The Estrada Real project and endemic diseases: the case of schistosomiasis, geoprocessing and tourism

Geographical Information System (GIS) is a tool that has recently been applied to better understand spatial disease distributions. Using meteorological, social, sanitation, mollusc distribution data and remote sensing variables, this study aimed to further develop the GIS technology by creating a model for the spatial distribution of schistosomiasis and to apply this model to an area with rural tourism in the Brazilian state of Minas Gerais (MG). The Estrada Real, covering about 1,400 km, is the largest and most important Brazilian tourism project, involving 163 cities in MG with different schistosomiasis prevalence rates. The model with three variables showed a R² = 0.34, with a standard deviation of risk estimated adequate for public health needs. The main variables selected for modelling were summer vegetation, summer minimal temperature and winter minimal temperature. The results confirmed the importance of Remote Sensing data and the valuable contribution of GIS in identifying priority areas for intervention in tourism regions which are endemic to schistosomiasis.

A comparative evaluation of endemic and non-endemic region of visceral leishmaniasis (Kala-azar) in India with ground survey and space technology

In visceral leishmaniasis, phlebotomine vectors are targets for control measures. Understanding the ecosystem of the vectors is a prerequisite for creating these control measures. This study endeavours to delineate the suitable locations of Phlebotomus argentipes with relation to environmental characteristics between endemic and non-endemic districts in India. A cross-sectional survey was conducted on 25 villages in each district. Environmental data were obtained through remote sensing images and vector density was measured using a CDC light trap. Simple linear regression analysis was used to measure the association between climatic parameters and vector density. Using factor analysis, the relationship between land cover classes and P. argentipes density among the villages in both districts was investigated. The results of the regression analysis indicated that indoor temperature and relative humidity are the best predictors for P. argentipes distribution. Factor analysis confirmed breeding preferences for P. argentipes by landscape element. Minimum Normalised Difference Vegetation Index, marshy land and orchard/settlement produced high loading in an endemic region, whereas water bodies and dense forest were preferred in non-endemic sites. Soil properties between the two districts were studied and indicated that soil pH and moisture content is higher in endemic sites compared to non-endemic sites. The present study should be utilised to make critical decisions for vector surveillance and controlling Kala-azar disease vectors.

Integració d'un sensor sonar d'escombrat lateral en un robot submarí

El grup de Visió per Computador i Robòtica (VICOROB) disposa de varis robotssubmarins per a la recerca i inspecció subaquàtica. Recentment s’ha adquiritun sensor sonar d’escombrat lateral el qual s’utilitza per realitzar imatgesacústiques del fons marí quan aquest es mou principalment a velocitat constanti mantenint el rumb.Els robots del grup VICOROB estan equipats amb diferents tipus de sensors icàmeres per analitzar el fons marí. Aquest sensors són de gran qualitat ipermeten conèixer de manera bastant satisfactòria l’entorn a les proximitats delrobot. Freqüentment però, aquest sensors estant sotmesos a diferentsrestriccions depenent de la seva naturalesa de funcionament, de tal maneraque es necessària la seva combinació per resoldre determinats problemes endiferents situacions.Amb aquest projecte, es pretén integrar un nou sistema de captura d’imatgessonores del fons marí, en un dels robots. Amb la integració d’aquest nousensor, s’espera obtenir una opció alternativa els sistemes actuals que puguiaportar informació addicional sobre el fons. Aquest sistema podrà ser utilitzatper realitzar tasques per les quals els altres sensors no estant preparats o béper complementar informació d’altres sensor

New tools for epidemiology: a space odyssey

Geographical Information Systems (GIS) facilitate access to epidemiological data through visualization and may be consulted for the development of mathematical models and analysis by spatial statistics. Variables such as land-cover, land-use, elevations, surface temperatures, rainfall etc. emanating from earth-observing satellites, complement GIS as this information allows the analysis of disease distribution based on environmental characteristics. The strength of this approach issues from the specific environmental requirements of those causative infectious agents, which depend on intermediate hosts for their transmission. The distribution of these diseases is restricted, both by the environmental requirements of their intermediate hosts/vectors and by the ambient temperature inside these hosts, which effectively govern the speed of maturation of the parasite. This paper discusses the current capabilities with regard to satellite data collection in terms of resolution (spatial, temporal and spectral) of the sensor instruments on board drawing attention to the utility of computer-based models of the Earth for epidemiological research. Virtual globes, available from Google and other commercial firms, are superior to conventional maps as they do not only show geographical and man-made features, but also allow instant import of data-sets of specific interest, e.g. environmental parameters, demographic information etc., from the Internet.

