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

Isabel Castillejo-González,Manuel Sánchez De La Orden,Alfonso García-Ferrer,Cristina Angueira

doi:10.3390/ijgi8030132

Isabel Castillejo-González, Manuel Sánchez De La Orden + Show 2 more

Open Access

https://doi.org/10.3390/ijgi8030132

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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.

Highlights

The Gran Chaco or Dry Chaco is a flat, semi-arid ecosystem characterized by a mix of woodlands and grasslands
While the Spectral Angle Mapper (SAM) algorithm continued having the lowest accuracies with overall accuracy (OA) values at 28.8% and 22.5% in the winter-13 and summer-14 images, respectively, the Decision Tree (DT) classifier surpassed the Support Vector Machine (SVM) algorithm, obtaining the highest accuracies of all the object-based classifications
An overview of the five classification methods applied to basic-spectral variables derived from the Landsat 8 imagery for landform classification showed that object-based analysis (OBIA) clearly outperformed pixel-based analysis, with increases of overall accuracy reaching up to 37% with the DT classifier

Summary

Introduction

The Gran Chaco or Dry Chaco is a flat, semi-arid ecosystem characterized by a mix of woodlands and grasslands. Soil types are usually delineated in the field, based on the relationship of soils and their natural surroundings, following tacit mental models [3] Those models are rarely described with clarity [4], are subject to personal bias, and are difficult to replicate, especially in quantitative studies [5]. The digital soil mapping (DSM) techniques combine field observations, laboratory measurements, terrain variables and remote sensing data, integrated with quantitative methods to map spatial patterns of soil properties [6]. This combination of techniques facilitates mapping inaccessible areas or areas with economical restrictions by reducing the need for extensive field surveys. As landforms can be suitable predictors of soil types as soil development often occurs in response to the underlying lithology and water movement in the landscape [7,8], the application of these techniques for the classification of landforms can be consider as the first stage of a future soil mapping procedure

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