J. A. (2020). The realities of climate change, conservation agriculture and soil carbon sequestration. In Global Change Biology (Vol. 26, Issue 6, pp. 3188–3189). Blackwell Publishing Ltd. https://doi.org/10.1111/gcb.15082spa
dc.relation.referencesKeskin, H., Grunwald, S., & Harris, W. G. (2019). Digital mapping of soil carbon fractions with machine learning. Geoderma, 339, 40–58. https://doi.org/10.1016/j.geoderma.2018.12.037spa
dc.relation.referencesKuang, B., Tekin, Y., & Mouazen, A. M. (2015). Comparison between artificial neural network and partial least squares for on-line visible and near infrared spectroscopy measurement of soil organic carbon, pH and clay content. Soil and Tillage Research, 146(PB), 243–252. https://doi.org/10.1016/j.still.2014.11.002spa
dc.relation.referencesKunkel, M. L., Flores, A. N., Smith, T. J., McNamara, J. P., & Benner, S. G. (2011). A simplified approach for estimating soil carbon and nitrogen stocks in semi-arid complex terrain. Geoderma, 165(1), 1–11. https://doi.org/10.1016/j.geoderma.2011.06.011spa
dc.relation.referencesLal, R., & Kimble, J. M. (1997). Conservation tillage for carbon sequestration. In Nutrient Cycling in Agroecosystems (Vol. 49). Kluwer Academic Publishers.spa
dc.relation.referencesLamichhane, S., Kumar, L., & Wilson, B. (2019). Digital soil mapping algorithms and covariates for soil organic carbon mapping and their implications: A review. In Geoderma (Vol. 352, pp. 395–413). Elsevier B.V. https://doi.org/10.1016/j.geoderma.2019.05.031spa
dc.relation.referencesLee, S., Evangelista, D.G., 2006. Earthquake-induced landslide susceptibility mappingusing an artificial neural network. Nat. Hazards Earth Syst. Sci. 6, 687–695.spa
dc.relation.referencesLizeth, A., & Blanco, T. (2018). Estimación de biomasa aérea de eucalipto (Eucalyptus grandis) y pino (Pinus spp) en plantaciones forestales comerciales, usando imágenes satelitales Sentinel.spa
dc.relation.referencesLópez González, E. (1998). TRATAMIENTO DE LA COLINEALIDAD EN REGRESIÓN MÚLTIPLE. In Psicothema (Vol. 10, Issue 2).spa
dc.relation.referencesMahmoudabadi, E., Karimi, A., Haghnia, G. H., & Sepehr, A. (2017). Digital soil mapping using remote sensing indices, terrain attributes, and vegetation features in the rangelands of northeastern Iran. Environmental Monitoring and Assessment, 189(10). https://doi.org/10.1007/s10661-017-6197-7spa
dc.relation.referencesMartínez, E., Fuentes, J. P., & Acevedo, E. (2008). Soil organic carbon and soil properties.spa
dc.relation.referencesMinasny, B., & McBratney, A. B. (2016a). Digital soil mapping: A brief history and some lessons. Geoderma, 264, 301–311. https://doi.org/10.1016/j.geoderma.2015.07.017spa
dc.relation.referencesMinasny, B., & McBratney, A. B. (2016b). Digital soil mapping: A brief history and some lessons. Geoderma, 264, 301–311. https://doi.org/10.1016/j.geoderma.2015.07.017spa
dc.relation.referencesMirzaee, S., Ghorbani-Dashtaki, S., Mohammadi, J., Asadi, H., & Asadzadeh, F. (2016). Spatial variability of soil organic matter using remote sensing data. Catena, 145, 118–127. https://doi.org/10.1016/j.catena.2016.05.023spa
dc.relation.referencesMouazen, A. M., Kuang, B., De Baerdemaeker, J., & Ramon, H. (2010). Comparison among principal component, partial least squares and back propagation neural network analyses for accuracy of measurement of selected soil properties with visible and near infrared spectroscopy. Geoderma, 158(1–2), 23–31. https://doi.org/10.1016/j.geoderma.2010.03.001spa
