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 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 EsteroFil: Castillejo González, Isabel Luisa. Universidad de Córdoba. Departamento de Ingeniería Gráfica y Geomática; EspañaFil: Angueira, Maria Cristina. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Santiago del Estero; ArgentinaFil: García Ferrer, Alfonso. Universidad de Córdoba. Departamento de Ingeniería Gráfica y Geomática; EspañaFil: Sánchez de la Orden, Manuel. Universidad de Córdoba. Departamento de Ingeniería Gráfica y Geomática; Españ

Mapping of Olive Trees Using Pansharpened QuickBird Images: An Evaluation of Pixel- and Object-Based Analyses

This study sought to verify whether remote sensing offers the ability to efficiently delineate olive tree canopies using QuickBird (QB) satellite imagery. This paper compares four classification algorithms performed in pixel- and object-based analyses. To increase the spectral and spatial resolution of the standard QB image, three different pansharpened images were obtained based on variations in the weight of the red and near infrared bands. The results showed slight differences between classifiers. Maximum Likelihood algorithm yielded the highest results in pixel-based classifications with an average overall accuracy (OA) of 94.2%. In object-based analyses, Maximum Likelihood and Decision Tree classifiers offered the highest precisions with average OA of 95.3% and 96.6%, respectively. Between pixel- and object-based analyses no clear difference was observed, showing an increase of average OA values of approximately 1% for all classifiers except Decision Tree, which improved up to 4.5%. The alteration of the weight of different bands in the pansharpen process exhibited satisfactory results with a general performance improvement of up to 9% and 11% in pixel- and object-based analyses, respectively. Thus, object-based analyses with the DT algorithm and the pansharpened imagery with the near-infrared band altered would be highly recommended to obtain accurate maps for site-specific management

Juan de Ochoa y su contribución a la arquitectura efímera del quinientos: un cadalso para el auto de fe cordobés de 1595

Juan de Ochoa fue uno de los canteros más relevantes de la segunda mitad del siglo XVI en la ciudad de Córdoba, maestro mayor del concejo municipal, del Obispado y del cabildo catedralicio hasta su fallecimiento en 1606. Sin embargo, una de sus facetas más desconocidas es la de arquitecto diseñador de estructuras efímeras, escenografías y decoraciones de corta durabilidad, manifestaciones de gran interés que alcanzaron su máximo esplendor en los siglos de la Edad Moderna. En el siguiente trabajo se documenta un interesante ejemplo que realizó, en colaboración con Francisco Coronado, para la celebración del auto de fe de 1595 en la capital cordobesa.Juan de Ochoa was a very important architect during the second half of the 16th Century in Cordoba. He worked for the municipal council, the bishopric and the cathedral until his death in 1606. However, one of its most unknown facets was to be designer of ephemeral architectures, scenographies and short-term decorations. These manifestations reached their maximum splendor in the centuries of the Modern Age. The following study documents an interesting example made by Ochoa, in collaboration with Francisco Coronado, for the celebration of the 1595 auto de fe in the Cordovan capital

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 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

Soil mapping based on landscape classification in the semiarid Chaco, Argentina

The semiarid Chaco is an ecosystem shared by Argentina, Paraguay, Bolivia, and Brazil where land use changes from forest to commercial agriculture and social conflicts have been intensive during the last decade. These changes and the lack of reliable soil information at suitable scales are threatening the sustainable development of the region. In Santiago del Estero province, Argentina, a soil survey was carried out with the objective of reducing the knowledge gap. Due to the large area, geomorphology diversity, limited funding, and high demand of information, a geopedologic survey using remote sensing and GIS was considered an appropriate approach. Map units were determined based on the integration of geoforms and soils, knowledge of landscape and soil forming factors, field observations, and laboratory determinations. Three main landscape units were recognized: (1) a fluvio-eolian Chaco plain including a megafan with Haplustolls and Torripsamments, (2) the Rio Dulce valley with Torripsamments, and (3) the alluvial migratory plain of Río Salado with Torripsamments, Ustifluvents, and Natraqualfs. The used approach helped speed up the soil information collection at appropriate scale for land use planningInst.de SuelosFil: Angueira, Maria Cristina. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Santiago del Estero; ArgentinaFil: Cruzate, Gustavo Adolfo. INTA, Centro de Investigación en Recursos Naturales. Instituto de Suelos; ArgentinaFil: Zamora, Eduardo Maximo. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi; ArgentinaFil: Olmedo, Guillermo Federico. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Mendoza; ArgentinaFil: Sayago, J.M. Universidad Nacional de Tucumán. Instituto de Geociencias y Medio Ambiente; ArgentinaFil: Castillejo González, I. Universidad de Córdoba. Departamento de Ingeniería Gráfica y Geomática; Españ

Detection of Planting Systems in Olive Groves Based on Open-Source, High-Resolution Images and Convolutional Neural Networks

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).This paper aims to evaluate whether an automatic analysis with deep learning convolutional neural networks techniques offer the ability to efficiently identify olive groves with different intensification patterns by using very high-resolution aerial orthophotographs. First, a sub-image crop classification was carried out. To standardize the size and increase the number of samples of the data training (DT), the crop images were divided into mini-crops (sub-images) using segmentation techniques, which used a different threshold and stride size to consider the mini-crop as suitable for the analysis. The four scenarios evaluated discriminated the sub-images efficiently (accuracies higher than 0.8), obtaining the largest sub-images (H = 120, W = 120) for the highest average accuracy (0.957). The super-intensive olive plantings were the easiest to classify for most of the sub-image sizes. Nevertheless, although traditional olive groves were discriminated accurately, too, the most difficult task was to distinguish between the intensive plantings and the traditional ones. A second phase of the proposed system was to predict the crop at farm-level based on the most frequent class detected in the sub-images of each crop. The results obtained at farm level were slightly lower than at the sub-images level, reaching the highest accuracy (0.826) with an intermediate size image (H = 80, W = 80). Thus, the convolutional neural networks proposed made it possible to automate the classification and discriminate accurately among traditional, intensive, and super-intensive planting systems

Detection of Planting Systems in Olive Groves Based on Open-Source, High-Resolution Images and Convolutional Neural Networks

This paper aims to evaluate whether an automatic analysis with deep learning convolutional neural networks techniques offer the ability to efficiently identify olive groves with different intensification patterns by using very high-resolution aerial orthophotographs. First, a sub-image crop classification was carried out. To standardize the size and increase the number of samples of the data training (DT), the crop images were divided into mini-crops (sub-images) using segmentation techniques, which used a different threshold and stride size to consider the mini-crop as suitable for the analysis. The four scenarios evaluated discriminated the sub-images efficiently (accuracies higher than 0.8), obtaining the largest sub-images (H = 120, W = 120) for the highest average accuracy (0.957). The super-intensive olive plantings were the easiest to classify for most of the sub-image sizes. Nevertheless, although traditional olive groves were discriminated accurately, too, the most difficult task was to distinguish between the intensive plantings and the traditional ones. A second phase of the proposed system was to predict the crop at farm-level based on the most frequent class detected in the sub-images of each crop. The results obtained at farm level were slightly lower than at the sub-images level, reaching the highest accuracy (0.826) with an intermediate size image (H = 80, W = 80). Thus, the convolutional neural networks proposed made it possible to automate the classification and discriminate accurately among traditional, intensive, and super-intensive planting systems