Published

2025-04-21

Temporal analysis of land cover change in Ayapel's alluvial gold mining zones, Colombia

Análisis temporal del cambio de coberturas terrestres en zonas de minería de oro de aluvión en Ayapel, Colombia

DOI:

https://doi.org/10.15446/esrj.v29n1.111372

Keywords:

Remote Sensing, alluvial gold mining, satellite imagery, Landsat, land cover change, sequential pattern mining (en)
Teledetección, minería de oro de aluvión, Procesamiento digital de imágenes satelitales, Landsat, cambio de coberturas terrestres, minería de patrones secuenciales (es)

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Authors

  • Maura Melissa Herrera Ceferino Universidad de Antioquia
  • David Stephen Fernández Universidad de Antioquia
  • Fabio de Jesús Vélez Universidad de Antioquia
  • Néstor Jaime Aguirre Universidad de Antioquia

Alluvial gold mining has been a longstanding economic activity in the municipality of Ayapel, Córdoba, dating back to the colonial era, as noted by historians. However, most of this mining has occurred outside legal frameworks, often facilitated by the presence of lawless groups. This illegality complicates efforts to monitor and implement ecological restoration plans in the area. Additionally, the municipality of Ayapel is home to a significant natural asset: La Ciénaga de Ayapel. Designated as a RAMSAR site since 2018, this wetland holds international recognition for its ecological importance. However, it faces the threat of contamination from the nearby mining activities. Therefore, monitoring this mining activity becomes a crucial point to ensure, among other components, the care of La Ciénaga de Ayapel. The objective of this study is to analyze land cover changes in areas affected by alluvial mining between 1985 and 2020, using Landsat images, digital image processing techniques, unsupervised classification, and sequential pattern mining for the multitemporal analysis. Regarding the analysis conducted for the study area, encompassing the Quebradona microbasin in the municipality of Ayapel, for the years 1985 to 2020, there was an increase in mining activity of 503 ha, equivalent to 12 times that of 1985, with a peak in 2010, 54 times the 1985 levels. Bare soil, on the other hand, increased by 96% by 2020. As for vegetation cover, dense or high vegetation remained relatively constant over time, going from 2434 ha in 1985 to 2446 ha in 2020. Low vegetation, typically corresponding to grasslands, increased from occupying 3855 ha in 1985 to 5859 ha by 2020. Medium vegetation decreased by 25% compared to 1985, going from 7588 ha to 5674 ha by 2020. Finally, compared to 2010, there was a 14% increase or recovery in medium vegetation, and high vegetation saw a 5% recovery. It is important to highlight that the images from 1989 and 2013 exhibited a significant presence of cloud cover, which led to an underestimation of the area classified for each type of land cover.

La minería de oro de aluvión ha sido una actividad económica de larga data en el municipio de Ayapel, Córdoba, con antecedentes que se remontan a la época colonial, según lo señalan los historiadores. Sin embargo, la mayor parte de esta minería ha ocurrido fuera de los marcos legales, a menudo facilitada por la presencia de grupos al margen de la ley. Esta ilegalidad dificulta los esfuerzos de monitoreo y la implementación de planes de restauración ecológica en la zona. Además, el municipio de Ayapel alberga un importante recurso natural: La Ciénaga de Ayapel. Este humedal ha sido designado como sitio RAMSAR desde 2018, lo que le otorga reconocimiento internacional por su importancia ecológica. No obstante, enfrenta la amenaza de contaminación debido a las actividades mineras cercanas. Por lo tanto, el monitoreo de esta actividad minera se vuelve un aspecto crucial para garantizar, entre otros elementos, la protección de La Ciénaga de Ayapel. El objetivo de este estudio es analizar los cambios en la cobertura terrestre en las áreas afectadas por la minería aluvial entre 1985 y 2020, utilizando imágenes Landsat, técnicas de procesamiento digital de imágenes, clasificación no supervisada y minería de patrones secuenciales para el análisis multitemporal. En cuanto al análisis realizado en la zona de estudio, que abarca la microcuenca Quebradona en el municipio de Ayapel, se observó un incremento en la actividad minera de 503 ha entre los años 1985 y 2020, lo que equivale a 12 veces la extensión de 1985, alcanzando un pico en 2010, cuando la actividad minera fue 54 veces mayor que en 1985. Por otro lado, el suelo desnudo aumentó en un 96% para 2020. En cuanto a la cobertura vegetal, la vegetación densa o alta se mantuvo relativamente constante a lo largo del tiempo, pasando de 2434 ha en 1985 a 2446 ha en 2020. La vegetación baja, que generalmente corresponde a pastizales, creció de 3855 ha en 1985 a 5859 ha en 2020. En contraste, la vegetación media disminuyó en un 25% respecto a 1985, pasando de 7588 ha a 5674 ha en 2020. Finalmente, en comparación con 2010, se evidenció una recuperación del 14% en la vegetación media y un 5% en la vegetación alta. Es importante destacar que las imágenes de los años 1989 y 2013 mostraron una presencia significativa de nubosidad, lo que llevó a una subestimación en la clasificación del área para cada tipo de cobertura terrestre.

