Community Based Disaster Risk Reduction 12.Mai 2009
Remote Sensing and GeoInformation System (GIS) Contribution to the Detection of Areas Prone to Natural Disasters in Asia
Prof. Dr.habil.Barbara Theilen-Willige Prof.Dr.habil.Barbara Theilen-Willige TU Berlin, Institute of Applied Geosciences, Germany E-mail:
[email protected]
Interactive Data Availability in the WWW - a planning base for natural hazard mitigation measurements
Near real time and basic environmental data availability are crucial for the work of envolved institutions. Information of natural hazards have to be available as actual as possible in a WEB-GIS that can be downloaded according to special needs.
Weather
Flooding
Fire
Lightning
Landslides
GIS-Architecture for Datamining Satellite Data LANDSAT-Imageries
Layers
Hazards
(15 m Resolution)
Shapefiles related to Infrastructure
Shapefiles related to Natural Hazards
Actual and Past Natural Hazards
RGB Image
Industrial Facilities
Flooding
NDVI Image
Airports
Earthquake Events >Magnitude 3
Thermal-Band
Railroads
Classification
Highways
SRTM derived Maps (90 – 50 m Resolution) Slope > 15 °
Bridges Dams
Fault Zones Landslide Events in the Past Flooded Areas in the Past
Electricity
Tsunami and Storm Surge prone Areas
………
Volcanic Activity
Height Level
Sea Level Rise prone Areas
Curvature Hillshade
Earthquakes Fire Lightning Landslides Volcanism High Precipitation, Snow Desertification
Vertical and horizontal movements
Drainage Flow Accumulation Open Source: Basic Data Base : Global Land Cover Facility, University of Maryland, USA: http://glcfapp.umiacs.umd.edu:8080/es di/index.jsp CGIAR Consortium for Spatial Information (CGIAR-CSI): http://srtm.csi.cgiar.org/SELECTION/in putCoord.asp Digital Image processing: BAGF
Can be digitized based on LANDSAT and Google Earth Data, if necessary, so the data ownership is no problem Open Source: http://biogeo.berkeley.edu/bgm/gdat a.php
Data can be partly derived by published literature, maps and open source data. However, data ownership has to be considered.
Fast Track information is needed. Data sponsering is needed.
Vulnerability Maps
Free Satellite and GIS Data
http://www.pdc.org/iweb/pdchome.html
http://nhss.cr.usgs.gov/aboutUs.htm
http://nhss.cr.usgs.gov/aboutUs.htm
Evaluations of Shuttle Radar Topography Mission (SRTM) Digital Elevation Data (DEM) and ASTER GDEM Data
Examples: Detection of steep slopes prone to landslides and soil erosion
Why ?
Detection of lowlands, basins, coastal areas prone to flooding
Detection of subsurface structures
Remote Sensing and GIS Contribution to the Detection of Causal Factors Influencing Earthquake Ground Motion and Secondary Effects in Northern Pakistan
Earthquake Hazardous Areas :
Due to soil amplification because of local site conditions
Due to liquefaction and compaction
Due to landslides Due to active fault zones and aseismic movements in the subsurface(horizontal and vertical)
Earthquakes in Northern Pakistan –
Data: USGS, ISC
Evaluations of Macroseismic Maps for the Detection of Local Site Conditions
http://asc-india.org/maps/hazard/haz-pakistan.htm
SCHNEIDER,1992, 2004, CRANSWICK et al,1990
Influence of topography and surface-near geology
According to GSHAP data, Pakistan lies in a region with moderate to high seismic hazard; the greatest hazard is in parts of the North West Frontier Province (NWFP), in the vicinity of Quetta and along the border with Iran. Historically, earthquakes in the M7.0 range have been experienced in Balochistan and along the border with Afghanistan and India.
Influence of Local Site Conditions decreasing intensity from the epicenter
It has been observed that at many sites surface motions are influenced primarily by top 20-30 m of soil. Therefore the subsurface geology has a role to play in earthquake awareness. In case of stronger earthquakes it is important not only to use circles when searching for affected areas.
Earthquakes in the Mingaora area
Information of fault zones and earthquake parameters such as depth, fault plane solution, mechanisms, etc. and local site conditions are necessary.
Surficial geologic properties influencing shaking intensities
Local site variations
Earthquake source
Soil Amplification in Relation to Surficial Geologic Properties Degree of shaking intensity
Groundwater table
Reuter, Klengel & Pasek, 1992 Perspective view of a Google Earth scene
Local site conditions influencing shaking intensity such as wetlands, moor areas, varying grain sizes and thickness of sediments, groundwater tables, etc.
