Skip to main navigation menu Skip to main content Skip to site footer

Multi-temporal analysis of the Peru-Colombia fluvial border by satellite images analysis based on geographical objects during the period 1989-2015

Abstract

This paper proposes a semi-automated method to study the fluvial dynamics of large rivers using multi-temporal geographic object-based image analysis (GEOBIA), which provides direct interoperability with geographic information systems, allows quantifying the variation of existing landforms, and is efficient in terms of computing time. The method is applied to the dynamics of the Amazon River, which has provoked a conflict between Peru and Colombia for being the borderline that separates both countries. The results obtained show the repercussions on its surface such as changes in sand islands and erosion, as well as the dynamics at the channel bottom during the 1989–2015 period caused by the sediment load and thalweg movement. These results are verified against data collected on field, finding 99.2 % accuracy between estimates and actual figures.

Keywords

GEOBIA, Satellite images, Fluvial border, Amazon River

PDF (Español)

Author Biography

Leidy Johanna Quiroga Olarte

Magíster en Geomática de la Universidad Nacional de Colombia.

Martha Patricia Bohórquez

Ph. D. en Ciencias Estadísticas, Universidad Nacional de Colombia. Miembro del grupo de investigación Estadística Espacial Universidad Nacional de Colombia.

Luis Fernando Santa Guzmán

Ph. D. in Information Management, Universidade Nova de Lisboa. Miembro del grupo de investigación Estadística Espacial Universidad Nacional de Colombia.


References

Abad, J. & Garcia, M. (2006). RVR Meander: A toolbox for re-meandering of channelized streams. Computers & Geosciences, 32(1), 92-101.

Aguirre, J., Seijmonsbergen, A. C. & Duivenvoorden, J. F. (2012). Optimizing land cover classification accuracy for change detection, a combined pixel-based and objectbased approach in a mountainous area in Mexico. Applied Geography, 34, 29-37. https://doi.org/10.1016/j.apgeog.2011.10.010.

Aksoy, B. & Ercanoglu, M. (2012). Landslide identification and classification by objectbased image analysis and fuzzy logic: An example from the Azdavay region (Kastamonu, Turkey). Computers and Geosciences, 38(1), 87-98. https://doi.org/10.1016/j.cageo.2011.05.010.

Al Fugara, A. M., Pradhan, B. & Mohamed, T. A. (2009). Improvement of land-use classification using object-oriented and fuzzy logic approach. Applied Geomatics, 1(4), 111-120. https://doi.org/10.1007/s12518-009-0011-3.

Aminipouri, M., Sliuzas, R. & Kuffer, M. (2009). Object-Oriented Analysis of Very High Resolution Orthophotos for Estimating the Population of Slum Areas, A Case of Dar-Es-Salaam, Tanzania. ISPRS Archives, 38 (1-4-7/W5), Conference Paper. Recuperado de http://www.isprs.org/proceedings/XXXVIII/1_4_7-W5/.

Ariza, A., Roa, O., Serrato, P., Aicardo, H. & Rincón, L. (2018). Uso de índices espectrales derivados de sensores remotos para la caracterización geomorfológica en zonas insulares del Caribe colombiano. Perspectiva Geográfica, 23(1), 105-122.

Baar, A., Boechat Albernaz, M., van Dijk, W. & Kleinhans, M. (2019). Critical dependence of morphodynamic models of fluvial and tidal systems on empirical downslope sediment transport. Nature Communications, 10(1), 4903. https://doi.org/10.1038/s41467-019-12753-x.

Calle, H. (14 de julio de 2018). La movediza frontera de Perú y Colombia. El Espectador. Recuperado de https://www.elespectador.com/noticias/medio-ambiente/lamovediza-frontera-de-peru-y-colombia-articulo-800173.

Charlton, R. (2007). Fundamentals of fluvial geomorphology. London: Routledge. Chen, G., Zhao, K. & Powers, R. (2014). Assessment of the image misregistration effects on object-based change detection. ISPRS Journal of Photogrammetry and Remote Sensing, 87, 19-27. https://doi.org/10.1016/j.isprsjprs.2013.10.007.

