Influence of Groundwater Irrigation on Chemical Properties of Soils in the Vicinity of Wastewater Drainage Canals in Al-Ahsa Oasis, Saudi Arabia

Hussain, Ghulam; Al-Zarah, Abdullah I.; Latif, M.

doi:10.3923/rjet.2013.1.17

Cited by 4 publications

(3 citation statements)

References 6 publications

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“…Moreover, a recent study carried out by [13] has revealed that the most of the arable lands in Al Hassa oasis have been affected or affecting by soil salinity, and they justify that by different factors one of them is poor quality irrigation of groundwater. The same study and other several studies [14] [19] have found that the soil in Al Hassa oasis's arable lands contents high EC ranging between 1.4…”

Section: Discussionsupporting

confidence: 69%

“…In our study area, this phenomenon was demonstrated by few studies [12] [13] [14], for instance, Allbed et al [13] have documented that arable lands in the Al Hassa oasis have been suffering from shifting sand and soil salinity as a major threat. For example, [13] found out that there is a very positive strong correlation between salinity and degradation in vegetation cover, and they detected that change in concentration of soil salinity resulting in a change of vegetation cover density.…”

Section: Introductionsupporting

confidence: 57%

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Detection, Mapping and Assessment Change in Urban and Croplands Area in Al-Hassa Oasis, Eastern Region in Saudi Arabia Using Remote Sensing and Geographic Information System

AlQarni¹,

Babiker²,

Salih³

2018

JGIS

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Croplands are decreasing due to the expansion of urban areas into rural communities and to some extent due to sand accumulations. Increases in population numbers, new development, in addition to the accumulation of sand and soil salinity are the major driving force leading to abandonment and shrinking of cropland. The aim of this study was to investigate and assess to what extent agricultural lands are affected by urban development in the Al Hassa oasis, Eastern region in Saudi Arabia by employing Landsat time series data of years 1988, 2000 and 2017 as the main source of information. A set of ground truth, control points (GCPs) was also used besides population census data. Unsupervised classifications approach, Normalized Difference Vegetation Index (NDVI) and change detection methods were used here. Urban area during 2000-2017 exhibits much higher increase compared to 1988-2000, while the arable lands declined to −3.4% in 1988-2000 and increased to 22% during 2000-2017. The data analysis results provided new accurate numerical information supported by a graphical representation in regard to the decrease and increase in urban and agricultural lands. Therefore the findings of this study should be considered by decision maker for improving and development the agriculture activities in rural and urban communities.

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Section: Discussionsupporting

confidence: 69%

Section: Introductionsupporting

confidence: 57%

Detection, Mapping and Assessment Change in Urban and Croplands Area in Al-Hassa Oasis, Eastern Region in Saudi Arabia Using Remote Sensing and Geographic Information System

AlQarni¹,

Babiker²,

Salih³

2018

JGIS

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“…This oasis covers an area of approximately 20,000 ha and is at an altitude of approximately 130 to 160 m above sea level [39,40]. The Al Hassa oasis is L-shaped and is actually composed of two separate oases [41].…”

Section: Study Areamentioning

confidence: 99%

Mapping and Modelling Spatial Variation in Soil Salinity in the Al Hassa Oasis Based on Remote Sensing Indicators and Regression Techniques

2014

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Soil salinity is one of the most damaging environmental problems worldwide, especially in arid and semi-arid regions. An integrated approach using remote sensing in addition to various statistical methods has shown success for developing soil salinity prediction models. The aim of this study was to develop statistical regression models based on remotely sensed indicators to predict and map spatial variation in soil salinity in the Al Hassa oasis. Different spectral indices were calculated from original bands of IKONOS images. Statistical correlation between field measurements of Electrical Conductivity (EC), spectral indices and IKONOS original bands showed that the Salinity Index (SI) and red band (band 3) had the highest correlation with EC. Combining these two remotely sensed variables into one model yielded the best fit with R 2 = 0.65. The results revealed that the high performance of this combined model is attributed to: (i) the spatial resolution of the images; (ii) the great potential of the enhanced images, derived from SI, by enhancing and delineating the spatial variation of soil salinity; and (iii) the superiority of band 3 in retrieving soil salinity features and patterns, which was explained by the high reflectance of the smooth and bright surface crust and the low reflectance of the coarse dark puffy crust. Soil salinity maps generated using the selected model showed that strongly saline soils (>16 dS/m) with variable spatial distribution were the dominant class over the study area. The spatial variability of this class over the investigated areas was attributed to a variety factors, including soil factors, management related factors and climate factors. The results demonstrate that modelling and mapping spatial variation in soil salinity based on OPEN ACCESSRemote Sens. 2014, 6 1138 regression analysis and remote sensing data is a promising approach, as it facilitates timely detection with a low-cost procedure and allows decision makers to decide what necessary action should be taken in the early stages to prevent soil salinity from becoming prevalent, sustaining agricultural lands and natural ecosystems.

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Soil salinity and vegetation cover change detection from multi-temporal remotely sensed imagery in Al Hassa Oasis in Saudi Arabia

Allbed

Kumar

Sinha

2017

Geocarto International

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Influence of Groundwater Irrigation on Chemical Properties of Soils in the Vicinity of Wastewater Drainage Canals in Al-Ahsa Oasis, Saudi Arabia

Cited by 4 publications

References 6 publications

Detection, Mapping and Assessment Change in Urban and Croplands Area in Al-Hassa Oasis, Eastern Region in Saudi Arabia Using Remote Sensing and Geographic Information System

Detection, Mapping and Assessment Change in Urban and Croplands Area in Al-Hassa Oasis, Eastern Region in Saudi Arabia Using Remote Sensing and Geographic Information System

Mapping and Modelling Spatial Variation in Soil Salinity in the Al Hassa Oasis Based on Remote Sensing Indicators and Regression Techniques

Soil salinity and vegetation cover change detection from multi-temporal remotely sensed imagery in Al Hassa Oasis in Saudi Arabia

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