Revisión de literatura sobre técnicas prospectivas del cambio de cobertura y uso del suelo

Objetivo. Se realizó una revisión sistemática de la literatura para identificar los avances en las aplicaciones basadas en algoritmos de aprendizaje automático y aprendizaje profundo para el análisis del cambio de cobertura y uso del suelo con fines prospectivos, sus covariables y las denominaciones...

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Tipo de recurso:
Fecha de publicación:
2025
Institución:
Universidad de Caldas
Repositorio:
Repositorio Institucional U. Caldas
Idioma:
spa
OAI Identifier:
oai:repositorio.ucaldas.edu.co:ucaldas/23646
Acceso en línea:
https://doi.org/10.17151/luaz.2025.60.5
Palabra clave:
Cobertura vegetal
Usos del suelo
Medio ambiente
Tierras
SIG
Land cover
Land use
Environment
Lands
GIS
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openAccess
License
Luna Azul - 2025
id REPOUCALDA_4100aefe0c0b0f4079cea153e60ca4ed
oai_identifier_str oai:repositorio.ucaldas.edu.co:ucaldas/23646
network_acronym_str REPOUCALDA
network_name_str Repositorio Institucional U. Caldas
repository_id_str
dc.title.none.fl_str_mv Revisión de literatura sobre técnicas prospectivas del cambio de cobertura y uso del suelo
Literature review on land cover change and land use
title Revisión de literatura sobre técnicas prospectivas del cambio de cobertura y uso del suelo
spellingShingle Revisión de literatura sobre técnicas prospectivas del cambio de cobertura y uso del suelo
Cobertura vegetal
Usos del suelo
Medio ambiente
Tierras
SIG
Land cover
Land use
Environment
Lands
GIS
title_short Revisión de literatura sobre técnicas prospectivas del cambio de cobertura y uso del suelo
title_full Revisión de literatura sobre técnicas prospectivas del cambio de cobertura y uso del suelo
title_fullStr Revisión de literatura sobre técnicas prospectivas del cambio de cobertura y uso del suelo
title_full_unstemmed Revisión de literatura sobre técnicas prospectivas del cambio de cobertura y uso del suelo
title_sort Revisión de literatura sobre técnicas prospectivas del cambio de cobertura y uso del suelo
dc.subject.none.fl_str_mv Cobertura vegetal
Usos del suelo
Medio ambiente
Tierras
SIG
Land cover
Land use
Environment
Lands
GIS
topic Cobertura vegetal
Usos del suelo
Medio ambiente
Tierras
SIG
Land cover
Land use
Environment
Lands
GIS
description Objetivo. Se realizó una revisión sistemática de la literatura para identificar los avances en las aplicaciones basadas en algoritmos de aprendizaje automático y aprendizaje profundo para el análisis del cambio de cobertura y uso del suelo con fines prospectivos, sus covariables y las denominaciones de las clasificaciones de cobertura y uso del suelo. Metodología. Se llevo a cabo una recuperación bibliográfica, consultando diferentes bibliotecas digitales, a partir de una ecuación de búsqueda delimitada al periodo de estudio entre los años 2000 a 2024. Posteriormente, se aplicó una lectura por niveles para extraer la información relevante al estudio, la cual consistió en la revisión de las secciones de introducción, métodos, figuras, tablas, resultados y conclusiones, para generar las anotaciones pertinentes con la investigación. Resultados. Se obtuvo un total de 55 estudios, entre artículos, libros y trabajos de tesis, de los cuales se les resumió y extrajo la información pertinente a la investigación, para luego relacionarla con la discriminación por tipos de cobertura y uso del suelo, factores impulsores del cambio, y las técnicas de aprendizaje automático frecuentemente utilizadas. Adicionalmente, se realizó una citación de los conceptos claves de la modelación del cambio de cobertura y uso del suelo con autómatas celulares. Conclusiones. Cuando el análisis del cambio de cobertura y uso del suelo apunta hacia objetivos prospectivos, el uso de autómatas celulares es destacable, por la posibilidad de generar simulaciones de posibles líneas futuras, posibilitando el análisis de escenarios, así como deducir y recrear las reglas de transición entre diferentes tipos de cobertura o usos del suelo, de forma automatizada y estadísticamente evaluable.