Chuvieco, E. (2016). Fundamentals of satellite remote sensing: An environmental approach. Boca Raton, Florida: CRC Press.

Congalton, R. G. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37(1), 35-46.

Corpoamazonia. (2014). Plan de manejo ambiental de la quebrada Yahuarcaca, municipio de Leticia, departamento de Amazonas.

Davies, A. & Robins, P. (2017). Residual flow, bedforms and sediment transport in a tidal channel modelled with variable bed roughness. Geomorphology, 295, 855-872.

Drâgut, L. & Blaschke, T. (2006). Automated classification of landform elements using object-based image analysis. Geomorphology, 81, 330-344. https://doi.org/10.1016/j.geomorph.2006.04.013.

Earth Explorer, U. E. (2016). USGS EarthExplorer. Recuperado de https://earthexplorer.usgs.gov/.

González, M. (2005). La zona de integración fronteriza (ZIF) colombo - peruana un esfuerzo por atender la realidad. Aldea Mundo, Revista Sobre Fronteras e Integración, 10(18), 29-35. Recuperado de http://www.saber.ula.ve/db/ssaber/Edocs/pubelectronicas/aldeamundo/ano10num18/articulo3.pdf.

Hansen, M. C. & Loveland, T. R. (2012). A review of large area monitoring of land cover change using Landsat data. Remote Sensing of Environment, 122, 66-74. https://doi.org/10.1016/j.rse.2011.08.024.

Hedhli, I., Moser, G., Zerubia, J. & Serpico, S. B. (2014). Fusion of multitemporal and multiresolution remote sensing data and application to natural disasters. En 2014 IEEE Geoscience and Remote Sensing Symposium (pp. 207-210). https://doi.org/10.1109/IGARSS.2014.6946393.

Li, M., Zang, S., Zhang, B., Li, S. & Wu, C. (2014). A review of remote sensing image classification techniques: The role of Spatio-contextual information. European Journal of Remote Sensing, 47(1), 389-411. https://doi.org/10.5721/EuJRS20144723

Lillesand, T., Kiefer, R. W. & Chipman, J. (2014). Remote sensing and image interpretation. Hoboken: John Wiley & Sons.

Lizarazo, I. (2012). Clasificación de la cobertura del suelo urbano usando objetos de imagen difusos. UD y la Geomática, 6, 97-109.

López, J. (2011). Modelación hidráulica y morfodinámica de cauces sinuosos aplicación a la quebrada la Marinilla (ANT). Boletín Ciencias de la Tierra, 30, 107-118.

Ma, L., Li, M., Ma, X., Cheng, L., Du, P., & Liu, Y. (2017). A review of supervised objectbased land-cover image classification. ISPRS Journal of Photogrammetry and Remote Sensing, 130, 277-293. https://doi.org/10.1016/j.isprsjprs.2017.06.001.

Maxwell, S. K. (2011). Generating land cover boundaries from remotely sensed data using object-based image analysis: overview and epidemiological application. Spat Spatiotemporal Epidemiol, 1(4), 231-237. https://doi.org/10.1016/j.sste.2010.09.005.

Mertes, L. a K., Daniel, D. L., Melack, J. M., Nelson, B., Martinelli, L. & Forsberg, B. R. (1995). Spatial patterns of hydrology, geomorphology, and vegetation on the floodplain of the Amazon river in Brazil from a remote sensing perspective. Geomorphology, 13(1-4), 215-232. https://doi.org/10.1016/0169-555X(95)00038-7.

Muñoz Vernaza, A. (1928). Exposición sobre el tratado de límites de 1916 entre el Ecuador y Colombia y análisis jurídico del tratado de límites de 1922 entre Colombia y el Perú. Quito: Talleres Gráficos de El Comercio.

Novak, F. & Namihas, S. (2011). Perú-Colombia: la construcción de una asociación estratégica y un desarrollo fronterizo. Lima: Pontificia Universidad Católica del Perú.

