Algoritmos de pedología cuantitativa para el Sistema de Información de Suelos de Latinoamérica y el Caribe, SISLAC

ilustraciones, fotografías, graficas, mapas

Autores:
Díaz Guadarrama, Sergio
Tipo de recurso:
Fecha de publicación:
2021
Institución:
Universidad Nacional de Colombia
Repositorio:
Universidad Nacional de Colombia
Idioma:
spa
OAI Identifier:
oai:repositorio.unal.edu.co:unal/81445
Acceso en línea:
https://repositorio.unal.edu.co/handle/unal/81445
https://repositorio.unal.edu.co/
Palabra clave:
000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores
Perfiles de suelo
SISLAC
Carbono orgánico del suelo
Variabilidad espacial
Depuración de datos
Segmentación
Soil profiles
Soil organic carbon
Spatial variability
Segmentation of soil profiles
Data validation
Datos geológicos
Procesamiento de datos
Geological data
Data processing
Rights
openAccess
License
Reconocimiento 4.0 Internacional
id UNACIONAL2_49ab5c778b16086164999d5f43ab8ac1
oai_identifier_str oai:repositorio.unal.edu.co:unal/81445
network_acronym_str UNACIONAL2
network_name_str Universidad Nacional de Colombia
repository_id_str
dc.title.spa.fl_str_mv Algoritmos de pedología cuantitativa para el Sistema de Información de Suelos de Latinoamérica y el Caribe, SISLAC
dc.title.translated.eng.fl_str_mv Quantitative pedology algorithms for the Soil Information System of Latin America and the Caribbean, SISLAC
title Algoritmos de pedología cuantitativa para el Sistema de Información de Suelos de Latinoamérica y el Caribe, SISLAC
spellingShingle Algoritmos de pedología cuantitativa para el Sistema de Información de Suelos de Latinoamérica y el Caribe, SISLAC
000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores
Perfiles de suelo
SISLAC
Carbono orgánico del suelo
Variabilidad espacial
Depuración de datos
Segmentación
Soil profiles
Soil organic carbon
Spatial variability
Segmentation of soil profiles
Data validation
Datos geológicos
Procesamiento de datos
Geological data
Data processing
title_short Algoritmos de pedología cuantitativa para el Sistema de Información de Suelos de Latinoamérica y el Caribe, SISLAC
title_full Algoritmos de pedología cuantitativa para el Sistema de Información de Suelos de Latinoamérica y el Caribe, SISLAC
title_fullStr Algoritmos de pedología cuantitativa para el Sistema de Información de Suelos de Latinoamérica y el Caribe, SISLAC
title_full_unstemmed Algoritmos de pedología cuantitativa para el Sistema de Información de Suelos de Latinoamérica y el Caribe, SISLAC
title_sort Algoritmos de pedología cuantitativa para el Sistema de Información de Suelos de Latinoamérica y el Caribe, SISLAC
dc.creator.fl_str_mv Díaz Guadarrama, Sergio
dc.contributor.advisor.none.fl_str_mv Rubiano Sanabria, Yolanda
Lizarazo Salcedo, Iván Alberto
dc.contributor.author.none.fl_str_mv Díaz Guadarrama, Sergio
dc.subject.ddc.spa.fl_str_mv 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores
topic 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores
Perfiles de suelo
SISLAC
Carbono orgánico del suelo
Variabilidad espacial
Depuración de datos
Segmentación
Soil profiles
Soil organic carbon
Spatial variability
Segmentation of soil profiles
Data validation
Datos geológicos
Procesamiento de datos
Geological data
Data processing
dc.subject.proposal.spa.fl_str_mv Perfiles de suelo
SISLAC
Carbono orgánico del suelo
Variabilidad espacial
Depuración de datos
Segmentación
dc.subject.proposal.eng.fl_str_mv Soil profiles
Soil organic carbon
Spatial variability
Segmentation of soil profiles
Data validation
dc.subject.unesco.spa.fl_str_mv Datos geológicos
Procesamiento de datos
dc.subject.unesco.eng.fl_str_mv Geological data
Data processing
description ilustraciones, fotografías, graficas, mapas
publishDate 2021
dc.date.issued.none.fl_str_mv 2021-05
dc.date.accessioned.none.fl_str_mv 2022-04-07T13:01:57Z
dc.date.available.none.fl_str_mv 2022-04-07T13:01:57Z
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/81445
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/81445
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
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spelling Reconocimiento 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Rubiano Sanabria, Yolanda019716440377435f3ee30eb40d6935daLizarazo Salcedo, Iván Albertob7a911d83f30c19f50a8d2f7b4e94e02Díaz Guadarrama, Sergio1ac99c09739fc5361ab553d26d173c9c2022-04-07T13:01:57Z2022-04-07T13:01:57Z2021-05https://repositorio.unal.edu.co/handle/unal/81445Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, fotografías, graficas, mapasLas bases de datos espaciales de suelos son una herramienta que ayuda en el modelamiento de diversos fenómenos en los que los suelos son determinantes, tales como el calentamiento global o la seguridad alimentaria. Sin embargo, existen problemas que dificultan su procesamiento, tales como la calidad de los datos y su alta dimensionalidad. El objetivo de esta investigación consistió en definir e implementar validaciones automatizadas para la depuración de los datos y mejorar la representación de los perfiles de suelo en función de la profundidad para así, mejorar la estimación de la variabilidad espacial del Carbono Orgánico del Suelo, COS. La zona de estudio comprendió la región sureste del departamento del Valle del Cauca, Colombia. Los datos utilizados fueron obtenidos del Sistema de Información de Suelos de Latinoamérica y el Caribe, SISLAC. La metodología implementada consistió en: (i) depurar los datos de errores e