Modeling temporal evolution of junco marshes radar signatures

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In this work, multitemporal synthetic aperture radar (SAR) data in conjunction with an electromagnetic (EM) model and a vegetation growth model were used to monitor and explain burn-regrowth events of junco vegetation in a wetland environment. The data used were from Radarsat-1, ENVISAT Advanced Syn...

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Detalles Bibliográficos
Autor principal: Grings, Francisco Matías
Otros Autores: Ferrazzoli, P., Karszenbaum, H., Tiffenberg, J., Kandus, P., Guerriero, L., Jacobo-Berrles, J.C
Formato: Capítulo de libro
Lenguaje:Inglés
Publicado: 2005
Materias:
RADAR
Acceso en línea:Registro en Scopus
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Biblioteca Central Dr. Luis F. Leloir (FCEN) de Universidad de Buenos Aires
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100 1 |a Grings, Francisco Matías 
245 1 0 |a Modeling temporal evolution of junco marshes radar signatures 
260 |c 2005 
270 1 0 |m Grings, F.; Institute de Astronomía y Física del Espacio, Ciudad Universitaria, 1428 Buenos Aires, Argentina; email: verderis@iafe.uba.ar 
504 |a Pope, K.O., Rejmankova, E., Paris, F.F., Woodruff, R., Detecting seasonal flooding cicles in marshes of the Yucatan peninsula with SIR-C polarimetric radar imagery (1997) Remote Sens. Environ., 59, pp. 157-166 
504 |a Kasischke, E.S., Smith, K.B., Bourgeau-Chavez, L.L., Romanowicz, E.A., Brunzell, S., Richardson, C.J., Effects of the seasonal hydrologic patterns in South Florida wetlands on radar backsatter measured on ERS-2 SAR image (2003) Remote Sens. Environ., 88, pp. 423-441 
504 |a Parmuchi, M.G., Karszenbaum, H., Kandus, P., Mapping the Paraná River delta wetland using multitemporal RADARSAT/SAR data and a decision based classifier (2002) Can. J. Remote Sens., 28, pp. 1202-1213 
504 |a Ramsey III, E.W., Sapkota, S.K., Barnes, F.G., Nelson, G.A., Monitoring the recovery of juncos roemerianus marsh burns with the normalized difference vegetation index and Landsat Thematic Mapper data (2002) Wetlands Ecol. Manage., 10, pp. 85-96 
504 |a Ramsey III, E.W., Nelson, G.A., Sapkota, S.K., Laine, S.C., Verdi, J., Krasznay, S., Using multiple polarization L-Band radar to monitor marsh burn recovery (1999) IEEE Trans, Geosci. Remote Sens., 37 (1), pp. 635-639. , Jan 
504 |a Richards, J.A., Woodgate, P.W., Skidmore, A.K., An explanation of enhance of radar backscattering from flooded forests (1987) Int. J. Remote Sens., 8, pp. 1093-1100 
504 |a Hess, L.L., Melack, G.M., Simonett, D.S., Radar detection of flooding beneath the forest canopy, a review (1990) Int. J. Remote Sens., 11, pp. 1313-1325 
504 |a Hess, L.L., Melack, J.M., Filoso, S., Wang, Y., Delineation of inundated area and vegetation along the amazon floodplain whit the SIR-C synthetic aperture radar (1995) IEEE Trans. Geosci. Remote Sens., 33 (4), pp. 896-902. , Jul 
504 |a Townsend, P.A., Mapping seasonal flooding in forested wetlands using multitemporal radarsat SAR (2001) Photogramm. Eng. Remote Sens., 67, pp. 857-864 
504 |a Estimating forest structure in wetlands using multitemporal SAR (2002) Remote Sens. Environ., 79, pp. 288-304 
504 |a Karszenbaum, H., Kandus, P., Martinez, J.M., Toan, T.L., Tiffenberg, J., Parmuchi, G., ERS-2, RADARSAT SAR backscattering characteristics of the Paraná River delta wetlands, Argentina (2000) ERS-Envisat Symp., , ESA-SP-461 
504 |a Kandus, P., Pratolongo, P., (2002), private communication; Bracaglia, M., Ferrazzoli, P., Guerriero, L., A fully polarimetric multiple scattering model for crops (1995) Remote Sens. Environ., 54, pp. 170-179 
504 |a El-Rayes, M.A., Ulaby, F.T., Microwave dielectric spectrum of vegetation - Part I: Experimental observations (1987) IEEE Trans. Geosci. Remote Sens., GE-25, pp. 541-549 
504 |a Karam, M.A., Fung, A.K., Electromagnetic scattering from a layer of finite length, randomly oriented, dielectric, circular cylinders over a rough interface with application to vegetation (1988) Int. J. Remote Sens., 9, pp. 1109-1134 
504 |a Schiffer, R., Reflectivity of a slightly rough surface (1987) Appl. Opt., 26, pp. 704-712 
504 |a Beckmann, P., Spizzichino, A., (1963) The Scattering of Electromagnetic Waves from Rough Surfaces, , New York: Pergamon 
506 |2 openaire  |e Política editorial 
