Automatic Identification of Transformation in the Colombian Tropical Dry Forest Using GMM and UBM-GMM
Today, machine learning methods have become a powerful tool to help curb the effects of global warming by solving ecological questions. In particular, the Colombian Tropical Dry Forest (TDF) is an important ecosystem that is currently under threat due to deforestation generated by cattle, mining, an...
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| Format: | Online |
| Language: | spa |
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Universidad Pedagógica y Tecnológica de Colombia
2020
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| Online Access: | https://revistas.uptc.edu.co/index.php/ingenieria/article/view/11752 |
| _version_ | 1801706089190785024 |
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| author | Rendón-Hurtado, Néstor David Isaza-Narváez, Claudia Victoria Rodríguez-Buriticá, Susana |
| author_facet | Rendón-Hurtado, Néstor David Isaza-Narváez, Claudia Victoria Rodríguez-Buriticá, Susana |
| author_sort | Rendón-Hurtado, Néstor David |
| collection | OJS |
| description | Today, machine learning methods have become a powerful tool to help curb the effects of global warming by solving ecological questions. In particular, the Colombian Tropical Dry Forest (TDF) is an important ecosystem that is currently under threat due to deforestation generated by cattle, mining, and urban development since colonial times. One of the urgent challenges in this area is to understand the threatened ecosystems landscape transformation and forest degradation. Traditionally, environmental conservation experts measure these changes using transformation levels (high, medium, low). These levels have been obtained through direct observation, counting species, and measures of spatial variation through the time. Therefore, these methods are invasive to the study landscapes and require large amounts of time analysis. A proficient alternative to classical methods is the passive acoustic monitoring, as they are less invasive to the environment, avoid seeing the difficulty of species from isolated individuals, and help reduce the time of researchers at the sites. Even though too much data is generated, and computational tools have been required for their analysis. This paper proposes a new method to automatically identify the transformation in the Colombian TDF. The method is based on Gaussian Mixture Models (GMM) and Universal Background Model (UBM). In addition, it includes an acoustic indices analysis to select the most informative variables. The GMM proposal was tested in two local sites (La Guajira and Bolivar regions) and achieved an accuracy of 93% and 89% for each one, and it was obtained 84% with the general UBM model. |
| format | Online |
| id | oai:oai.revistas.uptc.edu.co:article-11752 |
| institution | Revista Facultad de Ingeniería |
| language | spa |
| publishDate | 2020 |
| publisher | Universidad Pedagógica y Tecnológica de Colombia |
| record_format | ojs |
| spelling | oai:oai.revistas.uptc.edu.co:article-117522021-07-13T02:23:18Z Automatic Identification of Transformation in the Colombian Tropical Dry Forest Using GMM and UBM-GMM Identificación automática de transformación en el bosque seco tropical colombiano usando GMM y UBM-GMM Rendón-Hurtado, Néstor David Isaza-Narváez, Claudia Victoria Rodríguez-Buriticá, Susana acoustic index ecoacoustics gaussian mixture model machine learning ; maximum likelihood estimation universal background model ecoacústica modelos de mezclas gausianas índices acústicos machine learning estimación de máxima verosimilitud modelo universal Today, machine learning methods have become a powerful tool to help curb the effects of global warming by solving ecological questions. In particular, the Colombian Tropical Dry Forest (TDF) is an important ecosystem that is currently under threat due to deforestation generated by cattle, mining, and urban development since colonial times. One of the urgent challenges in this area is to understand the threatened ecosystems landscape transformation and forest degradation. Traditionally, environmental conservation experts measure these changes using transformation levels (high, medium, low). These levels have been obtained through direct observation, counting species, and measures of spatial variation through the time. Therefore, these methods are invasive to the study landscapes and require large amounts of time analysis. A proficient alternative to classical methods is the passive acoustic monitoring, as they are less invasive to the environment, avoid seeing the difficulty of species from isolated individuals, and help reduce the time of researchers at the sites. Even though too much data is generated, and computational tools have been required for their analysis. This paper proposes a