Spatiotemporal analysis of fine particulate matter (PM2.5) in Saudi Arabia using remote sensing data

AbstractRabigh is a thriving coastal city located at the eastern bank of the Red Sea, Saudi Arabia. The city has suffered from shoreline destruction because of the invasive tidal action powered principally by the wind speed and direction over shallow waters. This study was carried out to calibrate the water column depth in the vicinity of Rabigh. Optical and microwave remote sensing data from the European Space Agency were collected over 2 years (2017–2018) along with the analog daily monitoring of tidal data collected from the marine station of Rabigh. Depth invariant index (DII) was implemented utilizing the optical data, while the Wind Field Estimation algorithm was implemented utilizing the microwave data. The findings of the current research emphasis on the oscillation behavior of the depth invariant mean values and the mean astronomical tides resulted in R2 of 0.75 and 0.79, respectively. Robust linear regression was established between the astronomical tide and the mean values of the normalized DII (R2 = 0.81). The findings also indicated that January had the strongest wind speed solidly correlated with the depth invariant values (R2 = 0.92). Therefore, decision-makers can depend on remote sensing data as an efficient tool to monitor natural phenomena and also to regulate human activities in fragile ecosystems.

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Mapping Paleohydrologic Features in the Arid Areas of Saudi Arabia Using Remote-Sensing Data

Water ◽

10.3390/w12020417 ◽

2020 ◽

Vol 12 (2) ◽

pp. 417 ◽

Cited By ~ 2

Author(s):

Mohamed Abdelkareem ◽

Fathy Abdalla ◽

Samar Y. Mohamed ◽

Farouk El-Baz

Keyword(s):

Remote Sensing ◽

Saudi Arabia ◽

Volcanic Activity ◽

Remote Sensing Data ◽

Landsat 8 ◽

Optical Remote Sensing ◽

Alos Palsar ◽

Sensing Data ◽

The Past ◽

Aeolian Deposits

At present, the Arabian Peninsula is one of the driest regions on Earth; however, this area experienced heavy rainfall in the past thousand years. During this period, catchments received substantial amounts of surface water and sustained vast networks of streams and paleolakes, which are currently inactive. The Advanced Land Observing Satellite (ALOS) Phased Array Type L-band Synthetic Aperture Radar (PALSAR) data reveal paleohydrologic features buried under shallow aeolian deposits in many areas of the ad-Dawasir, Sahba, Rimah/Batin, and as-Sirhan wadis. Optical remote-sensing data support that the middle of the trans-peninsula Wadi Rimah/Batin, which extends for ~1200 km from the Arabian Shield to Kuwait and covers ~200,000 km2, is dammed by linear sand dunes formed by changes in climate conditions. Integrating Landsat 8 Operational Land Imager (OLI), Geo-Eye, Shuttle Radar Topography Mission (SRTM) digital elevation model, and ALOS/PALSAR data allowed for the characterization of paleodrainage reversals and diversions shaped by structural and volcanic activity. Evidence of streams abruptly shifting from one catchment to another is preserved in Wadi ad-Dawasir along the fault trace. Volcanic activity in the past few thousand years in northern Saudi Arabia has also changed the slope of the land and reversed drainage systems. Relics of earlier drainage directions are well maintained as paleoslopes and wide upstream patterns. This study found that paleohydrologic activity in Saudi Arabia is impacted by changes in climate and by structural and volcanic activity, resulting in changes to stream direction and activity. Overall, the integration of radar and optical remote-sensing data is significant for deciphering past hydrologic activity and for predicting potential water resource areas.

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Harbour dredging and fish mortality in an aquaculture zone: assessment of changes in suspended particulate matter using multi-sensor remote-sensing data

International Journal of Remote Sensing ◽

10.1080/01431161.2014.916446 ◽

2014 ◽

Vol 35 (11-12) ◽

pp. 4383-4398 ◽

Cited By ~ 4

Author(s):

Ming-Xia He ◽

Lianbo Hu ◽

Chuanmin Hu

Keyword(s):

Remote Sensing ◽

Particulate Matter ◽

Suspended Particulate Matter ◽

Remote Sensing Data ◽

Fish Mortality ◽

Sensing Data ◽

Suspended Particulate

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Damage Assessment of a Salt Dome in Jizan, Southwestern Saudi Arabia, Using High Spatial Resolution Remote Sensing Data

Frontiers in Earth Science ◽

10.3389/feart.2021.700337 ◽

2021 ◽

Vol 9 ◽

Author(s):

Habes Ghrefat ◽

Ahmed Hakami ◽

Elkhedr Ibrahim ◽

Saad Mogren ◽

Saleh Qaysi ◽

...

