Literatura académica sobre el tema "Remote-sensing maps"
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Artículos de revistas sobre el tema "Remote-sensing maps"
Asylbekova, A. y A. Kikkarina. "Updating topographic maps using Remote Sensing". Journal of Geography and Environmental Management 42, n.º 1 (2016): 168–74. http://dx.doi.org/10.26577/jgem.2016.1.296.
Texto completoVillmann, Thomas, Erzsébet Merényi y Barbara Hammer. "Neural maps in remote sensing image analysis". Neural Networks 16, n.º 3-4 (abril de 2003): 389–403. http://dx.doi.org/10.1016/s0893-6080(03)00021-2.
Texto completoZeug, Gunter y Olaf Kranz. "Remote Sensing Based Population Maps for Crisis Response". Photogrammetrie - Fernerkundung - Geoinformation 2010, n.º 1 (1 de febrero de 2010): 33–46. http://dx.doi.org/10.1127/1432-8364/2010/0038.
Texto completoKing, Trude V. V., Raymond F. Kokaly, Todd M. Hoefen y Michaela R. Johnson. "Hyperspectral remote sensing data maps minerals in Afghanistan". Eos, Transactions American Geophysical Union 93, n.º 34 (21 de agosto de 2012): 325–26. http://dx.doi.org/10.1029/2012eo340002.
Texto completoBabayev, A. G. y N. G. Kharin. "REMOTE SENSING METHODS FOR COMPILING MAPS OF DESERTIFICATION". Mapping Sciences and Remote Sensing 26, n.º 4 (octubre de 1989): 325–30. http://dx.doi.org/10.1080/07493878.1989.10641780.
Texto completoBedell, Richard. "Remote Sensing in Mineral Exploration". SEG Discovery, n.º 58 (1 de julio de 2004): 1–14. http://dx.doi.org/10.5382/segnews.2004-58.fea.
Texto completoYakubov, Gayrat, Khamid Mubarakov, Ilkhomjon Abdullaev y Azizjon Ruziyev. "Creating large-scale maps for agriculture using remote sensing". E3S Web of Conferences 227 (2021): 03002. http://dx.doi.org/10.1051/e3sconf/202122703002.
Texto completoZhu, Zhiqin, Yaqin Luo, Guanqiu Qi, Jun Meng, Yong Li y Neal Mazur. "Remote Sensing Image Defogging Networks Based on Dual Self-Attention Boost Residual Octave Convolution". Remote Sensing 13, n.º 16 (6 de agosto de 2021): 3104. http://dx.doi.org/10.3390/rs13163104.
Texto completoHuang, Liang, Qiuzhi Peng y Xueqin Yu. "Change Detection in Multitemporal High Spatial Resolution Remote-Sensing Images Based on Saliency Detection and Spatial Intuitionistic Fuzzy C-Means Clustering". Journal of Spectroscopy 2020 (23 de marzo de 2020): 1–9. http://dx.doi.org/10.1155/2020/2725186.
Texto completoAnugraha, A. S. y H. J. Chu. "LAND USE CLASSIFICATION FROM COMBINED USE OF REMOTE SENSING AND SOCIAL SENSING DATA". ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4 (19 de septiembre de 2018): 33–39. http://dx.doi.org/10.5194/isprs-archives-xlii-4-33-2018.
Texto completoTesis sobre el tema "Remote-sensing maps"
Kannich, Rosene. "Automated selection of topographic base information for thematic maps". Thesis restricted. Connect to e-thesis to view abstract, 2007. http://theses.gla.ac.uk/544/.
Texto completoMSc(R) thesis submitted to the Faculty of Physical Sciences, Department of Geographical and Earth Sciences, University of Glasgow, 2007. Includes bibliographical references. Print version also available.
Horstman, Kevin Charles. "Geological, remote sensing, and geophysical investigation of the greater Arivaca region, Pima and Santa Cruz counties, Arizona". Diss., The University of Arizona, 1996. http://hdl.handle.net/10150/565552.
Texto completoNecsoiu, Dorel Marius. "A Data Fusion Framework for Floodplain Analysis using GIS and Remotely Sensed Data". Thesis, University of North Texas, 2000. https://digital.library.unt.edu/ark:/67531/metadc2557/.