Biotic factors and occurrence of Lutzomyia longipalpis in endemic area of visceral leishmaniasis, Mato Grosso do Sul, Brazil

The relationships between environmental exposure to risk agents and health conditions have been studied with the aid of remote sensing imagery, a tool particularly useful in the study of vegetation cover. This study aims to evaluate the influence of environmental variables on the spatial distribution of the abundance of Lutzomyia longipalpis and the reported canine and human visceral leishmaniasis (VL) cases at an urban area of Campo Grande, state of Mato Grosso do Sul. The sandfly captures were performed in 13 residences that were selected by raffle considering four residences or collection station for buffer. These buffers were generated from the central house with about 50, 100 and 200 m from it in an endemic area of VL. The abundance of sandflies and human and canine cases were georreferenced using the GIS software PCI Geomatica. The normalized difference vegetation index (NDVI) and percentage of land covered by vegetation were the environmental variables extracted from a remote sensing IKONOS-2 image. The average NDVI was considered as the complexity of habitat and the standard deviation as the heterogeneity of habitat. One thousand three hundred sixty-seven specimens were collected during the catch. We found a significant positive linear correlation between the abundance of sandflies and the percentage of vegetation cover and average NDVI. However, there was no significant association between habitat heterogeneity and the abundance of these flies.

Delimitation of kala-azar risk areas in the district of Vaishali in Bihar (India) using a geo-environmental approach

Remote sensing and geographical information technologies were used to discriminate areas of high and low risk for contracting kala-azar or visceral leishmaniasis. Satellite data were digitally processed to generate maps of land cover and spectral indices, such as the normalised difference vegetation index and wetness index. To map estimated vector abundance and indoor climate data, local polynomial interpolations were used based on the weightage values. Attribute layers were prepared based on illiteracy and the unemployed proportion of the population and associated with village boundaries. Pearson's correlation coefficient was used to estimate the relationship between environmental variables and disease incidence across the study area. The cell values for each input raster in the analysis were assigned values from the evaluation scale. Simple weighting/ratings based on the degree of favourable conditions for kala-azar transmission were used for all the variables, leading to geo-environmental risk model. Variables such as, land use/land cover, vegetation conditions, surface dampness, the indoor climate, illiteracy rates and the size of the unemployed population were considered for inclusion in the geo-environmental kala-azar risk model. The risk model was stratified into areas of "risk"and "non-risk"for the disease, based on calculation of risk indices. The described approach constitutes a promising tool for microlevel kala-azar surveillance and aids in directing control efforts.

Forest fires cluster detection with space-time scan statistics

Forest fires are defined as uncontrolled fires often occurring in wildland areas, but that can also affect houses or agricultural resources. Causes are both natural (e.g.,lightning phenomena) and anthropogenic (human negligence or arsons).Major environmental factors influencing the fire ignition and propagation are climate and vegetation. Wildfires are most common and severe during drought period and on windy days. Moreover, under water-stress conditions, which occur after a long hot and dry period, the vegetation is more vulnerable to fire. These conditions are common in the United State and Canada, where forest fires represent a big problem. We focused our analysis on the state of Florida, for which a big dataset on forest fires detection is readily available. USDA Forest Service Remote Sensing Application Center, in collaboration with NASA-Goddard Space Flight Center and the University of Maryland, has compiled daily MODIS Thermal Anomalies (fires and biomass burning images) produced by NASA using a contextual algorithm that exploits the strong emission of mid-infrared radiation from fires. Fire classes were converted in GIS format: daily MODIS fire detections are provided as the centroids of the 1 kilometer pixels and compiled into daily Arc/INFO point coverage.

