Revisión bibliométrica de las principales metodologías para la evaluación del uso y cobertura del suelo
Esta investigación presenta una revisión bibliométrica centrada en examinar las metodologías empleadas en la clasificación supervisada y no supervisada del uso y cobertura del suelo. Se realizaron búsquedas en Google Scholar, Science Direct y Scopus para seleccionar 31 artículos entre 2018 y 2024. L...
- Autores:
-
Vargas Becerra, Edison Fabian
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2024
- Institución:
- Universidad Santo Tomás
- Repositorio:
- Repositorio Institucional USTA
- Idioma:
- spa
- OAI Identifier:
- oai:repository.usta.edu.co:11634/54822
- Acceso en línea:
- http://hdl.handle.net/11634/54822
- Palabra clave:
- Land use
Systematic review
Plant cover
Uso del suelo
Cobertura vegetal
Revisión sistemática
- Rights
- openAccess
- License
- Atribución-NoComercial-SinDerivadas 2.5 Colombia
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Revisión bibliométrica de las principales metodologías para la evaluación del uso y cobertura del suelo |
| title |
Revisión bibliométrica de las principales metodologías para la evaluación del uso y cobertura del suelo |
| spellingShingle |
Revisión bibliométrica de las principales metodologías para la evaluación del uso y cobertura del suelo Land use Systematic review Plant cover Uso del suelo Cobertura vegetal Revisión sistemática |
| title_short |
Revisión bibliométrica de las principales metodologías para la evaluación del uso y cobertura del suelo |
| title_full |
Revisión bibliométrica de las principales metodologías para la evaluación del uso y cobertura del suelo |
| title_fullStr |
Revisión bibliométrica de las principales metodologías para la evaluación del uso y cobertura del suelo |
| title_full_unstemmed |
Revisión bibliométrica de las principales metodologías para la evaluación del uso y cobertura del suelo |
| title_sort |
Revisión bibliométrica de las principales metodologías para la evaluación del uso y cobertura del suelo |
| dc.creator.fl_str_mv |
Vargas Becerra, Edison Fabian |
| dc.contributor.advisor.none.fl_str_mv |
Avellaneda Diaz, Elisa Maria |
| dc.contributor.author.none.fl_str_mv |
Vargas Becerra, Edison Fabian |
| dc.contributor.corporatename.spa.fl_str_mv |
Universidad Santo Tomás |
| dc.subject.keyword.spa.fl_str_mv |
Land use Systematic review Plant cover |
| topic |
Land use Systematic review Plant cover Uso del suelo Cobertura vegetal Revisión sistemática |
| dc.subject.proposal.spa.fl_str_mv |
Uso del suelo Cobertura vegetal Revisión sistemática |
| description |
Esta investigación presenta una revisión bibliométrica centrada en examinar las metodologías empleadas en la clasificación supervisada y no supervisada del uso y cobertura del suelo. Se realizaron búsquedas en Google Scholar, Science Direct y Scopus para seleccionar 31 artículos entre 2018 y 2024. Los artículos analizados utilizan metodologías de teledetección o sistemas de información geográfica (SIG) y presentan resultados relacionados con la precisión de las metodologías de evaluación de LULC. Se observó que la clasificación supervisada fue la más utilizada, mientras que las técnicas de clasificación más empleadas fueron el índice NDVI, seguido por el algoritmo de máxima verosimilitud. Estos hallazgos contribuyen a aumentar la precisión y fiabilidad de los análisis de uso y cobertura del suelo para abordar los desafíos ambientales y sociales asociados con el desarrollo urbano y rural. |
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2024 |
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2024-04-24T19:33:37Z |
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2024-04-24T19:33:37Z |
| dc.date.issued.none.fl_str_mv |
2024-04-10 |
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Trabajo de Grado |
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info:eu-repo/semantics/acceptedVersion |
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http://purl.org/coar/resource_type/c_7a1f |
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info:eu-repo/semantics/bachelorThesis |
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Vargas Becerra, E. (2024). Revisión bibliométrica de las principales metodologías para la evaluación del uso y cobertura del suelo. [Trabajo de Grado, Universidad Santo Tomás]. Repositorio Institucional |
