Aplicación del enfoque multi-índice con imágenes Sentinel-2 para obtener áreas urbanas en la estación seca (Zonas semiáridas en el noreste de Argelia)

[EN] The mapping of urban areas mostly presents a big difficulty, particularly, in arid and semi-arid environments. For that reason, in this research, we expect to increase built up accuracy mapping for Bordj Bou Arreridj city in semi-arid regions (North-East Algeria) by focusing on the identification of appropriate combination of the remotely sensed spectral indices. The study applies the ‘k–means’ classifier. In this regard, four spectral indexes were selected, namely normalized difference tillage index (NDTI) for built-up, and both bare soil index (BSI) and dry bare-soil index (DBSI), which are related to bare soil, as well as the normalized difference vegetation index (NDVI). All previous spectral indices mentioned were derived from Sentinel-2 data acquired during the dry season. Two combinations of them were generated using layer stack process, keeping both of NDTI and NDVI index constant in both combinations so that the multi-index NDTI/BSI/NDVI was the first single dataset combination, and the multi-index NDTI/DBSI/NDVI as the second component. The results show that BSI index works better with NDTI index compared to the use of DBSI index. Therefore, BSI index provides improvements: bare soil classes and built-up were better discriminated, where the overall accuracy increased by 5.67% and the kappa coefficient increased by 12.05%. The use of k-means as unsupervised classifier provides an automatic and a rapid urban area detection. Therefore, the multi-index dataset NDTI/ BSI / NDVI was suitable for mapping the cities in dry climate, and could provide a better urban management and future remote sensing applications in semi-arid areas particularly.[ES] La cartografía de las zonas urbanas presenta una gran dificultad, especialmente en los entornos áridos y semiáridos. Por esa razón, en esta investigación esperamos aumentar la precisión de la cartografía de la ciudad de Bordj Bou Arreridj en las regiones semiáridas (noreste de Argelia) centrándose en la identificación de la combinación adecuada de los índices espectrales obtenidos por teledetección. El estudio aplica el clasificador ‘k-means’. A este respecto, se seleccionaron cuatro índices espectrales, a saber, el índice de labranza de diferencia normalizada (NDTI) para el área construida, el índice de suelo desnudo (BSI) y el índice de suelo desnudo seco (DBSI), que están relacionados con el suelo desnudo, así como el índice de vegetación de diferencia normalizada (NDVI). Todos los índices espectrales anteriores mencionados se derivaron de datos Sentinel-2 adquiridos durante la estación seca (agosto). Se generaron dos combinaciones de ellas utilizando el proceso de superposición de capas, manteniendo constante tanto el índice NDTI como el índice NDVI en ambas combinaciones, de modo que el multi-índice NDTI/BSI/NDVI fue la primera combinación de conjuntos de datos, y el multi-índice NDTI/DBSI/NDVI fue el segundo componente. Los resultados muestran que el índice BSI funciona mejor con NDTI en comparación con el uso de DBSI. Por lo tanto, BSI proporciona mejoras: las clases de suelo desnudo y la de construcciones fueron mejor discriminadas, aumentando la precisión global en un 5,67%, y el coeficiente kappa un 12,05%. El uso de k-means como clasificador no supervisado proporciona una detección del área urbana automática y rápida. Por lo tanto, el conjunto de datos de varios índices NDTI/ BSI/ NDVI fue adecuado para cartografiar las ciudades en clima seco, y podría proporcionar una mejor gestión urbana y futuras aplicaciones de teledetección en zonas semiáridas en particular.Rouibah, K.; Belabbas, M. (2020). Applying Multi-Index approach from Sentinel-2 Imagery to Extract Urban Area in dry season (Semi-Arid Land in North East Algeria). Revista de Teledetección. 0(56):89-101. https://doi.org/10.4995/raet.2020.13787OJS89101056Al-Quraishi, A. ( 2011). Drought mapping using Geoinformation technology for some sites in the Iraqi Kurdistan region. 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Influence of Pavement Structure, Traffic, and Weather on Urban Flexible Pavement Deterioration