Texto completoUmbert, Ceresuela Marta. "Exploiting the multiscale synergy among ocean variables : application to the improvement of remote sensing salinity maps". Doctoral thesis, Universitat Politècnica de Catalunya, 2015. http://hdl.handle.net/10803/321115.
Texto completoRemote sensing imagery of the ocean surface provides a synoptic view of the complex geometry of ocean circulation, which is dominated by mesoscale variability. The signature of filaments and vortices is present in different ocean scalars advected by the oceanic flow. The most probable origin of the observed structures is the turbulent character of ocean currents, and those signatures are persistent over time scales compatible with ocean mesoscale dynamics. At spatial scales of kilometers or more, turbulence is mainly 2D, and a complex geometry, full of filaments and eddies of different sizes, emerges in remote sensing images of surface chlorophyll-a concentration and surface salinity, as well as in other scalars acquired with higher quality such as surface temperature and absolute dynamic topography. The aim of this thesis is to explore and apply mapping methodologies to improve the quality of remote sensing maps in general, but focusing in the case of remotely sensed sea surface salinity (SSS) data. The different methodologies studied in this thesis have been applied with the specific goal of improving surface salinity maps generated from data acquired by the European Space Agency's mission SMOS, the first satellite able to measure soil moisture and ocean salinity from space at a global scale. The first part of this thesis will introduce the characteristics of the operational SMOS Level 2 (L2) SSS products and the classical approaches to produce the best possible SSS maps at Level 3 (L3), namely data filtering, weighted average and Optimal Interpolation. In the course of our research we will obtain a set of recommendations about how to process SMOS data starting from L2 data. A fusion technique designed to exploit the common turbulent signatures between different ocean variables is also explored in this thesis, in what represents a step forward from L3 to Level 4 (L4). This fusion technique is theoretically based on the geometrical properties of advected tracers. Due to the effect of the strong shear in turbulent flows, the spatial structure of tracers inherit some properties of the underlying flow and, in particular, its geometrical arrangement. As a consequence, different ocean variables exhibit scaling properties, similar to the turbulent energy cascade. The fusion method takes a signal affected by noise, data gaps and/or low resolution, and improves it in a geophysically meaningful way. This signal improvement is achieved by using an appropriate data, which is another ocean variable acquired with higher quality, greater spatial coverage and/or finer resolution. A key point in this approach is the assumption of the existence of a multifractal structure in ocean images, and that singularity lines of the different ocean variables coincide. Under these assumptions, the horizontal gradients of both variables, signal and template, can be related by a smooth matrix. The first, simplest approach to exploit such an hypothesis assumes that the relating matrix is proportional to the identity, leading to a local regression scheme. As shown in the thesis, this simple approach allows reducing the error and improving the coverage of the resulting Level 4 product; Moreover, information about the statistical relationship between the two fields is obtained since the functional dependence between signal and template is determined at each point.
Tyoda, Zipho. "Landslide susceptibility mapping : remote sensing and GIS approach". Thesis, Stellenbosch : Stellenbosch University, 2013. http://hdl.handle.net/10019.1/79856.
Texto completoLandslide susceptibility maps are important for development planning and disaster management. The current synthesis of landslide susceptibility maps largely applies GIS and remote sensing techniques. One of the most critical stages on landslide susceptibility mapping is the selection of landslide causative factors and weighting of the selected causative factors, in accordance to their influence to slope instability. GIS is ideal when deriving static factors i.e. slope and aspect and most importantly in the synthesis of landslide susceptibility maps. The integration of landslide causative thematic maps requires the selection of the weighting method; in order to weight the causative thematic maps in accordance to their influence to slope instability. Landslide susceptibility mapping is based on the assumption that future landslides will occur under similar circumstances as historic landslides. The weight of evidence method is ideal for landslide susceptibility mapping, as it calculates the weights of the causative thematic maps using known landslides points. This method was applied in an area within the Western Cape province of South Africa, the area is known to be highly susceptible to landslide occurrences. A prediction rate of 80.37% was achieved. The map combination approach was also applied and achieved a prediction rate of 50.98%. Satellite remote sensing techniques can be used to derive the thematic information needed to synthesize landslide susceptibility maps and to monitor the variable parameters influencing landslide susceptibility. Satellite remote sensing techniques can contribute to landslide investigation at three distinct phases namely: (1) detection and classification of landslides (2) monitoring landslide movement and identification of conditions leading up to an event (3) analysis and prediction of slope failures. Various sources of remote sensing data can contribute to these phases. Although the detection and classification of landslides through the remote sensing techniques is important to define landslide controlling parameters, the ideal is to use remote sensing data for monitoring of areas susceptible to landslide occurrence in an effort to provide an early warning. In this regard, optical remote sensing data was used successfully to monitor the variable conditions (vegetation health and productivity) that make an area susceptible to landslide occurrence.
