Índices radiométricos, multiespectrales y SAR, para la evaluación a gran escala de la calidad de hábitat en bosque húmedo tropical en zonas del Magdalena Medio, Colombia

ilustraciones, diagramas, mapas, planos

Autores:: Romero Jiménez, Luis Hernando

Tipo de recurso:

Fecha de publicación:: 2024

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:

id	UNACIONAL2_667c15b44d1752a40ddc2ddb354e165b
oai_identifier_str	oai:repositorio.unal.edu.co:unal/85570
network_acronym_str	UNACIONAL2
network_name_str	Universidad Nacional de Colombia
repository_id_str
dc.title.spa.fl_str_mv	Índices radiométricos, multiespectrales y SAR, para la evaluación a gran escala de la calidad de hábitat en bosque húmedo tropical en zonas del Magdalena Medio, Colombia
dc.title.translated.eng.fl_str_mv	Radiometric, multispectral and SAR indices, for the large-scale evaluation of habitat quality in tropical humid forest in areas of Magdalena Medio, Colombia
title	Índices radiométricos, multiespectrales y SAR, para la evaluación a gran escala de la calidad de hábitat en bosque húmedo tropical en zonas del Magdalena Medio, Colombia
spellingShingle	Índices radiométricos, multiespectrales y SAR, para la evaluación a gran escala de la calidad de hábitat en bosque húmedo tropical en zonas del Magdalena Medio, Colombia 500 - Ciencias naturales y matemáticas::507 - Educación, investigación, temas relacionados Recursos naturales Habitat Natural resources Ecosistema Ecosystem Multispectral Synthetic Aperture Radar Supervised Learning Machine Learning Ecological Integrity Habitat Quality
title_short	Índices radiométricos, multiespectrales y SAR, para la evaluación a gran escala de la calidad de hábitat en bosque húmedo tropical en zonas del Magdalena Medio, Colombia
title_full	Índices radiométricos, multiespectrales y SAR, para la evaluación a gran escala de la calidad de hábitat en bosque húmedo tropical en zonas del Magdalena Medio, Colombia
title_fullStr	Índices radiométricos, multiespectrales y SAR, para la evaluación a gran escala de la calidad de hábitat en bosque húmedo tropical en zonas del Magdalena Medio, Colombia
title_full_unstemmed	Índices radiométricos, multiespectrales y SAR, para la evaluación a gran escala de la calidad de hábitat en bosque húmedo tropical en zonas del Magdalena Medio, Colombia
title_sort	Índices radiométricos, multiespectrales y SAR, para la evaluación a gran escala de la calidad de hábitat en bosque húmedo tropical en zonas del Magdalena Medio, Colombia
dc.creator.fl_str_mv	Romero Jiménez, Luis Hernando
dc.contributor.advisor.none.fl_str_mv	Fagua González, Jose Camilo Rodríguez Buriticá, Susana
dc.contributor.author.none.fl_str_mv	Romero Jiménez, Luis Hernando
dc.contributor.researchgroup.spa.fl_str_mv	Biodiversidad, Biotecnología y Conservación de Ecosistemas
dc.contributor.orcid.spa.fl_str_mv	https://orcid.org/0000-0002-1977-0545
dc.contributor.cvlac.spa.fl_str_mv	https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000131287
dc.subject.ddc.spa.fl_str_mv	500 - Ciencias naturales y matemáticas::507 - Educación, investigación, temas relacionados
topic	500 - Ciencias naturales y matemáticas::507 - Educación, investigación, temas relacionados Recursos naturales Habitat Natural resources Ecosistema Ecosystem Multispectral Synthetic Aperture Radar Supervised Learning Machine Learning Ecological Integrity Habitat Quality
dc.subject.agrovoc.spa.fl_str_mv	Recursos naturales Habitat
dc.subject.agrovoc.eng.fl_str_mv	Natural resources
dc.subject.decs.spa.fl_str_mv	Ecosistema
dc.subject.decs.eng.fl_str_mv	Ecosystem
dc.subject.proposal.eng.fl_str_mv	Multispectral Synthetic Aperture Radar Supervised Learning Machine Learning Ecological Integrity Habitat Quality
description	ilustraciones, diagramas, mapas, planos
publishDate	2024
dc.date.accessioned.none.fl_str_mv	2024-01-31T19:41:11Z
dc.date.available.none.fl_str_mv	2024-01-31T19:41:11Z
dc.date.issued.none.fl_str_mv	2024-01-30
dc.type.spa.fl_str_mv	Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/masterThesis
dc.type.coarversion.spa.fl_str_mv	http://purl.org/coar/version/c_dc82b40f9837b551
dc.type.redcol.spa.fl_str_mv	http://purl.org/redcol/resource_type/TM
dc.identifier.uri.none.fl_str_mv	https://repositorio.unal.edu.co/handle/unal/85570
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/85570 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
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dc.publisher.branch.spa.fl_str_mv	Universidad Nacional de Colombia - Sede Bogotá
institution	Universidad Nacional de Colombia
