Metodologías de identificación de impactos sobre la cobertura vegetal generados por la minería a cielo abierto

La explotación minera a cielo abierto implica la generación de impactos ambientales negativos complejos que recaen en gran medida sobre la destrucción de la cobertura vegetal, así como en los demás recursos naturales. Teniendo en cuenta las afectaciones a nivel ambiental que refleja la minería de su...

Full description

Autores:
Guzmán Rocha, Angie Julieth
Tipo de recurso:
Trabajo de grado de pregrado
Fecha de publicación:
2024
Institución:
Universidad Militar Nueva Granada
Repositorio:
Repositorio UMNG
Idioma:
spa
OAI Identifier:
oai:repository.umng.edu.co:10654/47521
Acceso en línea:
https://hdl.handle.net/10654/47521
Palabra clave:
Minería
Cobertura vegetal
Impactos
Teledetección
Imágenes satelitales
Mining
Vegetation cover
Impacts
Remote sensing
Satellite imagery
Rights
openAccess
License
http://creativecommons.org/licenses/by-nc-nd/4.0/
id UNIMILTAR2_2abeadbbb7ac70bfef86d45d68dee953
oai_identifier_str oai:repository.umng.edu.co:10654/47521
network_acronym_str UNIMILTAR2
network_name_str Repositorio UMNG
repository_id_str
dc.title.spa.fl_str_mv Metodologías de identificación de impactos sobre la cobertura vegetal generados por la minería a cielo abierto
Methodologies for identifying impacts on vegetation cover caused by open-pit mining
title Metodologías de identificación de impactos sobre la cobertura vegetal generados por la minería a cielo abierto
spellingShingle Metodologías de identificación de impactos sobre la cobertura vegetal generados por la minería a cielo abierto
Minería
Cobertura vegetal
Impactos
Teledetección
Imágenes satelitales
Mining
Vegetation cover
Impacts
Remote sensing
Satellite imagery
title_short Metodologías de identificación de impactos sobre la cobertura vegetal generados por la minería a cielo abierto
title_full Metodologías de identificación de impactos sobre la cobertura vegetal generados por la minería a cielo abierto
title_fullStr Metodologías de identificación de impactos sobre la cobertura vegetal generados por la minería a cielo abierto
title_full_unstemmed Metodologías de identificación de impactos sobre la cobertura vegetal generados por la minería a cielo abierto
title_sort Metodologías de identificación de impactos sobre la cobertura vegetal generados por la minería a cielo abierto
dc.creator.fl_str_mv Guzmán Rocha, Angie Julieth
dc.contributor.author.none.fl_str_mv Guzmán Rocha, Angie Julieth
dc.subject.proposal.spa.fl_str_mv Minería
Cobertura vegetal
Impactos
Teledetección
Imágenes satelitales
topic Minería
Cobertura vegetal
Impactos
Teledetección
Imágenes satelitales
Mining
Vegetation cover
Impacts
Remote sensing
Satellite imagery
dc.subject.proposal.eng.fl_str_mv Mining
Vegetation cover
Impacts
Remote sensing
Satellite imagery
description La explotación minera a cielo abierto implica la generación de impactos ambientales negativos complejos que recaen en gran medida sobre la destrucción de la cobertura vegetal, así como en los demás recursos naturales. Teniendo en cuenta las afectaciones a nivel ambiental que refleja la minería de superficie, las cuales pueden incidir de la misma forma sobre la población, se desarrolló este estudio con el fin de realizar una revisión bibliográfica descriptiva a través de diferentes artículos de investigación desarrollados en el tema, con el fin de realizar una síntesis que reúna cuáles son las metodologías más utilizadas y precisas para evaluar la afectación de la cobertura vegetal por minería a cielo abierto y destacar las herramientas más aplicadas que puedan ser de utilidad para el desarrollo de investigaciones futuras. Se logró concluir que la teledetección es la metodología más utilizada, ya que provee versatilidad para incluir herramientas que fortalezcan el análisis de imágenes, la correlación estadística y modelos con escenarios futuros, potenciándose con el avance de la tecnología, también se encontró de manera atípica, metodologías que incluyen trabajo de campo interdisciplinar, lo cual implica mayores costos y mayor cantidad de tiempo en comparación con la teledetección.