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http://hdl.handle.net/11634/54822 |
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Vargas Becerra, E. (2024). Revisión bibliométrica de las principales metodologías para la evaluación del uso y cobertura del suelo. [Trabajo de Grado, Universidad Santo Tomás]. Repositorio Institucional reponame:Repositorio Institucional Universidad Santo Tomás instname:Universidad Santo Tomás repourl:https://repository.usta.edu.co |
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http://hdl.handle.net/11634/54822 |
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spa |
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spa |
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Adugna, T., Xu, W., & Fan, J. (2022). Comparison of Random Forest and Support Vector Machine Classifiers for Regional Land Cover Mapping Using Coarse Resolution FY-3C Images. Remote sensing, 14(3). https://doi.org/https://doi.org/10.3390/rs14030574 Chen, H., Song, J., Wu, C., Du, B., & Yokoya, N. (2023). Exchange means change: An unsupervised single-temporal change detection framework based on intra- and inter-image patch exchange. ISPRS Journal of Photogrammetry and Remote Sensing, 206, 87-105. https://doi.org/https://doi.org/10.1016/j.isprsjprs.2023.11.004 Chopade, M. R., Mahajan, S., & Chaube, N. (2023). Assessment of land use, land cover change in the mangrove forest of Ghogha area, Gulf of Khambhat, Gujarat. Expert Systems with Applications, 212. https://doi.org/https://doi.org/10.1016/j.eswa.2022.118839 Chowdhury, S. (2024). Comparison of accuracy and reliability of random forest, support vector machine, artificial neural network and maximum likelihood method in land use/cover classification of urban setting. Environmental Challenges, 14. https://doi.org/https://doi.org/10.1016/j.envc.2023.100800 Chughtai, A. H., Abbasi, H., & Karas, I. R. (2021). A review on change detection method and accuracy assessment for land use land cover. Remote Sensing Applications: Society and Environment, 22. da Silva, V. S., Salami, G., da Silva, M. I., & Silva, E. A. (2020). Methodological evaluation of vegetation indexes in land use and land cover (LULC) classification. Geology, Ecology, and Landscapes, 4(2). https://doi.org/https://doi-org.crai-ustadigital.usantotomas.edu.co/10.1080/24749508.2019.1608409 Dargains, A., & Cabral, P. (2021). A GIS-based methodology for sustainable farming planning: Assessment of land use/cover changes and carbon dynamics at farm level. Land Use Policy, 111. https://doi.org/https://doi.org/10.1016/j.landusepol.2021.105788 Darvishi, A., Yousefi, M., & Marullb, J. (2020). Modelling landscape ecological assessments of land use and cover change scenarios. Application to the Bojnourd Metropolitan Area (NE Iran). Land Use Policy, 99. https://doi.org/https://doi.org/10.1016/j.landusepol.2020.105098 Davis, F., & Balaji Bhaskar, M. S. (2022). Assessment of water, soil contamination and land cover changes in Sims and Vince Bayou urban watersheds of Houston, Texas. Watershed Ecology and the Environment, 4, 73-85. https://doi.org/https://doi-org.crai-ustadigital.usantotomas.edu.co/10.1016/j.wsee.2022.08.002 Demarchi, L., Canters , F., Cariou, C., Licciardi , G., & Cheung-Wai Chan, J. (2018). Assessing the performance of two unsupervised dimensionality reduction techniques on hyperspectral APEX data for high resolution urban land-cover mapping. ISPRS Journal of Photogrammetry and Remote Sensing, 87, 166-179. https://doi.org/https://doi.org/10.1016/j.isprsjprs.2013.10.012 Du, L., Dong, C., Kang, X., Xinglong, Q., & Gu, L. (2023). Spatiotemporal evolution of land cover changes and landscape ecological risk assessment in the Yellow River Basin, 2015–2020. Journal of Environmental Management, 332. https://doi.org/https://doi.org/10.1016/j.jenvman.2022.117149 Edosa, B., & Nagasa, M. (2024). Spatiotemporal assessment of land use land cover change, driving forces, and consequences using geospatial techniques: The case of Naqamte city and hinterland, western Ethiopia. Environmental Challenges, 14. https://doi.org/https://doi.org/10.1016/j.envc.2023.100830 Espinoza Guzmán, M. A., Borrego, D. A., & Sahagún Sánchez, F. J. (2023). Evaluation of recent land-use and land-cover change in a mountain region. Trees, Forests and