new method to automatically identify the transformation in the Colombian TDF. The method is based on Gaussian Mixture Models (GMM) and Universal Background Model (UBM). In addition, it includes an acoustic indices analysis to select the most informative variables. The GMM proposal was tested in two local sites (La Guajira and Bolivar regions) and achieved an accuracy of 93% and 89% for each one, and it was obtained 84% with the general UBM model. Hoy, los métodos de aprendizaje automático se han convertido en una herramienta para ayudar a frenar los efectos del calentamiento global, al resolver cuestiones ecológicas. En particular, el bosque seco tropical (BST) de Colombia se encuentra actualmente amenazado por la deforestación generada, desde la época colonial, por la ganadería, la minería y el desarrollo urbano. Uno de los desafíos urgentes en esta área es comprender la transformacion y degradación de los bosques. Tradicionalmente, los cambios de los ecosistemas se miden por varios niveles de transformación (alto, medio, bajo). Estos se obtienen a través de observación directa, recuento de especies y medidas de variación espacial a lo largo del tiempo. Por ende, estos métodos son invasivos y requieren de largos lapsos de observación en los lugares de estudio. Una alternativa eficaz a los métodos clásicos es el monitoreo acústico pasivo, que es menos invasivo, ya que evita el aislamiento de las especies y reduce el tiempo de los investigadores en los sitios. Sin embargo, implica la generación de múltiples datos y la necesidad de herramientas computacionales destinadas al análisis de las grabaciones. Este trabajo propone un método para identificar automáticamente la transformación del BST mediante grabaciones acústicas, aplicando dos modelos de clasificación: Gaussian Mixture Models (GMM), por cada región estudiada, y Universal Background Model (UBM), para un modelo general. Además, contiene un análisis de índices acústicos, con el fin de detectar los más representativos para las transformaciones del BST. Nuestra propuesta de GMM alcanzó una precisión de 93% y 89% para las regiones de La Guajira y Bolívar. El modelo general UBM logró 84% de precisión. Universidad Pedagógica y Tecnológica de Colombia 2020-09-18 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf application/xml https://revistas.uptc.edu.co/index.php/ingenieria/article/view/11752 10.19053/01211129.v29.n54.2020.11752 Revista Facultad de Ingeniería; Vol. 29 No. 54 (2020): Continuos Publication; e11752 Revista Facultad de Ingeniería; Vol. 29 Núm. 54 (2020): Publicación Continua; e11752 2357-5328 0121-1129 spa https://revistas.uptc.edu.co/index.php/ingenieria/article/view/11752/9618 https://revistas.uptc.edu.co/index.php/ingenieria/article/view/11752/10009 Copyright (c) 2020 Néstor David Rendón-Hurtado, Claudia Victoria Isaza-Narváez, Ph. D., Susana Rodríguez-Buriticá, Ph. D. |
| spellingShingle | acoustic index ecoacoustics gaussian mixture model machine learning ; maximum likelihood estimation universal background model ecoacústica modelos de mezclas gausianas índices acústicos machine learning estimación de máxima verosimilitud modelo universal Rendón-Hurtado, Néstor David Isaza-Narváez, Claudia Victoria Rodríguez-Buriticá, Susana Automatic Identification of Transformation in the Colombian Tropical Dry Forest Using GMM and UBM-GMM |
| title | Automatic Identification of Transformation in the Colombian Tropical Dry Forest Using GMM and UBM-GMM |
| title_alt | Identificación automática de transformación en el bosque seco tropical colombiano usando GMM y UBM-GMM |
| title_full | Automatic Identification of Transformation in the Colombian Tropical Dry Forest Using GMM and UBM-GMM |
| title_fullStr | Automatic Identification of Transformation in the Colombian Tropical Dry Forest Using GMM and UBM-GMM |
| title_full_unstemmed | Automatic Identification of Transformation in the Colombian Tropical Dry Forest Using GMM and UBM-GMM |
| title_short | Automatic Identification of Transformation in the Colombian Tropical Dry Forest Using GMM and UBM-GMM |
| title_sort | automatic identification of transformation in the colombian tropical dry forest using gmm and ubm gmm |
| topic | acoustic index ecoacoustics gaussian mixture model machine learning ; maximum likelihood estimation universal background model ecoacústica modelos de mezclas gausianas índices acústicos machine learning estimación de máxima verosimilitud modelo universal |
| topic_facet | acoustic index ecoacoustics gaussian mixture model machine learning ; maximum likelihood estimation universal background model ecoacústica modelos de mezclas gausianas índices acústicos machine learning estimación de máxima verosimilitud modelo universal |
| url | https://revistas.uptc.edu.co/index.php/ingenieria/article/view/11752 |
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