Combining object-based image analysis with topographic data for landform mapping: a case study in the semi-arid Chaco ecosystem, Argentina
This paper presents an object-based approach to mapping a set of landforms located in the fluvio-eolian plain of Rio Dulce and alluvial plain of Rio Salado (Dry Chaco, Argentina), with two Landsat 8 images collected in summer and winter combined with topographic data. The research was conducted in t...
| Autores principales: | , , , |
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| Formato: | info:ar-repo/semantics/artículo |
| Lenguaje: | Inglés |
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MDPI
2019
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| Materias: | |
| Acceso en línea: | https://www.mdpi.com/2220-9964/8/3/132 http://hdl.handle.net/20.500.12123/5306 https://doi.org/10.3390/ijgi8030132 |
| _version_ | 1855035475144736768 |
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| author | Castillejo González, Isabel Luisa Angueira, Maria Cristina García Ferrer, Alfonso Sánchez de la Orden, Manuel |
| author_browse | Angueira, Maria Cristina Castillejo González, Isabel Luisa García Ferrer, Alfonso Sánchez de la Orden, Manuel |
| author_facet | Castillejo González, Isabel Luisa Angueira, Maria Cristina García Ferrer, Alfonso Sánchez de la Orden, Manuel |
| author_sort | Castillejo González, Isabel Luisa |
| collection | INTA Digital |
| description | This paper presents an object-based approach to mapping a set of landforms located in the fluvio-eolian plain of Rio Dulce and alluvial plain of Rio Salado (Dry Chaco, Argentina), with two Landsat 8 images collected in summer and winter combined with topographic data. The research was conducted in two stages. The first stage focused on basic-spectral landform classifications where both pixel- and object-based image analyses were tested with five classification algorithms: Mahalanobis Distance (MD), Spectral Angle Mapper (SAM), Maximum Likelihood (ML), Support Vector Machine (SVM) and Decision Tree (DT). The results obtained indicate that object-based analyses clearly outperform pixel-based classifications, with an increase in accuracy of up to 35%. The second stage focused on advanced object-based derived variables with topographic ancillary data classifications. The combinations of variables were tested in order to obtain the most accurate map of landforms based on the most successful classifiers identified in the previous stage (ML, SVM and DT). The results indicate that DT is the most accurate classifier, exhibiting the highest overall accuracies with values greater than 72% in both the winter and summer images. Future work could combine both, the most appropriate methodologies and combinations of variables obtained in this study, with physico-chemical variables sampled to improve the classification of landforms and even of types of soil. |
| format | info:ar-repo/semantics/artículo |
| id | INTA5306 |
| institution | Instituto Nacional de Tecnología Agropecuaria (INTA -Argentina) |
| language | Inglés |
| publishDate | 2019 |
| publishDateRange | 2019 |
| publishDateSort | 2019 |
| publisher | MDPI |
| publisherStr | MDPI |
| record_format | dspace |
| spelling | INTA53062019-06-12T15:02:47Z Combining object-based image analysis with topographic data for landform mapping: a case study in the semi-arid Chaco ecosystem, Argentina Castillejo González, Isabel Luisa Angueira, Maria Cristina García Ferrer, Alfonso Sánchez de la Orden, Manuel Minería de Datos Accidentes Geográficos Imágenes por Satélites Cartografía Ecosistema Data Mining Landforms Satellite Imagery Cartography Ecosystems Mapeo Región Chaco Semiárido, Argentina Mapping This paper presents an object-based approach to mapping a set of landforms located in the fluvio-eolian plain of Rio Dulce and alluvial plain of Rio Salado (Dry Chaco, Argentina), with two Landsat 8 images collected in summer and winter combined with topographic data. The research was conducted in two stages. The first stage focused on basic-spectral landform classifications where both pixel- and object-based image analyses were tested with five classification algorithms: Mahalanobis Distance (MD), Spectral Angle Mapper (SAM), Maximum Likelihood (ML), Support Vector Machine (SVM) and Decision Tree (DT). The results obtained indicate that object-based analyses clearly outperform pixel-based classifications, with an increase in accuracy of up to 35%. The second stage focused on advanced object-based derived variables with topographic ancillary data classifications. The combinations of variables were tested in order to obtain the most accurate map of landforms based on the most successful classifiers identified in the previous stage (ML, SVM and DT). The results indicate that DT is the most accurate classifier, exhibiting the highest overall accuracies with values greater than 72% in both the winter and summer images. Future work could combine both, the most appropriate methodologies and combinations of variables obtained in this study, with physico-chemical variables sampled to improve the classification of landforms and even of types of soil. EEA Santiago del Estero Fil: Castillejo González, Isabel Luisa. Universidad de Córdoba. Departamento de Ingeniería Gráfica y Geomática; España Fil: Angueira, Maria Cristina. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Santiago del Estero; Argentina Fil: García Ferrer, Alfonso. Universidad de Córdoba. Departamento de Ingeniería Gráfica y Geomática; España Fil: Sánchez de la Orden, Manuel. Universidad de Córdoba. Departamento de Ingeniería Gráfica y Geomática; España 2019-06-12T15:00:57Z 2019-06-12T15:00:57Z 2019-03 info:ar-repo/semantics/artículo info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion https://www.mdpi.com/2220-9964/8/3/132 http://hdl.handle.net/20.500.12123/5306 2220-9964 https://doi.org/10.3390/ijgi8030132 eng info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) application/pdf MDPI ISPRS International Journal of Geo-Information 8 (3) : 132 (2019) |
| spellingShingle | Minería de Datos Accidentes Geográficos Imágenes por Satélites Cartografía Ecosistema Data Mining Landforms Satellite Imagery Cartography Ecosystems Mapeo Región Chaco Semiárido, Argentina Mapping Castillejo González, Isabel Luisa Angueira, Maria Cristina García Ferrer, Alfonso Sánchez de la Orden, Manuel Combining object-based image analysis with topographic data for landform mapping: a case study in the semi-arid Chaco ecosystem, Argentina |
| title | Combining object-based image analysis with topographic data for landform mapping: a case study in the semi-arid Chaco ecosystem, Argentina |
| title_full | Combining object-based image analysis with topographic data for landform mapping: a case study in the semi-arid Chaco ecosystem, Argentina |
| title_fullStr | Combining object-based image analysis with topographic data for landform mapping: a case study in the semi-arid Chaco ecosystem, Argentina |
| title_full_unstemmed | Combining object-based image analysis with topographic data for landform mapping: a case study in the semi-arid Chaco ecosystem, Argentina |
| title_short | Combining object-based image analysis with topographic data for landform mapping: a case study in the semi-arid Chaco ecosystem, Argentina |
| title_sort | combining object based image analysis with topographic data for landform mapping a case study in the semi arid chaco ecosystem argentina |
| topic | Minería de Datos Accidentes Geográficos Imágenes por Satélites Cartografía Ecosistema Data Mining Landforms Satellite Imagery Cartography Ecosystems Mapeo Región Chaco Semiárido, Argentina Mapping |
| url | https://www.mdpi.com/2220-9964/8/3/132 http://hdl.handle.net/20.500.12123/5306 https://doi.org/10.3390/ijgi8030132 |
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