Ojaghi, S., Ahmadi, F. F. & Ebadi, H. (2016). A new method for semi-automatic classification of remotely sensed images developed based on the cognitive approaches for producing spatial data required in geomatics applications. Arabian Journal of Geosciences, 9(19), 724. https://doi.org/10.1007/s12517-016-2730-1.

Posada, E., Ramírez Daza, H. M. & Espejo, N. (2012). Manual de prácticas de percepción remota con el programa ERDAS IMAGINE 2011. Bogotá: Instituto Geográfico Agustín Codazzi.

Quiroga, L. (2018). Análisis de detección de cambios en el espacio empleando interpretación de imágenes satelitales y estadística espacial. (Tesis de Maestría en Geomática). Bogotá, Universidad Nacional de Colombia.

Ramírez, C., Bocanegra, R. & Sandoval, M. (2006). Modelación morfológica del río Cauca en el tramo La Balsa-Juanchito. Ingeniería y Competitividad, 8(2), 80-93.

Rozo, M. G., Nogueira, A. C. R. & Soto, C. (2014). Remote sensing-based analysis of the planform changes in the Upper Amazon River over the period 1986-2006. Journal of South American Earth Sciences, 51, 28-44. https://doi.org/10.1016/j.jsames.2013.12.004.

Rozo, M. & Soto, C. (2009). Multitemporal analysis of the amazon river between corea island (Colombia) and Aramosa Island (Brazil). Ingeniería, Investigación y Desarrollo, 9(2), 13-17. Recuperado de https://revistas.uptc.edu.co/index.php/ingenieria_sogamoso/article/view/902.

Saadat, H., Bonnell, R., Sharifi, F., Mehuys, G., Namdar, M. & Ale-Ebrahim, S. (2008). Landform classification from a digital elevation model and satellite imagery. Geomorphology, 100(3-4), 453-464. https://doi.org/10.1016/j.geomorph.2008.01.011-

Salgar Antolínez, D. (2 de octubre de 2014). Santa Rosa, ¿tierra de nadie? El Espectador.
Recuperado de https://www.elespectador.com/noticias/elmundo/santa-rosatierra-de-nadie-articulo-520150.

Shah, P., & Vayada, M. G. (2014). Review on Satellite Image Classification using Fuzzy Logic. International Journal of Science and Research (IJSR), 4(12), 1245-1248.

Uca Avci, Z. D., Karaman, M., Ozelkan, E., Kumral, M. & Budakoglu, M. (2014). OBIA based hierarchical image classification for industrial lake water. Science of the Total Environment, 487(1), 565-573. https://doi.org/10.1016/j.scitotenv.2014.04.048.

United States Geological Survey (USGS). (2016). Landsat Processing Details. Recuperado de http://landsat.usgs.gov/Landsat_Processing_Details.php.

Vargas, G. (Agosto, 2012). Geología , geomorfología y dinámica fluvial aplicada a hidráulica de ríos. Ponencia presentada en el XX Seminario Nacional de Hidráulica e Hidrología, Barranquilla, Colombia.

Wei, W., Chen, X. & Ma, A. (2005). Object-oriented Information Extraction and Application in High-resolution Remote Sensing Image. Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, IGARSS ‘05, pp. 3803-3806. https://doi.org/10.1109/IGARSS.2005.1525737.

Wu, S., Bai, Y., & Chen, H. (2017). Change detection methods based on low-rank sparse representation for multi-temporal remote sensing imagery. Cluster Computing, 22, 9951-9966. https://doi.org/10.1007/s10586-017-1022-1.

Yang, C., Cai, X., Wang, X., Yan, R., Zhang, T., Zhang, Q., & Lu, X. (2015). Remotely Sensed Trajectory Analysis of Channel Migration in Lower Jingjiang Reach during the Period of 1983-2013. Remote Sensing, 7(12), 16241-16256. https://doi.org/10.3390/rs71215828.

Downloads

Download data is not yet available.

Similar Articles

1 2 > >> 

You may also start an advanced similarity search for this article.