Evaluating total inorganic nitrogen in coastal waters through fusion of multi-temporal RADARSAT-2 and optical imagery using random forest algorithm

International Journal of Applied Earth Observation and Geoinformation

Volumn 33, Issue 1, 2014, Pages 192-202

  Liu, Meiling ^a   Liu, Xiangnan ^a   Li, Jin ^b   Ding, Chao ^a   Jiang, Jiale ^a  

^a CHINA UNIVERSITY OF GEOSCIENCES   (China)

^b NATIONAL MARINE DATA AND INFORMATION SERVICE   (China)

Author keywords

Multisensor data fusion; Optical images; RADARSAT 2 images; Random forest algorithm; Total inorganic nitrogen

Indexed keywords

ALGORITHM; COASTAL WATER; INORGANIC NITROGEN; NUTRIENT; OPEN WATER; RADAR IMAGERY; RADARSAT; REMOTE SENSING; SATELLITE IMAGERY; SPATIAL DISTRIBUTION; SYNTHETIC APERTURE RADAR; TEMPORAL DISTRIBUTION;

CHINA; HONG KONG;

EID: 84904761645     PISSN: 15698432     EISSN: 1872826X     Source Type: Journal    
DOI: 10.1016/j.jag.2014.05.009     Document Type: Article

References (63)

