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Bühl, J., S. Alexander, S. Crewell, A. Heymsfield, H. Kalesse, A. Khain, M. Maahn, K. Van Tricht, and M. Wendisch. "Remote Sensing." Meteorological Monographs 58 (January 1, 2017): 10.1–10.21. http://dx.doi.org/10.1175/amsmonographs-d-16-0015.1.
Abstract State-of-the-art remote sensing techniques applicable to the investigation of ice formation and evolution are described. Ground-based and spaceborne measurements with lidar, radar, and radiometric techniques are discussed together with a global view on past and ongoing remote sensing measurement campaigns concerned with the study of ice formation and evolution. This chapter has the intention of a literature study and should illustrate the major efforts that are currently taken in the field of remote sensing of atmospheric ice. Since other chapters of this monograph mainly focus on aircraft in situ measurements, special emphasis is put on active remote sensing instruments and synergies between aircraft in situ measurements and passive remote sensing methods. The chapter concentrates on homogeneous and heterogeneous ice formation in the troposphere because this is a major topic of this monograph. Furthermore, methods that deliver direct, process-level information about ice formation are elaborated with a special emphasis on active remote sensing methods. Passive remote sensing methods are also dealt with but only in the context of synergy with aircraft in situ measurements.
Image analysis plays a crucial role in many computer vision applications in which images of the same scene with different geometrical orientations need to be compared for further processing. This thesis describes the design and implementation of a model-based vision system for the recognition of aerial images. The main objective is to register two remote sensing images taken at different times. First, some distinctive features are extracted and matched then, these matched features are used as marking points in defining a geometric mapping function. Once registered, the reference image can be used as an aid to automatic interpretation and as a framework for detecting changes between successive images. A two stage matching procedure is used for this task. In the first part, corners are extracted and matched in both images and an initial estimation of the mapping function is computed. This initial function is then used in the second part to estimate the parameters of a global mapping function for the entire image. The process ends when all the extracted features in one image are either mapped to features in the other image, or rejected if no match could be found.
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Budgett, David Mortimer. "Remote sensing of the epicardium." Thesis, Imperial College London, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.363025.
Lemos, Pinto J. de. "Remote sensing in refractive turbulence." Thesis, University of Hull, 1986. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.381887.
Sayer, Andrew Mark. "Aerosol Remote Sensing Using AATSR." Thesis, University of Oxford, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.526115.
A remote sensing near infrared suspended sediment algorithm is developed from first principles and applied to Compact Airborne Spectrographic Imagery (CASI) data flown over the Humber Estuary. Laboratory measurements were used as the basis for the algorithm development, with the resulting spectra indicating that the ideal wavelength for a suspended sediment algorithm is the near infrared. The resulting algorithm took the form of a waveband ratio which was subsequently validated with a semi-analytical water optics model based on the absorption/scattering properties of the optically active constituents. The model was then used to derive a global water-leaving radiance algorithm, which is independent of the sediment type. The algorithm was applied to the CASI data collected during August and September 1993, and the resulting SPM maps were compared with contemporaneous in-situ measurements. The in-situ measurements include calculations of the diffuse attenuation coefficient (Kd), which was correlated with the SPM concentration. Further developments to the algorithm through the use of an atmospheric correction are outlined.
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Jago, Rosemary Alison. "Remote sensing of contaminated land." Thesis, University of Southampton, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.243094.