Aleong-Mackay, Kathryn. "Landsat imagery and small-scale vegetation maps : data supplementation and verification : a case study of the Maralal area, northern Kenya". Thesis, McGill University, 1987. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=66182.
Texto completoCamacho, Mark A. "Depth analysis of Midway Atoll using Quickbird multi-spectral imaging over variable substrates". Thesis, Monterey California. Naval Postgraduate School, 2006. http://hdl.handle.net/10945/2674.
Texto completoPark, Kyoung Jin. "Generating Thematic Maps from Hyperspectral Imagery Using a Bag-of-Materials Model". The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1366296426.
Texto completoBaxter, Katrina. "Linking seafloor mapping and ecological models to improve classification of marine habitats : opportunities and lessons learnt in the Recherche Archipelago, Western Australia". University of Western Australia. School of Plant Biology, 2008. http://theses.library.uwa.edu.au/adt-WU2008.0181.
Texto completoCarmody, James Daniel Physical Environmental & Mathematical Sciences Australian Defence Force Academy UNSW. "Deriving bathymetry from multispectral and hyperspectral imagery". Awarded by:University of New South Wales - Australian Defence Force Academy. School of Physical, Environmental and Mathematical Sciences, 2007. http://handle.unsw.edu.au/1959.4/38654.
Texto completoLibros sobre el tema "Remote-sensing maps"
Yao gan zhuan ti fen xi yu di xue tu pu. Beijing: Ke ue chu ban she, 2002.
Buscar texto completoNelson, Elizabeth. BOREAS hardcopy maps. Greenbelt, Md: National Aeronautics and Space Administration, Goddard Space Flight Center, 2000.
Buscar texto completoBeckel, Lothar. Österreich: Satelliten-Bild-Atlas. Salzburg: Druckhaus Nonntal-Bücherdienst, 1988.
Buscar texto completoSanFilipo, John R. Satellite image maps of Pakistan. [Reston, VA: U.S. Dept. of the Interior, U.S. Geological Survey, 1997.
Buscar texto completoTaiwan gang wan ji hai an shu wei tu xiang zi liao ku jian li zhi yan jiu. Taibei Shi: Jiao tong bu yun shu yan jiu suo, 2002.
Buscar texto completoGao fen bian lü yao gan ying xiang zhong dao lu ti qu fang fa de yan jiu. Beijing: Ke xue chu ban she, 2012.
Buscar texto completoSurvey, United States Geological. Controlled photomosaic of the MTM 20197 quadrangle, Orcus Patera region of Mars. Reston, VA: U.S. Geological Survey, 1995.
Buscar texto completoUnited States Geological Survey. Controlled photomosaic of the MTM 45002 quadrangle, Acidalia Planitia region of Mars. Reston, VA: U.S. Geological Survey, 1995.
Buscar texto completoPohn, Howard A. Radar and landsat lineament maps of the Glens Falls 1 x 2 quadrangle, New York, Vermont, and New Hampshire. [Denver, Colo.?]: Dept. of the Interior, U.S. Geological Survey, 1986.
Buscar texto completoPohn, Howard A. Radar and landsat lineament maps of the Glens Falls 1p0s x 2p0s quadrangle, New York, Vermont, and New Hampshire. [Denver, Colo.?]: Dept. of the Interior, U.S. Geological Survey, 1986.
Buscar texto completoCapítulos de libros sobre el tema "Remote-sensing maps"
Czaplewski, Raymond L. "Accuracy Assessment of Maps of Forest Condition". En Remote Sensing of Forest Environments, 115–40. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-0306-4_5.
Texto completoPopelka, Stanislav. "Eye-Tracking Evaluation of Non-Photorealistic Maps of Cities and Photo-Realistic Visualization of an Extinct Village". En Remote Sensing and Cognition, 87–109. Boca Raton, FL : Taylor & Francis, 2018.: CRC Press, 2018. http://dx.doi.org/10.1201/9781351040464-5.