publishDate 2024
dc.date.issued.none.fl_str_mv 2024-12-06
dc.date.accessioned.none.fl_str_mv 2025-11-04T16:44:58Z
dc.date.available.none.fl_str_mv 2025-11-04T16:44:58Z
dc.type.local.spa.fl_str_mv Tesis/Trabajo de grado - Monografía - Especialización
dc.type.driver.none.fl_str_mv info:eu-repo/semantics/bachelorThesis
dc.type.coar.none.fl_str_mv http://purl.org/coar/resource_type/c_7a1f
format http://purl.org/coar/resource_type/c_7a1f
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/10654/47521
dc.identifier.instname.spa.fl_str_mv instname:Universidad Militar Nueva Granada
dc.identifier.reponame.spa.fl_str_mv reponame:Repositorio Institucional Universidad Militar Nueva Granada
dc.identifier.repourl.none.fl_str_mv repourl:https://repository.umng.edu.co
url https://hdl.handle.net/10654/47521
identifier_str_mv instname:Universidad Militar Nueva Granada
reponame:Repositorio Institucional Universidad Militar Nueva Granada
repourl:https://repository.umng.edu.co
dc.language.iso.none.fl_str_mv spa
language spa
dc.relation.references.none.fl_str_mv Aires, U. R. V., Santos, B. S. M., Coelho, C. D., Da Silva, D. D., & Calijuri, M. L. (2017). Changes in land use and land cover as a result of the failure of a mining tailings dam in Mariana, MG, Brazil. Land Use Policy, 70, 63-70. https://doi.org/10.1016/j.landusepol.2017.10.026
Andaryani, S., Nourani, V., Ershadfath, F., Hashemi, F., Haghighi, A. T., & Keesstra, S. (2022). The detection and monitoring of pollution caused by gold mining using a vegetation cover index. Environmental Science And Pollution Research, 30(3), 8020-8035. https://doi.org/10.1007/s11356-022-22773-8
Bernal Navarro, L. F. (2021). Análisis de la pérdida de cobertura arbórea en el complejo del Cerrejón en los municipios Albania, Barranca y Hatonuevo (La Guajira) desde el 2001 hasta 2018. Repositorio Universidad Militar. https://repository.unimilitar.edu.co/server/api/core/bitstreams/21c20674-a3d9-44be-a233-1cd660220ea8/content
Biney, E., Biney, N., Dadzie, I., Harris, E., Quartey, G. A., Asare, Y. M., Bessah, E., & Forkuo, E. K. (2022). Impact of mining on vegetation cover: A case study of Prestea Huni-Valley municipality. Scientific African, 17, e01387. https://doi.org/10.1016/j.sciaf.2022.e01387
Blanche, M. F., Dairou, A. A., Juscar, N., Romarice, O. M. F., Arsene, M., Bernard, T. L., & Leroy, M. N. L. (2024). Assessment of land cover degradation due to mining activities using remote sensing and digital photogrammetry. ENVIRONMENTAL SYSTEMS RESEARCH, 13(1). https://doi.org/10.1186/s40068-024-00372-5
Ceballos Espinosa, D. de J., & Toro R, L. J. (2012). EVALUACIÓN DE LA SUSCEPTIBILIDAD A LA EROSIÓN POR EL CAMBIO DE COBERTURA DEBIDO A LA MINERÍA, EN EL MUNICIPIO DE ANORÍ, ANTIOQUIA, COLOMBIA. https://revistas.unal.edu.co/index.php/gestion/article/view/36280
Chen, L., Zhang, H., Zhang, X., Liu, P., Zhang, W., & Ma, X. (2021). Vegetation changes in coal mining areas: Naturally or anthropogenically Driven? CATENA, 208, 105712. https://doi.org/10.1016/j.catena.2021.105712
Copacean, L., Popescu, C., & Cojocariu, L. (2015). Remote sensing methods for analyzing vegetation and urban expansion. Research Journal Of Agricultural Science, 47 (4), 2015. https://rjas.ro/download/paper_version.paper_file.b05783e4a672dd18.636f70616365616e2e706466.pdf
Doyama, K., Yamaji, K., Haruma, T., Ichihara, Y., Tamura, K., Jiang, Q., Tsunashima, Y., Fukuyama, K., & Yasutaka, T. (2024). Vegetation at the former open-pit Ningyo-toge mine, 36 years after closure treatment: Impact of soil cover on woody plant establishment and dominance of the perennial herb Miscanthus sinensis. Journal Of Environmental Management, 362, 121292. https://doi.org/10.1016/j.jenvman.2024.121292
Firozjaei, M. K., Sedighi, A., Firozjaei, H. K., Kiavarz, M., Homaee, M., Arsanjani, J. J., Makki, M., Naimi, B., & Alavipanah, S. K. (2020). A historical and future impact assessment of mining activities on surface biophysical characteristics change: A remote sensing-based approach. Ecological Indicators, 122, 107264. https://doi.org/10.1016/j.ecolind.2020.107264
Florez-Yepes, G. Y., Rincon-Santamaría, A., Cardona, P. S., & Alvarez, A. M. A. (2017). Análisis multitemporal de las coberturas vegetales en el área de influencia de las minas de oro ubicadas en la parte alta del sector de Maltería en Manizales, Colombia. DYNA, 84(201), 95. https://doi.org/10.15446/dyna.v84n201.55759
Gallwey, J., Robiati, C., Coggan, J., Vogt, D., & Eyre, M. (2020). A Sentinel-2 based multispectral convolutional neural network for detecting artisanal small-scale mining in Ghana: Applying deep learning to shallow mining. Remote Sensing Of Environment, 248, 111970. https://doi.org/10.1016/j.rse.2020.111970
Guirao-Goris, J. A., Olmedo Salas, A., & Ferrer Ferrandis, E. (2008). El artículo de revisión. Universitat de València. https://www.uv.es/joguigo/valencia/Recerca_files/el_articulo_de_revision.pdf
Guo, Y., Huang, Y., Li, J., Ouyang, S., Wu, L., & Qi, W. (2023). Study on the influence of mining disturbance on the variation characteristics of vegetation index: A case study of Lingwu Mining Area. Environmental Development, 45, 100811. https://doi.org/10.1016/j.envdev.2023.100811
