Monitoring soil salinity via remote sensing technology under data scarce conditions: A case study from Turkey

2017 ◽  
Vol 74 ◽  
pp. 384-391 ◽  
Author(s):  
Taha Gorji ◽  
Elif Sertel ◽  
Aysegul Tanik
Keyword(s):  
Remote Sensing ◽  
Soil Salinity ◽  
Remote Sensing Technology ◽  
Sensing Technology

scholarly journals SOIL SALINITY MAPPING USING MULTITEMPORAL LANDSAT DATA

2016 ◽  
Vol XLI-B7 ◽  
pp. 3-9 ◽  
Author(s):  
A. Azabdaftari ◽  
F. Sunar
Keyword(s):  
Remote Sensing ◽  
Linear Regression ◽  
Correlation Analysis ◽  
Soil Salinity ◽  
Vegetation Index ◽  
Landsat Images ◽  
Ground Truth Data ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Salinity Index

Soil salinity is one of the most important problems affecting many areas of the world. Saline soils present in agricultural areas reduce the annual yields of most crops. This research deals with the soil salinity mapping of Seyhan plate of Adana district in Turkey from the years 2009 to 2010, using remote sensing technology. In the analysis, multitemporal data acquired from LANDSAT 7-ETM<sup>+</sup> satellite in four different dates (19 April 2009, 12 October 2009, 21 March 2010, 31 October 2010) are used. As a first step, preprocessing of Landsat images is applied. Several salinity indices such as NDSI (Normalized Difference Salinity Index), BI (Brightness Index) and SI (Salinity Index) are used besides some vegetation indices such as NDVI (Normalized Difference Vegetation Index), RVI (Ratio Vegetation Index), SAVI (Soil Adjusted Vegetation Index) and EVI (Enhamced Vegetation Index) for the soil salinity mapping of the study area. The field’s electrical conductivity (EC) measurements done in 2009 and 2010, are used as a ground truth data for the correlation analysis with the original band values and different index image bands values. In the correlation analysis, two regression models, the simple linear regression (SLR) and multiple linear regression (MLR) are considered. According to the highest correlation obtained, the 21st March, 2010 dataset is chosen for production of the soil salinity map in the area. Finally, the efficiency of the remote sensing technology in the soil salinity mapping is outlined.

scholarly journals SOIL SALINITY MAPPING USING MULTITEMPORAL LANDSAT DATA

2016 ◽  
Vol XLI-B7 ◽  
pp. 3-9 ◽  
Author(s):  
A. Azabdaftari ◽  
F. Sunar
Keyword(s):  
Remote Sensing ◽  
Linear Regression ◽  
Correlation Analysis ◽  
Soil Salinity ◽  
Vegetation Index ◽  
Landsat Images ◽  
Ground Truth Data ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Salinity Index

Soil salinity is one of the most important problems affecting many areas of the world. Saline soils present in agricultural areas reduce the annual yields of most crops. This research deals with the soil salinity mapping of Seyhan plate of Adana district in Turkey from the years 2009 to 2010, using remote sensing technology. In the analysis, multitemporal data acquired from LANDSAT 7-ETM<sup>+</sup> satellite in four different dates (19 April 2009, 12 October 2009, 21 March 2010, 31 October 2010) are used. As a first step, preprocessing of Landsat images is applied. Several salinity indices such as NDSI (Normalized Difference Salinity Index), BI (Brightness Index) and SI (Salinity Index) are used besides some vegetation indices such as NDVI (Normalized Difference Vegetation Index), RVI (Ratio Vegetation Index), SAVI (Soil Adjusted Vegetation Index) and EVI (Enhamced Vegetation Index) for the soil salinity mapping of the study area. The field’s electrical conductivity (EC) measurements done in 2009 and 2010, are used as a ground truth data for the correlation analysis with the original band values and different index image bands values. In the correlation analysis, two regression models, the simple linear regression (SLR) and multiple linear regression (MLR) are considered. According to the highest correlation obtained, the 21st March, 2010 dataset is chosen for production of the soil salinity map in the area. Finally, the efficiency of the remote sensing technology in the soil salinity mapping is outlined.

scholarly journals The Utilization of ALOS PALSAR Image to Estimate Natural Forest Biomass: Case Study at Bogani Nani Wartabone National Park

Jurnal Wasian ◽  
2014 ◽  
Vol 1 (1) ◽  
pp. 15
Author(s):  
Nurlita Indah Wahyuni
Keyword(s):  
Remote Sensing ◽  
National Park ◽  
Satellite Image ◽  
Forest Biomass ◽  
Research Area ◽  
Natural Forest ◽  
Alos Palsar ◽  
Remote Sensing Technology ◽  
Sensing Technology

The development of remote sensing technology makes it possible to utilize its data in many sectors including forestry. Remote sensing image has been used to map land cover and monitor deforestation. This paper presents utilization of ALOS PALSAR image to estimate and map aboveground biomass at natural forest of Bogani Nani Wartabone National Park especially SPTN II Doloduo and SPTN III Maelang. We used modeling method between biomass value from direct measurement and digital number of satellite image. There are two maps which present the distribution of biomass and carbon from ALOS PALSAR image with 50 m spatial resolution. These maps were built based on backscatter polarization of HH and HV bands. The maps indicate most research area dominated with biomass stock 0-5.000 ton/ha.

scholarly journals Soil Salinity: Causes and Impacts on Agriculture and the Environment: ملوحة التربة: أسبابها وآثارها على الزراعة والبيئة

2019 ◽  
Vol 3 (4) ◽  
Author(s):  
Sleem Ali Saleem Kreba
Keyword(s):  
Remote Sensing ◽  
Soil Salinity ◽  
Scientific Knowledge ◽  
Arid Regions ◽  
Review Article ◽  
Important Method ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
The Right ◽  
Semi Arid

  Soil salinity is an important issue for agriculture and the environment, especially in arid and semi-arid regions. Soil salinity influences agricultural productivity and soil properties. It is strongly related to irrigation and groundwater. This review article considers collecting published scientific knowledge about the soil salinity issue. It considers introducing the soil salinity, its types, its causes, its impacts on agriculture and the environment, its measuring methods, and its reclamation methods. The article considers also the remote sensing technology and its use in monitoring and predicting soil salinity. This article was prepared to help farmers, students, scientists, and researchers in agricultural and environmental sectors. Conserving affected lands with salinity is costly and time consuming, and choosing the right crops and plant species is the most important method to deal with this issue.    

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