Document Type : Research Paper
Authors
- Maral Khodadadi 1
- Mohammad Sadegh Askari 1
- Fereydoon Sarmadian 2
- Hossein Gholi Refahi 3
- Ali Akbar Norouzi 4
- Ahmad Heidari 5
- Hamid Reza Matinfar 6
1 MSc Student, Faculty of Agricultural Engineering, Tehran University, Iran
2 Associate Professor, Faculty of Agricultural Engineering, Tehran University, Iran
3 Professor, Faculty of Agricultural Engineering and Technology, Tehran University, Iran
4 Scientific Board, Soil Conservation and Watershed Management Research Institute, Iran
5 Assistant Professor, Faculty of Agricultural Engineering and Technology, Tehran University, Iran
6 Assistant Professor, Faculty of Agriculture, Lorestan University, Iran
Abstract
Salinity is the major factors of soil degradation in semi arid and arid regions. The main aim of this study was to evaluate the capability of Landsat ETM+ data for soil Salinity mapping in the selected part of the Qazvin plain, an area of arid environment. In this study spectral classes carried out on remotely sensed data and with the help of field observation and soil analysis were regrouped to soil salinity classes to prepare soil salinity map.. Soil sampling was implemented using stratified random sampling method, depending on landscape complexity and homogeneity as well as on the representativeness of Landsat ETM+ data. Also in each soil map unit at least one profile was studied for subsoil salinity variations. Field samples taken by using augur and profiles were analyzed in laboratory for Na+ , Ca2+ , Mg2+ cations, as well as soil texture, ECe and pH. We have analyzed the effectiveness of additional data such as digital elevation model to improve the accuracy of classification. Also NDVI, SRVI, PVI, SAVI, SI, BI and NDSI indices, PCA and Tasseled cap were analyzed. Soil salinity map of each selected bands produced and with ground truth map crossed. The results indicated that combination of DEM with ETM+ bands has highest accuracy. This study addressed that thermal band of ETM+ can increase the classification accuracy which illustrated its effective role to classify the soil salinity. Tasseled cap and other indices had almost high accuracy among studied image processing techniques. The SI and BI indices had the highest correlation with EC and could distinguish the saline and non saline soils while the optimum index factor had overall low accuracy.
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