Keyword(s):

Remote Sensing ◽

Saudi Arabia ◽

Spatial Resolution ◽

Damage Assessment ◽

High Spatial Resolution ◽

Remote Sensing Data ◽

Salt Dome ◽

Visual Interpretation ◽

Sensing Data ◽

Residential Neighborhood

The salt dome in Jizan, southwestern Saudi Arabia, has caused several problems related to underground dissolution, particularly in the old part of the city. Examples of these problems include surface collapse, building failure, fracturing, tilting, and road cracking. Analysis of the salt dome using X-ray diffraction (XRD) revealed the dominance of gypsum, anhydrite, and halite. This study evaluates the damage assessment using multitemporal high spatial resolution data of the GeoEye-1, and QuickBird-2 sensors. Change detection technique, textural analysis, and visual interpretation were applied to these data. Analysis of the data recorded before and after a particular damage event revealed that three neighborhoods located above the Jizan salt dome—Al-Ashaima, Shamiya, and Aljabal—were affected to the greatest extent. The entire residential neighborhood of Al-Ashaima was evacuated, and the buildings located in it were demolished. Several buildings in the Shamiya and Aljabal neighborhoods were also demolished. Therefore, high spatial remote sensing data are effective in assessing building damage and for anticipating future damage, thus benefiting decision making for the affected cities.

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Mapping the Distribution of Shallow Groundwater Occurrences Using Remote Sensing-Based Statistical Modeling over Southwest Saudi Arabia

Remote Sensing ◽

10.3390/rs12091361 ◽

2020 ◽

Vol 12 (9) ◽

pp. 1361 ◽

Cited By ~ 3

Author(s):

Fahad Alshehri ◽

Mohamed Sultan ◽

Sita Karki ◽

Essam Alwagdani ◽

Saleh Alsefry ◽

...

Keyword(s):

Remote Sensing ◽

Saudi Arabia ◽

Brightness Temperature ◽

Field Data ◽

Shallow Groundwater ◽

Remote Sensing Data ◽

Data Sets ◽

Backscatter Coefficient ◽

Groundwater Levels ◽

Sensing Data

Identifying shallow (near-surface) groundwater in arid and hyper-arid areas has significant societal benefits, yet it is a costly operation when traditional methods (geophysics and drilling) are applied over large domains. In this study, we developed and successfully applied methodologies that rely heavily on readily available temporal, visible, and near-infrared radar and thermal remote sensing data sets and field data, as well as statistical approaches to map the distribution of shallow (1–5 m deep) groundwater occurrences in Al Qunfudah Province, Saudi Arabia, and to identify the factors controlling their development. A four-fold approach was adopted: (1) constructing a digital database to host relevant geologic, hydrogeologic, topographic, land use, climatic, and remote sensing data sets, (2) identifying the distribution of areas characterized by shallow groundwater levels, (3) developing conceptual and statistical models to map the distribution of shallow groundwater occurrences, and (4) constructing an artificial neural network (ANN) and multivariate regression (MR) models to map the distribution of shallow groundwater, test the models over areas of known depth to groundwater (area of Al Qunfudah city and surroundings: 294 km2), and apply the better of the two models to map the shallow groundwater occurrences across the entire Al Qunfudah Province (area: 4680 km2). Findings include: (1) high performance for the ANN (92%) and MR (88%) models in predicting the distribution of shallow groundwater using temporal-derived remote sensing products (e.g., normalized difference vegetation index (NDVI), radar backscatter coefficient, precipitation, and brightness temperature) and field data (depth to water table), (2) areas witnessing shallow groundwater levels show high NDVI (mean and standard deviation (STD)), radar backscatter coefficient values (mean and STD), and low brightness temperature (mean and STD) compared to their surroundings, (3) correlations of temporal groundwater levels and satellite-based precipitation suggest that the observed (2017–2019) rise in groundwater levels is related to an increase in precipitation in these years compared to the previous three years (2014–2016), and (4) the adopted methodologies are reliable, cost-effective, and could potentially be applied to identify shallow groundwater along the Red Sea Hills and in similar settings worldwide.

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