Abstract:
Current study monitored Electerical Conductivity (EC) as soil salinity index and Organic Matter (OM) in the area of Harat in Yazd, Iran, through remote sensing technology with high spatial and spectral resolution. The images were selected from IRS, LISS III satellites between the years 2008 and 2012. After preprocessing and analyzing the images, the relationship between parameters of (EC) and (OM) spectral reflections were determined, and both two-satellite images were classified using maximum likelihood method. Results showed that during the period (2008-2012) organic matter content of all farmlands increased and the area of saline land decreased. This trend showed that agriculture activities help reduction of desertification. Accuracy classification and coefficient kappa obtained for salinity map in 2008 were equal to 82% and 0.73, and in 2012, were equal to 84% and 0.70 respectively. Accuracy of classification and coefficient kappa obtained for Organic matter map in 2008 were equal to 85.5% and 0.76 and in 2012, were equal to 84% and 0.74 respectively. This research indicates that remote sensing data, especially IRS-LissIII images, have high efficiency for detection of soil salinity and organic matter changes and natural resources management.
Machine summary:
Monitoring of organic matter and soil salinity by using IRS - LissIII satellite data in the Harat plain, of Yazd province M.
of Desert Management, Faculty of Natural Resources, Yazd University, Yazd, Iran Received: 11 April 2016; Received in revised form: 23 September 2017; Accepted: 4 November 2017 Abstract Current study monitored Electerical Conductivity (EC) as soil salinity index and Organic Matter (OM) in the area of Harat in Yazd, Iran, through remote sensing technology with high spatial and spectral resolution.
This research indicates that remote sensing data, especially IRS- LissIII images, have high efficiency for detection of soil salinity and organic matter changes and natural resources management.
Considering time and cost factors, soil degradation studies in large areas, using remote sensing and GIS seems to be a more effective technique (Gao and Liu 2008; El-Baroudy and Moghanm 2014).
2. Research Methodology To detect the changes in soil salinity and organic matter in the agricultural lands, the LISS III data of 30 October 2008 and 5 November 2012 used.
The results show that the accuracy of most of spectral maps (classified maps) is over 70%, which is indicative of the efficacy of IRS - LissIII satellite images for the soil science studies and classification of soil salinity ranges.
The results showed that the accuracy of most of spectral maps (classified maps) is over 70%, which is indicative of the efficacy of IRS-LissIII satellite images for the soil science studies and classification of soil salinity ranges.