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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_abf2Fagua González, Jose Camiloc8bc1a68873be62c740ff8a96db3689bRodríguez Buriticá, Susana961305fd226b4085be9a7f1602b20a9cRomero Jiménez, Luis Hernando3e8052e51570beb2ea52ac5af319da12Biodiversidad, Biotecnología y Conservación de Ecosistemashttps://orcid.org/0000-0002-1977-0545https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=00001312872024-01-31T19:41:11Z2024-01-31T19:41:11Z2024-01-30https://repositorio.unal.edu.co/handle/unal/85570Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, diagramas, mapas, planosEl uso de información espacial para el estudio de fenómenos ecológicos que involucran múltiples especies, es fundamental para el planteamiento de estrategias para la conservación, planeación y monitoreo de la biodiversidad. El hábitat, definido en este trabajo como las condiciones biofísicas que permiten la persistencia de la población de una especie en el espacio y en el tiempo, ha sido analizado tradicionalmente por medio de diferentes productos derivados de sensores remotos (SR), con la desventaja de que muchos de estos carecen de validación en campo, o tienen resoluciones espaciales o temáticas insuficientes para la planeación y el monitoreo de acciones de conservación. Para superar estas limitaciones, integramos información espectral de datos SAR (Radar de Apertura Sintética como Sentinel-1 y PALSAR), datos multiespectrales (Sentinel-2 y MODIS), y registros de especies tomados en campo y consultados en bases de datos nacionales e internacionales. Utilizando cinco algoritmos de aprendizaje (Machine Learning), proponemos un índice de calidad de hábitat específico para ecosistemas de bosque húmedo tropical, en una zona de estudio ubicada en el Magdalena Medio, entre los municipios de Puerto Berrío, Yondó, Cantagallo y Puerto Wilches. Como aproximación a la medida de calidad de bosques se utilizó el Índice de Condición Estructural de Hansen (SCI), que evalúa la estructura de los bosques, aunque no considera otros elementos relacionados con la integridad de este ecosistema, como su composición y función. Por esta razón, se modificó el SCI con datos de registros biológicos, ajustados para representar la dependencia que las especies tienen del bosque, y se construyó un índice de calidad de hábitat integral con datos de sensores remotos e información de campo. El algoritmo de Random Forest fue el que mostró el error más bajo de estimación del SCI (Accuracy = 0.675 y Kappa = 0.532); mientras que para la estimación de la dependencia de las especies hacia el bosque, fue el algoritmo de Máquinas de Soporte Vectorial (Accuracy = 0.643 y Kappa= 0.397). Se encontró que las variables que aportan mayor información para la estimación del SCI son Red edge 1, Red, SWIR_2, Green, el índice PSRI de Sentinel-2A y los índices de Radar HV, VHdivVV y VHdivHH. Se comprobó que los valores de exactitud temática son más bajos al utilizar las 18 categorías de SCI, por lo que se simplificó a cinco categorías. De manera similar, para la estimación de la calidad del bosque se encontró que las variables que aportan mayor información son HVdivHH y HV de Sentinel-1A y el Tasseled cap wetness, el índice MNDWI y la banda SWIR_1. Finalmente, el modelo que integra el SCI con la calidad de los bosques resultó con la mayor exactitud temática, desarrollado con el algoritmo de Potenciación del Gradiente (Accuracy= 0.724 y Kappa= 0.493), permitiendo identificar áreas de incongruencia entre estos dos componentes. La exactitud de los modelos evidencia que las variables predictoras derivadas de SR, presentan relaciones que no son capturadas por las variables originales del SCI y que pueden contribuir a su mejoramiento, mientras que la estimación de dependencia de las especies al bosque refleja un sesgo en el muestreo. No obstante, el modelo final incorpora la incertidumbre de los dos primeros modelos, lo que fortalece los resultados encontrados en los modelos 1 y 2, pero así mismo con la capacidad de retroalimentarse con una mayor disponibilidad de registros biológicos curados. (Texto tomado de la fuente)The use of spatial information for the study of ecological phenomena that involve multiple species, is fundamental for the approach of strategies for the conservation, planning and monitoring of biodiversity. The habitat, defined in this work as the biophysical conditions that allow the persistence of the population of a species in space and time, has traditionally been analyzed by means of different products derived from remote sensing (RS), with the disadvantage of that many of these lack validation in the field, or have insufficient spatial or thematic resolution for planning and monitoring conservation actions. To overcome these limitations, we integrate spectral information from SAR data (Synthetic Aperture Radar such as Sentinel-1 and PALSAR), multispectral