Biomass estimation of wetland vegetation in Poyang Lake area using ENVISAT advanced synthetic aperture radar data

Journal of Applied Remote Sensing

Volumn 7, Issue 1, 2013, Pages

  Liao, Jingjuan ^a   Shen, Guozhuang ^a   Dong, Lei ^b  

^a CENTER FOR EARTH OBSERVATION AND DIGITAL EARTH   (China)

^b INSTITUTE OF REMOTE SENSING APPLICATIONS   (China)

Author keywords

biomass inversion; Michigan Microwave Canopy Scattering; neural network algorithm; synthetic aperture radar; wetland

Indexed keywords

BIOMASS; ECOSYSTEMS; FORESTRY; GEODETIC SATELLITES; LAKES; RADAR IMAGING; RADIATIVE TRANSFER; VEGETATION; WETLANDS;

ADVANCED SYNTHETIC APERTURE RADARS; ALTERNATING POLARIZATION; ENVIRONMENTAL CHANGE; HYDROLOGICAL CYCLES; MICHIGAN MICROWAVE CANOPY SCATTERINGS; NEURAL NETWORK ALGORITHM; RADIATIVE TRANSFER MODEL; WETLAND VEGETATION;

SYNTHETIC APERTURE RADAR;

EID: 84897763654     PISSN: None     EISSN: 19313195     Source Type: Journal    
DOI: 10.1117/1.JRS.7.073579     Document Type: Article

References (23)

1. Y. Pan et al., "Measurement of ecological capital of Chinese terrestrial ecosystem based on remote sensing," Sci. China Ser. D Earth Sci. 48(6), 786-796 (2005), http://dx.doi.org/10 .1360/03yd0264.
2. D. Lu, "The potential and challenge of remote sensing-based biomass estimation," Int. J. Rem. Sens. 27(7), 1297-1328 (2006), http://dx.doi.org/10.1080/01431160500486732.
3. R. Li and J. Liu, "An estimation of wetland vegetation biomass in the poyang lake using landsat ETM data," Acta Geograph. Sinica 56(5), 532-540 (2001).
4. J. Li, X. Shu, and S. Chen, "Establishment of wetland vegetation biomass model by in-situ and remote sensing observation in Poyang Lake area," J. Guangzhou Univ. (Nat. Sci. Ed.) 4(6), 494-498 (2005), http://dx.doi.org/10.3969/jissn.1671-4229.2005.06.005.
5. T. Le Toan et al., "Rice crop mapping and monitoring using ERS-1 data based on experiment and modeling results," IEEE Trans. Geosci. Rem. Sens. 35(1), 41-56 (1997), http://dx .doi.org/10.1109/36.551933.
6. S. Paloscia, G. Macelloni, and P. Pampaloni, "The relations between backscattering coefficient and biomass of narrow and wide leaf crops," in Proc. IEEE Int. Geosci. and Remote Sens. Symp., Vol. 1, pp. 100-102, IEEE, Seattle, Washington (1998).
7. F. Ribbes and L. T Thuy, "Coupling radar data and rice growth model for yield estimation," in Proc. IEEE Int. Geosci. andRemote Sens. Symp., Vol. 4, pp. 2336-2338, IEEE, Hamburg (1999).
8. Y. Shao, J. Liao, and C. Wang, "Analysis of temporal radar backscatter of rice: A comparison of SAR observations with modeling results," Can. J. Rem. Sens. 28(2), 128-138 (2002), http://dx.doi.org/10. 5589/m02-019.
9. S. Moreau and T. Le Toan, "Biomass quantification of andean wetland forages using ERS satellite SAR data for optimizing livestock management," Rem. Sens. Environ. 84(4), 477-492 (2003), http://dx.doi.org/10.1016/S0034- 4257(02)00111-6.
10. E. S. Kasischke et al., "Effects of seasonal hydrologic patterns in south Florida wetlands on radar backscatter measured from ERS-2 SAR imagery," Rem. Sens. Environ. 88(4), 423-441 (2003), http://dx.doi.org/10. 1016/j.rse.2003.08.016.
11. M. P. F Costa et al., "Biophysical properties and mapping of aquatic vegetation during the hydrological cycle of the Amazon floodplain using JERS-1 and Radarsat," Int. J. Rem. Sens. 23(7), 1401-1426 (2002), http://dx.doi.org/10.1080/01431160110092957.
12. F D. Frate and D. Solimini, "On neural network algorithms for retrieving forest biomass from SAR data," IEEE Trans. Geosci. Rem. Sens. 42(1), 24-34 (2004), http://dx.doi.org/10 .1109/TGRS.2003.817220.
13. M. C. Dobson et al., "Dependence of radar backscatter on coniferous forest biomass," IEEE Trans. Geosci. Rem. Sens. 30(2), 412-415 (1992), http://dx.doi.org/10.1109/36.134090.
14. E. S. Kasischke, Jr., N. L. Christensen, and L. L. Bourgeau-Chavez, "Correlating radar backscatter with components of biomass in loblolly pine forests," IEEE Trans. Geosci. Rem. Sens. 33(3), 643-659 (1995), http://dx.doi.org/10.1109/36.387580.
15. P. F. Polatin, K. Sarabandi, and F. T. Ulaby, "An iterative inversion algorithm with application to the polarimetric radar response of vegetation canopies," IEEE Trans. Geosci. Rem. Sens. 32(1), 62-71 (1994), http://dx.doi.org/10.1109/36.285189.
16. E. Rignot et al., "Radar estimates of aboveground biomass in boreal forests of interior Alaska," IEEE Trans. Geosci. Rem. Sens. 32(5), 1117-1124 (1994), http://dx.doi.org/10 .1109/36.312903.
17. F D. Frateand and L. F Wang, "Sunflower biomass estimation using a scattering model and a neural network algorithm," Int. J. Rem. Sens. 22(7), 1235-1244 (2001), http://dx.doi.org/10.1080/01431160151144323.
18. S. Wang and D. Guan, "Remote sensing method of forest biomass estimation by artificial neural network models," Ecol. Environ. 16(1), 108-111 (2007), http://dx.doi.org/10.3969/j issn.1674-5906.2007.01.020.
19. Y. Peng, Y. Jian, and R. Li, "Community diversity of aquatic plants in the lakes of Poyang plain district of China," J. Cent. South Forest. Univ. 23(4), 22-27 (2003), http://dx.doi.org/10.3969/j.issn.1673-923X.2003.04. 014.
20. F T. Ulaby et al., "Michigan microwave canopy scattering model," Int. J. Rem. Sens. 11(7), 1223-1253 (1990), http://dx.doi.org/10. 1080/01431169008955090.
21. C. Xie et al., "Analysis of ALOS PALSAR INSAR data for mapping water level changes in Yellow River delta wetlands," Int. J. Rem. Sens. 34(6), 2047-2056 (2013), http://dx.doi.org/10.1080/01431161.2012.731541.
22. Y Wang et al., "Understanding the radar backscattering from flooded and nonflooded Amazonian forests: results from canopy backscatter modeling," Rem. Sens. Environ. 54(3), 324-332 (1995), http://dx.doi.org/10. 1016/0034-4257(95)00140-9.
23. M. S. Dawson, A. K. Fung, and M. T. Manry, "Surface parameter retrieval using fast learning neural networks," Rem. Sens. Rev. 7(1), 1-18 (1993), http://dx.doi.org/10.1080/02757259309532163.