publishDate 2025
dc.date.none.fl_str_mv 2025-09-11T00:00:00Z
2025-09-11T00:00:00Z
2025-09-11
dc.type.none.fl_str_mv Artículo de revista
http://purl.org/coar/resource_type/c_6501
Text
info:eu-repo/semantics/article
Journal article
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status_str publishedVersion
dc.identifier.none.fl_str_mv 0122-5391
10.17151/luaz.2025.60.5
1909-2474
https://doi.org/10.17151/luaz.2025.60.5
identifier_str_mv 0122-5391
10.17151/luaz.2025.60.5
1909-2474
url https://doi.org/10.17151/luaz.2025.60.5
dc.language.none.fl_str_mv spa
language spa
dc.relation.none.fl_str_mv 60
Luna Azul
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Núm. 60 , Año 2025 : Enero-Junio
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dc.rights.none.fl_str_mv Luna Azul - 2025
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Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
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dc.publisher.none.fl_str_mv Universidad de Caldas
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spelling Revisión de literatura sobre técnicas prospectivas del cambio de cobertura y uso del sueloLiterature review on land cover change and land useCobertura vegetalUsos del sueloMedio ambienteTierrasSIGLand coverLand useEnvironmentLandsGISObjetivo. Se realizó una revisión sistemática de la literatura para identificar los avances en las aplicaciones basadas en algoritmos de aprendizaje automático y aprendizaje profundo para el análisis del cambio de cobertura y uso del suelo con fines prospectivos, sus covariables y las denominaciones de las clasificaciones de cobertura y uso del suelo. Metodología. Se llevo a cabo una recuperación bibliográfica, consultando diferentes bibliotecas digitales, a partir de una ecuación de búsqueda delimitada al periodo de estudio entre los años 2000 a 2024. Posteriormente, se aplicó una lectura por niveles para extraer la información relevante al estudio, la cual consistió en la revisión de las secciones de introducción, métodos, figuras, tablas, resultados y conclusiones, para generar las anotaciones pertinentes con la investigación. Resultados. Se obtuvo un total de 55 estudios, entre artículos, libros y trabajos de tesis, de los cuales se les resumió y extrajo la información pertinente a la investigación, para luego relacionarla con la discriminación por tipos de cobertura y uso del suelo, factores impulsores del cambio, y las técnicas de aprendizaje automático frecuentemente utilizadas. Adicionalmente, se realizó una citación de los conceptos claves de la modelación del cambio de cobertura y uso del suelo con autómatas celulares. Conclusiones. Cuando el análisis del cambio de cobertura y uso del suelo apunta hacia objetivos prospectivos, el uso de autómatas celulares es destacable, por la posibilidad de generar simulaciones de posibles líneas futuras, posibilitando el análisis de escenarios, así como deducir y recrear las reglas de transición entre diferentes tipos de cobertura o usos del suelo, de forma automatizada y estadísticamente evaluable.Objetive. A systematic literature review was conducted to identify advances in applications based on Machine Learning and Deep Learning algorithms for the analysis of land cover change and land use for prospective purposes, their covariates and the names of land cover and land use classifications. Methodology. A bibliographic retrieval was carried out consulting different digital libraries based on a search equation limited to the study period from 2000 to 2024. Subsequently, a layered reading was applied to extract information relevant to the study, which consisted in reviewing the introduction, methods, figures, tables, results and conclusions, in order to generate annotations relevant to the research. Results. A total of 55 studies were obtained, including articles, books and thesis, from which the information pertinent to the research was summarized and extracted, then related to discrimination by land coverage and land use types, drivers of change, and frequently used machine learning techniques. Additionally, a citation of the key concepts of land cover and land use with cellular automata was provided. Conclusions. When analyzing land cover and land use change toward prospective objectives, the use of cellular automata is remarkable, for the possibility of generating simulations of possible future lines, enabling scenario analysis, as well as deducing and recreating the transition rules between different land cover types or land uses, in an automated and statistically evaluable way.Universidad de Caldas2025-09-11T00:00:00Z2025-09-11T00:00:00Z2025-09-11Artículo de revistahttp://purl.org/coar/resource_type/c_6501Textinfo:eu-repo/semantics/articleJournal articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_2df8fbb1application/pdftext/html0122-539110.17151/luaz.2025.60.51909-2474https://doi.org/10.17151/luaz.2025.60.5https://revistasojs.ucaldas.edu.co/index.php/lunazul/article/view/10806spa60Luna AzulAgudelo-Hz, W. J., Castillo-Barrera, N. C., y Uriel, M. G. (2023). 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