References

Acosta Díaz, D. M. (2017). Estudio multitemporal de la dinámica de explotación de oro de aluvión del Bajo Cauca Antioqueño en los años 2014 y 2017 a través de imágenes satelitales. Universidad Militar Nueva Granada.

Agencia Nacional de Minería (ANM). (2019). El título minero y sus etapas. Recuperado de https://www.anm.gov.co/?q=content/el-titulo-minero-y-sus-etapas.

Almeida-Filho, R., & Shimabukuro, Y. E. (2002). Digital processing of a Landsat-TM time series for mapping and monitoring degraded areas caused by independent gold miners, Roraima State, Brazilian Amazon. Remote Sensing of Environment. https://doi.org/10.1016/S0034-4257(01)00237-1

Anaya, L., & Diaz, A. (2016). Análisis de la fragmentación de coberturas naturales producida por la minería a cielo abierto en el Municipio La Jagua de Ibirico, Cesar. Journal of Chemical Information and Modeling, 54.

Arroyo-Rodríguez, V., Melo, F. P., Martínez-Ramos, M., Bongers, F., Chazdon, R. L., Meave, J. A., ... & Tabarelli, M. (2017). Multiple successional pathways in human-modified tropical landscapes: new insights from forest succession, forest fragmentation and landscape ecology research. Biological Reviews, 92(1), 326-340

Borrero Morales, N. (2014). Aluvión: a cielo abierto. Semana Sostenible. Recuperado de https://www.semana.com/medio-ambiente/multimedia/mineria-aluvion-cielo-abierto/32234/

Bradley, S. (2020). Mining’s impacts on forests. Chatham House. https://www.chathamhouse.org

Chazdon, R. L. (2008). Beyond deforestation: restoring forests and ecosystem services on degraded lands. Science, 320(5882), 1458-1460.

Chazdon, R. L. (2014). Second growth: The promise of tropical forest regeneration in an age of deforestation. University of Chicago Press.

Chica-Olmo, M., Abarca-Hernández, F., & Tolosana-Delgado, R. (2013). Land cover change analysis of a Mediterranean area in Spain using different sources of data: Multi-seasonal Landsat images, land surface temperature, digital terrain models and texture. International Journal of Applied Earth Observation and Geoinformation, 20, 37–49. https://doi.org/10.1016/j.jag.2011.12.002

Congalton, R. G., & Green, K. (2019). Assessing the Accuracy of Remotely Sensed Data: Principles and Practices (3rd ed.). CRC Press.

DeFries, R. S., Foley, J. A., & Asner, G. P. (2004). Land-use choices: Balancing human needs and ecosystem function. Frontiers in Ecology and the Environment, 2(5), 249-257.

