Volume 5, Issue 2 (2017)
ECOPERSIA 2017, 5(2): 1771-1786 |
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Mousavi S Z, Habibnejad M, Kavian A, Solaimani K, Khormali F. Digital Mapping of Topsoil Salinity Using Remote Sensing Indices in Agh-Ghala Plain, Iran. ECOPERSIA 2017; 5 (2) :1771-1786
URL: http://ecopersia.modares.ac.ir/article-24-9565-en.html
URL: http://ecopersia.modares.ac.ir/article-24-9565-en.html
Seyedeh Zohreh Mousavi * 
1, Mahmood Habibnejad2 , Ataollah Kavian3 , Karim Solaimani2 , Farhad Khormali4
1, Mahmood Habibnejad2 , Ataollah Kavian3 , Karim Solaimani2 , Farhad Khormali4
1- Ph.D Student of Watershed Engineering, Faculty of Natural Resources, Sari University of Agricultural Science and Natural Resources, Sari, Iran
2- Professor of Watershed Engineering, Faculty of Natural Resources, Sari University of Agricultural Science and Natural Resources, Sari, Iran
3- Associate Professor of Watershed Engineering, Faculty of Natural Resources, Sari University of Agricultural Science and Natural Resources, Sari, Iran
4- Professor of Pedology, Faculty of Water and Soil Engineering, Gorgan University of Agricultural Science and Natural Resources, Gorgan, Iran
2- Professor of Watershed Engineering, Faculty of Natural Resources, Sari University of Agricultural Science and Natural Resources, Sari, Iran
3- Associate Professor of Watershed Engineering, Faculty of Natural Resources, Sari University of Agricultural Science and Natural Resources, Sari, Iran
4- Professor of Pedology, Faculty of Water and Soil Engineering, Gorgan University of Agricultural Science and Natural Resources, Gorgan, Iran
Abstract: (6024 Views)
Background: Soil salinization is a world-wide land degradation process in arid and semi-arid regions that leads to sever economic and social consequences.
Materials and Methods: We analyzed soil salinity by two statistical linear (multiple linear regression) and non-linear (artificial neural network) models using Landsat OLI data in Agh-Ghala plain located in north east of Iran. In situ soil electrical conductivity (EC) of 156 topsoil samples (depth of 0-15cm) was also determined. A Pearson correlation between 26 spectral indices derived from Landsat OLI data and in situ measured ECs was used to apply efficient indices in assessing soil salinity. The best correlated indices such as blue, green and red bands, intensity indices (Int1, Int2), soil salinity indices (Si1, Si2, Si3, Si11, Aster-Si), vegetation Indices (NDVI, DVI, RVI, SAVI), greenness and wetness indices were used to develop two models.
Results: Comparison between two estimation models showed that the performance of ANN model (R2=0.964 and RMSE=2.237) was more reliable than that of MLR model (R2=0.506 and RMSE=9.674) in monitoring and predicting soil salinity. Out of the total area, 66% and 55.8% was identified as non-saline, slightly and very slightly saline for ANN and MLR models, respectively.
Conclusions: This shows that remote sensing data can be effectively used to model and map spatial variations of soil salinity.
Materials and Methods: We analyzed soil salinity by two statistical linear (multiple linear regression) and non-linear (artificial neural network) models using Landsat OLI data in Agh-Ghala plain located in north east of Iran. In situ soil electrical conductivity (EC) of 156 topsoil samples (depth of 0-15cm) was also determined. A Pearson correlation between 26 spectral indices derived from Landsat OLI data and in situ measured ECs was used to apply efficient indices in assessing soil salinity. The best correlated indices such as blue, green and red bands, intensity indices (Int1, Int2), soil salinity indices (Si1, Si2, Si3, Si11, Aster-Si), vegetation Indices (NDVI, DVI, RVI, SAVI), greenness and wetness indices were used to develop two models.
Results: Comparison between two estimation models showed that the performance of ANN model (R2=0.964 and RMSE=2.237) was more reliable than that of MLR model (R2=0.506 and RMSE=9.674) in monitoring and predicting soil salinity. Out of the total area, 66% and 55.8% was identified as non-saline, slightly and very slightly saline for ANN and MLR models, respectively.
Conclusions: This shows that remote sensing data can be effectively used to model and map spatial variations of soil salinity.
Keywords: Artificial Neural Network, Electrical conductivity, Landsat OLI data, Multiple linear regression, Iran
Article Type: مقاله Ø§Ø³ØªØ®Ø±Ø§Ø Ø´Ø¯Ù‡ از پایان نامه |
Subject:
Watershed Management & Engineering
Received: 2016/11/4 | Accepted: 2017/03/10 | Published: 2017/06/1
Received: 2016/11/4 | Accepted: 2017/03/10 | Published: 2017/06/1
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