People, 11. https://doi.org/https://doi.org/10.1016/j.tfp.2023.100370 Geta Bihonegn, B., & Gebeyehu Awoke , A. (2023). Evaluating the impact of land use and land cover changes on sediment yield dynamics in the upper Awash basin, Ethiopia the case of Koka reservoir. Heliyon, 12(9). https://doi.org/https://doi.org/10.1016/j.heliyon.2023.e23049 Haque, I., & Basak, R. (2017). Land cover change detection using GIS and remote sensing techniques: A spatio-temporal study on Tanguar Haor, Sunamganj, Bang. The Egyptian Journal of Remote Sensing and Space Science. https://doi.org/https://doi.org/10.1016/j.ejrs.2016.12.003 Hasmadi, M., HZ, P., & MF, S. (2009). Evaluating supervised and unsupervised techniques for land cover mapping using remote sensing data. Journal of Society and Space, 5, 1-10. Jing, Q., He, J., Li, Y., Yang, X., Peng, Y., Wang, H., Zhang, X. (2024). Analysis of the spatiotemporal changes in global land cover from 2001 to 2020. Science of The Total Environment, 908. Jung, M., & Chang, E. (2019). NDVI-based land-cover change detection using harmonic analysis. International Journal of Remote Sensing (36). https://doi.org/https://doi-org.crai-ustadigital.usantotomas.edu.co/10.1080/01431161.2015.1007252 Kumar, A., & Kumar Gorai, A. (2023). A comparative evaluation of deep convolutional neural network and deep neural network-based land use/land cover classifications of mining regions using fused multi-sensor satellite data. Advances in Space Research, 72(11). https://doi.org/https://doi.org/10.1016/j.asr.2023.08.057 Long Feng, Q., Liu, J., & Gong, J. (2018). Urban Flood Mapping Based on Unmanned Aerial Vehicle Remote Sensing and Random Forest Classifier—A Case of Yuyao, China. Water, 74, 1437-1455. https://doi.org/https://doi.org/10.3390/w7041437 Long, X., Lin, H., An, X., Chen, S., Qi, S., & Zhang, M. (2022). Evaluation and analysis of ecosystem service value based on land use/cover change in Dongting Lake wetland. Ecological Indicators, 136. https://doi.org/https://doi.org/10.1016/j.ecolind.2022.108619 Matsa, M., Mupepi, O., Musasa, T., & Defe, R. (2020). A GIS and remote sensing aided assessment of land use/cover changes in resettlement areas; a case of ward 32 of Mazowe district, Zimbabwe. Journal of Environmental Management, 276. https://doi.org/https://doi.org/10.1016/j.jenvman.2020.111312 Meng, Y., Yang, M., Liu, S., Mou, Y., Peng, C., & Zhou, X. (2021). Quantitative assessment of the importance of bio-physical drivers of land cover change based on a random forest method. Ecological Informatics, 61. https://doi.org/https://doi.org/10.1016/j.ecoinf.2020.101204 Ngadi Scarpetta, Y., Lebourgeois, V., Laques, A. E., dieye, M., Bourgoin, J., & Bégué, A. (2023). BFASTm-L2, an unsupervised LULCC detection based on seasonal change detection – An application to large-scale land acquisitions in Senegal. International Journal of Applied Earth Observation and Geoinformation, 121. https://doi.org/https://doi.org/10.1016/j.jag.2023.103379 Paradis, E. (2022). Probabilistic unsupervised classification for large-scale analysis of spectral imaging data. International Journal of Applied Earth Observation and Geoinformation, 107. https://doi.org/https://doi.org/10.1016/j.jag.2022.102675 Prasad, P., Loveson, V. J., Chandra, P., & Kotha , M. (2022). Evaluation and comparison of the earth observing sensors in land cover/land use studies using machine learning algorithms. Ecological Informatics, 68. https://doi.org/https://doi.org/10.1016/j.ecoinf.2021.101522 Purwanto, Latifah, S., Yonariza, Akhsani , F., Sofiana, E. I., & Riski Ferdiansah, M. (2023). Land cover change assessment using random forest and CA markov from remote sensing images in the protected forest of South Malang, Indonesia. Remote Sensing Applications: Society and Environment, 32. https://doi.org/https://doi.org/10.1016/j.rsase.2023.101061 Rash, A., Mustafa, Y., & Hamad, R. (2023). Quantitative assessment of Land use/land cover changes in a developing region using machine learning algorithms: A case study in the Kurdistan Region, Iraq. Heliyon, 9(11). https://doi.org/https://doi.org/10.1016/j.heliyon.2023.e21253 Ruggeri, S., Henao Cespedes, V., Garcés Gómez, Y., & Parra Uzcátegui, A. (2021). Optimized unsupervised CORINE Land Cover mapping using linear spectral mixture analysis and object-based image analysis. The