520 3 |a In this work, multitemporal synthetic aperture radar (SAR) data in conjunction with an electromagnetic (EM) model and a vegetation growth model were used to monitor and explain burn-regrowth events of junco vegetation in a wetland environment. The data used were from Radarsat-1, ENVISAT Advanced Synthetic Aperture Radar (ASAR), and European Remote Sensing 2 (ERS-2) temporal series. The EM model is based on radiative transfer theory and describes junco vegetation as a set of vertical dielectric cylinders on a flat flooded surface. It was used, together with the vegetation growth model, to predict the temporal evolution of the radar response during a burn-regrowth event. This simulation was compared with the ERS-2 vertical (VV) data. It was observed a "bell-shaped" temporal trend that was confirmed by the simulated data with a mean error of 2.5 dB. Additionally, in view of current and future ENVISAT ASAR Alternating Polarization Mode Precision data, the horizontal (HH) SAR temporal response was also simulated giving as a result strong differences between simulated HH and VV temporal trends. These differences are in good agreement with the ones observed between Radarsat-1 HH and ERS-2 VV SAR data acquired at close dates and also with the same differences observed between HH and VV ENVISAT ASAR data. Electromagnetic modeling results provide a sound theoretical interpretation of these observations. © 2005 IEEE.  |l eng 
536 |a Detalles de la financiación: Secretaria de Ciencia y Tecnica, Universidad de Buenos Aires 
536 |a Detalles de la financiación: Consejo Nacional de Investigaciones Científicas y Técnicas 
536 |a Detalles de la financiación: Ministero degli Affari Esteri e della Cooperazione Internazionale 
536 |a Detalles de la financiación: Consejo Nacional de Investigaciones Científicas y Técnicas 
536 |a Detalles de la financiación: European Space Agency, AO 667, AO3 232 
536 |a Detalles de la financiación: Comisión Nacional de Actividades Espaciales 
536 |a Detalles de la financiación: National Institutes for Water Resources 
536 |a Detalles de la financiación: Manuscript received August 10, 2004; revised May 24, 2005. This work was supported in part by the Argentine institutions Consejo Nacional de Investiga-ciones Científicas y Técnicas (CONICET) and the University of Buenos Aires FCEyN and in part by the Argentine-Italy, Secretaria de Ciencia y Tecnologia (SeCyT), Ministero Affari Esteri (MAE) cooperation project. 
536 |a Detalles de la financiación: The authors thank J. M. Martinez who helped with the analysis of the ERS-2 temporal series at the beginning of the work with radar data, and graduate students P. Pratolongo, G. Parmuchi, and G. Trilla for their contribution during field work campaigns. The authors specially thank the Canadian Centre for Remote Sensing GLOBESAR II program for providing RADARSAT-1 data, the European Space Agency for the continuous support through AO3 232 and AO 667 supported projects, the National Commission for Space Activities (CONAE) for the optical data, the National Water Institute (INA), and the National Hydrologic Service (SHN) for providing us river water levels and precipitation data. 
593 |a Institute de Astronomía y Física del Espacio, Ciudad Universitaria, 1428 Buenos Aires, Argentina 
593 |a Tor Vergata University, Ingegneria - DISP, 00133 Roma, Italy 
593 |a Universidad de Buenos Aires, FCEyN, Ciudad Universitaria, 1428 Buenos Aires, Argentina 
593 |a IEEE, Argentina 
593 |a Science Advisory Group, European Space Agency's SMOS Project 
593 |a ENVISAT ASAR AO 667 Project, Argentina 
593 |a Secretaría de Ciencia y Tecnología, Argentina 
593 |a National Commission on Space Activities (CONAE), Argentina 
593 |a University of Buenos Aires (UBA), Buenos Aires, Argentina 
593 |a Remote Sensing Group, Astronomy and Space Physics Institute (IAFE), Buenos Aires, Argentina 
593 |a Computer Science Department, Faculty of Exact and Natural Sciences, University of Buenos Aires, Argentina 
650 1 7 |2 spines  |a RADAR 
690 1 0 |a ELECTROMAGNETIC (EM) MODELS 
690 1 0 |a WETLANDS 
690 1 0 |a ELECTROMAGNETIC (EM) MODELS 
690 1 0 |a VEGETATION GROWTH MODEL 
690 1 0 |a COMPUTER SIMULATION 
690 1 0 |a DATA ACQUISITION 
690 1 0 |a MAGNETOELECTRIC EFFECTS 
690 1 0 |a MATHEMATICAL MODELS 
690 1 0 |a REMOTE SENSING 
690 1 0 |a WETLANDS 
690 1 0 |a SYNTHETIC APERTURE RADAR 
700 1 |a Ferrazzoli, P. 
700 1 |a Karszenbaum, H. 
700 1 |a Tiffenberg, J. 
700 1 |a Kandus, P. 
700 1 |a Guerriero, L. 
700 1 |a Jacobo-Berrles, J.C. 
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856 4 0 |u https://doi.org/10.1109/TGRS.2005.855067  |y DOI 
856 4 0 |u https://hdl.handle.net/20.500.12110/paper_01962892_v43_n10_p2238_Grings  |y Handle 
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