dc.relation.referencesObu, J., Lantuit, H., Myers-Smith, I., Heim, B., Wolter, J., & Fritz, M. (2017). Effect of Terrain Characteristics on Soil Organic Carbon and Total Nitrogen Stocks in Soils of Herschel Island, Western Canadian Arctic. Permafrost and Periglacial Processes, 28(1), 92–107. https://doi.org/10.1002/ppp.1881spa
dc.relation.referencesPanagos, P., Borrelli, P., Meusburger, K., Alewell, C., Lugato, E., & Montanarella, L. (2015). Estimating the soil erosion cover-management factor at the European scale. Land use policy, 48, 38-50.spa
dc.relation.referencesPla Sentis, I. (1994). Labranza, propiedades físicas y producción de maíz en los llanos occidentales. SVCS N, 46, 32-42.spa
dc.relation.referencesQin, C., Zhu, A. X., Pei, T., Li, B., Zhou, C., & Yang, L. (2007). An adaptive approach to selecting a flow‐partition exponent for a multiple‐flow‐direction algorithm. International Journal of Geographical Information Science, 21(4), 443-458.spa
dc.relation.referencesRamifehiarivo, N., Brossard, M., Grinand, C., Andriamananjara, A., Razafimbelo, T., Rasolohery, A., Razafimahatratra, H., Seyler, F., Ranaivoson, N., Rabenarivo, M., Albrecht, A., Razafindrabe, F., & Razakamanarivo, H. (2017). Mapping soil organic carbon on a national scale: Towards an improved and updated map of Madagascar. Geoderma Regional, 9, 29–38. https://doi.org/10.1016/j.geodrs.2016.12.002spa
dc.relation.referencesReyes, L., Adhikari, K., & Ventura, S. (2018). Projecting Soil Organic Carbon Distribution in Central Chile under Future Climate Scenarios. Journal of Environmental Quality, 47(4), 735–745. https://doi.org/10.2134/jeq2017.08.0329spa
dc.relation.referencesRicardo, J., & Florez, M. (2019). Evaluación de imágenes de radar Sentinel-1A e imágenes multiespectrales Sentinel-2A en la clasificación de cobertura del suelo en diferentes niveles de detalle.spa
dc.relation.referencesRivas, M., Otero, J., Van der Hammen, T., Torres, A., Cadena, C., Pedraza, C., Rodríguez, N., Franco, C., Betancourth, J., Olaya, É., Posada, E., & Cárdenas, L. (2007). Atlas de páramos de Colombia.spa
dc.relation.referencesRoa Lobo, J., & Kamp, U. (2012). Use of the topographic wetness index (TWI) for the diagnosis of the river overflow threat, Trujillo State-Venezuela. Revista Geográfica Venezolana, 53(1), 109-126.spa
dc.relation.referencesRodriguez-Galiano, V. F., Ghimire, B., Rogan, J., Chica-Olmo, M., & Rigol-Sanchez, J. P. (2012). An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS Journal of Photogrammetry and Remote Sensing, 67(1), 93–104. https://doi.org/10.1016/j.isprsjprs.2011.11.002spa
dc.relation.referencesRomán-Sánchez, A., Vanwalleghem, T., Peña, A., Laguna, A., & Giráldez, J. V. (2018). Controls on soil carbon storage from topography and vegetation in a rocky, semi-arid landscapes. Geoderma, 311, 159–166. https://doi.org/10.1016/j.geoderma.2016.10.013spa
dc.relation.referencesRügnitz, M., M. Chacón & R. Porro. (2009). Guía para la determinación de carbono en pequeñas propiedades rurales. Lima, Perú.: Centro Mundial Agroflorestal (ICRAF) / Consorcio Iniciativa Amazónica (IA).spa
dc.relation.referencesSandoval, M., Stolpe, N., Venegas, E., Mardones, M., & Montano, J. (2003). El secuestro de carbono en la agricultura y su importancia en el calentamiento global. In Theoria (Vol. 12).spa