inconsistencias, (ii) armonizar el conjunto de datos utilizando por una parte, la función de segmentación de la librería Algorithms for Quantitative Pedology y por otra, una función de segmentación adaptada que mejora la representación de los valores de COS mediante una función spline de áreas equivalentes y (iii) comprobar si con esta última, se mejora la estimación de la variabilidad vertical y horizontal del COS en la zona de estudio. Las profundidades de mapeo fueron las establecidas por el GlobalSoilMap para los primeros 30 cm de profundidad. Los resultados mostraron que al depurar los datos y mejorar la representación de los perfiles utilizando la función de segmentación adaptada se mejoran las estimaciones de la variabilidad espacial hasta en un 15% con respecto a los datos originales a información de referencia del proyecto soilgrids, las mejoras ocurren principalmente en las zonas más superficiales. Los procesos de validación y mejoras en la segmentación permitieron generar información de la distribución espacial del COS más representativa de la realidad al considerar el cambio gradual de esta propiedad en función de la profundidad. La metodología generada es reproducible y puede adaptarse para analizar otras propiedades continuas del suelo. (Texto tomado de la fuente)Soil spatial databases are tools that can help in the modeling of various phenomenon in which soils are determinants, such as global warming or food security. However, there are two problems that make processing difficult: the quality of the data and the high dimensionality. The objective of this research was to define and implement automated validations for data validation and improve the representation of soil profile as a function of depth in order to improve the estimation of the spatial variability of Soil Organic Carbon, SOC. The study area comprised the southeast region of the Valle del Cauca department, Colombia. The data used were obtained from the Soil Information System for Latin America and The Caribbean, SISLAC. The methodology implemented consisted of: (i) debugging the data for errors and inconsistencies; (ii) harmonize the data set using, on the one hand ; the segmentation function, Algorithms for Quantitative Pedology library, AQP; and on the other, an the adapted segmentation function that improves the representation of SOC values by a spline function of equal areas; (iii) check if this last function improves the estimation of the spatial variability of the SOC in the study area. The mapping depths were those established by the GlobalSoilMap project for the first 30 cm of depth. The results showed that by refining the data and improving the representation of the profiles using the adapted segmentation function, the estimates of spatial variability are improved by up to 15%, mainly in the most superficial areas. The validation processes and improvements in segmentation made it possible to generate information on the spatial distribution of the COS that is more representative of reality when considering the gradual change of this property as a function of depth. The generated methodology is reproducible and can be adapted to analyze other continuous soil properties.MaestríaMagíster en GeomáticaTecnologías GeoespacialesCiencias Agronómicasxviii, 144 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ciencias Agrarias - Maestría en GeomáticaEscuela de posgradosFacultad de Ciencias AgrariasBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadoresPerfiles de sueloSISLACCarbono orgánico del sueloVariabilidad espacialDepuración de datosSegmentaciónSoil profilesSoil organic carbonSpatial variabilitySegmentation of soil profilesData validationDatos geológicosProcesamiento de datosGeological dataData processingAlgoritmos de pedología cuantitativa para el Sistema de Información de Suelos de Latinoamérica y el Caribe, SISLACQuantitative pedology algorithms for the Soil Information System of Latin America and the Caribbean, SISLACTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMAmirinejad, A. 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Global Ecology and Conservation, 21, e00849. https://doi.org/10.1016/j.gecco.2019.e00849AdministradoresBibliotecariosInvestigadoresPúblico generalORIGINAL591583_2021.pdf591583_2021.pdfTesis de Maestría en Geomáticaapplication/pdf6187610https://repositorio.unal.edu.co/bitstream/unal/81445/3/591583_2021.pdf42d4d1286f8d0cee5acebb76b161bb3bMD53LICENSElicense.txtlicense.txttext/plain; charset=utf-84074https://repositorio.unal.edu.co/bitstream/unal/81445/4/license.txt8153f7789df02f0a4c9e079953658ab2MD54THUMBNAIL591583_2021.pdf.jpg591583_2021.pdf.jpgGenerated Thumbnailimage/jpeg5377https://repositorio.unal.edu.co/bitstream/unal/81445/5/591583_2021.pdf.jpgc8e5f9e5dbb3e11aacb531fb44ef11d5MD55unal/81445oai:repositorio.unal.edu.co:unal/814452023-08-02 23:03:51.193Repositorio Institucional Universidad Nacional de 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EVESURBIFBPUiBMQSBTRUNSRVRBUsONQSBHRU5FUkFMLiAqTEEgVEVTSVMgQSBQVUJMSUNBUiBERUJFIFNFUiBMQSBWRVJTScOTTiBGSU5BTCBBUFJPQkFEQS4gCgpBbCBoYWNlciBjbGljIGVuIGVsIHNpZ3VpZW50ZSBib3TDs24sIHVzdGVkIGluZGljYSBxdWUgZXN0w6EgZGUgYWN1ZXJkbyBjb24gZXN0b3MgdMOpcm1pbm9zLiBTaSB0aWVuZSBhbGd1bmEgZHVkYSBzb2JyZSBsYSBsaWNlbmNpYSwgcG9yIGZhdm9yLCBjb250YWN0ZSBjb24gZWwgYWRtaW5pc3RyYWRvciBkZWwgc2lzdGVtYS4KClVOSVZFUlNJREFEIE5BQ0lPTkFMIERFIENPTE9NQklBIC0gw5psdGltYSBtb2RpZmljYWNpw7NuIDE5LzEwLzIwMjEK

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