Hamid Reza Matinfar; Fereydoun Sarmadian; Seyed Kazem Alavipanah
Volume 2, Issue 4 , January 2011, , Pages 211-220
Abstract
Soil salinity is one of the main increasing problems of the world. In the recent years application of remote sensing and GIS techniques in order to assess saline soils is used because they bring about vast uniform coverage of ground phenomena in a short time. The images of LISS III sensor of the Indian ...
Read More
Soil salinity is one of the main increasing problems of the world. In the recent years application of remote sensing and GIS techniques in order to assess saline soils is used because they bring about vast uniform coverage of ground phenomena in a short time. The images of LISS III sensor of the Indian satellite (IRS) were used in this research. The Brightness index (BI) could discriminate highly saline soils from non –saline and the salinity index (SI) show high potential to separate very high, high and non- saline soils. Results of supervised classification without combination DEM and remote sensing data have overall accuracy of 76%, producer accuracy of 78% and user's accuracy of 82%. While in supervised classification combination of remotely sensed data and DEM have overall accuracy of 98.1% , producer accuracy of 98.28% and user's accuracy of 98.4% .The reason for low accuracy of the classification , before combination of remote sensing and topographic data , can be explained by highly moist saline soils spectral interference with non-saline soils (soil with 25 to 65% gravels) , but these two soils had different topographic condition with 200 meters elevation difference , thus , with combination of the DEM , this kind of area and other areas with similar conditions have been separated from each other.
Maral Khodadadi; Mohammad Sadegh Askari; Fereydoon Sarmadian; Hossein Gholi Refahi; Ali Akbar Norouzi; Ahmad Heidari; Hamid Reza Matinfar
Volume 1, Issue 2 , July 2009, , Pages 99-110
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 ...
Read More
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.