Demattê, J. A. M., Fongaro, C. T., Rizzo, R., & Safanelli, J. L. (2018). Geospatial Soil Sensing System (GEOS3): A powerful data mining procedure to retrieve soil spectral reflectance from satellite images. Remote Sensing of Environment, 212, 161–175. https://doi.org/10.1016/j.rse.2018.04.047

Dethier, E.N., Silman, M., Leiva, J.D. et al. A global rise in alluvial mining increases sediment load in tropical rivers. Nature 620, 787–793 (2023). https://doi.org/10.1038/s41586-023-06309-9.

Donkor, A., Nartey, V., Bonzongo, J. C., & Adotey, D. (2009). Artisanal mining of gold with mercury in Ghana. West African Journal of Applied Ecology, 9. https://doi.org/10.4314/wajae.v9i1.45666

Duarte, F., Álvarez, M., & Gómez, J. (2017), quienes exploraron la clasificación de imágenes para análisis de la cobertura del suelo.

Espejo, J. C., Messinger, M., Román-Dañobeytia, F., Ascorra, C., Fernandez, L. E., & Silman, M. R. (2018). Deforestation and forest degradation due to gold mining in the Peruvian Amazon: A 34-Year Perspective. Remote Sensing, 10(12), 1903. https://doi.org/10.3390/rs10121903

Esri. (2023). How Accuracy Assessment works. ArcGIS Pro Documentation. https://pro.arcgis.com/en/pro-app/latest/tool-reference/spatial-analyst/how-accuracy-assessment-works.htm.

Feeley, K. J., Davies, S. J., Perez, R., Hubbell, S. P., & Foster, R. B. (2011). Directional changes in the species composition of a tropical forest. Ecology, 92(4), 871-882.

Fernández-Manso, A., Quintano, C., & Roberts, D. (2012). Evaluation of multiple endmember spectral mixture analysis (MESMA) for surface coal mining affected area mapping. Remote Sensing of Environment, 127, 181–193. https://doi.org/10.1016/j.rse.2012.08.028

Franco, R. (2017). Composiciones LANDSAT en ArcGis: Guía básica. Bogotá, Colombia. 45p. Disponible en http://wp.me/p2IwQU-1bh

Foody, G. M. (2002). Status of land cover classification accuracy assessment. Remote Sensing of Environment, 80(1), 185-201.

Gastauer, M., Souza Filho, P. W. M., Ramos, S. J., Caldeira, C. F., Silva, J. R., Siqueira, J. O., & Furtini Neto, A. E. (2019). Mine land rehabilitation in Brazil: Goals and techniques in the context of legal requirements. Ambio, 48(1), 74-88.

Gerson, J. R., Szponar, N., Zambrano, A. A., et al. (2022). Amazon forests capture high levels of atmospheric mercury pollution from artisanal gold mining. Nature Communications, 13(1), 27997. https://doi.org/10.1038/s41467-022-27997-3

Gillanders, S. N., Coops, N. C., Wulder, M. A., & Goodwin, N. R. (2008). Application of Landsat satellite imagery to monitor land-cover changes at the Athabasca Oil Sands, Alberta, Canada. Canadian Geographer. https://doi.org/10.1111/j.1541-0064.2008.00225.x

Gistec. (2012). Comparison of NDVI and SAVI for vegetation detection. Retrieved from www.gistec.com

González, M. (2015). Análisis espectral de solidos suspendidos en aguas continentales con presencia de actividades mineras: caso de estudio Río Sipí, Pacifico Colombiano. Universidad Militar Nueva Granada, 14. http://hdl.handle.net/10654/13462

Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2017.06.031

Hartigan, A., & Wong, M. A. (1979). A K-Means Clustering Algorithm. Journal of the Royal Statistical Society.