The detection and mapping of recent and ancient wetland and river meander areas is important when dealing with soil amplification effects. Mingora
Wald et al. (2004) first, and Wald and Allen (2007) describe a methodology for deriving maps of seismic site conditions using topographic slope as a proxy. Vs30 measurements (the average shear-velocity down to 30 m) are correlated against topographic slope to develop two sets of coefficients for deriving Vs30: one for active tectonic regions that possess dynamic topographic relief, and one for stable continental regions where changes in topography are more subdued. They also compared topographic slope-based Vs30 maps to existing site condition maps based on geology and observed Vs30 measurements, where they were available, and found favorable results. http://earthquake.usgs.gov/hazards/apps/vs30/
http://earthquake.usgs.gov/hazards/apps/vs30/custom.php
Wald and Allen (2007) note significant limitations to this simplified approach. Users should be aware of these limitations and should exercise caution in using this approach for anything other than regional scale Vs30-based site amplification estimates. As always, site-specific Vs30 values should be used at finer scales or at particular locations.
Vs30 data interpolation map of Northern Pakistan
Lineament Analysisfor the Detection of Subsurface Structures
LANDSAT NDVIsatellite imageries for fault zone detection
Lineaments
Special attention is focused on the mapping of structural features visible on satellite imageries in order to investigate the tectonic setting and to detect surface traces of fracture and fault zones that might influence the damage intensity in case of stronger earthquakes. Linear features visible on remote sensing - data are mapped as lineaments.
Linear features visible on remote sensing data
Morphometric Maps MODIS
SRTM -DEM
)
ASTER (TIR) ASTER (SWIR
SRTM DEM (90 m) ASTER DEM (30 m)
Shuttle Radar Topography Mission (SRTM)
ASTER (VNIR)
ENVI
MISR MOPITT CERES
DEM
http://geog.hkbu.edu.hk/geog3600/Lect09.pdf
Minimum Curv.
Slope
Maximum Curvature
Systematic, Standardized GIS-Approach for The Elaboration of Basic Earthquake Susceptibility Maps (no cost approach)
Database and Methods SRTM, ASTER-DEM Extraction of
Satellite Imageries Tsunami Catalogues Digital Image Proc. Earthquake Catalogues
Morphometric Maps
Causal Factors
Causal Factors
Height Level Maps
Lowest Area: 0 - 20 m - ?
Vegetation
Slope Gradient Maps
Slope Gradient: < 10 °
Landuse
Causal Factors
Source
Geologic, Seismotectonic Bathymetric Maps Causal Factors
Depth
Unconsolidated Sedimentary Covers
Distance
Tectonic Pattern Lithologic Units
Curvature Maps
Drainage Maps
Curvature Maps: Lowest Curvature
Infrastructure
Intensity/ Magnitude
Uplift, Subsidence
Drainage Maps: Open river mouths, deltas
Watersheds
Watersheds
Flow Accumulation, Length
Highest Flow Accumulation
3D-Structure
………
……… Open for Further Data
…….
The Weighted Overlay-Method in ArcGIS for the elaboration of susceptibility maps
Factors influencing earthquake shock intensity at the surface such as outcropping unconsolidated sedimentary covers, surface morphology (curvature, slope gradient, morphologic setting), surfacenear faults, etc, are displayed as layers in ArcGIS, converted into .grid-formats and weighted.
Minimum Curvature Slope Gradient < 15° Height Level < 920 m Outcrop of Quatern.Sediments
The percentage of influence of a factor is changing due to seasonal and climatic reasons. A stronger earthquake during a wet season will probably cause more secondary effects than during a dry season.
+ Further factors have to be included such as active faults, uplift / subsidence,……..
The susceptibility is calculated by adding every layer with a weighted % influence and by summarizing all layers. The result can be divided into susceptibility classes and presented as a susceptibility map.
Merging assumed shear velocity data from USGS with the soil amplification susceptibility map according to the weighted overlay-approach
When evaluating the different datasets the highest amount of damage can be assumed during a stronger earthquake in the dark-red areas of the soilamplification-susceptibility map (= assumed highest susceptibility to soil amplification due to the accumulation of factors influencing ground motion) and in areas underlain by the active fault zones. Merging the Weighted-Overlay susceptibility map with the Vs-contourlines derived by Wald & Allen (2007) there is a coincidence of areas assumed to be more susceptible to soil amplification according to the weighted overlay approach with areas of estimated lower shear wave velocities (Vs < 300).
Overlay of the Susceptibility Map to Soil Amplification and Vs30-Contour Lines The estimated Vs30 values < 300 m/sec coincide with the highest degree of susceptibility to soil amplification.