Pendant les 20 dernières années, notre connaissance de la déformation de la Terre a été complètement bouleversée par l’introduction de deux techniques de Géodésie spatiale. D’une part, ce que l’on appelle positionnement satellitaire (‘point positioning’) pas seulement à partir du système GPS (Global Positioning System) mais également à partir du système DORIS (Doppler Orbitography and Radio-positioning Integrated from Space). D’autre part, ont été développées des techniques d’imagerie satellitaire de corrélation d’images et d’interférométrie SAR (Synthetic Aperture Radar) ainsi que les méthodes de mesures de décalages sur des images panchromatiques à haute résolution spatiale. Ces nouvelles techniques, ont permis une série d’avancement scientifiques notamment la confirmation et l’amélioration de la théorie de la tectonique de plaques, la cartographie fine de déplacements sismiques et asismiques, l’amélioration de la compréhension des phénomènes de relaxation post sismiques, la détection de séismes lents et ‘silencieux’, la détection de signaux précurseurs de séismes ou d’éruptions volcaniques. Actuellement des nombreuses questions clés restent ouvertes. Notamment : l’importance relative de la déformation accommodée sismiquement en bords de plaques par rapport à la déformation totale et à la contrainte tectonique ; la contribution des séismes à la déformation par rapport au déplacement asismique sur les discontinuités. Le but de la présente Thèse de Doctorat, est d’étudier le potentiel, les limitations et la complémentarité des données issues de l’Observation de la Terre pour prendre en compte et essayer de répondre à certains aspects des questions exposées ci-dessus.
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Charlton, Fergus. "Remote sensing of freshwater phytoplankton." Thesis, University of Edinburgh, 1998. http://hdl.handle.net/1842/21140.
This study researches the potential for using hyperspectral remote sensing to identify the phytoplanktonic composition of a freshwater bloom. Six novel analytical techniques were developed to identify phytoplankton class from reflectance spectra. These techniques offer the water manager a variety of means to identify the dominant phytoplankton class in a target water body. Identification of phytoplankton class is possible because certain photosynthetic pigments contained within phytoplankton cells are taxonomically significant, being indicative of a particular class. The detection of these pigments can be used to identify the presence of a particular phytoplanktonic class in an aquatic system. It is possible to identify these pigments using optical methods because they exhibit unique spectral absorption signatures. Such pigment absorption features are manifest in the composite reflectance signature from water bodies as measured by remote sensing instruments. However, due to the presence of the spectral features from other photosynthetic pigments and the other optically active components of water bodies, extracting from reflectance spectra the spectral information pertaining to individual class marker pigments can be difficult. The phytoplankton class identification techniques presented in this study were developed using absorption and reflectance spectra from pure cultures of phytoplankton. The reflectance spectra were measured in the controlled environment of a laboratory based experimental tank designed for this study. The class identification techniques were tested on field and airborne reflectance spectra measured from a eutrophic inland lake.
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Qi, Jiaguo. "Compositing multitemporal remote sensing data." Diss., The University of Arizona, 1993. http://hdl.handle.net/10150/186327.
In order to reduce the problems of clouds, atmospheric variations, view angle effects, and the soil background variations in the high temporal frequency AVHRR data, a compositing technique is usually employed. Current compositing techniques use a single pixel selection criterion of outputting the input pixel of maximum value NDVI. Problems, however, exist due to the use of the NDVI classifier and to the imperfection of the pixel selection criteria of the algorithm itself. The NDVI was found not to have the maximum value under an ideal observation condition, while the single pixel selection criterion favors the large off-nadir sensor view angles. Consequently, the composited data still consist of substantial noise. To further reduce the noise, several data sets were obtained to study these external factor effects on the NDVI classifier and other vegetation indices. On the basis of the studies of these external factors, a new classifier was developed to further reduce the soil noise. Then, a new set of pixel selection criteria was proposed for compositing. The new compositing algorithm with the new classifier was used to composite two AVHRR data sets. The alternative approach showed that the high frequency noises were greatly reduced, while more valuable data were retained. The proposed alternative compositing algorithm not only further reduced the external factor related noises, but also retained more valuable data. In this dissertation, studies of external factor effects on remote sensing data and derived vegetation indices are presented in the first four chapters. Then the development of the new classifier and the alternative compositing algorithm were described. Perspectives and limitations of the proposed algorithms are also discussed.