He, D., Le, B. T., Xiao, D., Mao, Y., Shan, F., & Ha, T. T. L. (2019). Coal mine area monitoring method by machine learning and multispectral remote sensing images. Infrared Physics and Technology, 103, 1350–4495. https://doi.org/10.1016/j.infrared.2019.103070

Helmi, S., & Banaei-Kashani, F. (2016). Mining frequent episodes from multivariate spatiotemporal event sequences. Proceedings of the 7th ACM SIGSPATIAL International Workshop on GeoStreaming, IWGS 2016. https://doi.org/10.1145/3003421.3003428

Hengkai, L., Feng, X., & Qin, L. (2020). Remote sensing monitoring of land damage and restoration in rare earth mining areas in 6 counties in southern Jiangxi based on multisource sequential images. Journal of Environmental Management, 267, 1–9. https://doi.org/10.1016/j.jenvman.2020.110653

Huang, D., & Liu, Q. (2013). Remote sensing monitoring and effect evaluation on ecological restoration of heidaigou coal mining area. International Conference on Remote Sensing, Environment and Transportation Engineering, RSETE 2013, 160–163. https://doi.org/10.2991/rsete.2013.40

Huete, A. R. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment. https://doi.org/10.1016/0034-4257(88)90106-X

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(July), 102194. https://doi.org/10.1016/j.jag.2020.102194

Karakacan Kuzucu, A., & Bektas Balcik, F. (2017). Testing the potential of vegetation indices for land use/cover classification using high resolution data. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 4, 279–283. https://doi.org/10.5194/isprs-annals-IV-4-W4-279-2017

Karan, S. K., Samadder, S. R., & Maiti, S. K. (2016). Assessment of the capability of remote sensing and GIS techniques for monitoring reclamation success in coal mine degraded lands. Journal of Environmental Management, 182, 272–283. https://doi.org/10.1016/j.jenvman.2016.07.070

Landis, J. R., & Koch, G. G. (1977). The Measurement of Observer Agreement for Categorical Data. Biometrics. https://doi.org/10.2307/2529310

Lei, S., Ren, L., & Bian, Z. (2016). Time–space characterization of vegetation in a semiarid mining area using empirical orthogonal function decomposition of MODIS NDVI time series. Environmental Earth Sciences, 75(6), 1–11. https://doi.org/10.1007/s12665-015-5122-z

Li, H., Lei, J., & Wu, J. (2017). Evolution analysis of vegetation cover under the disturbance of rare earth mining: A case in Lingbei mining area. Journal of Applied Science and Engineering, 20(3), 393–400. https://doi.org/10.6180/jase.2017.20.3.14

Li, J., Liang, J., Wu, Y., Yin, S., Yang, Z., & Hu, Z. (2021). Quantitative evaluation of ecological cumulative effect in mining area using a pixel-based time series model of ecosystem service value. Ecological Indicators, 120. https://doi.org/10.1016/j.ecolind.2020.106873

Li, J., Yan, X., Cao, Z., Yang, Z., Liang, J., Ma, T., & Liu, Q. (2020). Identification of successional trajectory over 30 Years and evaluation of reclamation effect in coal waste dumps of surface coal mine. Journal of Cleaner Production, 269. https://doi.org/10.1016/j.jclepro.2020.122161

Lillesand, T., Kiefer, R. W., & Chipman, J. (2015). Remote sensing and image interpretation (7th ed.). John Wiley & Sons.

Lu, D., Mausel, P., Brondízio, E., & Moran, E. (2004). Change detection techniques. International Journal of Remote Sensing, 25(12), 2365-2401.

LU, X., HU, Z. qi, LIU, W. jie, & HUANG, X. yan. (2007). Vegetation Growth Monitoring Under Coal Exploitation Stress by Remote Sensing in the Bulianta Coal Mining Area. Journal of China University of Mining and Technology, 17(4), 479–483. https://doi.org/10.1016/S1006-1266(07)60129-1

Decreto 356 de 2018, 6 (2018). Recuperado de https://www.funcionpublica.gov.co/eva/gestornormativo/norma.php?i=85440

Esri. (2020). ArcGIS Web AppBuilder (Version 2.15) [Computer software]. https://www.esri.com/en-us/arcgis/products/web-appbuilder/overview.

