Método para estimar la distribución espacial del contenido de carbono orgánico en el suelo de páramo con base en datos de sensores remotos
ilustraciones, diagramas, mapas
- Autores:
-
Serrano Agudelo, Pablo Cesar
- Tipo de recurso:
- Fecha de publicación:
- 2023
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/85803
- Palabra clave:
- 550 - Ciencias de la tierra::551 - Geología, hidrología, meteorología
Carbono orgánico del suelo
Instrumentos de medición
Páramos
soil organic carbon
measuring instruments
moors
Páramo
Carbono orgánico del suelo
Sensores remotos
Aprendizaje automatizado
Soil organic carbon
Remote sensors
Automated learning
Paramo
- Rights
- openAccess
- License
- Reconocimiento 4.0 Internacional
| id |
UNACIONAL2_5e881d4d523207b3f0c1a68ad3da33da |
|---|---|
| oai_identifier_str |
oai:repositorio.unal.edu.co:unal/85803 |
| network_acronym_str |
UNACIONAL2 |
| network_name_str |
Universidad Nacional de Colombia |
| repository_id_str |
|
| dc.title.spa.fl_str_mv |
Método para estimar la distribución espacial del contenido de carbono orgánico en el suelo de páramo con base en datos de sensores remotos |
| dc.title.translated.eng.fl_str_mv |
Method for estimating the spatial distribution of organic carbon content in paramo soil based on remote sensing data |
| title |
Método para estimar la distribución espacial del contenido de carbono orgánico en el suelo de páramo con base en datos de sensores remotos |
| spellingShingle |
Método para estimar la distribución espacial del contenido de carbono orgánico en el suelo de páramo con base en datos de sensores remotos 550 - Ciencias de la tierra::551 - Geología, hidrología, meteorología Carbono orgánico del suelo Instrumentos de medición Páramos soil organic carbon measuring instruments moors Páramo Carbono orgánico del suelo Sensores remotos Aprendizaje automatizado Soil organic carbon Remote sensors Automated learning Paramo |
| title_short |
Método para estimar la distribución espacial del contenido de carbono orgánico en el suelo de páramo con base en datos de sensores remotos |
| title_full |
Método para estimar la distribución espacial del contenido de carbono orgánico en el suelo de páramo con base en datos de sensores remotos |
| title_fullStr |
Método para estimar la distribución espacial del contenido de carbono orgánico en el suelo de páramo con base en datos de sensores remotos |
| title_full_unstemmed |
Método para estimar la distribución espacial del contenido de carbono orgánico en el suelo de páramo con base en datos de sensores remotos |
| title_sort |
Método para estimar la distribución espacial del contenido de carbono orgánico en el suelo de páramo con base en datos de sensores remotos |
| dc.creator.fl_str_mv |
Serrano Agudelo, Pablo Cesar |
| dc.contributor.advisor.spa.fl_str_mv |
Martínez Martínez, Luis Joel |
| dc.contributor.author.spa.fl_str_mv |
Serrano Agudelo, Pablo Cesar |
| dc.contributor.researchgroup.spa.fl_str_mv |
GIPSO |
| dc.subject.ddc.spa.fl_str_mv |
550 - Ciencias de la tierra::551 - Geología, hidrología, meteorología |
| topic |
550 - Ciencias de la tierra::551 - Geología, hidrología, meteorología Carbono orgánico del suelo Instrumentos de medición Páramos soil organic carbon measuring instruments moors Páramo Carbono orgánico del suelo Sensores remotos Aprendizaje automatizado Soil organic carbon Remote sensors Automated learning Paramo |
| dc.subject.agrovoc.spa.fl_str_mv |
Carbono orgánico del suelo Instrumentos de medición Páramos |
| dc.subject.agrovoc.eng.fl_str_mv |
soil organic carbon measuring instruments moors |
| dc.subject.proposal.spa.fl_str_mv |
Páramo Carbono orgánico del suelo Sensores remotos Aprendizaje automatizado |
| dc.subject.proposal.eng.fl_str_mv |
Soil organic carbon Remote sensors Automated learning Paramo |
| description |
ilustraciones, diagramas, mapas |
| publishDate |
2023 |
| dc.date.issued.none.fl_str_mv |
2023 |
| dc.date.accessioned.none.fl_str_mv |
2024-03-12T20:54:59Z |
| dc.date.available.none.fl_str_mv |
2024-03-12T20:54:59Z |
| dc.type.spa.fl_str_mv |
Trabajo de grado - Maestría |
| dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/masterThesis |
| dc.type.version.spa.fl_str_mv |
info:eu-repo/semantics/acceptedVersion |
| dc.type.content.spa.fl_str_mv |
Text |
| dc.type.redcol.spa.fl_str_mv |
http://purl.org/redcol/resource_type/TM |
| status_str |
acceptedVersion |
| dc.identifier.uri.none.fl_str_mv |
https://repositorio.unal.edu.co/handle/unal/85803 |
| dc.identifier.instname.spa.fl_str_mv |
Universidad Nacional de Colombia |
| dc.identifier.reponame.spa.fl_str_mv |
Repositorio Institucional Universidad Nacional de Colombia |
| dc.identifier.repourl.spa.fl_str_mv |
https://repositorio.unal.edu.co/ |
| url |
https://repositorio.unal.edu.co/handle/unal/85803 https://repositorio.unal.edu.co/ |
| identifier_str_mv |
Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia |
| dc.language.iso.spa.fl_str_mv |
spa |
| language |
spa |
| dc.relation.indexed.spa.fl_str_mv |
Agrosavia Agrovoc |
| dc.relation.references.spa.fl_str_mv |