He, T., Zhang, M., Guo, A., Zhai, G., Wu, C., & Xiao, W. (2023). A novel index combining temperature and vegetation conditions for monitoring surface mining disturbance using Landsat time series. CATENA, 229, 107235. https://doi.org/10.1016/j.catena.2023.107235
Himmy, O., Nguyen, T. T., Hemmler, K., Loulad, S., Rhinane, H., & Buerkert, A. (2024). Leveraging machine learning and Landsat time series for high-resolution mapping of mining-induced vegetation changes in Ouagadougou, Burkina Faso. Environmental Challenges, 101026. https://doi.org/10.1016/j.envc.2024.101026
Ibrahim, E., Lema, L., Barnabé, P., Lacroix, P., & Pirard, E. (2020). Small-scale surface mining of gold placers: Detection, mapping, and temporal analysis through the use of free satellite imagery. International Journal Of Applied Earth Observation And Geoinformation, 93, 102194. https://doi.org/10.1016/j.jag.2020.102194
Kayet, N., Pathak, K., Singh, C., Chowdary, V., Bhattacharya, B. K., Kumar, D., Kumar, S., & Shaik, I. (2022). Vegetation health conditions assessment and mapping using AVIRIS-NG hyperspectral and field spectroscopy data for -environmental impact assessment in coal mining sites. Ecotoxicology And Environmental Safety, 239, 113650. https://doi.org/10.1016/j.ecoenv.2022.113650
Kayet, N., Pathak, K., Singh, C. P., Chaturvedi, R. K., Brahmandam, A. S., & Mandal, C. (2024). Assessment and estimation of coal dust impact on vegetation using VIs difference model and PRISMA hyperspectral data in mining sites. Journal Of Environmental Management, 367, 121935. https://doi.org/10.1016/j.jenvman.2024.121935
Kruse, F., Nobles, G. R., De Jong, M., Van Bodegom, R. M. K., Van Oortmerssen, G. J. M., Kooistra, J., Van Den Berg, M., Küchelmann, H. C., Schepers, M., Leusink, E. H. P., Cornelder, B. A., Kruijer, J. D., & Dee, M. W. (2021). Human–environment interactions at a short-lived Arctic mine and the long-term response of the local tundra vegetation. Polar Record, 57. https://doi.org/10.1017/s0032247420000418
Kusimi, J. M. (2008). Assessing land use and land cover change in the Wassa West District of Ghana using remote sensing. GeoJournal, 71(4), 249-259. https://doi.org/10.1007/s10708-008-9172-6
Li, N., Guo, Y., Wang, L., Wang, Q., Yan, D., Zhao, S., & Lei, T. (2023). Evaluation and quantitative characterization for the ecological environment impact of open pit mining on vegetation destruction from landsat time series: A case study of Wulishan limestone mine. Ecological Indicators, 158, 111371. https://doi.org/10.1016/j.ecolind.2023.111371
Li, X., Chen, W., Cheng, X., & Wang, L. (2016). A Comparison of Machine Learning Algorithms for Mapping of Complex Surface-Mined and Agricultural Landscapes Using ZiYuan-3 Stereo Satellite Imagery. Remote Sensing, 8(6), 514. https://doi.org/10.3390/rs8060514
Liu, Y., & Zuo, R. (2023). Monitoring Mining Activity and Vegetation Recovery in Rare Earth Element Mining Areas. Mathematical Geosciences, 56(4), 811-839. https://doi.org/10.1007/s11004-023-10113-6
Manuel, R., Brito, M., Chichorro, M., & Rosa, C. (2017). Remote Sensing for Mineral Exploration in Central Portugal. Minerals, 7(10), 184. https://doi.org/10.3390/min7100184
Ming, L., Wang, Y., Liu, G., Meng, L., & Chen, X. (2024). Analysis of vegetation dynamics from 2001 to 2020 in China’s Ganzhou rare earth mining area using time series remote sensing and SHAP-enhanced machine learning. Ecological Informatics, 102887. https://doi.org/10.1016/j.ecoinf.2024.102887
Ministerio de Ambiente y Desarrollo Sostenible. (2019). Sentencia T 445 de agosto de 2016 - Documento técnico de Investigación científica y sociológica respecto a los impactos de la actividad minera y la explotación ilícita de minerales, en los ecosistemas del territorio colombiano. https://www.minambiente.gov.co/wp-content/uploads/2021/06/Investigacion-cientifica-y-sociologica-respecto-a-los-impactos-de-la-actividad-minera-y-la-explotacion-ilicita-de-minerales.pdf
Mora Cruz, G. (2023). Estado actual de la minería en Colombia y la identificación de impactos. https://repository.unilibre.edu.co/bitstream/handle/10901/25485/Estado%20%20actual%20de%20la%20Miner%C3%ADa%20en%20Colombia%20y%20la%20identificaci%C3%B3n%20de%20impactos.pdf?sequence=1&isAllowed=y
Padró, J., Carabassa, V., Balagué, J., Brotons, L., Alcañiz, J. M., & Pons, X. (2018). Monitoring opencast mine restorations using Unmanned Aerial System (UAS) imagery. The Science Of The Total Environment, 657, 1602-1614. https://doi.org/10.1016/j.scitotenv.2018.12.156
Perilla, G. A., & Mas, J. (2020). Google Earth Engine (GEE): una poderosa herramienta que vincula el potencial de los datos masivos y la eficacia del procesamiento en la nube. Investigaciones Geográficas Boletín del Instituto de Geografía, 101. https://doi.org/10.14350/rig.59929
Pinto, J. F., Mesías, L. C., & Chamorro, M. C. (2015). Análisis de la perdida de cobertura vegetal y los impactos ambientales hacia las comunidades utilizando imágenes satelitales, minería de carbón en Cerrejón y La Loma. ResearchGate. https://www.researchgate.net/publication/289529568_ANALISIS_DE_LA_PERDIDA_DE_COBERTURA_VEGETAL_Y_LOS_IMPACTOS_AMBIENTALES_HACIA_LAS_COMUNIDADES_UTILIZANDO_IMAGENES_SATELITALES_MINERIA_DE_CARBON_EN_CERREJON_Y_LA_LOMA