Texto completoStefanidis, C. N. y A. P. Cracknell. "Self Organised Maps: the Combined Utilisation of Feature and Novelty Detectors". En Neurocomputation in Remote Sensing Data Analysis, 242–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-642-59041-2_27.
Texto completoKinne, Stefan. "Remote sensing data combinations: superior global maps for aerosol optical depth". En Satellite Aerosol Remote Sensing over Land, 361–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-69397-0_12.
Texto completoStolte, Nilo. "Visualizing Remotely Sensed Depth Maps using Voxels". En Machine Vision and Advanced Image Processing in Remote Sensing, 170–80. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-60105-7_15.
Texto completoBoekaerts, P., E. Nyssen y J. Cornelis. "A Comparative Study of Topological Feature Maps Versus Conventional Clustering for (Multi-Spectral) Scene Identification in METEOSAT Imagery". En Neurocomputation in Remote Sensing Data Analysis, 232–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-642-59041-2_26.
Texto completoMillhauser, John K. y Christopher T. Morehart. "The Ambivalence of Maps: A Historical Perspective on Sensing and Representing Space in Mesoamerica". En Digital Methods and Remote Sensing in Archaeology, 247–68. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40658-9_11.
Texto completoBabonis, Gregory S. y Matthew W. Becker. "Use of Multifrequency Synthetic Aperture Radar (SAR) to Support Regional-Scale Groundwater Potential Maps". En Remote Sensing of the Terrestrial Water Cycle, 383–95. Hoboken, NJ: John Wiley & Sons, Inc, 2014. http://dx.doi.org/10.1002/9781118872086.ch23.
Texto completoErener, Arzu, Gulcan Sarp y Muhammet İbrahim Karaca. "Building Hights and Floor Estimation Using 3D Maps, Central Part of Kucukcekmece, Istanbul, Turkey". En Advances in Remote Sensing and Geo Informatics Applications, 159–62. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01440-7_37.
Texto completoMarques, Nuno C. y Ning Chen. "Border Detection on Remote Sensing Satellite Data Using Self-Organizing Maps". En Progress in Artificial Intelligence, 294–307. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-24580-3_35.
Texto completoActas de conferencias sobre el tema "Remote-sensing maps"
Parkes, Stephen D., Matthew F. McCabe, Samir K. Al-Mashhawari y Jorge Rosas. "Reproducibility of crop surface maps extracted from Unmanned Aerial Vehicle (UAV) derived digital surface maps". En SPIE Remote Sensing, editado por Christopher M. U. Neale y Antonino Maltese. SPIE, 2016. http://dx.doi.org/10.1117/12.2241280.
Texto completoMarangi, Carmela, Licinio Angelini, F. De Carlo, Giuseppe Nardulli, M. Pellicoro y Sebastiano Stramaglia. "Clustering by inhomogeneous chaotic maps in landmine detection". En Europto Remote Sensing, editado por Sebastiano B. Serpico. SPIE, 2001. http://dx.doi.org/10.1117/12.413888.
Texto completoAlonso-Benito, Alfonso, Pedro A. Hernandez-Leal, Manuel Arbelo, Alejandro Gonzalez-Calvo, Jose A. Moreno-Ruiz y Jose R. Garcia-Lazaro. "Satellite image based methods for fuels maps updating". En SPIE Remote Sensing, editado por Christopher M. U. Neale y Antonino Maltese. SPIE, 2016. http://dx.doi.org/10.1117/12.2241990.
Texto completoKressler, Florian, Klaus Steinnocher, Andreas Busch, André Streilein y Michael Franzen. "Supporting the update of maps by object-oriented classification of orthophotos". En Remote Sensing, editado por Manfred Ehlers y Ulrich Michel. SPIE, 2006. http://dx.doi.org/10.1117/12.688843.
Texto completoVilla, Alberto, Jocelyn Chanussot, Jon Atli Benediktsson y Christian Jutten. "Supervised super-resolution to improve the resolution of hyperspectral images classification maps". En Remote Sensing, editado por Lorenzo Bruzzone. SPIE, 2010. http://dx.doi.org/10.1117/12.864938.
Texto completoNasr, Ayman H. "Comparative study for the DEM generation from RADARSAT stereoscopic data and topographic maps". En Remote Sensing, editado por Francesco Posa. SPIE, 2005. http://dx.doi.org/10.1117/12.620609.
