Traditional methods and a lack of consensus and quality control in the delimitation of lithological units lead to differences and offsets at the contact zones between map sheets. On some geological maps, such inconsistencies considerably hinder exploration work. This study evaluates the contribution of remote sensing combined with a geoscientific knowledge base to produce bedrocks maps in a geologically complex and semi-arid region of the Peruvian Andes. The region is characterized by the presence of vegetation, snow and hydrothermal alteration. The results demonstrate the potential of ASTER-TIR emissivity data to discriminate lithological units characterized by differences in silica content. These units correspond to felsic plutonic, intermediate volcanic and sedimentary carbonate rocks with a map accuracy of 72% when compared to the available geological map. Specifically with regards to quartz zones, mapping by the two methods resulted in a comparative precision of 99%. The methodological approach also demonstrated that the potential of SWIR spectral bands is sufficiently high (precision of 84%) to detect many of the minerals associated with argillic and advanced argillic alteration in the study area.

El retroceso espacial de los glaciares ubicados en los Andes peruanos está estrechamente vinculado al cambio climático global. Los nevados de la Cordillera Blanca son una fuente de agua muy importante para la zona andina; pero ellos vienen sufriendo un retroceso espacial acelerado. Se ha aplicado las técnicas de la Teledetección y Sistemas de Información Geográfica (SIG) para estimar la variación del área y volumen glaciar a partir de datos imágenes TM y ASTER. Los resultados de área y volumen glaciar se correlacionan con datos de índice de vegetación (NDVI), temperatura de brillo y datos de precipitación. De los modelos de elevación digital de 30 m de resolución espacial (horizontal) para los tres modelos ASTER, IGN y GDEM, se ha desarrollado hipsometría glaciar para estimar el volumen glaciar por rango altitudinal. La evolución del área glaciar de los nevados Huascarán y Champará tiene una tendencia al retroceso. En el periodo 1973-2010, el área glaciar del nevado Huascarán se redujo 16,036 km2 (lo que representa el 24% del área glaciar en 1973), considerando una tasa de disminución de 0,422 km2/año; y el nevado Champará perdió 5,092 km2 de área glaciar (30% del área glaciar en 1973), considerando una tasa de disminución de 0,134 km2/año. Por otro lado, el volumen glaciar del nevado Huascarán ha disminuido en 5,364 km3 (que representa el 33%) en el periodo 1973-2010. Y el nevado Champará ha perdido 1,349 km3 (que representa el 52%).