Porras Gomez, F. A. (2022). Análisis del cambio de coberturas naturales producida por la explotación a cielo abierto en canteras de magnesita en Bolívar - Valle del Cauca. Repositorio Universidad Antonio Nariño. https://repositorio.uan.edu.co/server/api/core/bitstreams/fc1e3e39-05e2-4567-bf92-4d337d6028de/content
Quash, Y., Kross, A., & Jaeger, J. A. (2023). Assessing the impact of gold mining on forest cover in the Surinamese Amazon from 1997 to 2019: A semi-automated satellite-based approach. Ecological Informatics, 80, 102442. https://doi.org/10.1016/j.ecoinf.2023.102442
Ranjan, A. K., Parida, B. R., Dash, J., & Gorai, A. K. (2022). Quantifying the impacts of opencast mining on vegetation dynamics over eastern India using the long-term Landsat-series satellite dataset. Ecological Informatics, 71, 101812. https://doi.org/10.1016/j.ecoinf.2022.101812
Rebordosa, X., & López, J. (2010). Aplicación de la teledetección en el seguimiento temporal de las aplicaciones mineras a cielo abierto. Dialnet. https://dialnet.unirioja.es/servlet/articulo?codigo=3395277
Salinas, E. S., & Hernández, L. O. (2014). Escenarios ambientales y sociales de la minería a cielo abierto. Dialnet. https://dialnet.unirioja.es/servlet/articulo?codigo=4733822
Siddiqui, A. U., & Jain, M. K. (2021). Change analysis in land use land cover due to surface mining in Jharia coalfield through Landsat time series data. Materials Today Proceedings, 49, 3462-3468. https://doi.org/10.1016/j.matpr.2021.04.277
Song, D., Hu, Z., Zeng, J., & Sun, H. (2024). Influence of mining on vegetation in semi-arid areas of western China based on the coupling of above ground and below ground – A case study of Daliuta coalfield. Ecological Indicators, 161, 111964. https://doi.org/10.1016/j.ecolind.2024.111964
Sun, X., Zhou, Y., Jia, S., Shao, H., Liu, M., Tao, S., & Dai, X. (2024). Impacts of mining on vegetation phenology and sensitivity assessment of spectral vegetation indices to mining activities in arid/semi-arid areas. Journal Of Environmental Management, 356, 120678. https://doi.org/10.1016/j.jenvman.2024.120678
Thakur, T. K., Dutta, J., Upadhyay, P., Patel, D. K., Thakur, A., Kumar, M., & Kumar, A. (2021). Assessment of land degradation and restoration in coal mines of central India: A time series analysis. Ecological Engineering, 175, 106493. https://doi.org/10.1016/j.ecoleng.2021.106493
Tripathi, S. (2018). Monitoring Land Use/Cover Change Using Digital Classification Techniques: A Case Study of Sadhera Mines, Satna, Madhya Pradesh, India. www.academia.edu. https://www.academia.edu/81584238/Monitoring_Land_Use_Cover_Change_Using_Digital_Classification_Techniques_A_Case_Study_of_Sadhera_Mines_Satna_Madhya_Pradesh_India?auto=download&work_id=81584238
Wang, Y., Qin, K., Zhang, Z., He, Q., & Cohen, J. (2024). Mapping open-pit mining area in complex mining and mixed land cover zone using Landsat imagery. International Journal Of Applied Earth Observation And Geoinformation, 129, 103782. https://doi.org/10.1016/j.jag.2024.103782
Xiao, W., Deng, X., He, T., & Chen, W. (2020). Mapping Annual Land Disturbance and Reclamation in a Surface Coal Mining Region Using Google Earth Engine and the LandTrendr Algorithm: A Case Study of the Shengli Coalfield in Inner Mongolia, China. Remote Sensing, 12(10), 1612. https://doi.org/10.3390/rs12101612
Yang, Y., Erskine, P. D., Lechner, A. M., Mulligan, D., Zhang, S., & Wang, Z. (2018). Detecting the dynamics of vegetation disturbance and recovery in surface mining area via Landsat imagery and LandTrendr algorithm. Journal Of Cleaner Production, 178, 353-362. https://doi.org/10.1016/j.jclepro.2018.01.050
Yu, H., & Zahidi, I. (2022). Spatial and temporal variation of vegetation cover in the main mining area of Qibaoshan Town, China: Potential impacts from mining damage, solid waste discharge and land reclamation. The Science Of The Total Environment, 859, 160392. https://doi.org/10.1016/j.scitotenv.2022.160392
Yu, H., Zahidi, I., & Chow, M. F. (2023). Vegetation as an ecological indicator in assessing environmental restoration in mining areas. iScience, 26(9), 107667. https://doi.org/10.1016/j.isci.2023.107667
Yu, H., Zahidi, I., & Liang, D. (2023). Spatiotemporal variation of vegetation cover in mining areas of Dexing City, China. Environmental Research, 225, 115634. https://doi.org/10.1016/j.envres.2023.115634
Yu, L., Xu, Y., Xue, Y., Li, X., Cheng, Y., Liu, X., Porwal, A., Holden, E., Yang, J., & Gong, P. (2018). Monitoring surface mining belts using multiple remote sensing datasets: A global perspective. Ore Geology Reviews, 101, 675-687. https://doi.org/10.1016/j.oregeorev.2018.08.019
Zeng, X., Ma, Q., Wang, X., Liu, Z., & Wu, J. (2024). Quantifying the spatiotemporal patterns and environmental impacts of surface coal mining in the Xilingol Steppe, Inner Mongolia. Regional Environmental Change, 24(2). https://doi.org/10.1007/s10113-024-02216-5
