957 resultados para Synthetic aperture techniques
Resumo:
Results from electromagnetic induction surveys of sea-ice thickness in Storfjorden, Svalbard, reveal large interannual ice-thickness variations in a region which is typically characterized by a reoccurring polynya. The surveys were performed in March 2003, May 2006 and March 2007 with helicopter- and ship-based sensors. The thickness distributions are influenced by sea-ice and atmospheric boundary conditions 2 months prior to the surveys, which are assessed with synthetic aperture radar (SAR) images, regional QuikSCAT backscatter maps and wind information from the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis dataset. Locally formed thin ice from the Storfjorden polynya was frequently observed in 2003 and 2007 (mean thickness 0.55 and 0.37 m, respectively) because these years were characterized by prevailing northeasterly winds. In contrast, the entire fjord was covered with thick external sea ice in 2006 (mean thickness 2.21 m), when ice from the Barents Sea was driven into the fjord by predominantly southerly winds. The modal thickness of this external ice in 2006 increased from 1.2 m in the northern fjord to 2.4 m in the southern fjord, indicating stronger deformation in the southern part. This dynamically thickened ice was even thicker than multi-year ice advected from the central Arctic Ocean in 2003 (mean thickness 1.83 m). The thermodynamic ice thickness of fast ice as boundary condition is investigated with a one-dimensional sea-ice growth model (1DICE) forced with meteorological data from the weather station at the island of Hopen, southeast of Storfjorden. The model results are in good agreement with the modal thicknesses of fast-ice measurements in all years.
Resumo:
A circumpolar representative and consistent wetland map is required for a range of applications ranging from upscaling of carbon fluxes and pools to climate modelling and wildlife habitat assessment. Currently available data sets lack sufficient accuracy and/or thematic detail in many regions of the Arctic. Synthetic aperture radar (SAR) data from satellites have already been shown to be suitable for wetland mapping. Envisat Advanced SAR (ASAR) provides global medium-resolution data which are examined with particular focus on spatial wetness patterns in this study. It was found that winter minimum backscatter values as well as their differences to summer minimum values reflect vegetation physiognomy units of certain wetness regimes. Low winter backscatter values are mostly found in areas vegetated by plant communities typically for wet regions in the tundra biome, due to low roughness and low volume scattering caused by the predominant vegetation. Summer to winter difference backscatter values, which in contrast to the winter values depend almost solely on soil moisture content, show expected higher values for wet regions. While the approach using difference values would seem more reasonable in order to delineate wetness patterns considering its direct link to soil moisture, it was found that a classification of winter minimum backscatter values is more applicable in tundra regions due to its better separability into wetness classes. Previous approaches for wetland detection have investigated the impact of liquid water in the soil on backscatter conditions. In this study the absence of liquid water is utilized. Owing to a lack of comparable regional to circumpolar data with respect to thematic detail, a potential wetland map cannot directly be validated; however, one might claim the validity of such a product by comparison with vegetation maps, which hold some information on the wetness status of certain classes. It was shown that the Envisat ASAR-derived classes are related to wetland classes of conventional vegetation maps, indicating its applicability; 30% of the land area north of the treeline was identified as wetland while conventional maps recorded 1-7%.
Resumo:
A new approach for the estimation of soil organic carbon (SOC) pools north of the tree line has been developed based on synthetic aperture radar (SAR; ENVISAT Advanced SAR Global Monitoring mode) data. SOC values are directly determined from backscatter values instead of upscaling using land cover or soil classes. The multi-mode capability of SAR allows application across scales. It can be shown that measurements in C band under frozen conditions represent vegetation and surface structure properties which relate to soil properties, specifically SOC. It is estimated that at least 29 Pg C is stored in the upper 30 cm of soils north of the tree line. This is approximately 25 % less than stocks derived from the soil-map-based Northern Circumpolar Soil Carbon Database (NCSCD). The total stored carbon is underestimated since the established empirical relationship is not valid for peatlands or strongly cryoturbated soils. The approach does, however, provide the first spatially consistent account of soil organic carbon across the Arctic. Furthermore, it could be shown that values obtained from 1 km resolution SAR correspond to accounts based on a high spatial resolution (2 m) land cover map over a study area of about 7 × 7 km in NE Siberia. The approach can be also potentially transferred to medium-resolution C-band SAR data such as ENVISAT ASAR Wide Swath with ~120 m resolution but it is in general limited to regions without woody vegetation. Global Monitoring-mode-derived SOC increases with unfrozen period length. This indicates the importance of this parameter for modelling of the spatial distribution of soil organic carbon storage.
