Fractional snow cover mapping from MODIS data using wavelet-artificial intelligence hybrid models

Journal of Hydrology

Volumn 511, Issue , 2014, Pages 160-170

  Moosavi, Vahid ^a   Malekinezhad, Hossein ^a   Shirmohammadi, Bagher ^b  

^a YAZD UNIVERSITY   (Iran)

^b TARBIAT MODARES UNIVERSITY   (Iran)

Author keywords

Artificial intelligence; ETM+; MODIS; Snow cover mapping; Sub pixel classification; Wavelet transformation

Indexed keywords

EID: 84893921323     PISSN: 00221694     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jhydrol.2014.01.015     Document Type: Article

References (70)

1. Ackerman S., Strabala K., Menzel W., Frey R., Moeller C., Gumley L. Discriminating clear sky from clouds with MODIS. J. Geophys. Res. 1998, 103(D24):32141-32157.
2. Adamowski J.F. Development of a short-term river flood forecasting method for snowmelt driven floods based on wavelet and cross-wavelet analysis. J. Hydrol. 2008, 353(3-4):247-266.
3. Adamowski J., Fung Chan H. A wavelet neural network conjunction model for groundwater level forecasting. J. Hydrol. 2011, 407:28-40.
4. Adamowski J., Sun K. Development of a coupled wavelet transform and neural network method for flow forecasting of non-perennial rivers in semi-arid watersheds. J. Hydrol. 2010, 390(1-2):85-91.
5. Akansu A., Haddad R. Multiresolution Signal Decomposition: Transforms, Wavelets 1992, Academic Press, Subbands.
6. Anctil F., Tape D. An exploration of artificial neural network rainfall-runoff forecasting combined with wavelet decomposition. J. Environ. Eng. Sci. 2004, 3:121-128.
7. Aussem A., Campbell J., Murtagh F. Wavelet-based feature extraction and decomposition strategies for financial forecasting. J. Comput. Intell. Finance 1998, 13:5-12.
8. Avci E. An expert system based on wavelet neural network-adaptive norm entropy for scale invariant texture classification. Expert Syst. Appl. 2007, 32:919-926.
9. Bari H., Kousari M.R., Asadi Zarch M. Prediction of drought in dry lands through feedforward artificial neural network abilities. Arab. J. Geosci. 2013, 6:1417-1433.
10. Barnes W.L., Pagano T.S., Salomonson V.V. Prelaunch characteristics of the moderate resolution imaging spectroradiometer (MODIS) on EOS-AM1. IEEE Trans. Geosci. Remote Sens. 1998, 36(4):1088-1100.
11. Barton, J.S., Hall, D.K., Riggs, G.A., 2000. Remote sensing of fractional snow cover using moderate resolution imaging spectroradiometer (MODIS) data. In: Proceedings of the 57th Eastern Snow Conference, pp. 171-183.
12. Bishop C. Neural Networks for Pattern Recognition 1995, Oxford University Press, Oxford.
13. Borel C., Gerstl S. Nonlinear spectral mixing models for vegetative and soil surfaces. Remote Sens. Environ. 1994, 47:403-416.
14. Cannas, B., Fanni, A., Sias, G., Tronei, S., Zedda, M.K., 2006. River flow forecasting using neural networks and wavelet analysis. In: Proceedings of the European Geosciences Union, pp. 234-243.
15. Dobreva I.D., Klein A.G. Fractional snow cover mapping through artificial neural network analysis of MODIS surface reflectance. Remote Sens. Environ. 2011, 115:3355-3366.
16. Dong L., Xiao D., Liang Y., Liu Y. Rough set and fuzzy wavelet neural network integrated with least square weighted fusion algorithm based fault diagnosis research for power transformers. Electr. Power Syst. Res. 2008, 78:129-136.
17. Foody G.M., Cox D.P. Subpixel land cover composition estimation using a linear mixture model and fuzzy membership functions. Int. J. Remote Sens. 1994, 15(3):619-631.
18. Gao Y., Xie H., Yao T., Xue C. Integrated assessment on multi-temporal and multi-sensor combination for reducing cloud obscuration of MODIS snow cover products at the Pacific Northwestern USA. Remote Sens. Environ. 2010, 114(8):1662-1675.
19. Ghobadian A. Iranian Natural Resources in Relation to Agricultural Utilzation; Reconstruction and Reclamation 1990, Kerman University Press, Kerman, Iran.
20. Hadad K., Pourahmadi M., Majidi-Maraghi H. Fault diagnosis and classification based on wavelet transform and neural network. Prog. Nucl. Energy 2011, 53:41-47.
21. Hall D.K., Riggs G.A. Accuracy assessment of the MODIS snow products. Hydrol. Process. 2007, 21(12):1534-1547.
