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Application of remote sensing and geographical information system for the study of mass movements in Lebanon

Abstract : Among the various natural hazards, mass movements (MM) are probably the most damaging to the natural and human environment in the Mediterranean countries, including Lebanon which represents a good case study of mountainous landscape. Although affecting vast areas in the country, the phenomenon was not studied at regional scale, and related maps are still lacking. Therefore, this research deals with the use of remote sensing and geographic information system (GIS) techniques in studying MM in Lebanon. In this context, the first part reviews existing knowledge on the topics of mass movements (MM) specifically in the Mediterranean region, and defines research gaps. It exposes the diverse types of MM, their magnitudes, the causative agents and their bad consequences. It clarifies confusions related to MM-terms (hazard, susceptibility, risk, etc.), and compares the efficiencies of the most used methods for MM susceptibility/hazard zonation. It includes also a statement on remote sensing and GIS benefits and constraints in mass movement studies, pointing out possible ways of research. The second part is dedicated to the detailed description of the study area "the Mediteranean slopes of central to north Lebanon" within Lebanon. Physical/morphodynamic and socioeconomic characteristics of the area are exposed, as well as the natural hazards, MM events, their socio-economic impacts and mitigation measures. All previous studies about MM hazard in Lebanon are reviewed. The studied area, extending from the Mediterranean coast to around 3000 m elevation, covers ~36% of the total area of Lebanon. It represents the geoenvironmental diversity of this country in terms of geology, soil, hydrography, land cover and climate. It is characterized by problematic human activities (e.g., chaotic urban expansion, artificial recharge of groundwater, overgrazing, forest fire) enhancing environmental decline and inducing MM, with minimal government control. The third part compares the applicability of different satellite sensors (Landsat TM, IRS, SPOT4) and preferred image processing techniques (False Color Composite "FCC", Pansharpen, Principal component analysis "PCA", Anaglyph) for the mapping of MM recognized as landslides, rock/debris falls and earth flows. Results from the imagery have been validated by field surveys and analysis of IKONOS imagery (1 m) acquired in some locations witnessing major MM during long periods. Then, levels of accuracies of detected MM from satellite imageries were plotted. This study has demonstrated that the anaglyph produced from the two panchromatic stereo-pairs SPOT4 images remains the most effective tool setting the needed 3-D properties for visual interpretation and showing maximum accuracy of 69%. The PCA pan-sharpen Landsat TM-IRS image gave better results in detecting MM, among other processing techniques, with maximum accuracy level of 62%. The errors in interpretation fluctuate not only according to the processing technique, but also due to the difference in MM type. They are minimal once 3D anaglyph SPOT4 is considered, varying between 31% (landslides), 36% (rock and debris falls) and reaching 46% in the case of earth and debris flows. The fourth part explores relationships between MM occurrence and different factor terrain parameters. Parameters expressed by: 1- preconditioning factors, like: elevation, slope gradient, slope aspect, slope curvature, lithology, proximity to fault line, karst type, distance to quarries, soil type, distance to drainage line, distance to water sources, land cover/use, and proximity to roads, and 2- triggering MM factors, like: rainfall quantity, seismic events,floods and forest fires, were correlated with MM using GIS-approaches. This study indicates, depending on bivariate remote sensing and GIS statistical correlations (Kendall Tau-b correlation), that lithology is the most influencing on MM occurrence, having the highest correlation with other parameters (i.e. 7 times correlated at 1% level of significance and 3 times at 5%). It also shows that statistical correlations to mass movements exist best between parameters at the following decreasing order of importance: soil type/distance to water sources (acting similarly on MM occurrence), karst/distance to quarries/land cover-use, proximity to faults, slope gradient/proximity to roads/floods, seismic events, elevation/slope aspect/forest fires. These correlations were verified and checked through field observations and explained using univariate statistical correlations. Therefore, they could be extrapolated to other Mediterranean countries having similar geoenvironmental conditions. The fifth part proposes a mathematical decision making method - Valuing Analytical Bi- Univariate (VABU) that considers two-level weights for mapping MM susceptibility/hazard (1:50,000 cartographic scale) within the study area. The reliability of this method is examined through field surveys and depending on a GIS comparison with other statistical methods - Valuing accumulation Area (VAA) (depending on one weight level) and Information Value (InfoVal) (requiring detailed measurements of MM areas). Three susceptibility maps were derived using preconditioning parameters, while hazard maps were produced from triggering ones. The coincidence values of overlapping susceptibility maps were found to be equal to 47.5% (VABU/VAA), 54% (VABU/InfoVal) and 38% (VAA/InfoVal). The agreement between hazard maps showed closer values than susceptibility ones, oscillating between 36.5% (VAA/InfoVal), 39% (VABU/VAA), and 44 % (VABU/InfoVal). Field verification indicates that the total precision of the produced susceptibility maps ranges from 52.5% (VAA method), 67.5% (InfoVal method) and 77.5% (VABU method). This demonstrates the efficiency of our method, which consequently can be adopted for predictive mapping of MM susceptibility/hazard in other areas in Lebanon and may be easily extrapolated using the functional capacities of GIS. The sixth part predicts the geographic distribution and volume of block falls (m3) across the study area using GIS decision-tree modelling. Such mapping was unavailable in Lebanon, but also in many other countries putting effort on landslide research rather than other types of MM. Several decision-tree models were developed using (1) all terrain parameters, (2) topographic parameters only, (3) geologic parameters only, and adopting various processing techniques (pruned and unpruned trees). The best regression tree model combined all parameters and explained 80% of the variability in field blocks falls' measurements. The unpruned model built using four geological parameters (lithology, soil type, proximity to fault line, and karst type) seems also interesting, classifying 68% of block falls and referring to a small amount of input data (4 parameters). The produced predictive quantitative block falls' map at 1:50,000 appears extremely useful for decision-making, helping adoption of mitigation measures to reduce the occurrence of harmful block falls. The seventh part focuses on monitoring MM activity through integrating space borne radar data and Global Positioning System (GPS) techniques. ERS radar imageries were processed using InSAR and permanent scatters techniques. The analysis showed difficulties in detecting ground deformations due to MM. Nevertheless, the analysis is still in its preliminary stage and future planned work will take into consideration other manipulating procedures for detecting the displacements. On the other hand, a GPS installation in Hammana area; one of the Lebanese villages lying in a major landslide, was conducted. Two campaigns were raised, but results are still lacking since there is not enough data accumulation. More observations are still needed to build up a comprehensive picture on the direction and velocity of the movement.
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Submitted on : Friday, March 15, 2013 - 11:25:12 AM
Last modification on : Tuesday, November 16, 2021 - 4:50:43 AM
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  • HAL Id : tel-00800759, version 1


Chadi Abdallah. Application of remote sensing and geographical information system for the study of mass movements in Lebanon. Tectonics. Université Pierre et Marie Curie - Paris VI, 2007. English. ⟨NNT : 2007PA066085⟩. ⟨tel-00800759⟩



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