Resumo:
The amount and quality of available biomass is a key factor for the sustainable livestock industry and agricultural management related decision making. Globally 31.5% of land cover is grassland while 80% of Ireland’s agricultural land is grassland. In Ireland, grasslands are intensively managed and provide the cheapest feed source for animals. This dissertation presents a detailed state of the art review of satellite remote sensing of grasslands, and the potential application of optical (Moderate–resolution Imaging Spectroradiometer (MODIS)) and radar (TerraSAR-X) time series imagery to estimate the grassland biomass at two study sites (Moorepark and Grange) in the Republic of Ireland using both statistical and state of the art machine learning algorithms. High quality weather data available from the on-site weather station was also used to calculate the Growing Degree Days (GDD) for Grange to determine the impact of ancillary data on biomass estimation. In situ and satellite data covering 12 years for the Moorepark and 6 years for the Grange study sites were used to predict grassland biomass using multiple linear regression, Neuro Fuzzy Inference Systems (ANFIS) models. The results demonstrate that a dense (8-day composite) MODIS image time series, along with high quality in situ data, can be used to retrieve grassland biomass with high performance (R2 = 0:86; p < 0:05, RMSE = 11.07 for Moorepark). The model for Grange was modified to evaluate the synergistic use of vegetation indices derived from remote sensing time series and accumulated GDD information. As GDD is strongly linked to the plant development, or phonological stage, an improvement in biomass estimation would be expected. It was observed that using the ANFIS model the biomass estimation accuracy increased from R2 = 0:76 (p < 0:05) to R2 = 0:81 (p < 0:05) and the root mean square error was reduced by 2.72%. The work on the application of optical remote sensing was further developed using a TerraSAR-X Staring Spotlight mode time series over the Moorepark study site to explore the extent to which very high resolution Synthetic Aperture Radar (SAR) data of interferometrically coherent paddocks can be exploited to retrieve grassland biophysical parameters. After filtering out the non-coherent plots it is demonstrated that interferometric coherence can be used to retrieve grassland biophysical parameters (i. e., height, biomass), and that it is possible to detect changes due to the grass growth, and grazing and mowing events, when the temporal baseline is short (11 days). However, it not possible to automatically uniquely identify the cause of these changes based only on the SAR backscatter and coherence, due to the ambiguity caused by tall grass laid down due to the wind. Overall, the work presented in this dissertation has demonstrated the potential of dense remote sensing and weather data time series to predict grassland biomass using machine-learning algorithms, where high quality ground data were used for training. At present a major limitation for national scale biomass retrieval is the lack of spatial and temporal ground samples, which can be partially resolved by minor modifications in the existing PastureBaseIreland database by adding the location and extent ofeach grassland paddock in the database. As far as remote sensing data requirements are concerned, MODIS is useful for large scale evaluation but due to its coarse resolution it is not possible to detect the variations within the fields and between the fields at the farm scale. However, this issue will be resolved in terms of spatial resolution by the Sentinel-2 mission, and when both satellites (Sentinel-2A and Sentinel-2B) are operational the revisit time will reduce to 5 days, which together with Landsat-8, should enable sufficient cloud-free data for operational biomass estimation at a national scale. The Synthetic Aperture Radar Interferometry (InSAR) approach is feasible if there are enough coherent interferometric pairs available, however this is difficult to achieve due to the temporal decorrelation of the signal. For repeat-pass InSAR over a vegetated area even an 11 days temporal baseline is too large. In order to achieve better coherence a very high resolution is required at the cost of spatial coverage, which limits its scope for use in an operational context at a national scale. Future InSAR missions with pair acquisition in Tandem mode will minimize the temporal decorrelation over vegetation areas for more focused studies. The proposed approach complements the current paradigm of Big Data in Earth Observation, and illustrates the feasibility of integrating data from multiple sources. In future, this framework can be used to build an operational decision support system for retrieval of grassland biophysical parameters based on data from long term planned optical missions (e. g., Landsat, Sentinel) that will ensure the continuity of data acquisition. Similarly, Spanish X-band PAZ and TerraSAR-X2 missions will ensure the continuity of TerraSAR-X and COSMO-SkyMed.
