Volume 2, Issue 3 (5-2021)
Degrad Rehabil Nat Land 2021, 2(3): 112-122 |
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nazarnejad H, Komaki C B, Servati M. Mapping Soil Salinity Changes in Miandoab Plain using Satellite Images. Degrad Rehabil Nat Land 2021; 2 (3) :112-122
URL: http://drnl.sanru.ac.ir/article-1-178-en.html
URL: http://drnl.sanru.ac.ir/article-1-178-en.html
Urmia University
Abstract: (1770 Views)
Abstract
Mapping soil salinity changes on a large scale is very costly and time consuming. One of the most cost-effective ways to map soil salinity is to use satellite imagery. Investigation of spectral characteristics of soils with saline surfaces and determining the extent of areas affected by this phenomenon, by using new images with relatively high spatial and spectral resolution can be used in preparing soil salinity maps. In recent years, many indicators have been developed to detect and extract the characteristics of saline surfaces from satellite images, most of which have been studied based on the spectral reflection of these surfaces in different bands of satellite images or intermediate ratios. The purpose of this study is to estimate the salinity changes from 2001 to 2016 in Miandoab region located in West Azerbaijan province. For this purpose, 42 samples with appropriate distribution were taken from the area. The samples were then transferred to the laboratory to measure soil salinity parameters. To evaluate soil salinity, in addition to sampling of topsoil, the NDSI, IBI, NDVI, SI7 and NIR, RED, SWIR2 bands of Landsat 7 for 2001 and Landsat 8 for 2016 were used. The regression equation for determining soil salinity for 2001 showed that soil salinity has a positive and significant correlation with NDSI index at the level of 5%, While for 2016, the highest significant correlation at the 5% level was with the RED and NIR bands of the Landsat 8 image. In 2001, the highest and lowest lands in the region were non-saline lands (39%) and very saline lands (12%), respectively; While in 2016, the highest and lowest lands in the region are relatively saline lands (35%) and non-saline lands (18%). The results of the present study showed that the salinity of the region can be easily calculated using Remote sensing indices. Due to its proximity to Lake Urmia, the study area faces soil salinity problems, so the identification and classification of saline areas is necessary for land management.
Mapping soil salinity changes on a large scale is very costly and time consuming. One of the most cost-effective ways to map soil salinity is to use satellite imagery. Investigation of spectral characteristics of soils with saline surfaces and determining the extent of areas affected by this phenomenon, by using new images with relatively high spatial and spectral resolution can be used in preparing soil salinity maps. In recent years, many indicators have been developed to detect and extract the characteristics of saline surfaces from satellite images, most of which have been studied based on the spectral reflection of these surfaces in different bands of satellite images or intermediate ratios. The purpose of this study is to estimate the salinity changes from 2001 to 2016 in Miandoab region located in West Azerbaijan province. For this purpose, 42 samples with appropriate distribution were taken from the area. The samples were then transferred to the laboratory to measure soil salinity parameters. To evaluate soil salinity, in addition to sampling of topsoil, the NDSI, IBI, NDVI, SI7 and NIR, RED, SWIR2 bands of Landsat 7 for 2001 and Landsat 8 for 2016 were used. The regression equation for determining soil salinity for 2001 showed that soil salinity has a positive and significant correlation with NDSI index at the level of 5%, While for 2016, the highest significant correlation at the 5% level was with the RED and NIR bands of the Landsat 8 image. In 2001, the highest and lowest lands in the region were non-saline lands (39%) and very saline lands (12%), respectively; While in 2016, the highest and lowest lands in the region are relatively saline lands (35%) and non-saline lands (18%). The results of the present study showed that the salinity of the region can be easily calculated using Remote sensing indices. Due to its proximity to Lake Urmia, the study area faces soil salinity problems, so the identification and classification of saline areas is necessary for land management.
Keywords: Electrical Conductivity, Land Use, Normalized Difference Salinity Index, Saline Land Expansion, Satellite Imagery
Type of Study: Research |
Received: 2021/09/6 | Accepted: 2022/04/27 | Published: 2021/05/31
Received: 2021/09/6 | Accepted: 2022/04/27 | Published: 2021/05/31
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