1. Algina.J., Keselman, H.J., 1999. Comparing squared multiple correlation coefficients: examination of a confidence interval and a test significance. Psychol. Methods 4(1), 76-83
2. Alpers, W., Ross, D.B., Rufenach, C.L., 1981. On the detectability of ocean surface waves by real and synthetic aperture radar. J. Geophys. Res. 86, 6481-6498
3. Arun, It, Langmead, C.J., 2006. Structure based chemical shift prediction using random forests non-linear regression. In: Proceedings of the 4th Asia-Pacific Bioinformatics Conference, Vol. 3, pp. 317-326
4. Beaucage, P., Glazer, A., Choisnard, J., Yu, W., Bernier, M., Benoit, R., Lafrance, G., 2007. Wind assessment in a coastal environment using synthetic aperture radar satellite imagery and a numerical weather prediction model. Can. J. Remote Sens. 33 (5), 368-377
5. Breiman, L., 1996. Bagging predictors. Mach. Learn. 24,123-140
6. Breiman, L., 2001. Random forests. Mach. Learn. 45, 5-32
7. Bruning, C, Alpers, W., Zambresky, L.F., Tilley, D.G., 1988. Validation of a synthetic aperture radar ocean wave imaging theory by the Shuttle Imaging Radar: B. Experiment overthe North Sea. J. Geophys. Res. 93,15403-15425
8. Cutler, M.E.J., Boyd, D.S., Foody, G.M., Vetrivel, A., 2012. Estimating tropical forest biomass with a combination of SAR image texture and Landsat TM data: an assessment of predictions between regions. ISPRS J. Photogramm. Remote Sens. 70,66-77
9. Diaz-Uriarte, R., de Andres, S.A., 2006. Gene selection and classification of microarray data using random forest. BMC Bioinformatics, 7
10. Dong, J.W., Xiao, X.M., Sheldon, S., Biradar, C, Xie, G.S., 2012. Mapping tropical forests and rubber plantations in complex landscapes by integrating PALSAR and MODIS imagery. ISPRS J. Photogramm. Remote Sens. 74, 20-33
11. Dugdale, R.C., Morel, A., Bricaud, A., Wilkerson, F.P., 1989. Modeling new production in upwelling centers - a case-study of modeling new production from remotely sensed temperature and color. J. Geophys. Res.: Oceans 94 (C12), 18119-18132
12. Freund, Y., Schapire, R.E., 1996. Game theory, on-line prediction and boosting. In: Proceedings of the 9th Annual Conference on Computing and Learning Theory, pp. 325-332
13. Furevik, B.R., Johannessen, O.M., Sandvik, A.D., 2002. SAR-retrieved wind in polar regions - comparison with in situ data and atmospheric model output. IEEE Trans. Geosci. Remote Sens. 40,1720-1732
14. Gislason, P., Benediktsson, J., Sveinsson, J., 2006. Random forests for land cover classification. Pattern Recogn. Lett. 27 (4), 294-300
15. Goes, J.I., Saino, T., Oaku, H., Ishizaka, J., Wong, C.S., Nojiri, Y., 2000. Basin scale estimates of sea surface nitrate and new production from remotely sensed sea surface temperature and chlorophyll. Geophys. Res. Lett. 27 (9), 1263-1266
16. Goes, J.I., Saino, T., Oaku, H., Jiang, D.L., 1999. A method for estimating sea surface nitrate concentrations from remotely sensed SSTand chlorophyll a - a case study for the North Pacific Ocean using OCTS ADEOS data. IEEE Trans. Geosci. Remote Sens. 37 (3), 1633-1644
17. Gordon, H.R., Morel, A.Y., 1983. Remote Assessment of Ocean Color for Interpretation of Satellite Imagery - A Review. Springer-Verlag, New York
18. Grimm, R., Behrens, T., Maerker, M., Elsenbeer, H., 2008. Soil organic carbon concentrations and stocks on Barro Colorado Island - digital soil mapping using random forests analysis. Geoderma 146 (1-2), 102-113
19. Guo, L., Chehata, N., Mallet, C, Boukir, S., 2011. Relevance of airborne lidar and multispectral image data for urban scene classification using random forests. ISPRS J. Photogramm. Remote Sens. 66 (1), 56-66
20. Hansen, M.W., Kudryavtsev, V., Chapron, B., Johannessen, J.A., Collard, F., Dagestad, K.F., Mouche, A.A., 2012. Simulation of radar backscatter and Doppler shifts of wave-current interaction in the presence of strong tidal current. Remote Sens. Environ. 120,113-122
21. Hatch, J.P., Hearne, E.M., Clark, G.M., 1982. A method of testing for serial correlation in univariate repeated-measures analysis of variance. Behav. Res. Methods Instrum. 14(5), 497-498
22. Kamykowski, D., Zentara, S.J., 2003. Can phytoplankton community structure be inferred from satellite-derived sea surface temperature anomalies calculated relative to nitrate depletion temperatures? Remote Sens. Environ. 86 (4), 444-457