Zhang, W., Dai, L., Yan, Y., Han, X., Teng, Y., Li, M., Zhu, Y., & Zhang, Y. (2024). Multiscale geographically weighted regression-based analysis of vegetation driving factors and mining-induced quantification in the Fengfeng District, China. Ecological Informatics, 80, 102506. https://doi.org/10.1016/j.ecoinf.2024.102506
dc.rights.uri.*.fl_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.accessrights.none.fl_str_mv info:eu-repo/semantics/openAccess
dc.rights.creativecommons.*.fl_str_mv Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.local.spa.fl_str_mv Acceso abierto
dc.rights.coar.none.fl_str_mv http://purl.org/coar/access_right/c_abf2
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
Attribution-NonCommercial-NoDerivatives 4.0 International
Acceso abierto
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.mimetype.spa.fl_str_mv applicaction/pdf
dc.coverage.sede.spa.fl_str_mv Calle 100
dc.publisher.program.spa.fl_str_mv Especialización en Gestión Integral Ambiental
dc.publisher.faculty.spa.fl_str_mv Facultad de Ingeniería
dc.publisher.grantor.spa.fl_str_mv Universidad Militar Nueva Granada
institution Universidad Militar Nueva Granada
bitstream.url.fl_str_mv https://repository.umng.edu.co/bitstreams/e17eeb34-9a43-42c5-97cf-6fd0eb269e47/download
https://repository.umng.edu.co/bitstreams/f91bf486-5449-4204-9159-95663dba1f0d/download
https://repository.umng.edu.co/bitstreams/61220e9d-3da7-4e0f-a53a-3c2cae07ace7/download
bitstream.checksum.fl_str_mv 098c15d77462b3163912f1f46f703fe4
81e3acf9df1aa1fe959862fa43bb5e45
6802868ff75eec73e0f26fd3ba7c7bb4
bitstream.checksumAlgorithm.fl_str_mv MD5
MD5
MD5
repository.name.fl_str_mv Repositorio Institucional UMNG
repository.mail.fl_str_mv bibliodigital@unimilitar.edu.co
_version_ 1851052853544615936
spelling Guzmán Rocha, Angie JuliethEspecialista en Gestión Integral Ambiental2025-11-04T16:44:58Z2025-11-04T16:44:58Z2024-12-06https://hdl.handle.net/10654/47521instname:Universidad Militar Nueva Granadareponame:Repositorio Institucional Universidad Militar Nueva Granadarepourl:https://repository.umng.edu.coLa explotación minera a cielo abierto implica la generación de impactos ambientales negativos complejos que recaen en gran medida sobre la destrucción de la cobertura vegetal, así como en los demás recursos naturales. Teniendo en cuenta las afectaciones a nivel ambiental que refleja la minería de superficie, las cuales pueden incidir de la misma forma sobre la población, se desarrolló este estudio con el fin de realizar una revisión bibliográfica descriptiva a través de diferentes artículos de investigación desarrollados en el tema, con el fin de realizar una síntesis que reúna cuáles son las metodologías más utilizadas y precisas para evaluar la afectación de la cobertura vegetal por minería a cielo abierto y destacar las herramientas más aplicadas que puedan ser de utilidad para el desarrollo de investigaciones futuras. Se logró concluir que la teledetección es la metodología más utilizada, ya que provee versatilidad para incluir herramientas que fortalezcan el análisis de imágenes, la correlación estadística y modelos con escenarios futuros, potenciándose con el avance de la tecnología, también se encontró de manera atípica, metodologías que incluyen trabajo de campo interdisciplinar, lo cual implica mayores costos y mayor cantidad de tiempo en comparación con la teledetección.Open-pit mining involves the generation of complex negative environmental impacts, largely affecting the destruction of vegetation cover as well as other natural resources. Considering the environmental impacts reflected by surface mining, which can similarly affect the population, this study was developed to conduct a descriptive literature review through diverse research articles on the topic, aiming to synthesize the most widely used and accurate methodologies for assessing the impact of open-pit mining on vegetation cover and highlight the most applied tools that may be useful for the development of future research. It was concluded that remote sensing is the most commonly used methodology, as it provides versatility by including tools that strengthen image analysis, statistical correlation, and models for future scenarios, enhanced by technological advancements. It was also found, albeit atypically, the use of methodologies that involve interdisciplinary fieldwork, which entails higher costs and more time compared to remote sensing.Especializaciónapplicaction/pdfspahttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivatives 4.0 InternationalAcceso abiertohttp://purl.org/coar/access_right/c_abf2Metodologías de identificación de impactos sobre la cobertura vegetal generados por la minería a cielo abiertoMethodologies for identifying impacts on vegetation cover caused by open-pit miningTesis/Trabajo de grado - Monografía - Especializacióninfo:eu-repo/semantics/bachelorThesishttp://purl.org/coar/resource_type/c_7a1fEspecialización en Gestión Integral AmbientalFacultad de IngenieríaUniversidad Militar Nueva GranadaAires, U. R. V., Santos, B. S. M., Coelho, C. D., Da Silva, D. D., & Calijuri, M. L. (2017). Changes in land use and land cover as a result of the failure of a mining tailings dam in