Soil Salinity Mapping Based on ETM+ Data in Arid Rangeland, Iran (Case Study: Damghan Region, Iran)
الموضوعات :
Mitra Shirazi
1
(Ph.D. in Combat Desertification, Yazd University, Yazd, Iran)
Ali Akbar Damavandi
2
(Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran)
الکلمات المفتاحية: PCA, ETM+, remote sensing, Soil Indices,
ملخص المقالة :
Soil salinity has concerned people in arid and semi-arid rangelands. One of the most essential cases in relation to information for natural resource managers is preparation of soil salinity maps. Developing such maps, using traditional methods spends a lot of time and costs. Satellite data have broadened and integrated our vision for this purpose. This study was conducted in order to develop a model for providing a salinity map using ETM+ satellite data collected in 2012 and salinity values in Damghan rangelands, Iran. The geometric and atmospheric correction of satellite images was carried out. Necessary processing such as fusion of multispectral bands with panchromatic bands, tasseled cap transformation, the analysis of Principal Components Analysis (PCA), and rationing for composite bands creation were also performed. A total number of 114 surface soil sample points with the depth of 0-15 cm were taken through a random sampling method and their Electrical Conductivity (EC) was measured. Different bands extracted spectral values for each sample and the relation between spectral values (i.e. main bands, Tasseled Cap bands, and soil and vegetation index) with EC values of the samples was investigated. Using PCA analysis, the variables were categorized into four principle components to develop soil EC map according to the highest correlation. Results revealed that there was the highest correlation between PCA1 and variables of blue, green, red bands (R=0.7), Tasseled cap 1, 2, 4 (R=0.68) and indicators SI1, SI2, SI3 (R=0.7), GVI, BI (R=0.68), INT1, INT2, MND, WDVI (R=0.7). In PCA2, the variables of NIR,OSAVI, NDVI, SAVI, VNIR1 and TVI had a significant correlation with PCA2. Finally, using stepwise regression, three models were developed to determine soil salinity maps according to the utilized independent variables. Results showed that Landsat ETM+ images are good tools to estimate salinity maps of arid rangelands.
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