Using airborne laser altimetry to improve river flood extents delineated from SAR data

Flood extent maps derived from SAR images are a useful source of data for validating hydraulic models of river flood flow. The accuracy of such maps is reduced by a number of factors, including changes in returns from the water surface caused by different meteorological conditions and the presence of emergent vegetation. The paper describes how improved accuracy can be achieved by modifying an existing flood extent delineation algorithm to use airborne laser altimetry (LiDAR) as well as SAR data. The LiDAR data provide an additional constraint that waterline (land-water boundary) heights should vary smoothly along the flooded reach. The method was tested on a SAR image of a flood for which contemporaneous aerial photography existed, together with LiDAR data of the un-flooded reach. Waterline heights of the SAR flood extent conditioned on both SAR and LiDAR data matched the corresponding heights from the aerial photo waterline significantly more closely than those from the SAR flood extent conditioned only on SAR data.

Global scale water stages from SAR imagery (GlosSARi) to support global flood forecasting

Despite the success of studies attempting to integrate remotely sensed data and flood modelling and the need to provide near-real time data routinely on a global scale as well as setting up online data archives, there is to date a lack of spatially and temporally distributed hydraulic parameters to support ongoing efforts in modelling. Therefore, the objective of this project is to provide a global evaluation and benchmark data set of floodplain water stages with uncertainties and assimilation in a large scale flood model using space-borne radar imagery. An algorithm is developed for automated retrieval of water stages with uncertainties from a sequence of radar imagery and data are assimilated in a flood model using the Tewkesbury 2007 flood event as a feasibility study. The retrieval method that we employ is based on possibility theory which is an extension of fuzzy sets and that encompasses probability theory. In our case we first attempt to identify main sources of uncertainty in the retrieval of water stages from radar imagery for which we define physically meaningful ranges of parameter values. Possibilities of values are then computed for each parameter using a triangular ‘membership’ function. This procedure allows the computation of possible values of water stages at maximum flood extents along a river at many different locations. At a later stage in the project these data are then used in assimilation, calibration or validation of a flood model. The application is subsequently extended to a global scale using wide swath radar imagery and a simple global flood forecasting model thereby providing improved river discharge estimates to update the latter.

Robust classification of SAR imagery

In this work the G(A)(0) distribution is assumed as the universal model for amplitude Synthetic Aperture (SAR) imagery data under the Multiplicative Model. The observed data, therefore, is assumed to obey a G(A)(0) (alpha; gamma, n) law, where the parameter n is related to the speckle noise, and (alpha, gamma) are related to the ground truth, giving information about the background. Therefore, maps generated by the estimation of (alpha, gamma) in each coordinate can be used as the input for classification methods. Maximum likelihood estimators are derived and used to form estimated parameter maps. This estimation can be hampered by the presence of corner reflectors, man-made objects used to calibrate SAR images that produce large return values. In order to alleviate this contamination, robust (M) estimators are also derived for the universal model. Gaussian Maximum Likelihood classification is used to obtain maps using hard-to-deal-with simulated data, and the superiority of robust estimation is quantitatively assessed.

Scheduling satellite-based SAR acquisition for sequential assimilation of water level observations into flood modelling

Satellite-based Synthetic Aperture Radar (SAR) has proved useful for obtaining information on flood extent, which, when intersected with a Digital Elevation Model (DEM) of the floodplain, provides water level observations that can be assimilated into a hydrodynamic model to decrease forecast uncertainty. With an increasing number of operational satellites with SAR capability, information on the relationship between satellite first visit and revisit times and forecast performance is required to optimise the operational scheduling of satellite imagery. By using an Ensemble Transform Kalman Filter (ETKF) and a synthetic analysis with the 2D hydrodynamic model LISFLOOD-FP based on a real flooding case affecting an urban area (summer 2007,Tewkesbury, Southwest UK), we evaluate the sensitivity of the forecast performance to visit parameters. We emulate a generic hydrologic-hydrodynamic modelling cascade by imposing a bias and spatiotemporal correlations to the inflow error ensemble into the hydrodynamic domain. First, in agreement with previous research, estimation and correction for this bias leads to a clear improvement in keeping the forecast on track. Second, imagery obtained early in the flood is shown to have a large influence on forecast statistics. Revisit interval is most influential for early observations. The results are promising for the future of remote sensing-based water level observations for real-time flood forecasting in complex scenarios.

