Information Extraction Method of Soil Salinity in Typical Areas of the Yellow River Delta Based on Landsat Imagery
ABSTRACT
In order to get RS method to extract soil salinity of the Yellow River Delta, we set Kenli County as typical Yellow River Delta to be research area and get data of soil salinity through field investigation. By using RS image of Landsat-8 of March 14, 2014 and analyzing information features of each band and surface spectral features of research areas, we select out sensitive bands and build Soil Salinity Information Extraction (SSIE) model and vegetation index NDVI model for comparison. And then, we accordingly classify grades of soil salinity and get soil salinity information by decision tree approach based on expert knowledge. The results show that overall accuracy of SSIE model is 93.04% and coefficient of Kappa is 0.7869, while overall accuracy of NDVI model is 83.67% and coefficient of Kappa is 0.7017 respectively. By comparing with measured proportions of each class, we see that results from SSIE model is more accurate, which indicates significant advantage for soil salinity information extraction. This research provides scientific basis to get and monitoring soil salinity of the Yellow River Delta region quickly and accurately.
In order to get RS method to extract soil salinity of the Yellow River Delta, we set Kenli County as typical Yellow River Delta to be research area and get data of soil salinity through field investigation. By using RS image of Landsat-8 of March 14, 2014 and analyzing information features of each band and surface spectral features of research areas, we select out sensitive bands and build Soil Salinity Information Extraction (SSIE) model and vegetation index NDVI model for comparison. And then, we accordingly classify grades of soil salinity and get soil salinity information by decision tree approach based on expert knowledge. The results show that overall accuracy of SSIE model is 93.04% and coefficient of Kappa is 0.7869, while overall accuracy of NDVI model is 83.67% and coefficient of Kappa is 0.7017 respectively. By comparing with measured proportions of each class, we see that results from SSIE model is more accurate, which indicates significant advantage for soil salinity information extraction. This research provides scientific basis to get and monitoring soil salinity of the Yellow River Delta region quickly and accurately.
Cite this paper
Zhang, T. , Zhao, G. , Chang, C. , Wang, Z. , Li, P. , An, D. and Jia, J. (2015) Information Extraction Method of Soil Salinity in Typical Areas of the Yellow River Delta Based on Landsat Imagery. Agricultural Sciences, 6, 71-77. doi: 10.4236/as.2015.61006.
Zhang, T. , Zhao, G. , Chang, C. , Wang, Z. , Li, P. , An, D. and Jia, J. (2015) Information Extraction Method of Soil Salinity in Typical Areas of the Yellow River Delta Based on Landsat Imagery. Agricultural Sciences, 6, 71-77. doi: 10.4236/as.2015.61006.
References
[1] Chen, X.-B., Yang, J.-S. and Hu, S.-J. (2007) Water Resources Development and Control of Soil Secondary Salnization in Yan-Qi Basin. Chinese Journal of Soil Science, 38, 233-237. [2] Dehaan, R.L. and Taylor, G.R. (2002) Field-Derived Spectra of Salinized Soils and Vegetation as Indicators of Irrigation-Induced Soil Salinization. Remote Sensing of Environment, 80, 406-417.
http://dx.doi.org/10.1016/S0034-4257(01)00321-2 [3] Abdellatif, D. and Mourad, L. (2012) Remote Sensing Techniques for Salt Affected Soil Mapping: Application to the Oran Region of Algeria. Procedia Engineering, 33, 188-198.
http://dx.doi.org/10.1016/j.proeng.2012.01.1193 [4] Kang, Q., Yu, R. and Zhang, Z.-X. (2005) Advances in Study on Remote Sensing Application of Soil Salinization. Remote Sensing Technology and Application, 20, 447-454. [5] Ding, J.-L., Wu, M.-C. and Liu, H.-X. (2012)Study on the Soil Salinization Monitoring Based on Synthetical Hyperspectral Index. Spectroscopy and Spectral Analysis, 32, 1918-1922. [6] Taylor, G.R., Mah, A.H., Kruse, F.A., et al. (1996) Characterization of Saline Soils Using Airborne Radar Imagery. Remote Sensing of Environment, 57, 127-142.