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Hick, Peter T. "Remote sensing of agricultural salinity." Thesis, Curtin University, 1987. http://hdl.handle.net/20.500.11937/877.
Salinity represents the major environmental threat to arable land in Western Australia and many other parts of the world. This study was designed to establish criteria for a practical remote sensing system using the visible, reflected and shortwave infrared for the early detection and mapping of salinity. The results are principally from a group of study sites on the CSIROs Yalanbee Experiment Station, and from other significant sites during the agricultural cycles of 1985-7.Analysis of imagery from the Geoscan Multispectral Airborne Scanner showed that best discrimination between study sites affected by salinity, and those not affected, was provided by bands 3 (650-700 nm), 4 (830-870nm) and band 6 (1980-2080nm). The maximum discrimination occurred in a September 1986 flight (spring-flush). Although excellent discrimination was also evident in August and November in 1985, this could not be reproduced in November 1986. The visible and reflected infrared bands 3 and 4 featured prominently, but the significance of the short wave infrared bands was evident especially when vegetative ground cover became a less dominant factor.Field spectra collected over the same period with the Geoscan Portable Field Spectroradiometer (PFS) supported the aircraft data to a certain extent. Detailed analysis of the fine non-correlated structure of narrow constructed bands, from PFS data, indicated that improved discrimination between sites could be provided over a wider time window extending into the summer and autumn. This is when weather-related conditions, i.e. cloud, soil moisture and sun angle, are more conducive to extensive surveys.The importance of at least one narrow band centred near 1985 nm was determined. Laboratory spectra of bare soil from sites measured on an Hitachi Spectrophotometer also provided the importance of the shortwave region adjacent to the 1900 nm water absorption.The study evaluated the spatial and spectral characteristics of existing satellite systems such as Thematic Mapper and the Multispectral Scanner on the Landsat series and determined that a spatial resolution of about 20-30 metres was most appropriate for detection of salinity at a scale whereby management could be implemented.Ground electromagnetic techniques were evaluated during the study and the EM-38 Ground Conductivity Unit proved valuable for characterizing salinity status of the sites. The Lowtran Computer Code was used to model atmospheric attenuation and results indicated that the positioning of a narrow shortwave infrared waveband, centred at 1985 nm, is possible.
Khorram, Siamak, Stacy A. C. Nelson, Frank H. Koch, and Cynthia F. van der Wiele. Remote Sensing. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4614-3103-9.
Buydos, John F. Remote sensing. Washington, D.C. (10 First St., S.E., Washington 20540-5232): Science Reference Section, Science and Technology Division, Library of Congress, 1993.
A, Beaumont E., Foster Norman H, and American Association of Petroleum Geologists., eds. Remote sensing. Tulsa, Okla: American Association of Petroleum Geologists, 1992.
Maliva, Robert, and Thomas Missimer. "Remote Sensing." In Arid Lands Water Evaluation and Management, 435–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29104-3_18.
West, P. W. "Remote Sensing." In Tree and Forest Measurement, 145–63. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14708-6_13.
Sholarin, Ebenezer A., and Joseph L. Awange. "Remote Sensing." In Environmental Science and Engineering, 231–38. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27651-9_11.
Lindenschmidt, Karl-Erich. "Remote Sensing." In River Ice Processes and Ice Flood Forecasting, 103–20. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-28679-8_5.
Singhal, B. B. S., and R. P. Gupta. "Remote sensing." In Applied Hydrogeology of Fractured Rocks, 53–86. Dordrecht: Springer Netherlands, 1999. http://dx.doi.org/10.1007/978-94-015-9208-6_4.
West, P. W. "Remote Sensing." In Tree and Forest Measurement, 135–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-95966-3_13.
Noyhouzer, Tomer, and Daniel Mandler. "Remote Sensing." In Environmental Analysis by Electrochemical Sensors and Biosensors, 667–90. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-0676-5_23.