Merging assumed shear velocity data from USGS with the soil amplification susceptibility map according to the weighted overlay-approach
Soil amplification susceptibility map according to the weighted overlay-approach
Flooding Hazards in the Bahraich District in India
Ghaghara River in Northern India
Bahraich District in Uttar Pradesh in India LANDSAT TM Perspective 3D View
N
Bahraich
Earthquakes
Height Level Map
Flooding Susceptibility Map
High
Drainage
Linear Features
Overlay of Height Contour Lines on the LANDSAT Image - in order to find suited places for flood shalters
Flooding Hazards in Myanmar
http://earthobservatory.nasa.gov/NaturalHazards/Archive/Apr2008/nargis_trm m_2008119_lrg.jpg
Cyclone Nargis
http://www.spiegel.de/wissenschaft/natur/0,1518,551981,00.html
The first cyclone of the 2008 season in the northern Indian Ocean was a devastating one for Burma (Myanmar). According to reports from Accuweather.com, Cyclone Nargis made landfall with sustained winds of 130 mph and gusts of 150-160 mph, which is the equivalent of a strong Category 3 or minimal Category 4 hurricane.. This pair of images from the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA’s Terra satellite use a combination of visible and infrared light to make floodwaters obvious. Water is blue or nearly black, vegetation is bright green, bare ground is tan, and clouds are white or light blue. On April 15 (top), rivers and lakes are sharply defined against a backdrop of vegetation and fallow agricultural land. The Irrawaddy River flows south through the lefthand side of the image, splitting into numerous distributaries known as the Mouths of the Irrawaddy. The wetlands near the shore are a deep blue green. Cyclone Nargis came ashore across the Mouths of the Irrawaddy and followed the coastline northeast. The entire coastal plain is flooded in the May 5 image (bottom). The fallow agricultural areas appear to have been especially hard hit. For example, Yangôn (population over 4 million) is almost completely surrounded by floods. Several large cities (population 100,000–500,000) are in the affected area. Muddy runoff colors the Gulf of Martaban turquoise. The high-resolution image provided above is at MODIS’ maximum spatial resolution (level of detail) of 250 meters per pixel. The MODIS Rapid Response Team provides twice-daily images of the region in additional resolutions and formats, including photo-like natural color. http://earthobservatory.nasa.gov/Newsroom/NewImages/images.php3?img_id=18019
Flooded Areas in May 2008
http://www.spiegel.de/wissenschaft/natur/0,1518,551981,00.html
This scene captured by the Ikonos satellite on May 7, 2008, illustrates the complete devastation Cyclone Nargis brought to Burma (Myanmar) when it barreled ashore on May 3. This tiny village was located about 27 kilometers (16 miles) south of Yangon (Rangoon), the country’s largest city. In the lower image, taken on May 3, 2002, trees and buildings line a single street, which is surrounded by fields of crops, probably rice. After the disaster, the trees and buildings are completely gone, replaced by messy piles of rubble. The fields are largely submerged under brown and green floodwater. The tiny canal that ran alongside the village on the left side of the image has disappeared into a wide, brown river. A faint curving line outlines the canal’s banks within the new river.
http://earthobservatory.nasa.gov/NaturalHazards/natural_hazards_v2.php3?img_id=14826
Decrease of Vegetation
LANDSAT MSS 1978
N LANDSAT ETM 2000
NDVI Vegetation Index
Red- almost vital vegetation
Landuse Change between 1978 and 2000
LANDSAT MSS 1978
http://news.bbc.co.uk/2/hi/science/nature/7385315.stm
LANDSAT ETM 2000
Large-scale conversion of mangroves into shrimp and fish farms were among the main destructive drivers. During the 1990s, about 2,000 hectares of mangrove forest were lost each year, which is about 0.3% being lost annually.
Die Mangrovenwälder, die als Puffer zwischen Wellen und Stürmen und bewohnten Gebieten gedient haben, wurden in ihrem Bestand deutlich dezimiert.
LANDSAT ETM – RGB 2,4,1 – Aufnahme (3. Mai 2000) vom Gebiet des Golfes von Martaban / Andamanisches Meer
Areas above 10 m height are shown in red.
Areas above 10 m could be recommended for the position an d construction of low cost flood shelters
Surface run-off of water
Position of potential shelter places should be above 10 m height and situated on morphologic watersheds
Potential sites (red points) for the construction of low cost flood and storm shelters
LANDSAT ETM scene
Potential sites (red points) for the construction of low cost flood and storm shelters
2 km radius around potential shelter places 2 km Radius um die möglichen Shelter-Plätze und Erreichbarkeit über Straßen und Wege
Distance of the proposed shelter places to each other
Conversion of in ArcGIS 9.3 created shapefiles into Google Earth-kml-data format
Shelter points as derived from ArcGIS based evaluations of LANDSAT- and SRTM -Data can be visualized in Google Earth. Using the high resolution satellite data of Google Earth the shelter points can now be placed more precisely.
Community Based Disaster Risk Reduction
New Methods
Research
Preparedness and Mitigation
Education
Operational Thank you for your Attention
Measurements Management