Decision fusion for the classification of hyperspectral data: Outcome of the 2008 GRS-S data fusion contest

The 2008 Data Fusion Contest organized by the IEEE Geoscience and Remote Sensing Data Fusion Technical Committee deals with the classification of high-resolution hyperspectral data from an urban area. Unlike in the previous issues of the contest, the goal was not only to identify the best algorithm but also to provide a collaborative effort: The decision fusion of the best individual algorithms was aiming at further improving the classification performances, and the best algorithms were ranked according to their relative contribution to the decision fusion. This paper presents the five awarded algorithms and the conclusions of the contest, stressing the importance of decision fusion, dimension reduction, and supervised classification methods, such as neural networks and support vector machines.

Multi-modal Change Detection, Application to the Detection of Flooded Areas: Outcome of the 2009-2010 data fusion contest

The 2009-2010 Data Fusion Contest organized by the Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society was focused on the detection of flooded areas using multi-temporal and multi-modal images. Both high spatial resolution optical and synthetic aperture radar data were provided. The goal was not only to identify the best algorithms (in terms of accuracy), but also to investigate the further improvement derived from decision fusion. This paper presents the four awarded algorithms and the conclusions of the contest, investigating both supervised and unsupervised methods and the use of multi-modal data for flood detection. Interestingly, a simple unsupervised change detection method provided similar accuracy as supervised approaches, and a digital elevation model-based predictive method yielded a comparable projected change detection map without using post-event data.

Multisensor change detection with nonlinear canonical correlation

The analysis of multi-modal and multi-sensor images is nowadays of paramount importance for Earth Observation (EO) applications. There exist a variety of methods that aim at fusing the different sources of information to obtain a compact representation of such datasets. However, for change detection existing methods are often unable to deal with heterogeneous image sources and very few consider possible nonlinearities in the data. Additionally, the availability of labeled information is very limited in change detection applications. For these reasons, we present the use of a semi-supervised kernel-based feature extraction technique. It incorporates a manifold regularization accounting for the geometric distribution and jointly addressing the small sample problem. An exhaustive example using Landsat 5 data illustrates the potential of the method for multi-sensor change detection.

Improved earthquake response via simulation and integrated space- and ground-based technologies: the TREMOR proposal

Earthquakes occurring around the world each year cause thousands ofdeaths, millions of dollars in damage to infrastructure, and incalculablehuman suffering. In recent years, satellite technology has been asignificant boon to response efforts following an earthquake and itsafter-effects by providing mobile communications between response teamsand remote sensing of damaged areas to disaster management organizations.In 2007, an international team of students and professionals assembledduring theInternational Space University’s Summer Session Program in Beijing, Chinato examine how satellite and ground-based technology could be betterintegrated to provide an optimised response in the event of an earthquake.The resulting Technology Resources for Earthquake MOnitoring and Response(TREMOR) proposal describes an integrative prototype response system thatwill implement mobile satellite communication hubs providing telephone anddata links between response teams, onsite telemedicine consultation foremergency first-responders, and satellite navigation systems that willlocate and track emergency vehicles and guide search-and-rescue crews. Aprototype earthquake simulation system is also proposed, integratinghistorical data, earthquake precursor data, and local geomatics andinfrastructure information to predict the damage that could occur in theevent of an earthquake. The backbone of these proposals is a comprehensiveeducation and training program to help individuals, communities andgovernments prepare in advance. The TREMOR team recommends thecoordination of these efforts through a centralised, non-governmentalorganization.

