Document Details
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Document Type |
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Thesis |
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Document Title |
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Study of Sand and Dust Storm Monitoring Using Remote Sensing Techniques Over Kingdom of Saudi Arabia دراسة مراقبة العواصف الرملية والترابية باستخدام تقنيات الاستشعار عن بعد فوق المملكة العربية السعودية |
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Subject |
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Faculty of Environmental Sciences |
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Document Language |
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Arabic |
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Abstract |
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This thesis is a contribution to the field of spaceborne remote sensing of sand and dust storm (SDS) by means of Moderate Resolution Imaging Spectroradiometer (MODIS). Satellite remote sensing has proved to be a valuable tool to monitor SDS in near real time. Satellite imagery can be used successfully for mapping SDS events and their characteristics under a wide variety of conditions.
SDS events have significant effects on environmental conditions thereby affecting the economic and human health in the region. The aim of this study is to apply various algorithms in order to monitor SDS events in the Kingdom of Saudi Arabia (KSA) and validate the algorithm results with observational data as well as with satellite data.
In a first step, the Normalized Difference Dust Index (NDDI), Global Dust Detection Index (GDDI), and Middle East Dust Index (MEDI) are applied on MODIS data for the SDS monitoring in the KSA. In the NDDI, spectral signatures of sand in MODIS band 3 and band 7 are used for the detection of SDS events while in the GDDI, spectral signatures of sand in MODIS band 4 and band 7 are used for the detection of SDS events. On the other hand, MEDI used brightness temperature values in MODIS bands 29, 31, and 32 for the detection of SDS events.
In a second step a new index named, Saudi Dust Detection Index (SDDI), is proposed and tested for the detection of SDS events over the KSA. For this, spectral signatures of sand in MODIS band 3, band 4, and band 7 are used. Threshold values are identified in order to highlight various SDS categories.
In a third step NDDI, GDDI, MEDI, and SDDI results are analysed to highlight the most appropriate algorithm. In a fourth step, the performance of NDDI, GDDI, MEDI, and SDDI are compared, and most appropriate algorithm is identified for the SDS monitoring over the KSA. For this, algorithm accuracy (AC), Probability Of Correct positive Detection (POCD), and Probability Of False positive Detection (POFD) of each algorithm used in the current study are calculated. The results of the study indicate that for NDDI, GDDI, MEDI, and SDDI the AC is 89%, 82%, 80%, and 96% respectively, POCD is 92%, 94%, 89%, and 97% respectively, whilst POFD is 4%, 7%, 12%, and 2% respectively. These results unambiguously reveal that the performance of SDDI is better than GDDI, NDDI, and MEDI for SDS monitoring over the KSA.
In the final step, the algorithms (NDDI, GDDI, MEDI, SDDI) based results are validated by using MODIS combined Dark Target (DT) and Deep Blue (DB) Aerosol Optical Depth (AOD) product, Meteosat satellite images, ground-based meteorological stations data, and AOD data from AERONET (Aerosol Robotic Network) stations in the KSA. The results show that the multi-source data, that is, MODIS combine DT-DB AOD product, Meteosat data, AERONET AOD data, and meteorological stations data can be very valuable for the research related with SDS events over the KSA. The outcome of this study could be very beneficial to understand SDS characteristics in the study region. As no such attempt in the past has been made in the KSA it is envisioned that the results of this study will be helpful in planning remote sensing data for the climate change study in the region. This thesis also demonstrates that application of algorithms for SDS monitoring need local calibration for effective and reasonable results. |
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Supervisor |
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Dr. Mazen Assiri |
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Thesis Type |
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Doctorate Thesis |
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Publishing Year |
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1444 AH
2023 AD |
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Co-Supervisor |
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Prof. Dr. Mohsen Butt |
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Added Date |
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Tuesday, August 8, 2023 |
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Researchers
| عصام محمد الغامدي | Alghamdi, Essam Mohammed | Researcher | Doctorate | |
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