Section: Earth Sciences through Earth Observation Natural Hazard Assessment in Western Saudi Arabia using Remote Sensing and GIS Methods Barbara Theilen-Willige and Helmut Wenzel Prof.Dr.habil. Barbara Theilen-Willige, Prof. Dr. Helmut Wenzel Wenzel Consulting Engineers GmbH Technische Universität (TU) Berlin Institute of Applied Geosciences, Sekr. BH 3-2 Hofstattgasse 22-21,1180 Vienna, Austria Ernst-Reuter-Platz 1, D-10587 Berlin Germany E-Mail: helmut.wenzel@wenzel-consult.com E-mail: barbara.theilen-willige@campus.tu-berlin.de Tel. +43 664 3302395
Natural Hazard Assessment in Western Saudi Arabia using Remote Sensing and GIS Methods 1. Introduction Overview of the different Natural Hazards 2. Methods and Workflow Digital Image Processing of Satellite Data Digital Processing of Digital Elevation Data 3. Results of the GIS integrated Evaluations of Satellite Data Weighted Overlay for the Detection of Areas Susceptible to Slope Failure Digital Image Processing of Satellite Data and Evaluations 4. Conclusions
Overview of the different Natural Hazards Western Saudi Arabia is prone to different natural hazards such as earthquakes, tsunamis and volcanic hazards, as well as flash floods after heavy rainfalls. Slope failure, especially rock fall, is a common phenomenon in the mountainous regions. Shifting sand dunes and dust storms are a serious natural hazard being faced. An inventory of past geohazards is one of the main prerequisites for an objective hazard assessment. Such a hazard assessment requires a multi-source, systematic record. The ability to undertake the assessment, monitoring and modeling can be improved to a considerable extent through the current advances in remote sensing and GIS technology. This is demonstrated in the scope of this research by the following examples: • Flooding: Detection of areas prone to flash floods • Seismic hazards: Mapping of traces fault and fracture zones and of structural features based on remote sensing data • Volcanismn: Inventory of volcanic features • Tsunami hazards: Detection of areas prone to tsunami flooding
1. Introduction: Overview of the different Natural Hazards Natural Hazards in W-Saudi Arabia Focus of Research based on Remote Sensing and GIS Methods • Flashfloods • Geodynamic movements due to plate tectonic activity • Earthquakes and earthquake induced secondary effects (mass movements, compaction of soils, tsunami waves) • Volcanism Flash Flood Monitoring • Dust Storms Traces of Volcanism • Slope failure - Change Earth flow, debris flow, gully erosion Detection Traces of geodynamic • Salt Tectonics movements Lineament Analysis • Karst Traces of compression Mapping of fault zones Structural evaluations • Climate Change Mapping of escarpments, increasing intensity of extreme weather events terraces such as flashfloods and dust storms . .
Flooding Erosion (Wind, Water) Drought, Soil and Water Salinizatio n Sedimentation Storms Mass Movements Volcanism Tectonic Movements Mass Movements (slope (Uplift, Subsidence) failure, soil erosion) Vegetation, Landuse and Earthquakes Ecosystem Changes
2. Methods and Workflow Digital Image Processing of GIS integrated Evaluation of Integration and Combination Optical and Radar Satellite Satellite Data of Geodata Data •Extraction of areas with higher • Integration of geophysic, soil moisture •RGB geologic, geomorphologic •Lineament analysis •NDWI-Wasser-Index for soil and pedologic data •Weighted Overlay moisture detection • Digital Elevation Data •Principal Component, (DEM) classifications • Vegetation, land use, •Filter techniques (Morphologic infrastructure Convolution) Multi-source, systematic Record Hazard Assessment Creation of a Data Bank