Texto completoBinaghi, Elisabetta, Pietro A. Brivio, Pier P. Ghezzi y Anna Rampini. "Assessing the accuracy of soft thematic maps using fuzzy set-based error matrices". En Remote Sensing, editado por Sebastiano B. Serpico. SPIE, 1999. http://dx.doi.org/10.1117/12.373256.
Texto completoIbarrola-Ulzurrun, Edurne, Consuelo Gonzalo-Martin y Javier Marcello-Ruiz. "Influence of pansharpening techniques in obtaining accurate vegetation thematic maps". En SPIE Remote Sensing, editado por Ulrich Michel, Karsten Schulz, Manfred Ehlers, Konstantinos G. Nikolakopoulos y Daniel Civco. SPIE, 2016. http://dx.doi.org/10.1117/12.2241501.
Texto completoSoares, Fernando y Giovanni Nico. "Waterline extraction in optical images and InSAR coherence maps based on the geodesic time concept". En Remote Sensing, editado por Lorenzo Bruzzone. SPIE, 2010. http://dx.doi.org/10.1117/12.864687.
Texto completoNikolakopoulos, Konstantinos, Dimitris Vaiopoulos y Georgios Skianis. "SRTM DTM versus one created from 1/50.000 topographic maps: the case of Kos Island". En Remote Sensing, editado por Francesco Posa. SPIE, 2005. http://dx.doi.org/10.1117/12.626794.
Texto completoInformes sobre el tema "Remote-sensing maps"
Toutin, Th. Map Making with Remote Sensing Data. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1999. http://dx.doi.org/10.4095/219719.
Texto completoDysart, Mary D. Remote Sensing and Mass Migration Policy Development. Fort Belvoir, VA: Defense Technical Information Center, febrero de 2011. http://dx.doi.org/10.21236/ada562466.
Texto completoDysart, Mary D. Remote Sensing and Mass Migration Policy Development. Fort Belvoir, VA: Defense Technical Information Center, octubre de 2012. http://dx.doi.org/10.21236/ada567838.
Texto completoBorrett, Veronica, Melissa Hanham, Gunnar Jeremias, Jonathan Forman, James Revill, John Borrie, Crister Åstot et al. Science and Technology for WMD Compliance Monitoring and Investigations. The United Nations Institute for Disarmament Research, diciembre de 2020. http://dx.doi.org/10.37559/wmd/20/wmdce11.
Texto completoGraham, D. F. y A. Ciesielski. Combining field observations and remote sensing to map the Grenville Front along the Pascagama River, Quebec. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1994. http://dx.doi.org/10.4095/193842.
Texto completoSuir, Glenn, Christina Saltus y Sam Jackson. Remote Assessment of Swamp and Bottomland Hardwood Habitat Condition in the Maurepas Diversion Project Area. Engineer Research and Development Center (U.S.), agosto de 2021. http://dx.doi.org/10.21079/11681/41563.
Texto completoSuhartono, Suhartono, Agoes Soegianto y Achmad Amzeri. Mapping of land potentially for maize plant in Madura Island-Indonesia using remote sensing data and geographic information systems (GIS). EM International, noviembre de 2020. http://dx.doi.org/10.21107/amzeri.2020.1.
Texto completoDouglas, Thomas A., Christopher A. Hiemstra, Stephanie P. Saari, Kevin L. Bjella, Seth W. Campbell, M. Torre Jorgenson, Dana R. N. Brown y Anna K. Liljedahl. Degrading Permafrost Mapped with Electrical Resistivity Tomography, Airborne Imagery and LiDAR, and Seasonal Thaw Measurements. U.S. Army Engineer Research and Development Center, julio de 2021. http://dx.doi.org/10.21079/11681/41185.
Texto completoWilkinson, L., P. Budkewitsch, D. F. Graham, J. Henderson y M. D'Iorio. Alternative methods of base map generation using remote sensing and GIS: a pilot study in the western Churchill Province, Northwest Territories. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1997. http://dx.doi.org/10.4095/208634.
Texto completoPokrzywinski, Kaytee, Kaitlin Volk, Taylor Rycroft, Susie Wood, Tim Davis y Jim Lazorchak. Aligning research and monitoring priorities for benthic cyanobacteria and cyanotoxins : a workshop summary. Engineer Research and Development Center (U.S.), agosto de 2021. http://dx.doi.org/10.21079/11681/41680.
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