McFeeters, S. K. (1996). The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing. https://doi.org/10.1080/01431169608948714

McKenna, P. B., Lechner, A. M., Phinn, S., & Erskine, P. D. (2020). Remote sensing of mine site rehabilitation for ecological outcomes: A global systematic review. In Remote Sensing. https://doi.org/10.3390/rs12213535

MDPI. (2023). Assessing ecological restoration in arid mining regions: A progressive evaluation system. Retrieved from https://www.mdpi.com

Moore, I. D., Grayson, R. B., & Ladson, A. R. (1991). Digital terrain modelling: A review of hydrological, geomorphological, and biological applications.

Olofsson, P., Foody, G. M., Herold, M., Stehman, S. V., Woodcock, C. E., & Wulder, M. A. (2014). Good practices for estimating area and assessing accuracy of land change. Remote Sensing of Environment, 148, 42-57.

Pérez-Rincón, M. . (2014). Conflictos ambientales en Colombia: inventario, caracterización y análisis. In Minería en Colombia: control público, memoria y justicia socio-ecológica, movimientos sociales y posconflicto.

Pérez Umaña, M. (2018). Análisis multitemporal de la zona de explotación minera cielo abierto en el municipio de Duitama a partir de imágenes Landsat 7 y Sentinel 2A. Universidad Militar Nueva Granada. http://repository.unimilitar.edu.co/handle/10654/17633

Petropoulos, G. P., & Partsinevelos, P. (2012). Change detection of surface mining activity and reclamation based on a machine learning approach of multi- temporal Landsat TM imagery. Geocarto International, 28(4), 323–342. https://doi.org/10.1080/10106049.2012.706648

Pueblo, D. del. (2015). La Minería sin control, un enfoque desde la vulderación de los Derechos Humanos. https://www.defensoria.gov.co

Quejada Palacios, J. L. (2019). Afectaciones por minería en la subzona hidrográfica del Río Quito, mendiante la interpretación de imágenes satelitales. Universidad de Manizales.

Raval, S., & Shamsoddini, A. (2014). A monitoring framework for land use around kaolin mining areas through Landsat TM images. Earth Sci Inform. https://doi.org/10.1007/s12145-014-0169-z

Remote Sensing: An Overview. Eart Data: Open Access for Open Science. NASA, 2022. Disponible en: https://earthdata.nasa.gov/learn/backgrounders/remote-sensing

Richards, J. A. (2013). Remote sensing digital image analysis: An introduction (5th ed.). Springer.

Rodríguez-Luna, S. (2012). Localización de áreas de potencial explotación minera que no afectan la dinámica ambiental y territorial del municipio de Marmató, utilizando Sistemas de Información Geográfica. 67. http://ridum.umanizales.edu.co:8080/xmlui/bitstream/handle/6789/999/SIG MINERIA ORDENAMIENTO TERRITORIAL Y AMBIENTAL.pdf?sequence=1

Rodríguez, I. C. C. (2018). Evaluación de daños ambientales por minería a cielo abierto en un lecho fluvial en Colombia, usando imágenes multiespectrales. Universidad de Manizales.

Rodríguez, P. A, (2019). Evaluación de daños ambientales por explotación de oro de aluvión en los Humedales de Ayapel. Repositorio Institucional Universidad de Manizales. Disponible en: https://ridum.umanizales.edu.co/xmlui/handle/20.500.12746/6255.