Workflow for Datamining Evaluation of publications , studies, research reports and Online media research, Topographicdata (Digital documentationsof the Elevation Models), integrationof data of geophysic, geologic, interactive Web-maps geologic maps, soil maps, geomorphologic and ESRI online database (NASA, ESA, etc.) geodetic knowledge Susceptibilitymaps related to the Hazard event database: different naturalhazards: Community based • flash floods • susceptibilty to flash floods disaster information: • heavy rainfall • susceptibilityto soil erosion • newspapers • lightning • Susceptibilityto higher • radio reports • drought periods earthquakeshock • TV • soil salinity • susceptibility to uplift / • dust storms If wished subsidence or horizontal • geodetic data movements • earthquakes, etc. Satellite data base: Information of active • MODIS Comparative evaluation of fault and fracture zones • Landsat satellite data since 1972, or morphodynamic • Aster • for the structural / tectonic processes influencing • Sentinel 1-radar data analysis (lineamentanalysis), • ALOS-PALSAR-radar data the safety of • For the detection of landscape • Sentinel 2 and 3 settlements and changes • High resolution satellite infrastructural facilities imageries
Digital Image Processing of the different Satellite Data Remote Sensing Data Image Processing for GIS integrated Evaluation Tectonic Analysis RGB of Landsat , Sentinel 2, Aster and Landsat 7, Landsat 8 Merging Datasets: Sentinel Radar, OrbView data, False Color Composite, Sentinel 2, ASTER Landsat -Data and Morphometric Maps Image Sharpening High Resolution Satellite Structural Analysis for the Detection of Imageries as OrbView Subsurface Features, Lineament Analysis Principal Component-Analysis Sentinel-1-Radar Data Digital Elevation Model (DEM) Analysis, Deriving of Morphometric Maps SRTM DEM Image Filtering (Morphologic ASTER DEM Convolution) Weighted Overlay of Causal Factors Integration of Geodata into a Data Bank Geophysic, Geologic, Geomorphologic, Pedologic and Meteorologic Data,….. V egetation, Land Use, Infrastructure
REMOTE SENSING AND GIS EVALUATION RESULTS FOR THE DETECTION AND MONITORING OF AREAS PRONE TO NATURAL HAZARDS using the Examples of Flash Floods Documentation of past hazards Detection of areas susceptible for future hazards Contribution to the preparedness and adaption to impacts of climate change
Height level Height < lowest local height level Slope gradient Slope < 10° Extraction of causal / preparatory factors influencing the susceptibility to geohazards Minimum curvature Minimum curvature - Deriving and extracting causal or preparatory factors > 250 from Digital Elevation Data (SRTM, ASTER, ALOS PALSAR- DEM) - Aggregation of Layers in ESRI-Grid-Format Drop raster Drop raster < < 100.000 SusceptibilityMapbased on the Weighted OverlayMethod in ArcGIS based on ASTER DEM Data The resulting maps are divided into susceptibility classes . The susceptibility to soil amplification is classified by values from 0 to 6, whereby the value 6 i s standing for factors influencing the Selection and the highest, assumed susceptibility due to the susceptibility to soil extraction of attributes aggregation of causal / preparatory factors . amplification
Workflow of the Weighted Overlay of Causal / Preparatory Factors influencing the Flooding Susceptibility Drop Raster Slope Degree Curvature =0 < 100.000 < 10° The weighted overlay approach in a GIS can be used for the detection and identification of lowest regional endangered lowland areas susceptible to Flow Accumu- Aspect =(-1) Height Level lation > 1 flooding. Due to the aggregation of the below mentioned, morphologic factors these areas are more susceptible to flooding than the Aggregation of causal, environment in case of flash floods. morphometric factors influencingflooding Based on SRTM, ASTER ALOS PALSAR Digital susceptibilityusing the Elevation (DEM) data the following weighted overlay-tool in ArcGIS morphometric factors are extracted and then aggregated in the weighted overlay tool of Result of the weighted overlay calculation ArcGIS: • Lowest, local height levels The resulting maps are • flat terrain, calculating terrain curvature divided into (curvature values= 0 , calculated in ArcMap, susceptibility classes . The susceptibility to minimum curvature > 250 , calculated in flooding is classified by ENVI) values from 0 to 6 or 7, • slope gradients < 10° whereby the value 6 i s • drop raster < 100.000 and standing for the • high flow accumulation values highest, assumed • aspect = flat (-1) susceptibility due to the aggregation of causal / preparatory factors .
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