22. Hall D.K., Riggs G.A., Salomonson V.V. Development of methods for mapping global snow cover using moderate resolution imaging spectroradiometer data. Remote Sens. Environ. 1995, 54:127-140.
23. Hall D.K., Riggs G., Salomonson V.V., DiGirolamo N.E., Bayr K.J. MODIS snow-cover products. Remote Sens. Environ. 2002, 83:181-194.
24. Haykin S. Neural Networks, A Comprehensive Foundation 1999, Prentice-Hall, Englewood Cliffs, NJ. second ed.
25. Hornik K., Stinchcombe M., White M. Multilayer feed forward networks are universal approximators. Neural Netw. 1989, 2:359-366.
26. IPCC Summary for policymakers. Climate Change. The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change 2007, Cambridge University Press, Cambridge. S. Solomon (Ed.).
27. Jang J.S.R., Sun C.T., Mizutani E. Neuro-fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence 1997, Prentice-Hall, Eaglewood cliffs, NJ, pp. 665-685.
28. Kasabov N. Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering 1996, MIT Press.
29. Kavzoglu T., Mather P.M. The use of back-propagating artificial neural networks in land cover classification. Int. J. Remote Sens. 2003, 24(23):4907-4938.
30. Kisi O. Stream flow forecasting using neuro-wavelet technique. Hydrol. Process. 2008, 22(20):4142-4152.
31. Kisi O. Wavelet regression model as an alternative to neural networks for river stage forecasting. Water Resour. Manage 2011, 25:579-600.
32. Kisi O., Cimen M. A wavelet-support vector machine conjunction model for monthly streamflow forecasting. J. Hydrol. 2011, 399:132-140.
33. Kisi O., Shiri J. Wavelet and neuro-fuzzy conjunction model for predicting water table depth fluctuations. Hydrol. Res. 2012, 43(3):286-300.
34. Kousari M.R., Ekhtesasi M.R., Taze M., Saremi M.A. An investigation of the Iranian climatic changes by considering the precipitation, temperature, and relative humidity parameters. Theoret. Appl. Climatol. 2011, 103(3-4):321-335.
35. Kousari M.R., Ahani H., Hendizadeh R. Temporal and spatial trend detection of maximum air temperature in Iran during 1960-2005. Global Planet. Change 2013, 111:97-110.
36. Lee S., Lathrop R.G. Subpixel analysis of Landsat ETM+ using Self-Organizing Map (SOM) neural networks for urban land cover characterization. IEEE Trans. Geosci. Remote Sens. 2006, 44:1642-1654.
37. Li W., Fang S., Dian Y., Guo J. Cloud detection in MODIS data based on spectrum analysis. Geomatics Inform. Sci. Wuhan Univ. 2005, 30(5):435-438.
38. Liang T.G., Huang X.D., Wu C.X., Liu X.Y., Li W.L., Guo Z.G., et al. An application of MODIS data to snow cover monitoring in a pastoral area: A case study in Northern Xinjiang, China. Remote Sens. Environ. 2008, 112(4):1514-1526.
39. Lobell D.B., Asner G.P. Cropland distributions from temporal un-mixing of MODIS data. Remote Sens. Environ. 2004, 93:412-422.
40. Maier H.R., Dandy G.C. Understanding the behavior and optimizing the performance of back-propagation neural networks: an empirical study. Environ. Model. Softw. 1998, 13:179-191.
41. Mallat S.G. A theory for multi resolution signal decomposition: the wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. 1989, 11(7):674-693.
42. Mellit A., Benghanem M., Kalogirou S.A. An adaptive wavelet-network model for forecasting daily total solar radiation. Appl. Energy 2006, 83(7):705-722.
43. Mishra A.K., Desai V.R. Drought forecasting using feed-forward recursive neural network. Ecol. Model. 2006, 198(1-2):127-138.
44. Mofidi A., Zarrin A., Janbaz Ghobadi Gh. Determining the synoptic pattern of autumn heavy and extreme precipitations on the southern coast of the Caspian Sea. J. Earth Space Phys. 2008, 33(3):30.
45. Moosavi V., Vafakhah M., Shirmohammadi B., Behnia N. A Wavelet-ANFIS hybrid model for groundwater level forecasting for different prediction periods. Water Resour. Manage 2013, 27:1301-1321.
46. Naderi R., Rahiminejad M.R., Eslami B., Afsharzadeh S. Flora and vegetation of Golestanak (Alborz Mts), Iran. Phytol. Balcanica 2012, 18(1):59-68.
47. Nash E.B., Conel J.E. Spectral reflectance systematics for mixtures of powdered hypersthene, labradorite, and ilmenite. J. Geophys. Res. 1974, 79:1615-1621.