Plantas apresentando folhas com deformações do tipo bolhas, menor quantidade de vagens de tamanho reduzido e contendo menor número de sementes, vagens que não completaram a maturação e coloração verde da parte aérea no final do ciclo foram observadas em campos de produção. As plantas foram analisadas visando à detecção de fitoplasmas e a sua identificação molecular. Para isto, o DNA total das plantas amostradas foi submetido ao teste de duplo PCR conduzido com primers específicos para a região do 16S rDNA de fitoplasmas. As amplificações evidenciaram que fitoplasmas estavam presentes em tecidos de plantas sintomáticas. O duplo PCR realizado com primers grupo-específicos revelou a ocorrência de fitoplasmas afiliados aos grupos 16SrI e 16SrIII. As análises virtuais de RFLP, baseadas no sequenciamento da referida região genômica, permitiram identificar um dos fitoplasmas como pertencente ao subgrupo 16SrI-B. Os valores de coeficientes de similaridade, calculados com base nos padrões de restrição gerados in silico por 17 enzimas, confirmaram a identidade deste fitoplasma. Ainda, mapas de restrição mostraram que o fitoplasma encontrado em plantas de soja apresentava sítios putativos de restrição idênticos a um fitoplasma típico do grupo 16SrI-B. A análise filogenética, envolvendo o fitoplasma alvo deste estudo e fitoplasmas representantes de alguns grupos e subgrupos relatados no Brasil, mostrou que o fitoplasma da soja estava estritamente relacionado com aqueles componentes do grupo 16SrI. O fitoplasma em estudo emergiu do mesmo ramo da árvore filogenética também compartilhado por fitoplasmas do grupo 16SrI, confirmando os resultados dos demais testes moleculares. Os resultados gerados neste trabalho evidenciaram que a soja pode ser considerada como mais um hospedeiro de fitoplasmas pertencentes aos grupos 16SrI e 16SrIII, os quais tem sido relatados em associação com diversas doenças de plantas que ocorrem no Brasil. Este estudo também gera informações que podem contribuir para aumentar os conhecimentos sobre este emergente grupo de agentes causais de doença. / Plants exhibiting leaf deformation type bubbles, low quantities of pods of reduced size containing few seeds, pods not mature, and green color of stem and leaves in the end of crop growth were observed in commercial fields. The plants were tested for the detection of phytoplasmas and their molecular identification. For this, the total DNA of plants sampled was submitted to nested PCR assays conducted with universal primers for amplification of a fragment corresponding to the 16S rDNA of phytoplasmas. The amplifications revealed the presence of phytoplasmas in tissues of symptomatic plants. For identification, nested PCR performed with group-specific primers demonstrated the occurrence of phytoplasmas affiliated to the groups 16SrI and 16SrIII. The virtual RFLP analysis, based on the sequencing of DNA fragments generated from nested PCR with universal primers, allowed the identification of a phytoplasma belonging to the subgroup 16SrI-B. The values of similarity coefficients, calculated on the basis of restriction patterns generated in silico for 17 enzymes, confirmed the identity of this phytoplasma. Furthermore, restriction maps showed that the phytoplasma found in soybean plants had putative restriction sites identical to those phytoplasma of the group 16SI-B. Phylogenetic analysis, involving the phytoplasma identified in the present study and representatives of some groups and subgroups previously reported in Brazil, showed that the soybean phytoplasma was closely related to phytoplasmas belonging to group 16SrI. The studied phytoplasma and phytoplasmas belonging to group 16SrI emerged from the same branch, confirming the results obtained by PCR and RFLP analysis. Also, based on the results, soybean could be considered as a host for phytoplasmas belonging to the group 16SrI and 16SrIII, which has been reported in association with various diseases that occur in Brazil. The present study may contribute to improve the knowledge about this emerging group of causal agents of disease.

This study was designed to observe the effects of selenium from plant material in sheep after a single, oral dose. Purified sodium selenite and selenomethionine were given as positive controls. The plant Symphyotrichum spathulatum (Western Mountain Aster) was collected, analyzed for selenium content, and administered orally to sheep at varying doses according to body weight. Clinical signs were observed for 7 days during which time whole blood, serum, and expired air were collected. Following euthanasia, tissues were collected for histopathological analysis and mineral analysis. Clinical signs were less apparent than expected and included depression and mild dyspnea in sheep receiving the highest doses of selenium as plant material, whereas pathologic lesions were prominent. Acute myocardial degeneration and necrosis was most severe in the highest dose animals, but present to lesser degrees as dose decreased. Pulmonary lesions of edema and congestion were less frequently observed. Thirteen animals died prior to study completion. Selenium concentration in tissues, brain, liver, kidney cortex, atrium, ventricle, and skeletal muscle, increased with increasing dose of plant material. Treatment had a significant impact on selenium concentration in all tissues collected for mineral analysis (P < 0.01). Whole blood and serum were collected to study the toxicokinetics of selenium in these sheep. Serum kinetic parameters that increased significantly with increasing dose included the elimination rate constant, peak selenium concentration, and area under the selenium concentration versus time curve. Serum kinetic parameters that significantly decreased with increasing dose included the absorption and elimination half-lives. Whole blood kinetic parameters that increased significantly with increasing dose included the elimination rate constant, peak selenium concentration, and area under the curve. Expired air was collected to study the respiratory toxicokinetics of selenium in the sheep. The selenium concentration in expired air from sheep receiving selenomethionine was significantly greater than all other treatments (P < 0.0001) at all collection time points. But an intriguing finding was the dramatic differences in elimination profile curves as selenium dose increased with the plant material. The highest dose group elimination curve continually increased through all collection time points. All other groups dosed with plant material saw a decrease in selenium elimination by the last collection time point.