Abuín, J. R. (2007). Regresión lineal múltiple. IdEyGdM-Ld Estadística, Editor, 32. Adhikari, 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.0105519 Africano, K., Cely, G., & Serrano, P. (2016). Potential CO2 Capture Associated with Edaphic Component in Moorlands Guantiva - La Rusia, Department of Boyacá, Colombia. Akpa, 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.2016.02.021 Alberto 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-6-53-2020 Armas, 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.2017.26-3.10 Ayala, 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). Ayala, 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). Bannari, 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/10.1080/02757259509532298 Bernardeau, 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. Behrens, 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.2009.07.010 Blanco, A., Sarría, F., & Castillo, F. (2014). Elaboración de un mapa de carbono orgánico del suelo en la Región de Murcia. Breiman, L. (2001). Random forests. Machine learning, 45 (1), 5-32. Brus, D., Kempen, B., & Heuvelink, G. (2011). Sampling for validation of digital soil maps. European Journal of Soil Science, 62(3), 394-407. Bou 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.2010.01.001 Buol, S. W., Southard, R. J., Graham, R. C., & McDaniel, P. A. (2011). Soil genesis and classification. John Wiley & Sons. Burbano, 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.183501.85 Campbell, J.B., 2002. Introduction to Remote Sensing. Taylor & Francis, London. Chang, 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/10.1559/152304091783805626 Chen, 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.2021.115567 Chenu, 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.2018.04.011 Conrad, 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-8-1991-2015 Croft, 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.2012.01.001 Conrad, 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. Conforti, 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. Cuervo, E., Cely, G., & Moreno, D. (2016). Determinación de las fracciones de carbono orgánico en el suelo del páramo La Cortadera, Boyacá. 139–149. Davy, 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/SR12353 De 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.2012.10.014 Dong, 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. Elachi, C., & Van Zyl, J. J. (2006). Introduction to the physics and techniques of remote sensing (Vol. 28). John Wiley & Sons. FAO 2017.Carbono orgánico del suelo: el potencial oculto. Organización para las Naciones Unidas para la alimentación. Roma Italia. 90P FAO - 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. Filippi, 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/237440368 Forkuor, 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. Gardi, 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. Gallant, J., & Dowling, I. (2003). A multiresolution index of valley bottom flatness for mapping depositional areas. Water Resources Research, 39(12). https://doi.org/10.1029/2002WR001426 Gholizadeh, 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.2018.09.015 Gomez, 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.2008.06.011 Gomes, 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/SR13306 Griffiths, 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.2008.08.010 Guo, 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. Gutiérrez, 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.1855 Hinge, 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-x Hengl, 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.0125814 Hobley, 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/s11104-015-2380-1 Hosseini, 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. Huamán-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.2021.010 Hunt, 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.15082 Keskin, 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.2018.12.037 Kuang, 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.2014.11.002 Kunkel, 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.2011.06.011 Lal, R., & Kimble, J. M. (1997). Conservation tillage for carbon sequestration. In Nutrient Cycling in Agroecosystems (Vol. 49). Kluwer Academic Publishers. Lamichhane, 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.2019.05.031 Lee, S., Evangelista, D.G., 2006. Earthquake-induced landslide susceptibility mappingusing an artificial neural network. Nat. Hazards Earth Syst. Sci. 6, 687–695. Lizeth, A., & Blanco, T. (2018). Estimación de biomasa aérea de eucalipto (Eucalyptus grandis) y pino (Pinus spp) en plantaciones forestales comerciales, usando imágenes satelitales Sentinel. López González, E. (1998). TRATAMIENTO DE LA COLINEALIDAD EN REGRESIÓN MÚLTIPLE. In Psicothema (Vol. 10, Issue 2). Mahmoudabadi, 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/s10661-017-6197-7 Martínez, E., Fuentes, J. P., & Acevedo, E. (2008). Soil organic carbon and soil properties. Minasny, 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.2015.07.017 Minasny, 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.2015.07.017 Mirzaee, 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.2016.05.023 Mouazen, 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.2010.03.001 Obu, 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.1881 Panagos, 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. Pla Sentis, I. (1994). Labranza, propiedades físicas y producción de maíz en los llanos occidentales. SVCS N, 46, 32-42. Qin, 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. Ramifehiarivo, 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.2016.12.002 Reyes, 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/jeq2017.08.0329 Ricardo, 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. Rivas, 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. Roa 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. Rodriguez-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.2011.11.002 Román-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.2016.10.013 Rügnitz, 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). Sandoval, 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). 