Mariana, MG, Brazil. Land Use Policy, 70, 63-70. https://doi.org/10.1016/j.landusepol.2017.10.026Andaryani, S., Nourani, V., Ershadfath, F., Hashemi, F., Haghighi, A. T., & Keesstra, S. (2022). The detection and monitoring of pollution caused by gold mining using a vegetation cover index. Environmental Science And Pollution Research, 30(3), 8020-8035. https://doi.org/10.1007/s11356-022-22773-8Bernal Navarro, L. F. (2021). Análisis de la pérdida de cobertura arbórea en el complejo del Cerrejón en los municipios Albania, Barranca y Hatonuevo (La Guajira) desde el 2001 hasta 2018. Repositorio Universidad Militar. https://repository.unimilitar.edu.co/server/api/core/bitstreams/21c20674-a3d9-44be-a233-1cd660220ea8/contentBiney, E., Biney, N., Dadzie, I., Harris, E., Quartey, G. A., Asare, Y. M., Bessah, E., & Forkuo, E. K. (2022). Impact of mining on vegetation cover: A case study of Prestea Huni-Valley municipality. Scientific African, 17, e01387. https://doi.org/10.1016/j.sciaf.2022.e01387Blanche, M. F., Dairou, A. A., Juscar, N., Romarice, O. M. F., Arsene, M., Bernard, T. L., & Leroy, M. N. L. (2024). Assessment of land cover degradation due to mining activities using remote sensing and digital photogrammetry. ENVIRONMENTAL SYSTEMS RESEARCH, 13(1). https://doi.org/10.1186/s40068-024-00372-5Ceballos Espinosa, D. de J., & Toro R, L. J. (2012). EVALUACIÓN DE LA SUSCEPTIBILIDAD A LA EROSIÓN POR EL CAMBIO DE COBERTURA DEBIDO A LA MINERÍA, EN EL MUNICIPIO DE ANORÍ, ANTIOQUIA, COLOMBIA. https://revistas.unal.edu.co/index.php/gestion/article/view/36280Chen, L., Zhang, H., Zhang, X., Liu, P., Zhang, W., & Ma, X. (2021). Vegetation changes in coal mining areas: Naturally or anthropogenically Driven? CATENA, 208, 105712. https://doi.org/10.1016/j.catena.2021.105712Copacean, L., Popescu, C., & Cojocariu, L. (2015). Remote sensing methods for analyzing vegetation and urban expansion. Research Journal Of Agricultural Science, 47 (4), 2015. https://rjas.ro/download/paper_version.paper_file.b05783e4a672dd18.636f70616365616e2e706466.pdfDoyama, K., Yamaji, K., Haruma, T., Ichihara, Y., Tamura, K., Jiang, Q., Tsunashima, Y., Fukuyama, K., & Yasutaka, T. (2024). Vegetation at the former open-pit Ningyo-toge mine, 36 years after closure treatment: Impact of soil cover on woody plant establishment and dominance of the perennial herb Miscanthus sinensis. Journal Of Environmental Management, 362, 121292. https://doi.org/10.1016/j.jenvman.2024.121292Firozjaei, M. K., Sedighi, A., Firozjaei, H. K., Kiavarz, M., Homaee, M., Arsanjani, J. J., Makki, M., Naimi, B., & Alavipanah, S. K. (2020). A historical and future impact assessment of mining activities on surface biophysical characteristics change: A remote sensing-based approach. Ecological Indicators, 122, 107264. https://doi.org/10.1016/j.ecolind.2020.107264Florez-Yepes, G. Y., Rincon-Santamaría, A., Cardona, P. S., & Alvarez, A. M. A. (2017). Análisis multitemporal de las coberturas vegetales en el área de influencia de las minas de oro ubicadas en la parte alta del sector de Maltería en Manizales, Colombia. DYNA, 84(201), 95. https://doi.org/10.15446/dyna.v84n201.55759Gallwey, J., Robiati, C., Coggan, J., Vogt, D., & Eyre, M. (2020). A Sentinel-2 based multispectral convolutional neural network for detecting artisanal small-scale mining in Ghana: Applying deep learning to shallow mining. Remote Sensing Of Environment, 248, 111970. https://doi.org/10.1016/j.rse.2020.111970Guirao-Goris, J. A., Olmedo Salas, A., & Ferrer Ferrandis, E. (2008). El artículo de revisión. Universitat de València. https://www.uv.es/joguigo/valencia/Recerca_files/el_articulo_de_revision.pdfGuo, Y., Huang, Y., Li, J., Ouyang, S., Wu, L., & Qi, W. (2023). Study on the influence of mining disturbance on the variation characteristics of vegetation index: A case study of Lingwu Mining Area. Environmental Development, 45, 100811. https://doi.org/10.1016/j.envdev.2023.100811He, T., Zhang, M., Guo, A., Zhai, G., Wu, C., & Xiao, W. (2023). A novel index combining temperature and vegetation conditions for monitoring surface mining disturbance using Landsat time series. CATENA, 229, 107235. https://doi.org/10.1016/j.catena.2023.107235Himmy, O., Nguyen, T. T., Hemmler, K., Loulad, S., Rhinane, H., & Buerkert, A. (2024). Leveraging machine learning and Landsat time series for high-resolution mapping of mining-induced vegetation changes in Ouagadougou, Burkina Faso. Environmental Challenges, 101026. https://doi.org/10.1016/j.envc.2024.101026Ibrahim, E., Lema, L., Barnabé, P., Lacroix, P., & Pirard, E. (2020). Small-scale surface mining of gold placers: Detection, mapping, and temporal analysis through the use of free satellite imagery. International Journal Of Applied Earth Observation And Geoinformation, 93, 102194. https://doi.org/10.1016/j.jag.2020.102194Kayet, N., Pathak, K., Singh, C., Chowdary, V., Bhattacharya, B. K., Kumar, D., Kumar, S., & Shaik, I. (2022). Vegetation health conditions assessment and mapping using AVIRIS-NG hyperspectral and field spectroscopy data for -environmental impact assessment in coal mining sites. Ecotoxicology And Environmental