Dengue is currently the most important arboviral disease globally and is usually associated with built environments in tropical areas. Control measures are currently focused on community participation in control of the vector Aedes aegypti and larval source reduction. In Costa Rica, dengue fever is a relatively recent re-emerging disease and has become a serious public health problem. Remotely sensed information can facilitate the study of urban mosquito-borne diseases like dengue by providing multiple temporal and spatial resolutions appropriate to investigate urban structure and ecological characteristics associated with infectious disease. Initial studies showed that although dengue is a serious public health problem in Costa Rica, there is a need for interdisciplinary scientific research to guide vector control. Therefore, the dengue situation in Puntarenas, Costa Rica, and applications of remote sensing to study infectious diseases like dengue within urban environments was analyzed. Satellite imagery of high and medium spatial resolution was obtained to evaluate relationships between urban structure and incidence of dengue fever at the locality level. Using the satellite imagery, a geographical sampling method was developed and applied for seasonal entomological field surveys in Puntarenas. Very high resolution imagery from QuickBird was utilized to determine the relationships between Ae. aegypti larval habitat abundance and tree cover or built areas. Results showed that the most relevant Ae. aegypti larval habitats in Puntarenas were outdoor miscellaneous containers, cans and plastic food containers that fill with rain water in the wet season, while washtubs were the most productive habitats in the dry season. Dengue incidence and abundance of larval habitats in the urban environment were directly associated with tree cover and inversely associated with built areas. Environmental conditions and urban structure, as well as human behavior were related in different ways to dengue incidence and Ae. aegypti larval habitats. Overall, remotely sensed information was useful in developing sampling strategies for field surveys and determining factors within the urban environment that may promote persistence of mosquito larval habitats and increased dengue risk. The geographical methods and relationships revealed will be useful in determining target areas for more efficient vector control.

In an area of interest- Sivas Basin, Turkey- where most of the units are sedimentary and show similar spectral characteristics, spectral settings of ASTER sensor may not be enough by itself. Therefore, considering other aspects, such as morphological variables, is reasonable in addition to spectral classifiers. The main objective of this study is to test usefulness of integration of spectral analysis and morphological information for geological mapping. Remotely sensed imagery obtained from ASTER sensor is used to classify different lithological units while DEM is used to characterize landforms related to these lithological units. Maximum Likelihood Classification (MLC) is used to integrate data streaming from different sources. The methodology involves integrating the surface properties of the classified geological units in addition to the spectral reflectances. Seven different classification trials were conducted: : 1. MLC using only nine ASTER bands, 2. MLC using ASTER bands and DEM, 3. MLC using ASTER bands and slope, 4. MLC using ASTER bands and plan curvature, 5. MLC using ASTER bands and profile curvature, 6. MLC using ASTER bands and drainage density and finally 7. MLC using ASTER bands and all ancillary data. The results revealed that integrating topographical parameters aid in improvement of classification where spectral information is not sufficient to discriminate between classes of interest. An increase of more than 5% is observed in overall accuracy for the all ancillary data integration case. Moreover more than 10% improvement for most of the classes was identified. However from the results it is evident that the areal extent of the classified units causes constraints on application of the methodology.