http://dx.doi.org/10.1016/0034-4257(95)00239-1 [7] Weng, Y.L. and Gong, P. (2006) A Review on Remote Sensing Technique for Salt-Affected Soils. Scientia Geographic Sinica, 26, 371-372. [8] Yang, J.-S. (1995)The Prospect of the Study on Soil Salinization. Soil, 01, 23-27. [9] Rao, B.R.M., Ravisankar, T., Dwivedi, R.S., et al. (1995)Spectral Behaviour of Salt-Affected Soils. Intemational Joumal of Remote Sensing, 16, 2125-2136.
http://dx.doi.org/10.1080/01431169508954546 [10] Li, J.-X., Wang, S.-R.-N. and Bao, Y.-H. (2007) Monitoring Salinification of Land in Zhalot Banner Using Remote Sensing and GIS. Journal of Arid Land Resources and Environment, 21, 57-63. [11] Khan Nasir, M., Rastoskuev Victor, V., Shalina Elena, V., et al. (2001) Mapping Salt-Affected Soils Using Remote Sensing Indicators—A Simple Approach with the Use of G1S IDRISI. Proceedings ACRS 2001—22nd Asian Conference on Remote Sensing, 1, 25-29. [12] Meng, Y., Zhao, G.-X. and Wang, J. (2009) Remote Sensing Image-Based Information Extraction for Land Salinized Degradation and Its Evolution in Kenli County. Areal Research and Development, 28, 135-139. [13] Bui, E.N. and Henderson, B.L. (2003) Vegetation Indicators of Salinity in Northern Queensland. Austral Ecology, 28, 520-560. http://dx.doi.org/10.1046/j.1442-9993.2003.01311.x [14] Han, H., Feng, Z.-D. and E, Y.-H. (2006) Study on the Saline Soil Changes Using TM in Minqin Oasis, Northwest China. Journal of Lanzhou University, 42, 1-6. [15] Wu, Z.-F., Zhao, S.-L. and Zhang, X.-L. (1994) Studies on Interrelation between Salt Vegetation and Soil Salinity in the Yellow River Delta. Journal of Plant Ecology, 18, 184-193. [16] Zhang, C.-W., Tang, J.-K., Yu, X.-J., Wang, C.-L. and Mi, S.-J. (2013) Quantitative Retrieval of Soil Salt Content Based on Remote Sensing in the Yellow River Delta. Journal of Graduate University of Chinese Academy of Sciences, 30, 220-227. [17] Lei, L., Tiyip, T., Ding, J.-L., Jiang, H.-N. and Kelimu, A. (2014) Study on the Soil Salinization Monitoring Based on Measured Hyperspectral and HSI Data. Spectroscopy and Spectral Analysis, 34, 1948-1953. [18] Qu, Y.H., Duan, X.L., Gao, H.Y., Chen, A.P., An, Y.Q., Song, J.L., et al. (2009) Quantitative Retrieval of Soil Salinity Using Hyperspectral Data in the Region of Inner Mongolia Hetao Irrigation District. Spectroscopy and Spectral Analysis, 29, 1362-1366. [19] Dwivedi, R.S. and Rao, B.R.M. (1992) The Selection of the Best Possible Landsat TM Band Combination for Delineating Salt-Affected Soils. International Journal of Remote Sensing, 13, 2051-2058. http://dx.doi.org/10.1080/01431169208904252
[1] Chen, X.-B., Yang, J.-S. and Hu, S.-J. (2007) Water Resources Development and Control of Soil Secondary Salnization in Yan-Qi Basin. Chinese Journal of Soil Science, 38, 233-237. [2] Dehaan, R.L. and Taylor, G.R. (2002) Field-Derived Spectra of Salinized Soils and Vegetation as Indicators of Irrigation-Induced Soil Salinization. Remote Sensing of Environment, 80, 406-417.
http://dx.doi.org/10.1016/S0034-4257(01)00321-2 [3] Abdellatif, D. and Mourad, L. (2012) Remote Sensing Techniques for Salt Affected Soil Mapping: Application to the Oran Region of Algeria. Procedia Engineering, 33, 188-198.