Optimizing a remotely sensed proxy for plankton biomass in Lake Kivu

Many regions of the world, including inland lakes, present with suboptimal conditions for the remotely sensed retrieval of optical signals, thus challenging the limits of available satellite data-processing tools, such as atmospheric correction models (ACM) and water constituent-retrieval (WCR) algorithms. Working in such regions, however, can improve our understanding of remote-sensing tools and their applicabil- ity in new contexts, in addition to potentially offering useful information about aquatic ecology. Here, we assess and compare 32 combinations of two ACMs, two WCRs, and three binary categories of data quality standards to optimize a remotely sensed proxy of plankton biomass in Lake Kivu. Each parameter set is compared against the available ground-truth match-ups using Spearman's right-tailed ρ. Focusing on the best sets from each ACM-WCR combination, their performances are discussed with regard to data distribution, sample size, spatial completeness, and seasonality. The results of this study may be of interest both for ecological studies on Lake Kivu and for epidemio- logical studies of disease, such as cholera, the dynamics of which has been associated with plankton biomass in other regions of the world.

Reorganization of a deeply incised drainage: role of deformation, sedimentation and groundwater flow

Deeply incised drainage networks are thought to be robust and not easily modified, and are commonly used as passive markers of horizontal strain. Yet, reorganizations (rearrangements) appear in the geologic record. We provide field evidence of the reorganization of a Miocene drainage network in response to strike-slip and vertical displacements in Guatemala. The drainage was deeply incised into a 50-km-wide orogen located along the North America-Caribbean plate boundary. It rearranged twice, first during the Late Miocene in response to transpressional uplift along the Polochic fault, and again in the Quaternary in response to transtensional uplift along secondary faults. The pattern of reorganization resembles that produced by the tectonic defeat of rivers that cross growing tectonic structures. Compilation of remote sensing data, field mapping, sediment provenance study, grain-size analysis and Ar(40)/Ar(39) dating from paleovalleys and their fill reveals that the classic mechanisms of river diversion, such as river avulsion over bedrock, or capture driven by surface runoff, are not sufficient to produce the observed diversions. The sites of diversion coincide spatially with limestone belts and reactivated fault zones, suggesting that solution-triggered or deformation-triggered permeability have helped breaching of interfluves. The diversions are also related temporally and spatially to the accumulation of sediment fills in the valleys, upstream of the rising structures. We infer that the breaching of the interfluves was achieved by headward erosion along tributaries fed by groundwater flow tracking from the valleys soon to be captured. Fault zones and limestone belts provided the pathways, and the aquifers occupying the valley fills provided the head pressure that enhanced groundwater circulation. The defeat of rivers crossing the rising structures results essentially from the tectonically enhanced activation of groundwater flow between catchments.

Mapping of Environmental Data Using Kernel-Based Methods

Recently, kernel-based Machine Learning methods have gained great popularity in many data analysis and data mining fields: pattern recognition, biocomputing, speech and vision, engineering, remote sensing etc. The paper describes the use of kernel methods to approach the processing of large datasets from environmental monitoring networks. Several typical problems of the environmental sciences and their solutions provided by kernel-based methods are considered: classification of categorical data (soil type classification), mapping of environmental and pollution continuous information (pollution of soil by radionuclides), mapping with auxiliary information (climatic data from Aral Sea region). The promising developments, such as automatic emergency hot spot detection and monitoring network optimization are discussed as well.

Los Mapas de riesgo de inundaciones: representación de la vulnerabilidad y aportación de las innovaciones tecnológicas

Los mapas de riesgo de inundaciones deberían mostrar las inundaciones en relación con los impactos potenciales que éstas pueden llegar a producir en personas, bienes y actividades. Por ello, es preciso añadir el concepto de vulnerabilidad al mero estudio del fenómeno físico. Así pues, los mapas de riesgo de daños por inundación son los verdaderos mapas de riesgo, ya que se elaboran, por una parte, a partir de cartografía que localiza y caracteriza el fenómeno físico de las inundaciones, y, por la otra, a partir de cartografía que localiza y caracteriza los elementos expuestos. El uso de las llamadas «nuevas tecnologías», como los SIG, la percepción remota, los sensores hidrológicos o Internet, representa un potencial de gran valor para el desarrollo de los mapas de riesgo de inundaciones, que es, hoy por hoy, un campo abierto a la investigación