dc.relation.referencesSankey, T. T., & Weber, K. T. (2009). Rangeland assessments using remote sensing: is NDVI useful. Final Report: Comparing Effects of Management Practices on Rangeland Health with Geospatial Technologies (NNX06AE47G), 168. Sankey, T. T., & Weber, K. T. (2009). Rangeland assessments using remote sensing: is NDVI useful. Final Report: Comparing Effects of Management Practices on Rangeland Health with Geospatial Technologies (NNX06AE47G), 168.spa
dc.relation.referencesSayão, V. M., & Demattê, J. A. M. (2018). Soil texture and organic carbon mapping using surface temperature and reflectance spectra in Southeast Brazil. Geoderma Regional, 14. https://doi.org/10.1016/j.geodrs.2018.e00174spa
dc.relation.referencesSchillaci, C., Acutis, M., Lombardo, L., Lipani, A., Fantappiè, M., Märker, M., & Saia, S. (2017). Spatio-temporal topsoil organic carbon mapping of a semi-arid Mediterranean region: The role of land use, soil texture, topographic indices and the influence of remote sensing data to modelling. Science of the Total Environment, 601–602, 821–832. https://doi.org/10.1016/j.scitotenv.2017.05.239spa
dc.relation.referencesSiewert, M. (2018). High-resolution digital mapping of soil organic carbon in permafrost terrain using machine learning: A case study in a sub-Arctic peatland environment. Biogeosciences, 15(6), 1663–1682. https://doi.org/10.5194/bg-15-1663-2018spa
dc.relation.referencesSreenivas, K., Dadhwal, V. K., Kumar, S., Harsha, G. S., Mitran, T., Sujatha, G., Suresh, G. J. R., Fyzee, M. A., & Ravisankar, T. (2016). Digital mapping of soil organic and inorganic carbon status in India. Geoderma, 269, 160–173. https://doi.org/10.1016/j.geoderma.2016.02.002spa
dc.relation.referencesStolbovoy,.V, Montanarella, L., Filippi, N., Selvaradjou, S., Panagos, P., & Gallego, J. (2005). Soil sampling protocol to certify the changes of organic carbon stock in mineral soils of European Union. vol EUR, 21576.Sumfleth, K., & Duttmann, R. (2008). Prediction of soil property distribution in paddy soil landscapes using terrain data and satellite information as indicators. Ecological Indicators, 8(5), 485–501. https://doi.org/10.1016/j.ecolind.2007.05.005spa
dc.relation.referencesThompson, J. A., Pena-Yewtukhiw, E. M., & Grove, J. H. (2006). Soil-landscape modeling across a physiographic region: Topographic patterns and model transportability. Geoderma, 133(1–2), 57–70. https://doi.org/10.1016/j.geoderma.2006.03.037spa
dc.relation.referencesTaghizadeh, R., Nabiollahi, K., & Kerry, R. (2016). Digital mapping of soil organic carbon at multiple depths using different data mining techniques in Baneh region, Iran. Geoderma, 266, 98–110. https://doi.org/10.1016/j.geoderma.2015.12.003spa
dc.relation.referencesTorres, J., Valdez, J., Pérez, G., De los Santos, H., & Aguirre, C. (2017). Inventory and mapping of a pine forest under timber management using data obtained with a SPOT 6 sensor. In Revista Mexicana de Ciencias Forestales (Vol. 8, Issue 39).spa
dc.relation.referencesWang, S., Zhuang, Q., Wang, Q., Jin, X., & Han, C. (2017). Mapping stocks of soil organic carbon and soil total nitrogen in Liaoning Province of China. Geoderma, 305, 250-263.spa
dc.relation.referencesWang, B., Waters, C., Orgill, S., Gray, J., Cowie, A., Clark, A., & Liu, D. L. (2018). High resolution mapping of soil organic carbon stocks using remote sensing variables in the semi-arid rangelands of eastern Australia. Science of the Total Environment, 630, 367–378. https://doi.org/10.1016/j.scitotenv.2018.02.204spa