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. 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. Sayão, 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.e00174 Schillaci, 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.2017.05.239 Siewert, 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-15-1663-2018 Sreenivas, 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.2016.02.002 Stolbovoy,.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.2007.05.005 Thompson, 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.2006.03.037 Taghizadeh, 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.2015.12.003 Torres, 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). Wang, 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. Wang, 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.2018.02.204 Wang, 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.2014.01.018 Ward, 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/10.1080/14693062.2015.1046413 Waters, 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/RJ14119 Were, 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.2014.12.028 Wilson, J. P., & Gallant, J. C. (Eds.). (2000). Terrain analysis: principles and applications. John Wiley & Sons. Wilson, J. P. (2012). Digital terrain modeling. Geomorphology, 137(1), 107–121. https://doi.org/10.1016/j.geomorph.2011.03.012 Wischmeier, W. H., & Smith, D. D. (1978). Predicting rainfall erosion losses: a guide to conservation planning (No. 537). Department of Agriculture, Science and Education Administration. Xie, 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. Xiao, 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.2019.111383 Yang, 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.x Yang, 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/srep21842 Yang, 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/land7010011 Zeraatpisheh, 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. Zhao, 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.2013.12.015 Zhou, Q., & Liu, X. (2004). Analysis of errors of derived slope and aspect related to DEM data properties. Computers & Geosciences, 30(4), 369-378. Zhou, 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.2020.138244 Zimmermann, 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/s10021-009-9300-2 Zomer, R. J., Bossio, D. A., Sommer, R., & Verchot, L. V. (2017). Global Sequestration Potential of Increased Organic Carbon in Crop Zuur, A. F., Ieno, E. N., & Smith, G. M. (2007). Analysing ecological data (Vol. 680). New York: Springer |
| dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
| dc.rights.license.spa.fl_str_mv |
Reconocimiento 4.0 Internacional |
| dc.rights.uri.spa.fl_str_mv |
http://creativecommons.org/licenses/by/4.0/ |
| dc.rights.accessrights.spa.fl_str_mv |
info:eu-repo/semantics/openAccess |
| rights_invalid_str_mv |
Reconocimiento 4.0 Internacional http://creativecommons.org/licenses/by/4.0/ http://purl.org/coar/access_right/c_abf2 |
| eu_rights_str_mv |
openAccess |
| dc.format.extent.spa.fl_str_mv |
ix, 74 páginas |
| dc.format.mimetype.spa.fl_str_mv |
application/pdf |
| dc.publisher.spa.fl_str_mv |
Universidad Nacional de Colombia |
| dc.publisher.program.spa.fl_str_mv |
Bogotá - Ciencias Agrarias - Maestría en Geomática |
| dc.publisher.faculty.spa.fl_str_mv |
Facultad de Ciencias Agrarias |
| dc.publisher.place.spa.fl_str_mv |
Bogotá, Colombia |
| dc.publisher.branch.spa.fl_str_mv |
Universidad Nacional de Colombia - Sede Bogotá |
| institution |
Universidad Nacional de Colombia |
| bitstream.url.fl_str_mv |
https://repositorio.unal.edu.co/bitstream/unal/85803/1/license.txt https://repositorio.unal.edu.co/bitstream/unal/85803/2/1015444449.2023.pdf..pdf https://repositorio.unal.edu.co/bitstream/unal/85803/3/1015444449.2023.pdf..pdf.jpg |
| bitstream.checksum.fl_str_mv |
eb34b1cf90b7e1103fc9dfd26be24b4a de9df834daf800ee66fecd3a74bb4b05 9a10d2b94be4f609544e206a5cf1af92 |
| bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 MD5 |
| repository.name.fl_str_mv |
Repositorio Institucional Universidad Nacional de Colombia |
| repository.mail.fl_str_mv |
repositorio_nal@unal.edu.co |
| _version_ |
1814089180539518976 |
| spelling |