Safety, 239, 113650. https://doi.org/10.1016/j.ecoenv.2022.113650Kayet, N., Pathak, K., Singh, C. P., Chaturvedi, R. K., Brahmandam, A. S., & Mandal, C. (2024). Assessment and estimation of coal dust impact on vegetation using VIs difference model and PRISMA hyperspectral data in mining sites. Journal Of Environmental Management, 367, 121935. https://doi.org/10.1016/j.jenvman.2024.121935Kruse, F., Nobles, G. R., De Jong, M., Van Bodegom, R. M. K., Van Oortmerssen, G. J. M., Kooistra, J., Van Den Berg, M., Küchelmann, H. C., Schepers, M., Leusink, E. H. P., Cornelder, B. A., Kruijer, J. D., & Dee, M. W. (2021). Human–environment interactions at a short-lived Arctic mine and the long-term response of the local tundra vegetation. Polar Record, 57. https://doi.org/10.1017/s0032247420000418Kusimi, J. M. (2008). Assessing land use and land cover change in the Wassa West District of Ghana using remote sensing. GeoJournal, 71(4), 249-259. https://doi.org/10.1007/s10708-008-9172-6Li, N., Guo, Y., Wang, L., Wang, Q., Yan, D., Zhao, S., & Lei, T. (2023). Evaluation and quantitative characterization for the ecological environment impact of open pit mining on vegetation destruction from landsat time series: A case study of Wulishan limestone mine. Ecological Indicators, 158, 111371. https://doi.org/10.1016/j.ecolind.2023.111371Li, X., Chen, W., Cheng, X., & Wang, L. (2016). A Comparison of Machine Learning Algorithms for Mapping of Complex Surface-Mined and Agricultural Landscapes Using ZiYuan-3 Stereo Satellite Imagery. Remote Sensing, 8(6), 514. https://doi.org/10.3390/rs8060514Liu, Y., & Zuo, R. (2023). Monitoring Mining Activity and Vegetation Recovery in Rare Earth Element Mining Areas. Mathematical Geosciences, 56(4), 811-839. https://doi.org/10.1007/s11004-023-10113-6Manuel, R., Brito, M., Chichorro, M., & Rosa, C. (2017). Remote Sensing for Mineral Exploration in Central Portugal. Minerals, 7(10), 184. https://doi.org/10.3390/min7100184Ming, L., Wang, Y., Liu, G., Meng, L., & Chen, X. (2024). Analysis of vegetation dynamics from 2001 to 2020 in China’s Ganzhou rare earth mining area using time series remote sensing and SHAP-enhanced machine learning. Ecological Informatics, 102887. https://doi.org/10.1016/j.ecoinf.2024.102887Ministerio de Ambiente y Desarrollo Sostenible. (2019). Sentencia T 445 de agosto de 2016 - Documento técnico de Investigación científica y sociológica respecto a los impactos de la actividad minera y la explotación ilícita de minerales, en los ecosistemas del territorio colombiano. https://www.minambiente.gov.co/wp-content/uploads/2021/06/Investigacion-cientifica-y-sociologica-respecto-a-los-impactos-de-la-actividad-minera-y-la-explotacion-ilicita-de-minerales.pdfMora Cruz, G. (2023). Estado actual de la minería en Colombia y la identificación de impactos. https://repository.unilibre.edu.co/bitstream/handle/10901/25485/Estado%20%20actual%20de%20la%20Miner%C3%ADa%20en%20Colombia%20y%20la%20identificaci%C3%B3n%20de%20impactos.pdf?sequence=1&isAllowed=yPadró, J., Carabassa, V., Balagué, J., Brotons, L., Alcañiz, J. M., & Pons, X. (2018). Monitoring opencast mine restorations using Unmanned Aerial System (UAS) imagery. The Science Of The Total Environment, 657, 1602-1614. https://doi.org/10.1016/j.scitotenv.2018.12.156Perilla, G. A., & Mas, J. (2020). Google Earth Engine (GEE): una poderosa herramienta que vincula el potencial de los datos masivos y la eficacia del procesamiento en la nube. Investigaciones Geográficas Boletín del Instituto de Geografía, 101. https://doi.org/10.14350/rig.59929Pinto, J. F., Mesías, L. C., & Chamorro, M. C. (2015). Análisis de la perdida de cobertura vegetal y los impactos ambientales hacia las comunidades utilizando imágenes satelitales, minería de carbón en Cerrejón y La Loma. ResearchGate. https://www.researchgate.net/publication/289529568_ANALISIS_DE_LA_PERDIDA_DE_COBERTURA_VEGETAL_Y_LOS_IMPACTOS_AMBIENTALES_HACIA_LAS_COMUNIDADES_UTILIZANDO_IMAGENES_SATELITALES_MINERIA_DE_CARBON_EN_CERREJON_Y_LA_LOMAPorras Gomez, F. A. (2022). Análisis del cambio de coberturas naturales producida por la explotación a cielo abierto en canteras de magnesita en Bolívar - Valle del Cauca. Repositorio Universidad Antonio Nariño. https://repositorio.uan.edu.co/server/api/core/bitstreams/fc1e3e39-05e2-4567-bf92-4d337d6028de/contentQuash, Y., Kross, A., & Jaeger, J. A. (2023). Assessing the impact of gold mining on forest cover in the Surinamese Amazon from 1997 to 2019: A semi-automated satellite-based approach. Ecological Informatics, 80, 102442. https://doi.org/10.1016/j.ecoinf.2023.102442Ranjan, A. K., Parida, B. R., Dash, J., & Gorai, A. K. (2022). Quantifying the impacts of opencast mining on vegetation dynamics over eastern India using the long-term Landsat-series satellite dataset. Ecological Informatics, 71, 101812. https://doi.org/10.1016/j.ecoinf.2022.101812Rebordosa, X., & López, J. (2010). Aplicación de la teledetección en el seguimiento temporal de las aplicaciones mineras a cielo abierto. Dialnet. https://dialnet.unirioja.es/servlet/articulo?codigo=3395277Salinas, E. S., & Hernández, L. O. (2014). Escenarios ambientales