dc.relation.referencesWang, X., Cammeraat, E. L. H., Cerli, C., & Kalbitz, K. (2014). Soil aggregation and the stabilization of organic carbon as affected by erosion and deposition. Soil Biology and Biochemistry, 72, 55–65. https://doi.org/10.1016/j.soilbio.2014.01.018spa
dc.relation.referencesWard, A., Dargusch, P., Grussu, G., & Romeo, R. (2015). Using carbon finance to support climate policy objectives in high mountain ecosystems. Climate Policy, 16(6), 732–751. https://doi.org/10.1080/14693062.2015.1046413spa
dc.relation.referencesWaters, C. M., Melville, G. J., Orgill, S. E., & Alemseged, Y. (2015). The relationship between soil organic carbon and soil surface characteristics in the semi-arid rangelands of southern Australia. Rangeland Journal, 37(3), 297–307. https://doi.org/10.1071/RJ14119spa
dc.relation.referencesWere, K., Bui, D. T., Dick, Ø. B., & Singh, B. R. (2015). A comparative assessment of support vector regression, artificial neural networks, and random forests for predicting and mapping soil organic carbon stocks across an Afromontane landscape. Ecological Indicators, 52, 394–403. https://doi.org/10.1016/j.ecolind.2014.12.028spa
dc.relation.referencesWilson, J. P., & Gallant, J. C. (Eds.). (2000). Terrain analysis: principles and applications. John Wiley & Sons.spa
dc.relation.referencesWilson, J. P. (2012). Digital terrain modeling. Geomorphology, 137(1), 107–121. https://doi.org/10.1016/j.geomorph.2011.03.012spa
dc.relation.referencesWischmeier, W. H., & Smith, D. D. (1978). Predicting rainfall erosion losses: a guide to conservation planning (No. 537). Department of Agriculture, Science and Education Administration.spa
dc.relation.referencesXie, H., Zhao, J., Wang, Q., Sui, Y., Wang, J., Yang, X., ... & Liang, C. (2015). Soil type recognition as improved by genetic algorithm-based variable selection using near infrared spectroscopy and partial least squares discriminant analysis. Scientific reports, 5(1), 10930.spa
dc.relation.referencesXiao, J., Chevallier, F., Gomez, C., Guanter, L., Hicke, J. A., Huete, A. R., Ichii, K., Ni, W., Pang, Y., Rahman, A. F., Sun, G., Yuan, W., Zhang, L., & Zhang, X. (2019). Remote sensing of the terrestrial carbon cycle: A review of advances over 50 years. Remote Sensing of Environment, 233. https://doi.org/10.1016/j.rse.2019.111383spa
dc.relation.referencesYang, Y., Fang, J., Tang, Y., Ji, C., Zheng, C., He, J., & Zhu, B. (2008). Storage, patterns and controls of soil organic carbon in the Tibetan grasslands. Global Change Biology, 14(7), 1592–1599. https://doi.org/10.1111/j.1365-2486.2008.01591.xspa
dc.relation.referencesYang, R. M., Zhang, G. L., Yang, F., Zhi, J. J., Yang, F., Liu, F., Zhao, Y. G., & Li, D. C. (2016). Precise estimation of soil organic carbon stocks in the northeast Tibetan Plateau. Scientific Reports, 6. https://doi.org/10.1038/srep21842spa
dc.relation.referencesYang, S., Sheng, D., Adamowski, J., Gong, Y., Zhang, J., & Cao, J. (2018). Effect of land use change on soil carbon storage over the last 40 years in the Shi Yang River Basin, China. Land, 7(1). https://doi.org/10.3390/land7010011spa
dc.relation.referencesZeraatpisheh, M., Ayoubi, S., Jafari, A., Tajik, S., & Finke, P. (2019). Digital mapping of soil properties using multiple machine learning in a semi-arid region, central Iran. Geoderma, 338, 445-452.spa