Mahmoud, Ayman A., Tamer T. Elazhary, and Amal Zaki. "Remote sensing CubeSat." In Remote Sensing, edited by Roland Meynart, Steven P. Neeck, and Haruhisa Shimoda. SPIE, 2010. http://dx.doi.org/10.1117/12.865116.
"Remote sensing techniques." In Proceedings of the IEEE/OES Eighth Working Conference on Current Measurement Technology. IEEE, 2005. http://dx.doi.org/10.1109/ccm.2005.1506334.
Schweitzer, Jeffrey S. "Subsurface Remote Sensing." In UNATTENDED RADIATION SENSOR SYSTEMS FOR REMOTE APPLICATIONS. AIP, 2002. http://dx.doi.org/10.1063/1.1513964.
"Remote sensing applications." In 2010 2nd International Conference on Image Processing Theory, Tools and Applications (IPTA). IEEE, 2010. http://dx.doi.org/10.1109/ipta.2010.5586822.
Kellarev, Alexander, and Dan Sheffer. "Terahertz remote sensing." In SPIE Defense, Security, and Sensing, edited by Mehdi Anwar, Nibir K. Dhar, and Thomas W. Crowe. SPIE, 2011. http://dx.doi.org/10.1117/12.883109.
Bin Wan Ibrahim, W. Mohd Azhar, and E. H. Mirza. "Remote sensing electrocardiography." In 2013 International Conference on Computer Medical Applications (ICCMA 2013). IEEE, 2013. http://dx.doi.org/10.1109/iccma.2013.6506179.
Ghouzlane, Souad. "Wildfire Remote Sensing Applications." In 6th International Students Science Congress. Izmir International Guest Student Association, 2022. http://dx.doi.org/10.52460/issc.2022.027.
There is a growing interest in mapping, monitoring, and assessing wildfires' risk, behavior, and environmental impacts. Recent developments in Remote sensing technology and tools facilitate the researcher's job in obtaining spatial information and monitoring land changes and hazards. Moreover, remote sensing technology coupled with geographic information systems permits uncovering the spatial potential, predicting Spatio-temporal change patterns, and supporting sustainable land management. Likewise, Using Remote sensing data and GIS tools in mapping wildfire incidents and their behavior has proven to be highly efficient for land managers and firefighters to control the fire and prevent disastrous consequences. This paper aims to uncover some uses of remote sensing data in assessing forest fire hazards at every phase of the fire management program.
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Wang Yuan and Xing Lining. "Remote sensing satellite networking technology and remote sensing system: A survey." In 2015 12th IEEE International Conference on Electronic Measurement & Instruments (ICEMI). IEEE, 2015. http://dx.doi.org/10.1109/icemi.2015.7494508.
Harris, J., E. Grunsky, and V. Singhroy. Radar remote sensing. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2008. http://dx.doi.org/10.4095/226013.
Schultz, J., S. Czuchlewski, and R. Karl. Advanced laser remote sensing. Office of Scientific and Technical Information (OSTI), November 1996. http://dx.doi.org/10.2172/399674.
Davis, Curtiss O. Airborne Hyperspectral Remote Sensing. Fort Belvoir, VA: Defense Technical Information Center, September 1999. http://dx.doi.org/10.21236/ada631001.
Alföldi, T., P. Catt, and P. R. Stephens. Definitions of Remote Sensing. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1993. http://dx.doi.org/10.4095/218103.
Davis, Curtiss O. Airborne Hyperspectral Remote Sensing. Fort Belvoir, VA: Defense Technical Information Center, September 2001. http://dx.doi.org/10.21236/ada625021.
Ouattara, T., R. Couture, P. T. Bobrowsky, and A. Moore. Remote sensing and geosciences. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2004. http://dx.doi.org/10.4095/214995.
McDowell, R. S., S. W. Sharpe, and J. F. Kelly. Infrared signatures for remote sensing. Office of Scientific and Technical Information (OSTI), April 1994. http://dx.doi.org/10.2172/10161758.
