Modelagem 3D e geovisualização aplicada a desastres naturais. Uma proposta de laboratório de ensino e pesquisa para monitoramento e previsão de escorregamentos.

Landslides have a high degree of uncertainty, requiring new methods for their analysis, monitoring and forecasting. In Brazil, Cemaden is responsible for actions related to natural disasters. Recently, it started, with partner institutions, a project sponsored by FINEP in order to monitor ten landslide prone areas located in different regions of the country. This paper presents the proposal of REDEGEO to implement a modeling and geovisualization laboratory to study landslide processes in urbanized areas. The laboratory consists of three parts: (A) Field surveys to obtain high resolution images from unmanned aerial vehicles, and utilizing geophysics methods (Resistivity and Ground Penetrating Radar – GPR) to obtain internal geometry of outcrops; (B) 3D modeling using the software Geovisionary®, which allows the analysis of image and geophysical datasets in different formats, considering their volumetric properties; (C) Geovisualization and Virtual Reality (VR), allowing images obtained in the field to be observed from a human-machine interface, so that researchers can have full immersion in the selected areas. The creation of a laboratory related to natural disasters, including geovisualization and VR capabilities, stimulates the active participation of research teams and creates mechanisms for participation by technology developers, managers, civil defense agents and even the population living in risk-prone areas

SIG e modelos de escorregamentos: avaliando métodos para reduzir as incertezas de dados de solos e precipitação [GIS and Landslides Models: Assessing Methods for Reducing Soil and Precipitation Data Uncertainties]

Os modelos matemáticos de base física se constituem uma importante ferramenta para auxiliar na previsão e gestão dos escorregamentos. Os dados de entrada (geotécnicos e hidroclimatológicos) destes modelos frequentemente trazem significativa incerteza na sua distribuição espacial. Este trabalho avalia dois métodos probabilísticos - lógica Fuzzy e Geoestatística - para representar as propriedades físicas e hidráulicas dos solos e a precipitação. A comparação entre um método determinístico (inverso do quadrado da distância) e a geoestatística mostrou que este último foi bem mais eficiente para detectar os efeitos da precipitação orográfica existente na região da bacia do Paraíba do Sul além de representar, de forma mais adequada, a complexidade e diversidade do mundo real. Também foi avaliado o grau de interação entre os Sistemas de Informação Geográfica (SIG) e os três modelos de escorregamento regionais (Shalstab, SINMAP e TRIGRS). Os modelos Shalstab e SINMAP possuem maior interação e podem funcionar como extensão no interior de um SIG, possibilitando que os mapas produzidos por métodos probabilísticos possam ser melhor utilizados nos modelos de escorregamentos. [The physically-based models can be an important tool to contribute to prediction and management of the landslides. Geotechnical and hydro-climatologic data used as input in the landslides models often have a high degree of uncertainty in their spatial distribution. This paper evaluates two probabilistic approaches (Fuzzy and Geostatistics). The comparison between a deterministic method (isohyets) and geostatistics showed that geostatistics is more effi cient in detecting the orographic eff ect in the Paraiba do Sul basin and represents, more adequately, the complexity and diversity of the real world. Three regional landslides models (Shalstab, SINMAP e TRIGRS) were analyzed in order to evaluate their degree of interaction with Geographical Information Systems (GIS). SHALSTAB and SINMAP models have higher degree of interaction and function as a GIS extension making possible that diff erent types of input data can be manipulated within the GIS environment; this facilitates that maps, produced by probabilistic approaches, can be better used in regional Landslide models.

Region-based classification of PolSAR data using radial basis kernel functions with stochastic distances

© 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group. Region-based classification of PolSAR data can be effectively performed by seeking for the assignment that minimizes a distance between prototypes and segments. Silva et al. [“Classification of segments in PolSAR imagery by minimum stochastic distances between wishart distributions.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 6 (3): 1263–1273] used stochastic distances between complex multivariate Wishart models which, differently from other measures, are computationally tractable. In this work we assess the robustness of such approach with respect to errors in the training stage, and propose an extension that alleviates such problems. We introduce robustness in the process by incorporating a combination of radial basis kernel functions and stochastic distances with Support Vector Machines (SVM). We consider several stochastic distances between Wishart: Bhatacharyya, Kullback-Leibler, Chi-Square, Rényi, and Hellinger. We perform two case studies with PolSAR images, both simulated and from actual sensors, and different classification scenarios to compare the performance of Minimum Distance and SVM classification frameworks. With this, we model the situation of imperfect training samples. We show that SVM with the proposed kernel functions achieves better performance with respect to Minimum Distance, at the expense of more computational resources and the need of parameter tuning. Code and data are provided for reproducibility

Effect of natural rubber latex on the conducting state of polyaniline blends determined by Raman spectroscopy

Blends possessing the elastomeric properties of natural rubber (NR) and the conducting properties of conducting polymer (polyaniline, PANI) were obtained, which are promising for further application in deformation sensors. Blends containing 20% (v/v) of PANI in 80% of NR latex were fabricated by casting in the form of free-standing films and treated either with HCl or with corona discharge, which lead PANI to its conducting state (doping process). Characterization was carried out by Raman spectroscopy, d.c. conductivity and thermogravimetric analysis. Evidence for chemical interaction between PANI and NR was observed, which allowed the conclusion that the NR latex itself is able partially to induce both the primary doping of PANI (by protonation) and the secondary doping of PANI (by changing the chain conformation). Further improvement in the primary doping could be obtained for the blends either by corona discharge or by exposing them to HCl the electrical conductivity reached in the blends was dependent on the doping conditions used, as observed by Raman scattering. Copyright (C) 2003 John Wiley Sons, Ltd