dc.relation.referencesZhao, M. S., Rossiter, D. G., Li, D. C., Zhao, Y. G., Liu, F., & Zhang, G. L. (2014). Mapping soil organic matter in low-relief areas based on land surface diurnal temperature difference and a vegetation index. Ecological Indicators, 39, 120–133. https://doi.org/10.1016/j.ecolind.2013.12.015spa
dc.relation.referencesZhou, Q., & Liu, X. (2004). Analysis of errors of derived slope and aspect related to DEM data properties. Computers & Geosciences, 30(4), 369-378.spa
dc.relation.referencesZhou, T., Geng, Y., Chen, J., Pan, J., Haase, D., & Lausch, A. (2020). High-resolution digital mapping of soil organic carbon and soil total nitrogen using DEM derivatives, Sentinel-1 and Sentinel-2 data based on machine learning algorithms. Science of the Total Environment, 729. https://doi.org/10.1016/j.scitotenv.2020.138244spa
dc.relation.referencesZimmermann, M., Meir, P., Silman, M. R., Fedders, A., Gibbon, A., Malhi, Y., Urrego, D. H., Bush, M. B., Feeley, K. J., Garcia, K. C., Dargie, G. C., Farfan, W. R., Goetz, B. P., Johnson, W. T., Kline, K. M., Modi, A. T., Rurau, N. M. Q., Staudt, B. T., & Zamora, F. (2010). No differences in soil carbon stocks across the tree line in the Peruvian Andes. Ecosystems, 13(1), 62–74. https://doi.org/10.1007/s10021-009-9300-2spa
dc.relation.referencesZomer, R. J., Bossio, D. A., Sommer, R., & Verchot, L. V. (2017). Global Sequestration Potential of Increased Organic Carbon in Cropspa
dc.relation.referencesZuur, A. F., Ieno, E. N., & Smith, G. M. (2007). Analysing ecological data (Vol. 680). New York: Springerspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.licenseReconocimiento 4.0 Internacionalspa
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/spa
dc.subject.agrovocCarbono orgánico del suelospa
dc.subject.agrovocsoil organic carboneng
dc.subject.agrovocInstrumentos de mediciónspa
dc.subject.agrovocmeasuring instrumentseng
dc.subject.agrovocPáramosspa
dc.subject.agrovocmoorseng
dc.subject.ddc550 - Ciencias de la tierra::551 - Geología, hidrología, meteorologíaspa
dc.subject.proposalPáramospa
dc.subject.proposalSoil organic carboneng
dc.subject.proposalRemote sensorseng
dc.subject.proposalAutomated learningeng
dc.subject.proposalParamoeng
dc.subject.proposalCarbono orgánico del suelospa
dc.subject.proposalSensores remotosspa
dc.subject.proposalAprendizaje automatizadospa
dc.titleMétodo para estimar la distribución espacial del contenido de carbono orgánico en el suelo de páramo con base en datos de sensores remotosspa
dc.title.translatedMethod for estimating the spatial distribution of organic carbon content in paramo soil based on remote sensing dataeng
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.professionaldevelopmentGrupos comunitariosspa
dcterms.audience.professionaldevelopmentInvestigadoresspa
dcterms.audience.professionaldevelopmentPúblico generalspa
dcterms.audience.professionaldevelopmentResponsables políticosspa
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2spa

Archivos

Bloque original

Mostrando 1 - 1 de 1
Miniatura
Nombre:
1015444449.2023.pdf..pdf
Tamaño:
1.03 MB
Formato:
Adobe Portable Document Format
Descripción:
Tesis de Maestría en Geomatica
Descargar

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:
Descargar

Colecciones

Maestría en Geomática