Reconocimiento 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Martínez Martínez, Luis Joel94d011bd9a7f169197ab0a1837a443b9Serrano Agudelo, Pablo Cesar5ca36c36fcdd61a8060060c235475277600GIPSO2024-03-12T20:54:59Z2024-03-12T20:54:59Z2023https://repositorio.unal.edu.co/handle/unal/85803Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, diagramas, mapasEn 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).In 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.MaestríaMagíster en GeomáticaGeoinformación para el uso sostenible de recursos naturalesix, 74 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ciencias Agrarias - Maestría en GeomáticaFacultad de Ciencias AgrariasBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá550 - Ciencias de la tierra::551 - Geología, hidrología, meteorologíaCarbono orgánico del sueloInstrumentos de mediciónPáramossoil organic carbonmeasuring instrumentsmoorsPáramoCarbono orgánico del sueloSensores remotosAprendizaje automatizadoSoil organic carbonRemote sensorsAutomated learningParamoMétodo para estimar la distribución espacial del contenido de carbono orgánico en el suelo de páramo con base en datos de sensores remotosMethod for estimating the spatial distribution of organic carbon content in paramo soil based on remote sensing dataTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMAgrosaviaAgrovocAbuín, J. R. (2007). Regresión lineal múltiple. IdEyGdM-Ld Estadística, Editor, 32.Adhikari, 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.0105519Africano, K., Cely, G., & Serrano, P. (2016). Potential CO2 Capture Associated with Edaphic Component in Moorlands Guantiva - La Rusia, Department of Boyacá, Colombia.Akpa, 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.2016.02.021Alberto 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-6-53-2020Armas, 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.2017.26-3.10Ayala, 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).Ayala, 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).Bannari, 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/10.1080/02757259509532298Bernardeau, 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.Behrens, 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.2009.07.010Blanco, A., Sarría, F., & Castillo, F. (2014). Elaboración de un mapa de carbono orgánico del suelo en la Región de Murcia.Breiman, L. (2001). Random forests. Machine learning, 45 (1), 5-32.Brus, D., Kempen, B., & Heuvelink, G. (2011). Sampling for validation of digital soil maps. European Journal of Soil Science, 62(3), 394-407.Bou 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.2010.01.001Buol, S. W., Southard, R. J., Graham, R. C., & McDaniel, P. A. (2011). Soil genesis and classification. John Wiley & Sons.Burbano, 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.183501.85Campbell, J.B., 2002. Introduction to Remote Sensing. Taylor & Francis, London.Chang, 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/10.1559/152304091783805626Chen, 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.2021.115567Chenu, 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.2018.04.011Conrad, 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-8-1991-2015Croft, 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.2012.01.001Conrad, 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.Conforti, 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.Cuervo, E., Cely, G., & Moreno, D. (2016). Determinación de las fracciones de carbono orgánico en el suelo del páramo La Cortadera, Boyacá. 139–149.Davy, 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/SR12353De 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.2012.10.014Dong, 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.Elachi, C., & Van Zyl, J. J. (2006). Introduction to the physics and techniques of remote sensing (Vol. 28). John Wiley & Sons.FAO 2017.Carbono orgánico del suelo: el potencial oculto. Organización para las Naciones Unidas para la alimentación. Roma Italia. 90PFAO - 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.Filippi, 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/237440368Forkuor, 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.Gardi, 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.Gallant, J., & Dowling, I. (2003). A multiresolution index of valley bottom flatness for mapping depositional areas. Water Resources Research, 39(12). https://doi.org/10.1029/2002WR001426Gholizadeh, 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.2018.09.015Gomez, 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.2008.06.011Gomes, 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/SR13306Griffiths, 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.2008.08.010Guo, 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.Gutiérrez, 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.1855Hinge, 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-xHengl, 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.0125814Hobley, 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/s11104-015-2380-1Hosseini, 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.Huamán-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.2021.010Hunt, 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.15082Keskin, 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.2018.12.037Kuang, 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.2014.11.002Kunkel, 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.2011.06.011Lal, R., & Kimble, J. M. (1997). Conservation tillage for carbon sequestration. In Nutrient Cycling in Agroecosystems (Vol. 49). Kluwer Academic