[EN] Various studies have been recently conducted to predict pavement condition, but most of them were developed in a certain region where climate conditions were kept constant and/or the research focused on specific road distresses using single parameters. Thus, this research aimed at determining the influence of pavement structure, traffic demand, and climate factors on urban flexible pavement condition over time. To do this, the Structural Number was used as an indicator of the pavement capacity, various traffic and climate variables were defined, and the Pavement Condition Index was used as a surrogate measure of pavement condition. The analysis was focused on the calibration of regression models by using the K-Fold Cross Validation technique. As a result, for a given pavement age, pavement condition worsens as the Equivalent Single Axle Load and the Annual Average Height of Snow increased. Likewise, a cold Annual Average Temperature (5¿15 °C) and a large Annual Average Range of Temperature (20¿30 °C) encourage a more aggressive pavement deterioration process. By contrast, warm climates with low temperature variations, which are associated with low precipitation, lead to a longer pavement service life. Additionally, a new classification of climate zones was proposed on the basis of the weather influence on pavement deterioration.This research was funded by the Spanish Ministry of Science and Innovation, grant number RTC-2017-6148-7, with the European Regional Development Fund.Llopis-Castelló, D.; García-Segura, T.; Montalbán-Domingo, L.; Sanz-Benlloch, MA.; Pellicer, E. (2020). Influence of Pavement Structure, Traffic, and Weather on Urban Flexible Pavement Deterioration. Sustainability. 12(22):1-20. https://doi.org/10.3390/su12229717S1201222Hajj, E. Y., Loria, L., & Sebaaly, P. E. (2010). Performance Evaluation of Asphalt Pavement Preservation Activities. Transportation Research Record: Journal of the Transportation Research Board, 2150(1), 36-46. doi:10.3141/2150-05Santero, N. J., & Horvath, A. (2009). Global warming potential of pavements. Environmental Research Letters, 4(3), 034011. doi:10.1088/1748-9326/4/3/034011Pérez-Acebo, H., Linares-Unamunzaga, A., Abejón, R., & Rojí, E. (2018). Research Trends in Pavement Management during the First Years of the 21st Century: A Bibliometric Analysis during the 2000–2013 Period. Applied Sciences, 8(7), 1041. doi:10.3390/app8071041Prozzi, J. A., & Madanat, S. M. (2004). Development of Pavement Performance Models by Combining Experimental and Field Data. Journal of Infrastructure Systems, 10(1), 9-22. doi:10.1061/(asce)1076-0342(2004)10:1(9)Ragnoli, A., De Blasiis, M., & Di Benedetto, A. (2018). Pavement Distress Detection Methods: A Review. Infrastructures, 3(4), 58. doi:10.3390/infrastructures3040058Osorio, A., Chamorro, A., Tighe, S., & Videla, C. (2014). Calibration and Validation of Condition Indicator for Managing Urban Pavement Networks. Transportation Research Record: Journal of the Transportation Research Board, 2455(1), 28-36. doi:10.3141/2455-04Loprencipe, G., Pantuso, A., & Di Mascio, P. (2017). Sustainable Pavement Management System in Urban Areas Considering the Vehicle Operating Costs. Sustainability, 9(3), 453. doi:10.3390/su9030453LTPP Data Analysis: Factors Affecting Pavement Smoothness. NCHRP Web Document 40http://onlinepubs.trb.org/onlinepubs/nchrp/nchrp_w40-a.pdfArambula, E., George, R., Xiong, W., & Hall, G. (2011). Development and Validation of Pavement Performance Models for the State of Maryland. Transportation Research Record: Journal of the Transportation Research Board, 2225(1), 25-31. doi:10.3141/2225-04Meegoda, J. N., & Gao, S. (2014). Roughness Progression Model for Asphalt Pavements Using Long-Term Pavement Performance Data. Journal of Transportation Engineering, 140(8), 04014037. doi:10.1061/(asce)te.1943-5436.0000682Pérez-Acebo, H., Mindra, N., Railean, A., & Rojí, E. (2017). Rigid pavement performance models by means of Markov Chains with half-year step time. International Journal of Pavement Engineering, 20(7), 830-843. doi:10.1080/10298436.2017.1353390Osorio-Lird, A., Chamorro, A., Videla, C., Tighe, S., & Torres-Machi, C. (2017). Application of Markov chains and Monte Carlo simulations for developing pavement performance models for urban network management. Structure and Infrastructure Engineering, 14(9), 1169-1181. doi:10.1080/15732479.2017.1402064Pérez-Acebo, H., Gonzalo-Orden, H., Findley, D. J., & Rojí, E. (2020). A skid resistance prediction model for an entire road network. Construction and Building Materials, 262, 120041. doi:10.1016/j.conbuildmat.2020.120041Pérez-Acebo, H., Linares-Unamunzaga, A., Rojí, E., & Gonzalo-Orden, H. (2020). IRI Performance Models for Flexible Pavements in Two-Lane Roads until First Maintenance and/or Rehabilitation Work. Coatings, 10(2), 97. doi:10.3390/coatings10020097Dong, Q., Huang, B., & Richards, S. H. (2015). Calibration and Application of Treatment Performance Models in a Pavement Management System in Tennessee. Journal of Transportation Engineering, 141(2), 04014076. doi:10.1061/(asce)te.1943-5436.0000738Hassan, R., Lin, O., & Thananjeyan, A. (2015). A comparison between three approaches for modelling deterioration of five pavement surfaces. International Journal of Pavement Engineering, 18(1), 26-35. doi:10.1080/10298436.2015.1030744Pérez-Acebo, H., Gonzalo-Orden, H., & Rojí, E. (2019). Skid resistance prediction for new two-lane roads. Proceedings of the Institution of Civil Engineers - Transport, 172(5), 264-273. doi:10.1680/jtran.17.00045Ziari, H., Maghrebi, M., Ayoubinejad, J., & Waller, S. T. (2016). Prediction of Pavement Performance: Application of Support Vector Regression with Different Kernels. Transportation Research Record: Journal of the Transportation Research Board, 2589(1), 135-145. doi:10.3141/2589-15Pérez-Acebo, H., Bejan, S., & Gonzalo-Orden, H. (2017). Transition Probability Matrices for Flexible Pavement Deterioration Models with Half-Year Cycle Time. International Journal of Civil Engineering, 16(9), 1045-1056. doi:10.1007/s40999-017-0254-zGarcía-Segura, T., Montalbán-Domingo, L., Llopis-Castelló, D., Lepech, M. D., Sanz, M. A., & Pellicer, E. (2020). Incorporating pavement deterioration uncertainty into pavement management optimization. International Journal of Pavement Engineering, 1-12. doi:10.1080/10298436.2020.1837827Qiao, Y., Flintsch, G. W., Dawson, A. R., & Parry, T. (2013). Examining Effects of Climatic Factors on Flexible Pavement Performance and Service Life. Transportation Research Record: Journal of the Transportation Research Board, 2349(1), 100-107. doi:10.3141/2349-12Mohd Hasan, M. R., Hiller, J. E., & You, Z. (2015). Effects of mean annual temperature and mean annual precipitation on the performance of flexible pavement using ME design. International Journal of Pavement Engineering, 17(7), 647-658. doi:10.1080/10298436.2015.1019504Anastasopoulos, P. C., & Mannering, F. L. (2015). Analysis of Pavement Overlay and Replacement Performance Using Random Parameters Hazard-Based Duration Models. Journal of Infrastructure Systems, 21(1), 04014024. doi:10.1061/(asce)is.1943-555x.0000208Alaswadko, N., & Hassan, R. (2016). Rutting progression models for light duty pavements. International Journal of Pavement Engineering, 19(1), 37-47. doi:10.1080/10298436.2016.115512

