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ISPRS Int. J. Geo-Inf. 2019, 8(3), 132; https://doi.org/10.3390/ijgi8030132

Combining Object-Based Image Analysis with Topographic Data for Landform Mapping: A Case Study in the Semi-Arid Chaco Ecosystem, Argentina

1
Department of Graphic Engineering and Geomatics, Campus de Rabanales, University of Cordoba, 14071 Córdoba, Spain
2
National Institute of Agricultural Technology (INTA), Agricultural Experimental Station of Santiago del Estero, Santiago del Estero G4200, Argentina
*
Author to whom correspondence should be addressed.
Received: 15 December 2018 / Revised: 1 March 2019 / Accepted: 4 March 2019 / Published: 7 March 2019
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Abstract

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. View Full-Text
Keywords: data mining algorithms; DEM-derived variables; geoforms classification; Landsat-8 imagery; OBIA data mining algorithms; DEM-derived variables; geoforms classification; Landsat-8 imagery; OBIA
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Castillejo-González, I.L.; Angueira, C.; García-Ferrer, A.; Sánchez de la Orden, M. Combining Object-Based Image Analysis with Topographic Data for Landform Mapping: A Case Study in the Semi-Arid Chaco Ecosystem, Argentina. ISPRS Int. J. Geo-Inf. 2019, 8, 132.

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