23. Kamykowski, D., Zentara, S.J., 2005. Changes in world ocean nitrate availability through the 20th century. Deep-Sea Res. Part I: Oceanogr. Res. Pap. 52 (9), 1719-1744
24. Kamykowski, D., Zentara, S.J., Morrison, J.M., Switzer, A.C., 2002. Dynamic global patterns of nitrate, phosphate, silicate, and iron availability and phytoplankton community composition from remote sensing data. Global Biogeochem. Cycles 16 (4), http://dx.doi.org/10.1029/2001GB001640
25. Kanda, J., Itoh, T., Ishikawa, D., Watanabe, Y., 2003. Environmental control of nitrate uptake in the East China Sea. Deep-Sea Res. Part II: Topical Stud. Oceanogr. 50 (2), 403-422
26. Kasilingam, D., Shi, J., 1997. Artificial neural network based inversion technique for extracting ocean surface wave spectra from SAR images. In: Proc. IGARSS'97. IEEE Press, Singapore, pp. 1193-1195
27. Le Hegarat-Mascle, S., Bloch, I., Vidal-Madjar, D., 1998. Introduction of neighborhood information in evidence theory and application to data fusion of radar and optical images with partial cloud cover. Pattern Recogn. 31 (11), 1811-1823
28. Li, A., Huang, C, Sun, G., Shi, H., Toney, C, Zhu, Z., Rollins, M.G., Goward, S.N., Masek, J.G., 2011. Modeling the height of young forests regenerating from recent disturbances in Mississippi using Landsat and ICESat data. Remote Sens. Environ. 115(8), 1837-1849
29. Lindell, T., Pierson, D., Premazzi, G., Zilioli, E., 1999. Manual for Monitoring European Lakes using Remote Sensing Techniques (EUR 18665 EN. Official Publications of the European Communities, Luxembourg, pp. 161
30. Loh, A.N., Bauer, J.E., 2000. Distribution, partitioning and fluxes of dissolved and particulate organic C, N and P in the eastern North Pacific and Southern Oceans. Deep-Sea Res. Part I: Oceanogr. Res. Pap. 47 (12), 2287-2316
31. Loosvelt, L., Peters, J., Skriver, H., Lievens, H., Van Coillie, F.M.B., De Baets, B., Verhoest, N.E.C., 2012. Random forests as a tool for estimating uncertainty at pixel-level in SAR image classification. Int.J. Appl. Earth Observ. Geoinform. 19,173-184
32. Macri-Pellizzeri, T., Oliver, C.J., Lombardo, P., 2002. Segmentation-based joint classification of SAR and optical images. IEEE Proc.-Radar Son. Nav. 149 (6), 281-296
33. McClanahan, T., Polunin, N., Done, T., 2002. Ecological states and the resilience of coral reefs. Conserv. Ecol. 6 (2), 18
34. Meissner, T., Wentz, F.J., 2004. The complex dielectric constant of pure and sea water from microwave satellite observations. IEEE Trans. Geosci. Remote Sens. 42 (9), 1836-1849
35. Moghaddam, M., Dungan, J.L., Acker, S., 2002. Forest variable estimation from fusion of SAR and multispectral optical data. IEEE Trans. Geosci. Remote Sens. 40 (10), 2176-2187
36. Nilsson, C.S., Tildesley, P.C., 1995. Imaging of oceanic features by ERS-1 synthetic aperture radar. J. Geophys. Res. 100, 953-967
37. Pal, M., 2005. Random forest classifier for remote sensing classification. Int.J. Remote Sens. 26, 217-222
38. Pardo-Iguzquiza, E., Rodriguez-Galiano, V.F., Chica-Olmo, M., Atkinson, P.M., 2011. Image fusion by spatially adaptive filtering using downscaling cokriging. ISPRS J. Photogramm. Remote Sens. 66 (3), 337-346
39. Paris, J.F., Kwong, H.H., 1988. Characterization of vegetation with combined Thematic Mapper and Shuttle Imaging radar (SIRB) image data. Photogramm. Eng. Remote Sens. 54 (8), 1187-1193
40. Plant, W.J., Zurk, L.M., 1997. Dominant wave directions and significant wave heights from synthetic aperture radar imagery of the ocean. J. Geophys. Res. 102, 3473-3482
41. Poulain, V., Inglada, J., Spigai, M., 2009. Fusion of high resolution optical and SAR images with vector data bases for change detection. In: IEEE International Geoscience and Remote Sensing Symposium, Vols. 1-5, pp. 3336-3339
42. Poulain, V., Inglada, J., Spigai, M., Tourneret, J.Y., Marthon, P., 2011. High-resolution optical and SAR image fusion for building database updating. IEEE Trans. Geosci. Remote Sens. 49 (8), 2900-2910
43. Sarangi, R.K.,Thangaradjou,T., Kumar, A.S., Balasubramanian.T., 2011. Development of nitrate algorithm for the Southwest Bay of Bengal water and its implication using remote sensing satellite datasets. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 4 (4), 983-991