Publishers.Lamichhane, 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.2019.05.031Lee, S., Evangelista, D.G., 2006. Earthquake-induced landslide susceptibility mappingusing an artificial neural network. Nat. Hazards Earth Syst. Sci. 6, 687–695.Lizeth, A., & Blanco, T. (2018). Estimación de biomasa aérea de eucalipto (Eucalyptus grandis) y pino (Pinus spp) en plantaciones forestales comerciales, usando imágenes satelitales Sentinel.López González, E. (1998). TRATAMIENTO DE LA COLINEALIDAD EN REGRESIÓN MÚLTIPLE. In Psicothema (Vol. 10, Issue 2).Mahmoudabadi, 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/s10661-017-6197-7Martínez, E., Fuentes, J. P., & Acevedo, E. (2008). Soil organic carbon and soil properties.Minasny, 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.2015.07.017Minasny, 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.2015.07.017Mirzaee, 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.2016.05.023Mouazen, 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.2010.03.001Obu, 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.1881Panagos, 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.Pla Sentis, I. (1994). Labranza, propiedades físicas y producción de maíz en los llanos occidentales. SVCS N, 46, 32-42.Qin, 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.Ramifehiarivo, 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.2016.12.002Reyes, 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/jeq2017.08.0329Ricardo, 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.Rivas, 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.Roa 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.Rodriguez-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.2011.11.002Román-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.2016.10.013Rügnitz, 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).Sandoval, 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).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. 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.Sayão, 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.e00174Schillaci, 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.2017.05.239Siewert, 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-15-1663-2018Sreenivas, 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.2016.02.002Stolbovoy,.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.2007.05.005Thompson, 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.2006.03.037Taghizadeh, 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.2015.12.003Torres, 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).Wang, 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.Wang, 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.2018.02.204Wang, 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.2014.01.018Ward, 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/10.1080/14693062.2015.1046413Waters, 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/RJ14119Were, 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.2014.12.028Wilson, J. P., & Gallant, J. C. (Eds.). (2000). Terrain analysis: principles and applications. John Wiley & Sons.Wilson, J. P. (2012). Digital terrain modeling. Geomorphology, 137(1), 107–121. https://doi.org/10.1016/j.geomorph.2011.03.012Wischmeier, W. H., & Smith, D. D. (1978). Predicting rainfall erosion losses: a guide to conservation planning (No. 537). Department of Agriculture, Science and Education Administration.Xie, 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.Xiao, 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.2019.111383Yang, 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.xYang, 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/srep21842Yang, 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/land7010011Zeraatpisheh, 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.Zhao, 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.2013.12.015Zhou, Q., & Liu, X. (2004). Analysis of errors of derived slope and aspect related to DEM data properties. Computers & Geosciences, 30(4), 369-378.Zhou, 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.2020.138244Zimmermann, 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/s10021-009-9300-2Zomer, R. J., Bossio, D. A., Sommer, R., & Verchot, L. V. (2017). Global Sequestration Potential of Increased Organic Carbon in CropZuur, A. F., Ieno, E. N., & Smith, G. M. (2007). Analysing ecological data (Vol. 680). New York: SpringerEstudiantesGrupos comunitariosInvestigadoresPúblico generalResponsables políticosLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/85803/1/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD51ORIGINAL1015444449.2023.pdf..pdf1015444449.2023.pdf..pdfTesis de Maestría en Geomaticaapplication/pdf1075996https://repositorio.unal.edu.co/bitstream/unal/85803/2/1015444449.2023.pdf..pdfde9df834daf800ee66fecd3a74bb4b05MD52THUMBNAIL1015444449.2023.pdf..pdf.jpg1015444449.2023.pdf..pdf.jpgGenerated Thumbnailimage/jpeg5161https://repositorio.unal.edu.co/bitstream/unal/85803/3/1015444449.2023.pdf..pdf.jpg9a10d2b94be4f609544e206a5cf1af92MD53unal/85803oai:repositorio.unal.edu.co:unal/858032024-03-12 23:04:22.943Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.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 |