http://dx.doi.org/10.1016/j.proeng.2012.01.1193 [4] Kang, Q., Yu, R. and Zhang, Z.-X. (2005) Advances in Study on Remote Sensing Application of Soil Salinization. Remote Sensing Technology and Application, 20, 447-454. [5] Ding, J.-L., Wu, M.-C. and Liu, H.-X. (2012)Study on the Soil Salinization Monitoring Based on Synthetical Hyperspectral Index. Spectroscopy and Spectral Analysis, 32, 1918-1922. [6] Taylor, G.R., Mah, A.H., Kruse, F.A., et al. (1996) Characterization of Saline Soils Using Airborne Radar Imagery. Remote Sensing of Environment, 57, 127-142.
http://dx.doi.org/10.1016/0034-4257(95)00239-1 [7] Weng, Y.L. and Gong, P. (2006) A Review on Remote Sensing Technique for Salt-Affected Soils. Scientia Geographic Sinica, 26, 371-372. [8] Yang, J.-S. (1995)The Prospect of the Study on Soil Salinization. Soil, 01, 23-27. [9] Rao, B.R.M., Ravisankar, T., Dwivedi, R.S., et al. (1995)Spectral Behaviour of Salt-Affected Soils. Intemational Joumal of Remote Sensing, 16, 2125-2136.
http://dx.doi.org/10.1080/01431169508954546 [10] Li, J.-X., Wang, S.-R.-N. and Bao, Y.-H. (2007) Monitoring Salinification of Land in Zhalot Banner Using Remote Sensing and GIS. Journal of Arid Land Resources and Environment, 21, 57-63. [11] Khan Nasir, M., Rastoskuev Victor, V., Shalina Elena, V., et al. (2001) Mapping Salt-Affected Soils Using Remote Sensing Indicators—A Simple Approach with the Use of G1S IDRISI. Proceedings ACRS 2001—22nd Asian Conference on Remote Sensing, 1, 25-29. [12] Meng, Y., Zhao, G.-X. and Wang, J. (2009) Remote Sensing Image-Based Information Extraction for Land Salinized Degradation and Its Evolution in Kenli County. Areal Research and Development, 28, 135-139. [13] Bui, E.N. and Henderson, B.L. (2003) Vegetation Indicators of Salinity in Northern Queensland. Austral Ecology, 28, 520-560. http://dx.doi.org/10.1046/j.1442-9993.2003.01311.x [14] Han, H., Feng, Z.-D. and E, Y.-H. (2006) Study on the Saline Soil Changes Using TM in Minqin Oasis, Northwest China. Journal of Lanzhou University, 42, 1-6. [15] Wu, Z.-F., Zhao, S.-L. and Zhang, X.-L. (1994) Studies on Interrelation between Salt Vegetation and Soil Salinity in the Yellow River Delta. Journal of Plant Ecology, 18, 184-193. [16] Zhang, C.-W., Tang, J.-K., Yu, X.-J., Wang, C.-L. and Mi, S.-J. (2013) Quantitative Retrieval of Soil Salt Content Based on Remote Sensing in the Yellow River Delta. Journal of Graduate University of Chinese Academy of Sciences, 30, 220-227. [17] Lei, L., Tiyip, T., Ding, J.-L., Jiang, H.-N. and Kelimu, A. (2014) Study on the Soil Salinization Monitoring Based on Measured Hyperspectral and HSI Data. Spectroscopy and Spectral Analysis, 34, 1948-1953. [18] Qu, Y.H., Duan, X.L., Gao, H.Y., Chen, A.P., An, Y.Q., Song, J.L., et al. (2009) Quantitative Retrieval of Soil Salinity Using Hyperspectral Data in the Region of Inner Mongolia Hetao Irrigation District. Spectroscopy and Spectral Analysis, 29, 1362-1366. [19] Dwivedi, R.S. and Rao, B.R.M. (1992) The Selection of the Best Possible Landsat TM Band Combination for Delineating Salt-Affected Soils. International Journal of Remote Sensing, 13, 2051-2058. http://dx.doi.org/10.1080/01431169208904252