Resumo:
Large uncertainties remain in the current and future contribution to sea level rise from Antarctica. Climate warming may increase snowfall in the continent's interior, but enhance glacier discharge at the coast where warmer air and ocean temperatures erode the buttressing ice shelves. Here, we use satellite interferometric synthetic-aperture radar observations from 1992 to 2006 covering 85% of Antarctica's coastline to estimate the total mass flux into the ocean. We compare the mass fluxes from large drainage basin units with interior snow accumulation calculated from a regional atmospheric climate model for 1980 to 2004. In East Antarctica, small glacier losses in Wilkes Land and glacier gains at the mouths of the Filchner and Ross ice shelves combine to a near-zero loss of 4 ± 61 Gt/yr. In West Antarctica, widespread losses along the Bellingshausen and Amundsen seas increased the ice sheet loss by 59% in 10 years to reach 132 ± 60 Gt/yr in 2006. In the Peninsula, losses increased by 140% to reach 60 ± 46 Gt/yr in 2006. Losses are concentrated along narrow channels occupied by outlet glaciers and are caused by ongoing and past glacier acceleration. Changes in glacier flow therefore have a significant, if not dominant impact on ice sheet mass balance.
Resumo:
An area in central Siberia (partial coverage of Turukhansky und Yeniseysky districts) was investigated using satellite data. It covers freshwater ecosystems of non-forested peatlands in boreal forests. The satellite data represent the growing seasons of 2003/2004. Microwave data were acquired by the Advanced Synthetic Aperture Radar (ASAR) instrument onboard ENVISAT. The multi-temporal capabilities and resolution (150mx150m in WS mode) of the ASAR wide swath mode enabled the detection of dynamic features >2ha over this vast area. Scatterometer (QuikScat) data could be employed to distinguish hydro-periods. Wetland types have been identified on the basis of seasonal changes in backscatter. Results for peatlands have been compared with Russian forest inventory data which contain information on wetland distribution.
Resumo:
Résumé : Dans les couverts forestiers, le suivi de l’humidité du sol permet de prévenir plusieurs désastres tels que la paludification, les incendies et les inondations. Comme ce paramètre est très dynamique dans l’espace et dans le temps, son estimation à grande échelle présente un grand défi, d’où le recours à la télédétection radar. Le capteur radar à synthèse d’ouverture (RSO) est couramment utilisé grâce à sa vaste couverture et sa résolution spatiale élevée. Contrairement aux sols nus et aux zones agricoles, le suivi de l’humidité du sol en zone forestière est très peu étudié à cause de la complexité des processus de diffusion dans ce type de milieu. En effet, la forte atténuation de la contribution du sol par la végétation et la forte contribution de volume issue de la végétation réduisent énormément la sensibilité du signal radar à l’humidité du sol. Des études portées sur des couverts forestiers ont montré que le signal radar en bande C provient principalement de la couche supérieure et sature vite avec la densité de la végétation. Cependant, très peu d’études ont exploré le potentiel des paramètres polarimétriques, dérivés d’un capteur polarimétrique comme RADARSAT-2, pour suivre l’humidité du sol sur les couverts forestiers. L’effet du couvert végétal est moins important avec la bande L en raison de son importante profondeur de pénétration qui permet de mieux informer sur l’humidité du sol. L’objectif principal de ce projet est de suivre l’humidité du sol à partir de données radar entièrement polarimétriques en bandes C et L sur des sites forestiers. Les données utilisées sont celles de la campagne terrain Soil Moisture Active Passive Validation EXperiment 2012 (SMAPVEX12) tenue du 6 juin au 17 juillet 2012 au Manitoba (Canada). Quatre sites forestiers de feuillus ont été échantillonnés. L’espèce majoritaire présente est le peuplier faux-tremble. Les données utilisées incluent des mesures de l’humidité du sol, de la rugosité de surface du sol, des caractéristiques des sites forestiers (arbres, sous-bois, litières…) et des données radar entièrement polarimétriques aéroportées et satellitaires acquises respectivement, en bande L (UAVSAR) à 30˚ et 40˚ et en bande C (RADARSAT-2) entre 20˚ et 30˚. Plusieurs paramètres polarimétriques ont été dérivés des données UAVSAR et RADARSAT-2 : les coefficients de corrélation (ρHHVV, φHHVV, etc); la hauteur du socle; l’entropie (H), l’anisotropie (A) et l’angle alpha extraits de la décomposition de Cloude-Pottier; les puissances de diffusion de surface (Ps), de double bond (Pd) extraites de la décomposition de Freeman-Durden, etc. Des relations entre les données radar (coefficients de rétrodiffusion multifréquences et multipolarisations (linéaires et circulaires) et les paramètres polarimétriques) et l’humidité du sol ont été développées et analysées. Les résultats ont montré que 1) En bande L, plusieurs paramètres optimaux permettent le suivi de l’humidité du sol en zone forestière avec un coefficient de corrélation significatif (p-value < 0,05): σ[indice supérieur 0] linéaire et σ[indice supérieur 0] circulaire (le coefficient de corrélation, r, varie entre 0,60 et 0,96), Ps (r entre 0,59 et 0,84), Pd (r entre 0,6 et 0,82), ρHHHV_30˚, ρVVHV_30˚, φHHHV_30˚ and φHHVV_30˚ (r entre 0,56 et 0,81) alors qu’en bande C, ils sont réduits à φHHHV, φVVHV et φHHVV (r est autour de 0,90). 