y sociales de la minería a cielo abierto. Dialnet. https://dialnet.unirioja.es/servlet/articulo?codigo=4733822Siddiqui, A. U., & Jain, M. K. (2021). Change analysis in land use land cover due to surface mining in Jharia coalfield through Landsat time series data. Materials Today Proceedings, 49, 3462-3468. https://doi.org/10.1016/j.matpr.2021.04.277Song, D., Hu, Z., Zeng, J., & Sun, H. (2024). Influence of mining on vegetation in semi-arid areas of western China based on the coupling of above ground and below ground – A case study of Daliuta coalfield. Ecological Indicators, 161, 111964. https://doi.org/10.1016/j.ecolind.2024.111964Sun, X., Zhou, Y., Jia, S., Shao, H., Liu, M., Tao, S., & Dai, X. (2024). Impacts of mining on vegetation phenology and sensitivity assessment of spectral vegetation indices to mining activities in arid/semi-arid areas. Journal Of Environmental Management, 356, 120678. https://doi.org/10.1016/j.jenvman.2024.120678Thakur, T. K., Dutta, J., Upadhyay, P., Patel, D. K., Thakur, A., Kumar, M., & Kumar, A. (2021). Assessment of land degradation and restoration in coal mines of central India: A time series analysis. Ecological Engineering, 175, 106493. https://doi.org/10.1016/j.ecoleng.2021.106493Tripathi, S. (2018). Monitoring Land Use/Cover Change Using Digital Classification Techniques: A Case Study of Sadhera Mines, Satna, Madhya Pradesh, India. www.academia.edu. https://www.academia.edu/81584238/Monitoring_Land_Use_Cover_Change_Using_Digital_Classification_Techniques_A_Case_Study_of_Sadhera_Mines_Satna_Madhya_Pradesh_India?auto=download&work_id=81584238Wang, Y., Qin, K., Zhang, Z., He, Q., & Cohen, J. (2024). Mapping open-pit mining area in complex mining and mixed land cover zone using Landsat imagery. International Journal Of Applied Earth Observation And Geoinformation, 129, 103782. https://doi.org/10.1016/j.jag.2024.103782Xiao, W., Deng, X., He, T., & Chen, W. (2020). Mapping Annual Land Disturbance and Reclamation in a Surface Coal Mining Region Using Google Earth Engine and the LandTrendr Algorithm: A Case Study of the Shengli Coalfield in Inner Mongolia, China. Remote Sensing, 12(10), 1612. https://doi.org/10.3390/rs12101612Yang, Y., Erskine, P. D., Lechner, A. M., Mulligan, D., Zhang, S., & Wang, Z. (2018). Detecting the dynamics of vegetation disturbance and recovery in surface mining area via Landsat imagery and LandTrendr algorithm. Journal Of Cleaner Production, 178, 353-362. https://doi.org/10.1016/j.jclepro.2018.01.050Yu, H., & Zahidi, I. (2022). Spatial and temporal variation of vegetation cover in the main mining area of Qibaoshan Town, China: Potential impacts from mining damage, solid waste discharge and land reclamation. The Science Of The Total Environment, 859, 160392. https://doi.org/10.1016/j.scitotenv.2022.160392Yu, H., Zahidi, I., & Chow, M. F. (2023). Vegetation as an ecological indicator in assessing environmental restoration in mining areas. iScience, 26(9), 107667. https://doi.org/10.1016/j.isci.2023.107667Yu, H., Zahidi, I., & Liang, D. (2023). Spatiotemporal variation of vegetation cover in mining areas of Dexing City, China. Environmental Research, 225, 115634. https://doi.org/10.1016/j.envres.2023.115634Yu, L., Xu, Y., Xue, Y., Li, X., Cheng, Y., Liu, X., Porwal, A., Holden, E., Yang, J., & Gong, P. (2018). Monitoring surface mining belts using multiple remote sensing datasets: A global perspective. Ore Geology Reviews, 101, 675-687. https://doi.org/10.1016/j.oregeorev.2018.08.019Zeng, X., Ma, Q., Wang, X., Liu, Z., & Wu, J. (2024). Quantifying the spatiotemporal patterns and environmental impacts of surface coal mining in the Xilingol Steppe, Inner Mongolia. Regional Environmental Change, 24(2). https://doi.org/10.1007/s10113-024-02216-5Zhang, W., Dai, L., Yan, Y., Han, X., Teng, Y., Li, M., Zhu, Y., & Zhang, Y. (2024). Multiscale geographically weighted regression-based analysis of vegetation driving factors and mining-induced quantification in the Fengfeng District, China. Ecological Informatics, 80, 102506. https://doi.org/10.1016/j.ecoinf.2024.102506MineríaCobertura vegetalImpactosTeledetecciónImágenes satelitalesMiningVegetation coverImpactsRemote sensingSatellite imageryCalle 100ORIGINALGuzmánRochaAngieJulieth2024.pdfGuzmánRochaAngieJulieth2024.pdfapplication/pdf357382https://repository.umng.edu.co/bitstreams/e17eeb34-9a43-42c5-97cf-6fd0eb269e47/download098c15d77462b3163912f1f46f703fe4MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-82987https://repository.umng.edu.co/bitstreams/f91bf486-5449-4204-9159-95663dba1f0d/download81e3acf9df1aa1fe959862fa43bb5e45MD52THUMBNAILGuzmánRochaAngieJulieth2024.pdf.jpgGuzmánRochaAngieJulieth2024.pdf.jpgIM Thumbnailimage/jpeg5161https://repository.umng.edu.co/bitstreams/61220e9d-3da7-4e0f-a53a-3c2cae07ace7/download6802868ff75eec73e0f26fd3ba7c7bb4MD5310654/47521oai:repository.umng.edu.co:10654/475212025-11-06 03:02:11.447http://creativecommons.org/licenses/by-nc-nd/4.0/open.accesshttps://repository.umng.edu.coRepositorio Institucional UMNGbibliodigital@unimilitar.edu.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

Publicaciones similares