data (Sentinel-2 and MODIS), and records of species taken in the field and consulted in national and international databases. Using five learning algorithms (Machine Learning), we propose a specific habitat quality index for tropical humid forest ecosystems, in a study area located in Magdalena Medio, between the municipalities of Puerto Berrío, Yondó, Cantagallo and Puerto Wilches. As an approximation to measure forest quality, we use the Hansen Structural Condition Index (SCI), which evaluates the structure of forests, although it does not consider other elements related to the integrity of this ecosystem, such as its composition and function. For this reason, the SCI was modified with biological records, adjusted to represent the dependence that species have on the forest, and a comprehensive habitat quality index was constructed with data from remote sensors and field information. The Random Forest algorithm was the one that showed the lowest SCI estimation error (Accuracy = 0.675 and Kappa = 0.532); while for the estimation of the dependence of the species on the forest, it was the Support Vector Machines algorithm (Accuracy = 0.643 and Kappa = 0.397). It was found that the variables that provide the most information for estimating the SCI are Red edge 1, Red, SWIR_2, Green, the PSRI index of Sentinel-2A and the Radar HV, VHdivVV and VHdivHH indices. It was found that the thematic accuracy values are lower when using the 18 SCI categories, so it was simplified to five categories. Similarly, for the estimation of forest quality it was found that the variables that provide the most information are HVdivHH and HV of Sentinel-1A and the Tasseled cap wetness, the MNDWI index and the SWIR_1 band. Finally, the model that integrates the SCI with the quality of the forests resulted with the greatest thematic accuracy, developed with the Gradient Boosting algorithm (Accuracy= 0.724 and Kappa= 0.493), allowing the identification of areas of incongruence between these two components. The accuracy of the models shows that the predictor variables derived from SR present relationships that are not captured by the original variables of the SCI and that can contribute to their improvement, while the estimate of dependence of the species on the forest reflects a bias in the sampling. However, the final model incorporates the uncertainty of the first two models, which strengthens the results found in models 1 and 2, but also with the ability to feed back with a greater availability of curated biological records.MaestríaMagíster en GeomáticaGeoinformación para el uso sostenible de los recursos naturales132 páginasapplication/pdf500 - Ciencias naturales y matemáticas::507 - Educación, investigación, temas relacionadosRecursos naturalesHabitatNatural resourcesEcosistemaEcosystemMultispectralSynthetic Aperture RadarSupervised LearningMachine LearningEcological IntegrityHabitat QualityÍndices radiométricos, multiespectrales y SAR, para la evaluación a gran escala de la calidad de hábitat en bosque húmedo tropical en zonas del Magdalena Medio, ColombiaRadiometric, multispectral and SAR indices, for the large-scale evaluation of habitat quality in tropical humid forest in areas of Magdalena Medio, ColombiaTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesishttp://purl.org/coar/version/c_dc82b40f9837b551http://purl.org/redcol/resource_type/TMBogotá - Ciencias Agrarias - Maestría en GeomáticaFacultad de Ciencias AgrariasBogotá, ColombiaUniversidad Nacional de Colombia - Sede BogotáColombiaMagdalena MedioAbdelkareem, M., Bamousa, A. 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Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-27851-8_204-1Instituto de Investigación de Recursos Biológicos Alexander von HumboldtEstudiantesInvestigadoresMaestrosLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/85570/1/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD51ORIGINAL1013646905.2023.pdf1013646905.2023.pdfTesis de Maestría en Geomáticaapplication/pdf4079894https://repositorio.unal.edu.co/bitstream/unal/85570/2/1013646905.2023.pdfa41a16743220f0243a641ec81835637eMD52THUMBNAIL1013646905.2023.pdf.jpg1013646905.2023.pdf.jpgGenerated Thumbnailimage/jpeg4548https://repositorio.unal.edu.co/bitstream/unal/85570/3/1013646905.2023.pdf.jpg3ad2cd3dff463e5a272d91a6948836e6MD53unal/85570oai:repositorio.unal.edu.co:unal/855702024-08-22 23:10:24.38Repositorio Institucional Universidad Nacional de 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Índices radiométricos, multiespectrales y SAR, para la evaluación a gran escala de la calidad de hábitat en bosque húmedo tropical en zonas del Magdalena Medio, Colombia

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