Mineral dust aerosols play an important role in climate and the Earth's energy budget. However, the nature and complexity of dust sources is poorly understood. Traditional techniques used for mapping the Sahara dust sources like the analysis of surface dust observations, back trajectory analysis of isobar data, and mineral tracers all tell a different story regarding the location of dust sources, they only agree that the Bodélé Depression and western Hoggar Mountains are important sources. Remote sensing techniques have more recently been used to identify Saharan dust sources and the different methods provide more agreement about more Saharan dust sources and also identify the Bodélé Depression and the western Hoggar Mountains as important source of dust. While there are several remote sensing techniques that can be used to identify desert dust sources yet no comprehensive comparison has yet been done to evaluate their utility. This thesis has evaluated the utility of nine different methods that can be employed to detect dust using MODIS have been investigated by comparing them to sun-photometer aerosol optical thickness (AOT) measurements at a wavelength of 1020μm from Banizoumbou (Niger). The five established techniques that were evaluated, these were: Ackerman (1989), Miller (2003), Handley (2004), Hansell et al., (2007) and the Deep Blue NASA aerosol product Hsu et al., (2003). Many of these methods employ brightness temperature differences (BTD). To determine the effectiveness of this approach all possible MODIS BTD’s were computed and evaluated, these were: BTD (12 μm-11 μm), BTD (3.7 μm-12 μm), BTD (8.6 μm-11 μm), and BTD (8.6 μm-12 μm). To evaluate the accuracy of these dust indices the correlation between the sun-photometer AOT and the result of each MODIS dust index were determined. The results show that the Deep Blue Algorithm gives the highest correlation (R²= 0.91), however the deep blue product has a 10km spatial resolution and thus is not good at locating dust sources, the ultimate aim of this project, furthermore the cloud mask applied to the product routinely masks out most of the dust. The other methods all have a spatial resolution of 1km and thus are more appropriate for this purpose. Of these methods the Ackerman (1989) shows a high R2 value (R²= 0.71) as do many other methods. The effect of different surface materials on dust detection was evaluate by studying five different backgrounds in order to see how the dust can be distinguished from these backgrounds using the M test. Over Limestone background, Miller (2003) has got the highest M value followed by Deep Blue and Ackerman (1989), excluding Deep Blue due to the poor cloud mask and visually difficult to trace the dust to their sources, Ackerman (1989) comes second and has an M value close to Miller (2003). However, Ackerman (1989) shows the best result visually. Based on the five tested backgrounds result using different techniques (visually, M test, and statistically) Deep Blue, Miller, and Ackerman present reasonable results. Based on these results Ackerman (1989) was selected to detect the dust sources over Cyrenaica (Libya) with using MODIS and SEVIRI data. SEVIRI images are also used to study the meteorology of the dust storms in order to provide more information on the wind direction, cause, and lifetime of dust storm. The highest percentage of the dust storms generated from Cyrenaica are caused by Anticyclone 70%. The majority last for six hours, starting at 8:00 am and ending at 14:00 pm. MODIS is used to detect the location of the dust sources and Landsat and Google Earth images are used to identify the geomorphology of the dust sources. Total of 53 dust sources are detected during 2008, 45% from alluvial fans, 15% from lake, 13% from alluvial plains, 6% from agriculture, 6% from river, 2% from multiple landforms in a single MODIS pixel and thus their nature could not be determined, while 13% were diffuse and no source could be detected. Alluvial fans were the most active sources and almost half of these dust sources are located at one large fan located south east of Benghazi.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:628039 |
| Date | January 2012 |
| Creators | Abushufa, Tarek |
| Publisher | King's College London (University of London) |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | https://kclpure.kcl.ac.uk/portal/en/theses/evaluating-different-remote-sensing-techniques-for-detection-of-saharan-dust-and-characterisation-of-dust-sources(c3e6c6f3-3483-4d92-8b37-c728474d3f7c).html |
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