Egyptian Journal of Remote Sensing and Space Science, 24(3), 1061-1069. https://doi.org/https://doi.org/10.1016/j.ejrs.2021.10.009 Sisay, G., Gitima, G., Mersha , M., & Alemu, A. (2021). Assessment of land use land cover dynamics and its drivers in Bechet Watershed Upper Blue Nile Basin, Ethiopia. Remote Sensing Applications: Society and Environment, 24. https://doi.org/https://doi.org/10.1016/j.rsase.2021.100648 Sobhani, P., Esmaeilzadeh, H., & Mostafavi, H. (2021). Simulation and impact assessment of future land use and land cover changes in two protected areas in Tehran, Iran. Sustainable Cities and Society, 75. https://doi.org/https://doi-org.crai-ustadigital.usantotomas.edu.co/10.1016/j.scs.2021.103296 Tnag, X., Woodcock, C., Olofsson, P., & Hutyra, L. (2021). Spatiotemporal assessment of land use/land cover change and associated carbon emissions and uptake in the Mekong River Basin. Remote Sensing of Environment, 256. https://doi.org/https://doi.org/10.1016/j.rse.2021.112336 Tola, B., & Deyassa, G. (2024). A modeling approach for evaluating and predicting the impacts of land use land cover changes on groundwater recharge in Walga Watershed, Upper Omo Basin, Central Ethiopia. Journal of Hydrology: Regional Studies, 51. https://doi.org/https://doi.org/10.1016/j.ejrh.2024.101659 Tolessa, T., Kidane, M., & Bezie, A. (2021). Assessment of the linkages between ecosystem service provision and land use/land cover change in Fincha watershed, North-Western Ethiopia. Heliyon, 7(7). https://doi.org/https://doi.org/10.1016/j.heliyon.2021.e07673 Traore, M., Son Lee, M., Rasul, A., & Balew, A. (2021). Assessment of land use/land cover changes and their impacts on land surface temperature in Bangui (the capital of Central African Republic). Environmental Challenges, 4. https://doi.org/https://doi.org/10.1016/j.envc.2021.100114 Trujillo-Jiménez, M., Liberoff, A. L., Pessacg, N., Pacheco, C., & Flaherty, S. (2020). Uso de Métodos de Aprendizaje Automático y teledetección para clasificación de uso y cobertura del suelo en un valle semiárido de la Patogonia. Velastegui Montoya , A., Escandón-Panchana, P., Peña-Villacreses, G., & Herrera-Franco, G. (2023). Land use/land cover of petroleum activities in the framework of sustainable development. Cleaner Engineering and Technology(15). https://doi.org/https://doi.org/10.1016/j.clet.2023.100659 Zhao, S., Tu, K., Ye, S., Tang, H., Hu, Y., & Xie, C. (2023). Land Use and Land Cover Classification Meets Deep Learning: A Review. Sensors, 23(21). https://doi.org/https://doi.org/10.3390/s23218966 Zhao, Z., Islam, F., Waseem, L. A., Tariq, A., Nawaz , M., Islam, I. U., Hatamleh, W. (2024). Comparison of Three Machine Learning Algorithms Using Google Earth Engine for Land Use Land Cover Classification. Rangeland Ecology & Management, 92, 129-137. https://doi.org/https://doi.org/10.1016/j.rama.2023.10.007 |
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Avellaneda Diaz, Elisa MariaVargas Becerra, Edison FabianUniversidad Santo Tomás2024-04-24T19:33:37Z2024-04-24T19:33:37Z2024-04-10Vargas Becerra, E. (2024). Revisión bibliométrica de las principales metodologías para la evaluación del uso y cobertura del suelo. [Trabajo de Grado, Universidad Santo Tomás]. Repositorio Institucionalhttp://hdl.handle.net/11634/54822reponame:Repositorio Institucional Universidad Santo Tomásinstname:Universidad Santo Tomásrepourl:https://repository.usta.edu.coEsta investigación presenta una revisión bibliométrica centrada en examinar las metodologías empleadas en la clasificación supervisada y no supervisada del uso y cobertura del suelo. Se realizaron búsquedas en Google Scholar, Science Direct y Scopus para seleccionar 31 artículos entre 2018 y 2024. Los artículos analizados utilizan metodologías de teledetección o sistemas de información geográfica (SIG) y presentan resultados relacionados con la precisión de las metodologías de evaluación de LULC. Se observó que la clasificación supervisada fue la más utilizada, mientras que las técnicas de clasificación más empleadas fueron el índice NDVI, seguido por el algoritmo de máxima verosimilitud. Estos hallazgos contribuyen a aumentar la precisión y fiabilidad de los análisis de uso y cobertura del suelo para abordar los desafíos ambientales y sociales asociados con el desarrollo urbano y rural.This research presents a