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Hong Kong Polytechnic University (2022)

The long-term spatiotemporal dynamics of sand dunes in arid environment studied with optical satellite imagery

Saleh, Eslam Ali Hussein

Titre : The long-term spatiotemporal dynamics of sand dunes in arid environment studied with optical satellite imagery

Auteur : Saleh, Eslam Ali Hussein

Université de soutenance : Hong Kong Polytechnic University

Grade : Doctor of Philosophy (PhD) 2022

Résumé partiel
Dunes are considered the most common landform in terrestrial and extraterrestrial deserts. In several desert areas on Earth, dune and sand sheet instabilities pose a significant threat to transportation networks, water supply routes, urban areas, and cultural sites. Monitoring dune migration in the spatiotemporal domains can, therefore, contribute to a deeper understanding of the underpinning aeolian processes and their interaction with environmental change. Moreover, information on dune migration can be used as an indicator of large-scale trends in windiness over large deserts (e.g., the Sahara). The scarcity of metrological stations in vast deserts hinders drawing a complete picture of dynamic dune behavior. Additionally, scaling measurements of single dunes at a specific time scale to a large spatiotemporal domain are considered a problem. Optical image cross-correlation (OICC) is considered a valuable tool for semi-automatic monitoring of deformation at the Earth’s surface (i.e., earthquakes, glaciers, landslides, and dunes) with dense spatiotemporal resolution down to 1/10th of the pixel. However, limited studies using OICC to monitor dune migration by adjacently matching dozens of images over several years do not provide a complete picture of the temporal evolution of dune migration, especially over short timescales, due to sparse temporal coverage. Additionally, the dependency on adjacent matching with low redundancy levels would affect the solution uncertainty and spatial coverage. Previous studies that monitored other moving targets (i.e., landslides and glaciers) employed the inversion of OICC measurements ; however, the inversion of time series is in its infancy. Time series inversion by constructing the full network of OICC measurements is considered promising in reducing signal-to-noise ratio (SNR), enhancing spatial coverage, and reducing uncertainty. However, the full network inversion suffers from the following : 1) high computational cost and data overhead, especially for free archives that provide images with a high temporal resolution, 2) the inversion results are affected by low-quality images and temporal decorrelation, and 3) false displacements may occur especially for targets with uniform albedo and apparent topographic fluctuations (e.g., dunes and glaciers). In response to these limitations, this work is strongly motivated by the availability of the free Landsat-8 and Sentinel-2 archives, which offer data with enhanced radiometric, spatial resolutions, co-registration, and orthorectification accuracies. Thus, the main objective of this work is to introduce new quantitative methods for measuring dune velocity using these free archives with high spatiotemporal resolution and reliability based on optical image matching.

The thesis is divided into three main parts : In part one, the improved optical image matching selection and inversion (OPTSI) algorithm is introduced to monitor the temporal evolution of dune movement in the Northwest Sand Sea of North Sinai (NSSS). This method is considered a small baseline subset (SBAS) simulation used extensively in the synthetic interferometric aperture radar (InSAR) domain. The methodological workflow of the OPTSI algorithm includes two main steps : (1) defining the baselines of the optical image matching and weighting these baselines according to their respective contribution to the uncertainty of the matching measurements, and (2) applying selection criteria that limit the baseline thresholds, especially the solar angles disparities while maintaining the connectivity of the network to reduce the oscillation of the singular value decomposition (SVD) inversion. To test the relationship between the baselines and the uncertainties, approximately 576 matching measurements are established over stable targets. The results show that the baselines can be weighted as follows : sun azimuth difference, sun elevation difference, temporal baseline, and spatial baseline. The OPTSI algorithm significantly reduces uncertainty on average up to 25% and improves spatial coverage on average up to 15% with low data overhead, especially for free image archives.


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Page publiée le 20 décembre 2022