Saini, V., Ravi, G., & Manoj, A. (2019). Environmental Monitoring in the Jharia Coalfield, India. In Coal and Peat Fires: A Global Perspective (pp. 359–385). https://doi.org/10.1016/b978-0-12-849885-9.00017-2

Salazar, A., Sanchez, A., Villegas, J. C., Salazar, J. F., Ruiz Carrascal, D., Sitch, S., Restrepo, J. D., Poveda, G., Feeley, K. J., Mercado, L. M., Arias, P. A., Sierra, C. A., Uribe, M. del R., Rendón, A. M., Pérez, J. C., Murray Tortarolo, G., Mercado-Bettin, D., Posada, J. A., Zhuang, Q., & Dukes, J. S. (2018). The ecology of peace: preparing Colombia for new political and planetary climates. Frontiers in Ecology and the Environment, 16(9), 525–531. https://doi.org/10.1002/fee.1950

Sonter, L. J., Herrera, D., Barrett, D. J., Galford, G. L., Moran, C. J., & Soares-Filho, B. S. (2017). Mining drives extensive deforestation in the Brazilian Amazon. Nature Communications, 8(1), 1013.

Suarez Prada, H. S. (2019). Protocolo de procesamiento de imágenes satelitales para el entrenamiento y prueba de una red neuronal de predicción de daño ecológico por extracción ilegal de minerales dentro del marco de celebración de los 100 años de la Fuerza Aérea Colombiana. Universidad Distrital Francisco José de Caldas.

Oficina de las Naciones Unidas contra la Droga y el Delito-UNODC (2016). Colombia, Explotación de oro de aluvión. Evidencias a partir de percepción remota, 2016. https://www.unodc.org/documents/colombia/2016/junio/Explotacion_de_Oro_de_Aluvion.pdf

Oficina de las Naciones Unidas contra la Droga y el Delito-UNODC (2019). Colombia, Explotación de oro de aluvión. Evidencias a partir de percepción remota, 2018.

Oficina de las Naciones Unidas contra la Droga y el Delito-UNODC (2021). Explotación de oro de aluvión: Evidencias a partir de percepción remota 2020.

Qi, J., Chehbouni, A., Huete, A. R., Kerr, Y. H., & Sorooshian, S. (1994). A modified soil adjusted vegetation index (MSAVI). Remote Sensing of Environment, 48(2), 119–126.

Talukdar, S., Singha, P., Mahato, S., Shahfahad, Pal, S., Liou, Y.-A., & Rahman, A. (2020). Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations—A Review. Remote Sensing, 12(7), 1135. https://doi.org/10.3390/rs12071135.

Uribe Ospina, D. (2019). Estimación de la contaminación causada por la minería en cuerpos de agua del Bajo Cauca a través de imágenes satelitales. Universidad EIA.

U.S. Geological Survey. (n.d.). Landsat soil adjusted vegetation index (SAVI). Retrieved January 16, 2025, from https://www.usgs.gov/landsat-missions/landsat-soil-adjusted-vegetation-index

Wang, J., Yang, R., & Feng, Y. (2017). Spatial variability of reconstructed soil properties and the optimization of sampling number for reclaimed land monitoring in an opencast coal mine. Arabian Journal of Geosciences. https://doi.org/10.1007/s12517-017-2836-0

Wang, X., Tan, K., Xu, K., Chen, Y., & Ding, J. (2019). Quantitative evaluation of the eco-environment in a coalfield based on multi-temporal remote sensing imagery: A case study of Yuxian, China. International Journal of Environmental Research and Public Health, 16(3). https://doi.org/10.3390/ijerph16030511

What is remote sensing and what is it used for? | U.S. Geological Survey. Tomado de: https://www.usgs.gov/faqs/what-remote-sensing-and-what-it-used

Wu, X., & Zhang, X. (2019). An efficient pixel clustering-based method for mining spatial sequential patterns from serial remote sensing images. Computers and Geosciences. https://doi.org/10.1016/j.cageo.2019.01.005

Xu, J., Zhao, H., Yin, P., Jia, D., & Li, G. (2018). Remote sensing classification method of vegetation dynamics based on time series Landsat image: a case of opencast mining area in China. EURASIP Journal on Image and Video Processing, 113.

Zaki, M. J. (2001). SPADE: An efficient algorithm for mining frequent sequences. Machine learning, 42, 31-60.

