Assessment of Urban Expansion’s Impact on Changes in Vegetation Patterns in Dhofar, Oman, Using Remote Sensing and GIS Techniques

Al-Mulla, Yaseen A.; Alruheili, Amna; Al-Lawati, A.; Parimi, Krishna; Ali, Ahsan; Al-Sadi, N.; Al-Harrasi, F.

doi:10.1109/access.2022.3198942

Cited by 8 publications

(3 citation statements)

References 23 publications

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“…Additionally, Allbed et al [29] and others [30,31] demonstrated the potential of spectral indices derived from image enhancement techniques for soil salinity studies, justifying their use for potentially improved results compared to raw bands. Furthermore, Al-Mulla and Al-Adawi [32] successfully identified temporal changes in soil salinity within the Al-Rumais region of Oman using various image enhancement techniques. This aligns with findings by Tripathi et al [33] and Al-Khaier [34], who demonstrated the superiority of salinity indices, often incorporating two SWIR bands, over single bands for pinpointing salt-affected soils.…”

Section: Introductionmentioning

confidence: 99%

Spatial Assessment of Groundwater Salinity and Its Impact on Vegetation Cover Conditions in the Agricultural Lands of Al-Ahsa Oasis, Saudi Arabia

Hassaballa

2024

Water

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This study investigated the complex relationship among groundwater salinity, soil texture, and vegetation health in Al-Ahsa Oasis, Saudi Arabia. Utilizing vegetation condition index data from 5 years of satellite imagery, alongside ground measurements of groundwater salinity and soil analysis, the study unveiled significant spatial heterogeneity. The vegetation health and groundwater salinity in the study area ranged from 27 to 70% and from 1.7 to 9.6 dS m−1, respectively, and exhibited contrasting patterns, with a high vegetation condition index (healthy vegetation) in the north and east versus high salinity in the southwest. The applied cross-tabulation statistics revealed a strong negative correlation between the vegetation condition index and salinity, with “good” and “normal” vegetation health primarily coinciding with “slightly saline” groundwater. Soil texture further modulated the vegetation response, as “Silt loam” soils corresponded with “good” VCI, while “silty clay loam” only supported “dry” and “normal” conditions. The applied spatial autocorrelation analysis confirmed this, showing low vegetation clustering with “silt” texture and high salinity in the mid-west of the study area (p < 0.05). Outlier districts hint at local factors beyond simple salinity–vegetation health relationships. Finally, a conditional plot, applied as a spatial statistical approach, revealed that low vegetation health spatially correlates with high groundwater salinity (>5 dS m−1) on “silt loam” in the mid-west, while a higher vegetation condition index aligned with lower groundwater salinity in the east and north. These findings highlight the critical role of spatial variability and the need for advanced modeling and data integration to understand and predict the responses of vegetation to environmental challenges, and will be crucial for optimizing water management to ensure the long-term sustainability of Al-Ahsa Oasis.

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Section: Introductionmentioning

confidence: 99%

Spatial Assessment of Groundwater Salinity and Its Impact on Vegetation Cover Conditions in the Agricultural Lands of Al-Ahsa Oasis, Saudi Arabia

Hassaballa

2024

Water

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“…One of the recent tools used for wetland monitoring and restoration is remote sensing. This technique can be used to monitor the status of a CW, and it can be used as a useful tool that can provide valuable information, e.g., about plant health, in a relatively short time [1,2,16,18].…”

Section: Introductionmentioning

confidence: 99%

“…Remote sensing techniques have been widely used to classify vegetation monitoring as well as to evaluate the targeted plants, soil, and water changes [14]. These techniques have assisted in dealing with some impacts of natural and anthropogenic causes by using spectral data for decision making such as the vegetation identification of different regions using different satellites and methods of vegetation extraction [18,19]. Various vegetation indices, which are pivotal for evaluating vegetation and crop well-being, have emerged as standard tools in vegetation health assessments.…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing Analysis for Vegetation Assessment of a Large-Scale Constructed Wetland Treating Produced Water Polluted with Oil Hydrocarbons

Al-Jabri,

Al-Mulla,

Melgani

et al. 2023

Remote Sensing

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The identification and assessment of plant stress using wetland satellite images is a major task in remote sensing. In this study, one of the largest constructed wetlands (CWs) in the world, located in the Sultanate of Oman, was examined, assessed, and evaluated using remote sensor data from Sentinel-2. This CW system treats produced water generated during oil exploration activities in a desert environment; thus, CW vegetation is subjected to stress induced by oil hydrocarbons and water salinity. This study examined the plant stress and detected changes between the years of 2017 and 2019. Sentinel satellite images were evaluated for vegetation status extraction. The Normalized Difference Vegetation Index (NDVI), Modified Soil-Adjusted Vegetation Index (MSAVI), and Normalized Difference Salinity Index (NDSI) were used to evaluate the vegetation change. The results showed a comprehensive mapping identification of the plant stress and water flow parameter factors including oil in water contamination (OIW), dissolved oxygen (DO), water temperature (WT), and water conductivity (COND). Among the three indices, it was found that the NDVI showed a very good correlation with all parameters in both years with average R2 = 0.78, 0.67, 0.75, and 0.60 for OIW, DO, WT, and COND, respectively. The same trend was found for MSAVI but with R2 = 0.59, 0.48, 0.55, and 0.56 for OIW, DO, WT, and COND, respectively. This shows that the NDVI performed better than the MSAVI in evaluating the water flow parameters. On the other hand, the NDSI showed a strong correlation with one flow parameter, that is, water conductivity, especially at the outlet cells of the CW with R2 = 0.86 and 0.82 for winter time and summer time, respectively. The synchronization and correlation between the water flow parameters and remote sensing vegetation indices in this study lead to a new approach to large-scale landscape wetland monitoring that improves and helps predict any degradation or stress on vegetation growth. Furthermore, the results of this work can help decision makers potentially modify the wetland design and water flow path to improve future expansion phases. The mapping of such a critical and massive industrial CW should consider the use of high spatial resolution sensors where identifications and classifications are further improved. In summary, this research demonstrates that it is feasible to estimate vegetation stress within the constructed wetland using remote sensing techniques across extensive regions when an ample dataset comprising field data, satellite imagery, and supporting information is accessible.

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Remote Sensing Techniques for Investigating Natural Resources Deterioration: Application on Agricultural Degradation in Sultanate Oman

Al-Mulla,

Ali,

Alalawi

et al. 2024

Earth and Environmental Sciences Library

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Assessment of Urban Expansion’s Impact on Changes in Vegetation Patterns in Dhofar, Oman, Using Remote Sensing and GIS Techniques

Cited by 8 publications

References 23 publications

Spatial Assessment of Groundwater Salinity and Its Impact on Vegetation Cover Conditions in the Agricultural Lands of Al-Ahsa Oasis, Saudi Arabia

Spatial Assessment of Groundwater Salinity and Its Impact on Vegetation Cover Conditions in the Agricultural Lands of Al-Ahsa Oasis, Saudi Arabia

Remote Sensing Analysis for Vegetation Assessment of a Large-Scale Constructed Wetland Treating Produced Water Polluted with Oil Hydrocarbons

Remote Sensing Techniques for Investigating Natural Resources Deterioration: Application on Agricultural Degradation in Sultanate Oman

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