48. Nourani V., Alami M.T., Aminfar M.H. A combined neural-wavelet model for prediction of watershed precipitation, Lighvanchai, Iran. Eng. Appl. Artif. Intell. 2009, 16:1-12.
49. Nourani V., Kisi O., Komasi M. Two hybrid Artificial Intelligence approaches for modeling rainfall-runoff process. J. Hydrol. 2011, 402(1-2):41-59.
50. Painter T.H., Rittger K., McKenzie C., Slaughter P., Davis R.E., Dozier J. Retrieval of subpixel snow covered area, grain size, and albedo from MODIS. Remote Sens. Environ. 2009, 113:868-879.
51. Partal T., Cigizoglu H.K. Estimation and forecasting of daily suspended sediment data using wavelet-neural networks. J. Hydrol. 2008, 358(3-4):317-331.
52. Rahimikhoob A. Estimation of evapotranspiration based on only air temperature data using artificial neural networks for a subtropical climate in Iran. Theoret. Appl. Climatol. 2010, 101:83-91.
53. Rajaee T., Mirbagheri S.A., Zounemat-Kermani M., Nourani V. Daily suspended sediment concentration simulation using ANN and neuro-fuzzy models. Sci. Total Environ. 2009, 407:4916-4927.
54. Raleigh M.S., Rittgerb K., Moore C.E., Henn B., Lutz J.A., Lundquist J.D. Ground-based testing of MODIS fractional snow cover in subalpine meadows and forests of the Sierra Nevada. Remote Sens. Environ. 2013, 128:44-57.
55. Roux S.G., Cenier T., Garcia S., Litaudon P., Buonviso N. A wavelet-based method for local phase extraction from a multi-frequency oscillatory signal. J. Neurosci. Methods 2007, 160(1):135-143.
56. Salminen M., Pulliainen J., Metsämäki S., Böttcher K., Heinilä K. MODIS-derived snow-free ground reflectance statistics of selected Eurasian non-forested land cover types for the application of estimating fractional snow cover. Remote Sens. Environ. 2013, 138:51-64.
57. Saroj K., Meher B., Shankar U., Ghosh A. Wavelet-feature-based classifiers for multispectral remote-sensing images. IEEE Trans. Geosci. Remote Sens. 2007, 45(6):1881-1886.
58. Shabanov N.V., Lo K., Gopal S., Myneni R.B. Subpixel burn detection in moderate resolution imaging spectroradiometer 500-m data with ARTMAP neural networks. J. Geophys. Res. - Atmos. 2005, 110(D3):D01105.
59. Shirmohammadi B., Vafakhah M., Moosavi V., Moghaddamnia A. Application of several data-driven techniques for predicting groundwater level. Water Resour. Manage 2013, 27:419-432.
60. Silvan-Cardenas, J.L., 2009. Sub-pixel Remote Sensing for Mapping and Modelling Invasive Tamarix: A Case Study in West Texas. Theses and Dissertations-Geography.
61. Singer R.B. Near-infrared spectral reflectance of mineral mixtures: systematic combinations of pyroxenes, olivine, and iron oxides. J. Geophys. Res. 1981, 86:7967-7982.
62. Sirguey P., Mathieu R., Arnaud Y. Subpixel monitoring of the seasonal snow cover with MODIS at 250 m spatial resolution in the Southern Alps of New Zealand: methodology and accuracy assessment. Remote Sens. Environ. 2009, 113:160-181.
63. Stocklin J. Northern Iran: Alborz Mountains. Geol. Soc., Lond., Special Publ. 1974, 4:213-234.
64. Swinnen, E., Eerens, H., Lissens, G., Canters, F., 2001. Sub-pixel land cover classification with SPOT-VEGETATION. In: Proceedings of IGARSS, Sydney, Australia, July 2001, p. 3.
65. Tang Z., Fishwick P.A. Feedforward neural nets as models for time series forecasting. ORSA J. Comput. 1993, 5(4):374-385.
66. Verbeiren S., Eerens H., Piccard I. Sub-pixel classification of SPOT-VEGETATION time series for the assessment of regional crop areas in Belgium. Int. J. Appl. Earth Obs. Geoinf. 2007, 25:45-57.
67. Wang W., Ding S. Wavelet network model and its application to the predication of hydrology. Nat. Sci. 2003, 1(1):67-71.
68. Wong F.S. Time series forecasting using backpropagation neural networks. Neurocomputing 1991, 2:147-159.
69. Zhang X., Qi J., Zhang R., Liu M., Hu Z., Xue H., Fan B. Prediction of programmed-temperature retention values of naphthas by wavelet neural networks. Comput. Chem. 2001, 25:25-133.
70. Zhu J., Shi J., Wang Y. Subpixel snow mapping of the Qinghai-Tibet Plateau using MODIS data. Int. J. Appl. Earth Obs. Geoinf. 2012, 18:251-262.