2) En bande L, les paramètres polarimétriques n’ont pas montré de valeur ajoutée par rapport aux signaux conventionnels multipolarisés d’amplitude, pour le suivi de l’humidité du sol sur les sites forestiers. En revanche, en bande C, certains paramètres polarimétriques ont montré de meilleures relations significatives avec l’humidité du sol que les signaux conventionnels multipolarisés d’amplitude.
Resumo:
A method for systematically tracking swells across oceanic basins is developed by taking advantage of high-quality data from space-borne altimeters and wave model output. The evolution of swells is observed over large distances based on 202 swell events with periods ranging from 12 to 18 s. An empirical attenuation rate of swell energy of about 4 × 10−7 m−1 is estimated using these observations, and the nonbreaking energy dissipation rates of swells far away from their generating areas are also estimated using a point source model. The resulting acceptance range of nonbreaking dissipation rates is −2.5 to 5.0 × 10−7 m−1, which corresponds to a dissipation e-folding scales of at least 2000 km for steep swells, to almost infinite for small-amplitude swells. These resulting rates are consistent with previous studies using in-situ and synthetic aperture radar (SAR) observations. The frequency dispersion and angular spreading effects during swell propagation are discussed by comparing the results with other studies, demonstrating that they are the two dominant processes for swell height attenuation, especially in the near field. The resulting dissipation rates from these observations can be used as a reference for ocean engineering and wave modeling, and for related studies such as air-sea and wind-wave-turbulence interactions.
Resumo:
Swells are found in all oceans and strongly influence the wave climate and air-sea processes. The poorly known swell dissipation is the largest source of error in wave forecasts and hindcasts. We use synthetic aperture radar data to identify swell sources and trajectories, allowing a statistically significant estimation of swell dissipation. We mined the entire Envisat mission 2003–2012 to find suitable storms with swells (13 < T < 18 s) that are observed several times along their propagation. This database of swell events provides a comprehensive view of swell extending previous efforts. The analysis reveals that swell dissipation weakly correlates with the wave steepness, wind speed, orbital wave velocity, and the relative direction of wind and waves. Although several negative dissipation rates are found, there are uncertainties in the synthetic aperture radar-derived swell heights and dissipation rates. An acceptable range of the swell dissipation rate is −0.1 to 6 × 10−7 m−1 with a median of 1 × 10−7 m−1.
Resumo:
Ocean wind retrievals from satellite sensors are typically performed for the standard level of 10 m. This restricts their full exploitation for wind energy planning, which requires wind information at much higher levels where wind turbines operate. A new method is presented for the vertical extrapolation of satellite-based wind maps. Winds near the sea surface are obtained from satellite data and used together with an adaptation of the Monin–Obukhov similarity theory to estimate the wind speed at higher levels. The thermal stratification of the atmosphere is taken into account through a long-term stability correction that is based on numerical weather prediction (NWP) model outputs. The effect of the long-term stability correction on the wind profile is significant. The method is applied to Envisat Advanced Synthetic Aperture Radar scenes acquired over the south Baltic Sea. This leads to maps of the long-term stability correction and wind speed at a height of 100 m with a spatial resolution of 0.02°. Calculations of the corresponding wind power density and Weibull parameters are shown. Comparisons with mast observations reveal that NWP model outputs can correct successfully for long-term stability effects and also, to some extent, for the limited number of satellite samples. The satellite-based and NWP-simulated wind profiles are almost equally accurate with respect to those from the mast. However, the satellite-based maps have a higher spatial resolution, which is particularly important in nearshore areas where most offshore wind farms are built.
