Remote Sensing and GeoInformation System (GIS ...

3 downloads 0 Views 12MB Size Report
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.
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