Change detection in vegetation cover through interpretation of Landsat images by artificial neural networks (ANN). Case study: Ecuadorian Amazon Region

L.V. Jaramillo, A.F. Antunes


The interpretation of classes and change detection in the vegetation cover of large areas are activities that are made possible by the use of technologies and methods associated to Remote Sensing. Satellite images of medium and high spatial and spectral resolution are fundamental tools for the execution of projects with objectives of classification of vegetal cover and detection of its temporal variations. To exploit the use of digital information of territory recovered by the satellite images, and in order to optimize the resources invested in the tasks of classification and interpretation, it is necessary to have tools and methods that allow the automation of the processes involved and prove to be the Artificial Neural Networks (ANNs) an adequate mechanism for the execution of these processes. The main objective of this work is to validate a methodology for the identification of changes in the vegetation cover of an area located in the Ecuadorian Amazon. The applied methodology seeks the change detection in the coverage of native forests prevailing in the study region.


Artificial Neural Network; change detection; satellite images

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Achard, F. 2002. Determination of Deforestation Rates of the World’s Humid Tropical Forests. Science, 297(5583), 999-1002. science.1070656

Ambrosio, G., González, J., Arévalo, V. 2009. Comparación de imágenes de satélite para la Detección de Cambios Temporales. Málaga, España. Dpto. Ingeniería de Sistemas y Automática. Universidad de Málaga, p. 1-6. Último acceso: 7 julio 2014, de drafts/ambrosio2003cis.pdf

Angelsen, A., Kaimowitz, D. 1999. Rethinking the causes of deforestation: lessons from economic models. The World Bank Research Observer, 14(1), 73-98.

Basogain, X. 2008. Redes neuronales artificiales y sus aplicaciones. Departamento de Ingeniería de Sistemas y Automática, Escuela Superior de Ingeniería Bilbao. Open Course Ware. País Vasco. Último acceso: 28 de ctubre 2014, de https://ocw.ehu. eus/pluginfile.php/9047/mod_resource/content/1/ redes_neuro/contenidos/pdf/libro-del-curso.pdf

Centeno, J. A. S. 2003. Sensoriamento remoto e processamento de imagens digitais. Curitiba: UFPR.

Chander, G., Markham, B. 2003. Revised Landsat-5 TM Radiometrie Calibration Procedures and Postcalibration Dynamic Ranges. IEEE Transactions on Geoscience and Remote Sensing, 41(11) Part II, 2674-2677. TGRS.2003.818464

Cohen, J. 1960. A Coefficient of Agreement for Nominal Scales. Educational and Psychological Measurement, 20(1), 37-46. https://doi. org/10.1177/001316446002000104

Crosta, A. P. 1999. Processamento digital de imagens de sensoriamento remoto. Campinas: UNICAMP/ Instituto de Geociências.

Haykin, S. 2001. Redes neurais: princípios e prática. Bookman, 900.

Ílsever, M., Ünsalan, C. 2012. Pixel-Based Change Detection Methods. Two-Dimensional Change Detection Methods. Springer. 7-22.

Isasi, P., Galván, I. 2004. Redes de neuronas artificiales. Un enfoque práctico. Madrid: Prentice Hall.

Kaufman, Y. J., Wald, A. E., Remer, L. A., Gao, B.- C., Li, R.-R., Flynn, L. 1997. The MODIS 2.1um channel-correlation with visible reflectance for use in remote sensing of aerosol. IEEE Transactions on Geoscience and Remote Sensing, 35(5), 1286-1298.

Keenan, R. J., Reams, G. A., Achard, F., De Freitas, J. V., Grainger, A., Lindquist, E. 2015. Dynamics of global forest area: results from the FAO Global Forest Resources Assessment 2015. Forest Ecology and Management, 352, 9-20.

Kotchenova, S. Y., Vermote, E. F., Matarrese, R., Frank J., Klemm, J. 2006. Validation of a vector version of the 6S radiative transfer code for atmospheric correction of satellite data. Part I: Path radiance. Applied Optics, 45(26), 6762-6774, set. Disponible en: Último acceso: 22 mayo 2015, de

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

MAE. (2012a). Metodología para la Representación Cartográfica de los Ecosistemas del Ecuador Continental. Ministerio del Ambiente del Ecuador. Último acceso: 29 de Agosto de 2014, de uploads/downloads/2012/09/Documento_ Metodolog+¡a_28_05_2012_v2_1.pdf

MAE (2012b). Línea Base de deforestación del Ecuador Continental. Ministerio del Ambiente de Ecuador. Último acceso en: 28 de octubre de 2014, de http:// mapa-parte1.pdf

Moya, A. 2012. Detección automática de nuevas construcciones a partir de ortofotos. Universitat de Valencia. Último acceso: 28 de agosto de 2014, de curso02/26092012Tesina.pdf

OpenForis 2009. Open Foris Geospatial Tools. Food and Agriculture Organization of United Nations (FA0). Último acceso: 25 de febrero de 2017, de

Schmidt, G., Jenkerson, C., Masek, J., Vermote, E., Gao, F. 2013. Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) Algorithm DescriptionOpen-file Report 2013-1057. U.S. Geologycal Survey Home Page. Último acceso: 16/ agosto/2017, de

Singh, A. 1989. Review Article Digital change detection techniques using remotely-sensed data. International Journal of Remote Sensing, 10(6), 989-1003. https://

SUIA 1990. Sistema Único de Información Ambiental - Ministerio del Ambiente del Ecuador. Mapa Interactivo Ambiental. Último acceso en: 30 de junio de 2015, de

USGS. Landsat Surface Reflectance Higher-Level Data Products. U.S. Geologycal Survey Home Page. Último acceso: 28 de mayo de 2015, de

Valencia, R., Cerón, C., Palacios, W., Sierra, R. (1999). Las formaciones naturales de la Amazonía del Ecuador. Sierra, R. (ed) Propuesta preliminar de un sistema de clasificación de vegetación para el Ecuador continental. Proyecto INEFAN/GEF-BIRF y EcoCiencia, Quito, 109-119.

Vermote, E. F., Tanré, D., Deuzé, J. L., Herman, M., Morcrette, J. J. 1997. Second simulation of the satellite signal in the solar spectrum, 6s: an overview. IEEE Transactions on Geoscience and Remote Sensing, 35(3), 675-686.

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