In conventional remote sensing, visible-near infrared (VNIR) and shortwave infrared (SWIR) part of the electromagnetic spectrum (EMS) have been utilized for lithological discrimination extensively. Additionally, TIR part of the EM spectrum can also be utilized for discrimination of surface materials either through emissivity characteristics of materials or through radiance as in VNIR and SWIR. In this study, ASTER thermal multispectral infrared data is evaluated in regard to lithological discrimination and mapping through emissivity values rather than conventional methods that utilize radiance values. In order to reach this goal, Principle Component Analysis (PCA) and Decorrelation Stretch techniques are utilized for ASTER VNIR and SWIR data. Furthermore, the spectral indices which directly utilize the radiance values in VNIR, SWIR and TIR are also included in the image analysis. The emissivity values are obtained through Temperature-Emissivity Separation (TES) algorithm. The results of the image analyses, except spectral indices, are displayed in RGB color composite along with the geological map for visual interpretation. The results showed that utilizing emissivity values possesses potential for discrimination of organic matter bearing surface mixtures which has not been possible through the conventional methods. Additionally, PCA of emissivity values may increase the level of discrimination even further. Since the emissivity utilization is rather unused throughout in literature and new, further assessment of accuracy is highly recommended along with the field validations.

No description available.

In this study, DEMs and orthoimages were generated from ASTER imagery and their accuracies were assessed. The study site covers an area of approximately 60 x 60 km and encloses the city of Ankara. First, DEMs were generated from stereo ASTER images. In order to find the best GCP combination, different number of GCPs (8, 16, 24, and 32) was used. The accuracies of the generated DEMs were then assessed based on the check points (CP), slopes and land cover types. It was found that 16 GCPs were good compromise to produce the most accurate DEM. The post processing and blunder removal increased the overall accuracy up to 38%. It was also found that there is a strong linear relationship between the accuracies of DEMs and the slopes of the terrain. The accuracies computed for water, urban, forest, mountainous, and other areas were found to be 5.01 m, 8.03 m, 12.69 m, 17.14 m, and 10.21 m, respectively. The overall accuracy was computed as 10.92 m. The orthorectification of the ASTER image was carried out using 12 different mathematical models. Based on the results, the models First Order 2D Polynomial, Direct Linear Transformation and First Order Polynomial with Relief have produced the worst results. On the other hand, the model Second Order Rational Function appears to be the best model to orthorectify the ASTER images. However, the developed model Second Order Polynomial with Relief provides simplicity, consistency and requires less number of GCPs when compared to the model Second Order Rational Function.

The Darwin-Hatherton glacial system is an outlet glacial system in the Transantarctic Mountains, Antarctica, which drains ice from the East Antarctic Ice Sheet into the Ross Ice Shelf. This research provides remotely sensed data that can be used in modeling research for the Darwin-Hatherton glacial system, which in turn can be used in mass balance research for the West Antarctic Ice Sheet. Two improved digital elevation models (DEM) are produced to cover the lower Darwin Glacier and to cover the upper Darwin and Hatherton Glaciers. The new improved DEMs are generated from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite data, with a resolution of 45 m. To produce the two final DEMs, multiple DEMs are firstly adjusted to remove systematic errors and are then stacked and averaged to increase the accuracy and produce the final two DEMs. For the lower Darwin Glacier, 5 DEMs were averaged and in the upper Darwin and Hatherton Glaciers, 6 DEMs were averaged. The accuracy is quantified by a remaining error of + 9 m for the lower Darwin Glacier DEM and + 37 m for the upper Darwin and Hatherton Glaciers DEM. This is a significant improvement from the existing 200 m resolution Radarsat Antarctic mapping project (RAMPv2) DEM which has a remaining error of + 138 m over the lower Darwin Glacier and + 152 m over the upper Darwin and Hatherton Glaciers. The accuracy is assessed by comparing the ASTER and RAMPv2 DEMs to highly accurate ice, cloud and land elevation satellite (ICESat) laser altimetry data. A 15 m resolution, true colour, orthorectified image is provided for the entire Darwin-Hatherton glacial system from ASTER satellite imagery. The DEMs used to orthorectify the ASTER satellite imagery are the two new 45 m resolution ASTER DEMs. Lastly feature tracking was explored as a method for measuring surface ice velocity. This research shows that feature tracking is unsuitable for the Darwin-Hatherton glacial system if using 15 m resolution satellite imagery over a 1 to 4 year time period.