bibliometric review focused on examining the methodologies used in the supervised and unsupervised classification of land use and land cover. Google Scholar, Science Direct, and Scopus were searched to select 31 articles between 2018 and 2024. The articles analyzed use remote sensing or geographic information systems (GIS) methodologies and present results related to the accuracy of LULC assessment methodologies. It was observed that supervised classification was the most used, while the most used classification techniques were the NDVI index, followed by the maximum likelihood algorithm. These findings contribute to improving the accuracy and reliability of land use and land cover analyzes to address environmental and social challenges associated with urban and rural development.Ingeniero AmbientalPregradoapplication/pdfspaUniversidad Santo TomásPregrado de Ingeniería AmbientalFacultad de Ingeniería AmbientalAtribución-NoComercial-SinDerivadas 2.5 Colombiahttp://creativecommons.org/licenses/by-nc-nd/2.5/co/Abierto (Texto Completo)info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Revisión bibliométrica de las principales metodologías para la evaluación del uso y cobertura del sueloLand useSystematic reviewPlant coverUso del sueloCobertura vegetalRevisión sistemáticaTrabajo de Gradoinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_7a1finfo:eu-repo/semantics/bachelorThesisCRAI-USTA TunjaAdugna, T., Xu, W., & Fan, J. (2022). Comparison of Random Forest and Support Vector Machine Classifiers for Regional Land Cover Mapping Using Coarse Resolution FY-3C Images. Remote sensing, 14(3). https://doi.org/https://doi.org/10.3390/rs14030574Chen, H., Song, J., Wu, C., Du, B., & Yokoya, N. (2023). Exchange means change: An unsupervised single-temporal change detection framework based on intra- and inter-image patch exchange. ISPRS Journal of Photogrammetry and Remote Sensing, 206, 87-105. https://doi.org/https://doi.org/10.1016/j.isprsjprs.2023.11.004Chopade, M. R., Mahajan, S., & Chaube, N. (2023). Assessment of land use, land cover change in the mangrove forest of Ghogha area, Gulf of Khambhat, Gujarat. Expert Systems with Applications, 212. https://doi.org/https://doi.org/10.1016/j.eswa.2022.118839Chowdhury, S. (2024). Comparison of accuracy and reliability of random forest, support vector machine, artificial neural network and maximum likelihood method in land use/cover classification of urban setting. Environmental Challenges, 14. https://doi.org/https://doi.org/10.1016/j.envc.2023.100800Chughtai, A. H., Abbasi, H., & Karas, I. R. (2021). A review on change detection method and accuracy assessment for land use land cover. Remote Sensing Applications: Society and Environment, 22.da Silva, V. S., Salami, G., da Silva, M. I., & Silva, E. A. (2020). Methodological evaluation of vegetation indexes in land use and land cover (LULC) classification. Geology, Ecology, and Landscapes, 4(2). https://doi.org/https://doi-org.crai-ustadigital.usantotomas.edu.co/10.1080/24749508.2019.1608409Dargains, A., & Cabral, P. (2021). A GIS-based methodology for sustainable farming planning: Assessment of land use/cover changes and carbon dynamics at farm level. Land Use Policy, 111. https://doi.org/https://doi.org/10.1016/j.landusepol.2021.105788Darvishi, A., Yousefi, M., & Marullb, J. (2020). Modelling landscape ecological assessments of land use and cover change scenarios. Application to the Bojnourd Metropolitan Area (NE Iran). Land Use Policy, 99. https://doi.org/https://doi.org/10.1016/j.landusepol.2020.105098Davis, F., & Balaji Bhaskar, M. S. (2022). Assessment of water, soil contamination and land cover changes in Sims and Vince Bayou urban watersheds of Houston, Texas. Watershed Ecology and the Environment, 4, 73-85. https://doi.org/https://doi-org.crai-ustadigital.usantotomas.edu.co/10.1016/j.wsee.2022.08.002Demarchi, L., Canters , F., Cariou, C., Licciardi , G., & Cheung-Wai Chan, J. (2018). Assessing the performance of two unsupervised dimensionality reduction techniques on hyperspectral APEX data for high resolution urban land-cover mapping. ISPRS Journal of Photogrammetry and Remote Sensing, 87, 166-179. https://doi.org/https://doi.org/10.1016/j.isprsjprs.2013.10.012Du, L., Dong, C., Kang, X., Xinglong, Q., & Gu, L. (2023). 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