44. Scheuchl, B., Cumming, I.G., 2002. Potential of RADARSAT-2 for sea ice classification. Process. IEEE IGARSS 4, 2185-2187
45. Sha, W., 2007. Comments on "Water quality retrievals from combined landsat TM data and ERS-2 SAR data in the Gulf of Finland-.- IEEE Trans. Geosci. Remote Sens. 45(6), 1896-1897
46. Shen, H., Perrie, W., Liu, Q.R., He, Y.J., 2014. Detection of macroalgae blooms by complex SAR imagery. Mar. Pollut. Bull. 78,190-195
47. Sherlock, V., Pickmere, S., Currie, K., Hadfield, M., Nodder, S., Boyd, P.W., 2007. Predictive accuracy of temperature-nitrate relationships for the oceanic mixed layer of the New Zealand region. J. Geophys. Res.: Oceans 112 (C06010), doi:06010.01029/02006JC003562
48. Shiozaki, T., Chen, Y.I.L., 2013. Different mechanisms controlling interannual phytoplankton variation in the South China Sea and the western North Pacific subtropical gyre: a satellite study. Adv. Space Res. 52 (4), 668-676
49. Silio-Calzada, A., Bricaud, A., Gentili, B., 2008. Estimates of sea surface nitrate concentrations from sea surface temperature and chlorophyll concentration in upwelling areas: a case study for the Benguela system. Remote Sens. Environ. 112 (6), 3173-3180
50. Smith, R.C., Baker, K.S., 1978. The bio-optical state of ocean waters and remote sensing. Limnol. Oceanogr. 23, 247-259
51. Solberg, A.H.S., Brekke, C, Husoy, P.O., 2007. Oil spill detection in Radarsat and Envisat SAR images. IEEE Trans. Geosci. Remote Sens. 45 (3), 746-755
52. Soria-Ruiz, J., Fernandez-Ordonez, Y., Woodhouse, I.H., 2010. Land-cover classification using radar and optical images: a case study in Central Mexico. Int.J. Remote Sens. 31 (12), 3291-3305
53. Traganza, E.D., Silva, V.M., Austin, D.M., Hanson, W.L., Bronsink, S.H., 1983. Nutrient mapping and recurrence of coastal upwelling centers by satellite remote sensing: its implication to primary production and the sediment record. In: Suess, E., Thiede, J. (Eds.), Coastal Upwelling: Its Sediment Record. Plenum Press, New York, USA, pp. 61-83 (Part A)
54. Vaglio Laurin, G., Liesenberg, V., Chen, Q, Guerriero, L., Del Frate, F., Bartolini, A., Coomes, D., Wilebore, B., Lindsell, J., Valentini, R., 2013. Optical and SAR sensor synergies for forest and land cover mapping in a tropical site in West Africa. Int. J. Appl. Earth Observ. Geoinform. 21 (2), 7-16
55. Vogelzang, J., Wensink, G.J., van der Kooij, M.W.A., van Swol, R., 1994. Mapping of sea bottom topography with ERS-1 C-band SAR. Space at the service of our environment. In: Proceedings of Second ERS-1 Symposium.European Space Agency (Special Publication), SP, SP-361 (2), pp. 945-948
56. Waske, B., Braun, M., 2009. Classifier ensembles for land cover mapping usingmultitemporal sar imagery. ISPRS J. Photogramm. Remote Sens. 64 (5), 450-457
57. Waske, B., van der Linden, S., 2008. Classifying multilevel imagery from SAR and optical sensors by decision fusion. IEEE Trans. Geosci. Remote Sens. 46 (5), 1457-1466
58. Woodruff, D.L., Stumpf, R.P., Scope, J.A., Paerl, H.W., 1999. Remote estimation of water clarity in optically complex estuarine waters. Remote Sens. Environ. 68 (1), 41-52
59. Yang, J., Zhang.J., Meng.J., 2010. Underwatertopography detection of Taiwan Shoal with SAR images. Chin. J. Oceanol. Limnol. 28 (3), 636-642
60. Zeng, I., Alpers, W., 2004. Generation of internal solitary waves in the Sulu Sea and their refraction by bottom topography studied by ERS SAR imagery and a numerical model. Int. J. Remote Sens. 25 (7-8), 1277-1281
61. Zhang, J.X., Yang, J.H., Zhao, Z., Li, H.T., Zhang, Y.H., 2010. Block-regression based fusion of optical and SAR imagery for feature enhancement. Int. J. Remote Sens. 31 (9), 2325-2345
62. Zhang, Y.Z., Pulliainen, J., Koponen, S., Hallikainen, M., 2002. Application of an empirical neural network to surface water quality estimation in the Gulf of Finland using combined optical data and microwave data. Remote Sens. Environ. 81 (2-3), 327-336
63. Zhang, Y.Z., Pulliainen, J.T., Koponen, S.S., Hallikainen, M.T., 2003. Water quality retrievals from combined Landsat TM data and ERS-2 SAR data, in the Gulf of Finland. IEEE Trans. Geosci. Remote Sens. 41 (3), 622-629