At the request of the authors, the proceedings paper [1] will be retracted. Four coauthors, Maurício Zacharias Moreira, Eloisa Domingues, Fernando Peres Dias and Luiz Roberto de Campos Jacintho, do not endorse the content in the paper presented by the main author. We apologize to our readership for any inconvenience caused.

Reference

1. Macedo, R.; Santos, J.; Dias, F.; Moreira, M.; Jacintho, L.; Domingues, E. Mapping of Land Use and Land Cover on Brazil. In Proceedings of the 1st International Electronic Conference on Remote Sensing, 22 June–5 July 2015; Sciforum Electronic Conference Series, 2015, Volume 1, d007; doi:10.3390/ecrs-1-d007.

Retraction of the Proceedings Paper: https://sciforum.net/conference/ecrs-1/paper/2834

The seismic vulnerability analysis of urban environments is an operational issue that concerns the comprehensive knowledge of both building structural features and soils geophysical parameters, especially when considering areas that are prone to hydrogeological and seismic disasters. The protection of such environments, together with the population growth and the urbanization processes, requires a multi-disciplinary approach aiming at providing both an effective assessment of urban resources and synthetic parameters for managing post crisis events, restoration activities and search & rescue operations. Within such a framework, airborne Light Detection and Ranging (LiDAR) and Hyperspectral sensors have demonstrated to be powerful remote sensing instruments, whose jointly use allow providing meaningful parameters to describe both the topographic settings of urbanized areas and the buildings properties, in terms of geometrical, spectral and structural features. Based on this rationale, in this study, the operational benefits obtained by combining airborne LiDAR and Hyperspectral measurements are provided to support the seismic vulnerability assessment of urban seismic areas. The digital elevation model as well as the building height and the shape of the observed area are gathered by using airborne LiDAR measurements. Spectral and structural information of urban buildings are provided through the supervised classification of IMSpectorV10E VNIR (wavelength range between 400 and 1000nm subdivided into 503 bands) measurements acquired by the IPERGEO sensor. The objective is to combine the different products provided by LiDAR and Hyperspectral image processing analysis within a Geographic Information System (GIS) platform, to evaluate the intrinsic properties of buildings (e.g. perimeter, covered area, height and type of roofs) together with the topographic features of the surrounding area (e.g. the surface height and slope) for providing synthetic parameters and thematic maps useful for seismic assessment and mitigation purposes, such as: (i) the identification of steep slope areas, (ii) the analysis of building roof typology for supporting the evaluation of structural load conditions, (iii) the detection of critical structures (e.g. asbestos buildings), (iv) the identification of primary roads (in terms of escape or access routes) for supporting search and rescue operations, (v) the analysis of main road conditions after building collapses. Meaningful experimental results, gathered for the historical center of Cosenza city (Italy), allow demonstrating the benefits of the proposed approach for both seismic assessment and mitigation purposes.

The present work is supported and funded by Ministero dell'Università, dell'Istruzione e della Ricerca (MIUR) under the project PON01-02710 "MASSIMO" - "Monitoraggio in Area Sismica di SIstemi MOnumentali".

Desertification is one of the major threats to the environment and the global community. Changing climate, deforestation, changing agriculture methods and demand of resources are the important causes for the desertification. Due to dependency of human life on land, monitoring and mitigating the desertification effect is getting more attention over the years. The main objective of the study was to assess the natural factors impact on desertification process on a regional level by using the remotely sensed parameters like TRMM-precipitation, MODIS-evapotranspiration, MODIS-net primary productivity, and ASTER-DEM with the aid of AHP-GIS model. From the above mentioned sensor parameters, the indices like aridity, rainfall, rainfall use efficiency, NPP and slope are prepared for 2000-2012. TRMM data were downscaled to 1km spatial resolution to match the other parameters spatial scale for analysis. All the parameters were classified using the frequency distribution. Based on the classes the ranks were assigned and the weight of the parameters assigned by the expert’s opinions based on questionnaire survey. We converted each data into an annual scale for time series analysis. From the 12 year average data analysis, results showed that, 9.22% of the area is characterized by highly sensitive to desertification which is the southeast part of the state. Most of the area comes under the moderate sensitive area (83.45%) and 4.83% comes under the low sensitive area. The very low sensitive area exhibits only 2.5 %, which is the hilly region of the study area. We discuss the climate parameters, vegetation and topography parameters impact on desertification in the study area.

The geophysical observatory in the Antarctic Spanish Station Juan Carlos I (BAE), on Livingston Island (62.6S, 60.4W), has been monitoring the magnetic field in the Antarctic region for more than fifteen years. In 2004, a vertical incidence ionospheric sounder was incorporated to the observatory, which brings a significant added value in a region with low density of geophysical data. A High Frequency (HF) communications system was installed in 2004 in order to transmit the geomagnetic station recordings throughout the year, due to the fact that the BAE is only accessible during the austral summer. As the power supply is very limited when the station is not accessible, we had to design a low-power HF transceiver with a very simple antenna, due to environmental restrictions. Moreover, the flow of information is unidirectional, so the modulation has to be extremely robust since there is no retransmission in case of error. This led us to study the main parameters of the ionospheric channel (Signal to Noise Ratio -SNR-, delay spread, Doppler spread and availability) with narrowband and wideband soundings, and the design of modulations specially adapted to very low SNR scenarios with high levels of interference. In this poster, a review of the design of our remote geophysical observatory and associated transmission system from Antarctica to Spain (12760 km) during the last decade is presented.

Although in the field of Underwater Acoustic Imaging a large number Automatic Target Detection (ATD) systems have long been developed and their bibliography has been classified, the present study aims to test the potentiality of using common and widely used feature based image classification approaches against small targets detection in SideScan Sonar (SSS) images by combining them with powerful data mining techniques. Conventional Acoustic Classification Systems (ACS) start by extracting numerous texture descriptors from distinct image neighbourhoods throughout the image and forming large Feature Vectors (FVs). In view of the FVs’ high dimensionality, prior to unsupervised classification, a component analysis technique, usually Principal Component Analysis (PCA), is performed to decompose them into a few un-correlated features that explain the majority of the image’s variance. However, small targets belong to subordinary image information and do not contribute significantly to the total information variance of the SSS image. Furthermore targets tend to be independent image characteristics rather than un-correlated ones. In this study, a newly available technique, called Independent Component Analysis (ICA), that decomposes the FVs into independent sources, is tested against its ability to separate SSS images into targets and background and lead to accurate target classification.

The proposed methodological scheme consists of the following stages: 1) windowed feature extraction, 2) ICA  decomposition, 3) selection of certain components that enhance potential targets through a maximum curtosis criterion, 4) decision of the  number of classes that the selected components need to be clustered into so that they are optimally separated in the Euclidean space through validation indices utilization, 5) unsupervised classification and 6) selection of the class or classes that most possibly correspond to areas containing potential targets via a minimum area definition. The above stages are included in the SonarClass Matlab ACS. The classification precision of the proposed system was assessed using a SSS dataset from Igoumenitsa Harbour, Greece, including more than 85 ground truthed man made targets. The classification accuracy of the proposed system was estimated as Pc=tp/(tp+fp), where tp is the number of true positive (expected) and fp the number of false positive (unexpected) predictions, and was compared to the accuracy of following conventional ACS procedures. The method exhibited unquestionable superiority indicating that ICA may worth further attention by ATD system researchers and developers.