As ações político-pedagógicas do Serviço de Assistência Rural-SAR

This program resumes the history of the political-pedagogic actions on the Serviço de Assistência Rural SAR, of Natal archdiocese, and analyses the contributions of this actions on the process of rural workers organization in the social movements on the countryside. The educative actions of the RAS are happening in a permanent tension between the pedagogic project of a church in change and, a pedagogy of the groups, communities and social movements, that is centered in the cultural action, in the culture lived from its condition of citizens. This research reveals that this entity fulfilled a strategic attribution for the Natal s church on the formation of the community leaderships, at a first moment and leaderships for social movements. Before the military dictatorship, the work methodology of this entity had as priority, begin from the reality leaved by the rural workers in the expectation that these became to qualify themselves for a more citizen participation in the call development. During the military regime, the entity goes measuring theirs activities in the new context, until the moment that redefines the work line. Goes then defining regions and thematic of operation supporting the fights for land, salary campaigns, women agricultural workers organizations. The pedagogy of work has as one of its supporters the Paulo Freire s pedagogy, privileging the dialog as a source of production of knowledge from the reality leaved in a permanent transformation. The actions of this entity, with the groups and social movements, produces the necessary knowledge for the organization of the rural workers while individual and social subjects of a changing world. The process of action-reflection of the activities intended, by a creative form, a permanent production of strategies of fight of the workers. Research ever, not to make accommodate itself to the new knowledge acquired in the action-reflection it is part of the pedagogical idea of this Institution. One searched in this process of formation of the man and the woman to question the reality, to create actionreflection-action spaces on the fights for a possible transition of an ingenuous conscience for a critical conscience, in view of the transformation of the structures that oppresses them

Sistema inteligente para detecção de manchas de óleo na superfície marinha através de imagens de SAR

Oil spill on the sea, accidental or not, generates enormous negative consequences for the affected area. The damages are ambient and economic, mainly with the proximity of these spots of preservation areas and/or coastal zones. The development of automatic techniques for identification of oil spots on the sea surface, captured through Radar images, assist in a complete monitoring of the oceans and seas. However spots of different origins can be visualized in this type of imaging, which is a very difficult task. The system proposed in this work, based on techniques of digital image processing and artificial neural network, has the objective to identify the analyzed spot and to discern between oil and other generating phenomena of spot. Tests in functional blocks that compose the proposed system allow the implementation of different algorithms, as well as its detailed and prompt analysis. The algorithms of digital image processing (speckle filtering and gradient), as well as classifier algorithms (Multilayer Perceptron, Radial Basis Function, Support Vector Machine and Committe Machine) are presented and commented.The final performance of the system, with different kind of classifiers, is presented by ROC curve. The true positive rates are considered agreed with the literature about oil slick detection through SAR images presents

Wavelets and decision trees for target detection over sea surface using cosmo-skymed SAR data

We are investigating the combination of wavelets and decision trees to detect ships and other maritime surveillance targets from medium resolution SAR images. Wavelets have inherent advantages to extract image descriptors while decision trees are able to handle different data sources. In addition, our work aims to consider oceanic features such as ship wakes and ocean spills. In this incipient work, Haar and Cohen-Daubechies-Feauveau 9/7 wavelets obtain detailed descriptors from targets and ocean features and are inserted with other statistical parameters and wavelets into an oblique decision tree. © 2011 Springer-Verlag.

A structure-activity relationship (SAR) study of neolignan compounds with anti-schistosomiasis activity

Um conjunto de dezoito compostos de neolignanas com atividade antiesquistossomose foi estudado com o método semi-empírico PM3 e outros métodos teóricos com o intuito de avaliar algumas propriedades (variáveis ou descritores) moleculares selecionadas e correlacioná-las com a atividade biológica. Análise exploratória dos dados (análise de componentes principais, PCA, e análise hierárquica de agrupamentos, HCA), análise discriminante (DA) e o método KNN foram utilizados na obtenção de possíveis correlações entre os descritores calculados e a atividade biológica em questão e na predição da atividade antiesquistossimose de algumas moléculas teste. Os descritores moleculares responsáveis pela separação entre os compostos ativos e inativos foram: energia de hidratação (HE), refratividade molecular (MR) e carga sobre o átomo C19 (Q19). Estes descritores fornecem informações a respeito do tipo de interação que pode ocorrer entre os compostos e seu respectivo receptor biológico. Após a construção do modelo para compostos ativos e inativos, os métodos PCA, HCA, DA e KNN foram empregados em um estudo de predição. Foram estudados 10 novos compostos e somente 5 deles foram classificados como ativos contra esquistossomose.