Zhu, D., & Chen, T. (2020). Monitoring the effects of open-pit mining on the eco-environment using a moving window-based remote sensing ecological index. Environmental Science and

How to Cite

APA

Herrera Ceferino, M. M., Fernández, D. S., Vélez, F. de J. & Aguirre, N. J. (2025). Temporal analysis of land cover change in Ayapel’s alluvial gold mining zones, Colombia. Earth Sciences Research Journal, 29(1), 1–13. https://doi.org/10.15446/esrj.v29n1.111372

ACM

[1]
Herrera Ceferino, M.M., Fernández, D.S., Vélez, F. de J. and Aguirre, N.J. 2025. Temporal analysis of land cover change in Ayapel’s alluvial gold mining zones, Colombia. Earth Sciences Research Journal. 29, 1 (Apr. 2025), 1–13. DOI:https://doi.org/10.15446/esrj.v29n1.111372.

ACS

(1)
Herrera Ceferino, M. M.; Fernández, D. S.; Vélez, F. de J.; Aguirre, N. J. Temporal analysis of land cover change in Ayapel’s alluvial gold mining zones, Colombia. Earth sci. res. j. 2025, 29, 1-13.

ABNT

HERRERA CEFERINO, M. M.; FERNÁNDEZ, D. S.; VÉLEZ, F. de J.; AGUIRRE, N. J. Temporal analysis of land cover change in Ayapel’s alluvial gold mining zones, Colombia. Earth Sciences Research Journal, [S. l.], v. 29, n. 1, p. 1–13, 2025. DOI: 10.15446/esrj.v29n1.111372. Disponível em: https://revistas.unal.edu.co/index.php/esrj/article/view/111372. Acesso em: 20 jun. 2025.

Chicago

Herrera Ceferino, Maura Melissa, David Stephen Fernández, Fabio de Jesús Vélez, and Néstor Jaime Aguirre. 2025. “Temporal analysis of land cover change in Ayapel’s alluvial gold mining zones, Colombia”. Earth Sciences Research Journal 29 (1):1-13. https://doi.org/10.15446/esrj.v29n1.111372.

Harvard

Herrera Ceferino, M. M., Fernández, D. S., Vélez, F. de J. and Aguirre, N. J. (2025) “Temporal analysis of land cover change in Ayapel’s alluvial gold mining zones, Colombia”, Earth Sciences Research Journal, 29(1), pp. 1–13. doi: 10.15446/esrj.v29n1.111372.

IEEE

[1]
M. M. Herrera Ceferino, D. S. Fernández, F. de J. Vélez, and N. J. Aguirre, “Temporal analysis of land cover change in Ayapel’s alluvial gold mining zones, Colombia”, Earth sci. res. j., vol. 29, no. 1, pp. 1–13, Apr. 2025.

MLA

Herrera Ceferino, M. M., D. S. Fernández, F. de J. Vélez, and N. J. Aguirre. “Temporal analysis of land cover change in Ayapel’s alluvial gold mining zones, Colombia”. Earth Sciences Research Journal, vol. 29, no. 1, Apr. 2025, pp. 1-13, doi:10.15446/esrj.v29n1.111372.

Turabian

Herrera Ceferino, Maura Melissa, David Stephen Fernández, Fabio de Jesús Vélez, and Néstor Jaime Aguirre. “Temporal analysis of land cover change in Ayapel’s alluvial gold mining zones, Colombia”. Earth Sciences Research Journal 29, no. 1 (April 21, 2025): 1–13. Accessed June 20, 2025. https://revistas.unal.edu.co/index.php/esrj/article/view/111372.

Vancouver

1.
Herrera Ceferino MM, Fernández DS, Vélez F de J, Aguirre NJ. Temporal analysis of land cover change in Ayapel’s alluvial gold mining zones, Colombia. Earth sci. res. j. [Internet]. 2025 Apr. 21 [cited 2025 Jun. 20];29(1):1-13. Available from: https://revistas.unal.edu.co/index.php/esrj/article/view/111372

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