Toward reduction of artifacts in fused images

Most fusion satellite image methodologies at pixel-level introduce false spatial details, i.e.artifacts, in the resulting fusedimages. In many cases, these artifacts appears because image fusion methods do not consider the differences in roughness or textural characteristics between different land covers. They only consider the digital values associated with single pixels. This effect increases as the spatial resolution image increases. To minimize this problem, we propose a new paradigm based on local measurements of the fractal dimension (FD). Fractal dimension maps (FDMs) are generated for each of the source images (panchromatic and each band of the multi-spectral images) with the box-counting algorithm and by applying a windowing process. The average of source image FDMs, previously indexed between 0 and 1, has been used for discrimination of different land covers present in satellite images. This paradigm has been applied through the fusion methodology based on the discrete wavelet transform (DWT), using the à trous algorithm (WAT). Two different scenes registered by optical sensors on board FORMOSAT-2 and IKONOS satellites were used to study the behaviour of the proposed methodology. The implementation of this approach, using the WAT method, allows adapting the fusion process to the roughness and shape of the regions present in the image to be fused. This improves the quality of the fusedimages and their classification results when compared with the original WAT metho

A new perspective on the competitiveness of nations

The capability of firms to survive and to have a competitive advantage in global markets depends on, amongst other things, the efficiency of public institutions, the excellence of educational, health and communications infrastructures, as well as on the political and economic stability of their home country. The measurement of competitiveness and strategy development is thus an important issue for policy-makers. Despite many attempts to provide objectivity in the development of measures of national competitiveness, there are inherently subjective judgments that involve, for example, how data sets are aggregated and importance weights are applied. Generally, either equal weighting is assumed in calculating a final index, or subjective weights are specified. The same problem also occurs in the subjective assignment of countries to different clusters. Developed as such, the value of these type indices may be questioned by users. The aim of this paper is to explore methodological transparency as a viable solution to problems created by existing aggregated indices. For this purpose, a methodology composed of three steps is proposed. To start, a hierarchical clustering analysis is used to assign countries to appropriate clusters. In current methods, country clustering is generally based on GDP. However, we suggest that GDP alone is insufficient for purposes of country clustering. In the proposed methodology, 178 criteria are used for this purpose. Next, relationships between the criteria and classification of the countries are determined using artificial neural networks (ANNs). ANN provides an objective method for determining the attribute/criteria weights, which are, for the most part, subjectively specified in existing methods. Finally, in our third step, the countries of interest are ranked based on weights generated in the previous step. Beyond the ranking of countries, the proposed methodology can also be used to identify those attributes that a given country should focus on in order to improve its position relative to other countries, i.e., to transition from its current cluster to the next higher one

Localized Regression

The main problem with localized discriminant techniques is the curse of dimensionality, which seems to restrict their use to the case of few variables. This restriction does not hold if localization is combined with a reduction of dimension. In particular it is shown that localization yields powerful classifiers even in higher dimensions if localization is combined with locally adaptive selection of predictors. A robust localized logistic regression (LLR) method is developed for which all tuning parameters are chosen data¡adaptively. In an extended simulation study we evaluate the potential of the proposed procedure for various types of data and compare it to other classification procedures. In addition we demonstrate that automatic choice of localization, predictor selection and penalty parameters based on cross validation is working well. Finally the method is applied to real data sets and its real world performance is compared to alternative procedures