Resumo:
“Seeing is believing” the proverb well suits for fluorescent imaging probes. Since we can selectively and sensitively visualize small biomolecules, organelles such as lysosomes, neutral molecules, metal ions, anions through cellular imaging, fluorescent probes can help shed light on the physiological and pathophysiological path ways. Since these biomolecules are produced in low concentrations in the biochemical pathways, general analytical techniques either fail to detect or are not sensitive enough to differentiate the relative concentrations. During my Ph.D. study, I exploited synthetic organic techniques to design and synthesize fluorescent probes with desirable properties such as high water solubility, high sensitivity and with varying fluorescent quantum yields. I synthesized a highly water soluble BOIDPY-based turn-on fluorescent probe for endogenous nitric oxide. I also synthesized a series of cell membrane permeable near infrared (NIR) pH activatable fluorescent probes for lysosomal pH sensing. Fluorescent dyes are molecular tools for designing fluorescent bio imaging probes. This prompted me to design and synthesize a hybrid fluorescent dye with a functionalizable chlorine atom and tested the chlorine re-activity for fluorescent probe design. Carbohydrate and protein interactions are key for many biological processes, such as viral and bacterial infections, cell recognition and adhesion, and immune response. Among several analytical techniques aimed to study these interactions, electrochemical bio sensing is more efficient due to its low cost, ease of operation, and possibility for miniaturization. During my Ph.D., I synthesized mannose bearing aniline molecule which is successfully tested as electrochemical bio sensor. A Ferrocene-mannose conjugate with an anchoring group is synthesized, which can be used as a potential electrochemical biosensor.
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The research project object of this thesis is focused on the development of an advanced analytical system based on the combination of an improved thin layer chromatography (TLC) plate coupled with infrared (FTIR) and Raman microscopies for the detection of synthetic dyes. Indeed, the characterization of organic colorants, which are commonly present in mixtures with other components and in a very limited amount, still represents a challenging task in scientific analyses of cultural heritage materials. The approach provides selective spectral fingerprints for each compound, foreseeing the complementary information obtained by micro ATR-RAIRS-FTIR and SERS-Raman analyses, which can be performed on the same separated spot. In particular, silver iodide (AgI) applied on a gold coated slide is proposed as an efficient stationary phase for the discrimination of complex analyte mixtures, such as dyes present in samples of art-historical interest. The gold-AgI-TLC plate shows high performances related both to the chromatographic separation of analytes and to the spectroscopic detection of components. The use of a mid-IR transparent inorganic salt as the stationary phase avoids interferences of the background absorption in FTIR investigations. Moreover, by ATR microscopy measurements performed on the gold-AgI surface, a considerable enhancement in the intensity of spectra is observed. Complementary information can be obtained by Raman analyses, foreseeing a SERS activity of the AgI substrate. The method has been tested for the characterization of a mixture of three synthetic organic colorants widely used in dyeing processes: Brilliant Green (BG1), Rhodamine B (BV10) and Methylene Blue (BB9).
Resumo:
Data of the strength of Earth’s magnetic field (paleointensity) in the geological past are crucial for understanding the geodynamo. Conventional paleointensity determination methods require heating a sample to a high temperature in one or more steps. Consequently, many rocks are unsuitable for these methods due to a heating-induced experimental alteration. Alternative non-heating paleointensity methods are investigated to assess their effectiveness and reliability using both natural samples from Lemptégy Volcano, France, and synthetic samples. Paleointensity was measured from the natural and synthetic samples using the Pseudo-Thellier, ARM, REM, REMc, REM’, and Preisach methods. For the natural samples, only the Pseudo-Thellier method was able to produce a reasonable paleointensity estimate consistent with previous paleointensity data. The synthetic samples yielded more successful estimates using all the methods, with the Pseudo-Thellier and ARM methods producing the most accurate results. The Pseudo-Thellier method appears to be the best alternative to the heating-based paleointensity methods.