IRDR DATA session Meeting

Date:2013-11-14Avenue: RADI Station, Sanya, ChinaTime: 9:00-12:00

Correlation analysis between disaster events and different data types -flood as showcase

Zhang Hongyue

Sanya,China 2013-11-14

Contents

Targets of this research Investigation of taxonomy of natural disasters Analysis of information requirement of disaster Correlation analysis between disaster event and data Knowledge base construction Concept model building Flood showcase Outlook

Targets of this research

Heterogeneity

•Lifespan: rapid-onset or slow-onset•extent of affected area : local ,regional ,global•Incentives: weather, hydrology, geology

Uncertainty •regular or irregular,•Seasonal or unseasonalProfound impact …...

Multi-sourceUnstructuredDistributed Large amountMultidisciplinary……..

Natural Hazards/ Disasters Disaster Related Data

How to link

targets:To study the mechanism for connecting exiting data to enable simple and faster discovery and access. To research on a unified data query and retrieval method by attributes of disaster events in response to research need to past disaster events.

Investigation:

Domestic

Four category

Five category

Seven category

Sect oral classification

the Eleventh Five-Year Plan

GB/T 28921-2012

others

International

EM-DAT

NatCatSERVICE

GLIDE

GRIP

et al

Comparison and analysis

Taxonomy of natural disaster for data-oriented management

Map and integration

Taxonomy of Natural Disasters

Information requirement analysis

topography, geology, geomorphology, soils, hydrology, land use,

vegetation etc.

infrastructure, settlements, population,

socioeconomic data etc

location, frequency, magnitude etc.

hospitals, fire brigades, police

stations, warehouses etc

environmentdisastrous

phenomenaDestroyed elements

Emergency relief

resources

In general the following types of Information are required:

Meteorological factorsHydrological factorsGeological factorsExposure factors

Other environment factors

Induce

Disaster information(time, location, strength)Response mechanism

Relief organizationremedy

Damage assessment( population,

architecture, livestock, crops, infrastructure）

Reconstruction planEt al.

Pre-disaster：Early warning and

preparedness

Disaster happening：

Relief and Rescue

Post-disaster：Damage assessment and

recovery

Domain dataMeasure data

Remote sensing dataTopographic data

Et al;

Basic geographic dataTopographic data

Remote sensing dataPopulation and

transportation data

Statistical yearbookGovernment report

Aerial dataEt al

Knowledge inference of disaster event process

Correlation analysis: Knowledge inference

Correlation analysis: Data requirement

Remote sensing dataPlatform: Spatial resolution Temporal resolution Sensor: Spectral resolution Radiometric resolutionMeteorological datatemperature, rainfall, wind, barometric pressure, relative humidity et al.Hydrological dataWater level & water depth & peak flow et al.Geophysical dataTectonic movementSurface deformationSocio-economic dataGovernment reportStatistical yearbookEt al.

Disaster event-time ,location ,magnitude

hazards-hydrological& meteorological& geophysical& topographical elements

Damaged features- socio-economic elements& infrastructure& transportation et al.

Recovery features- transportation & building et al.

MonitorRecord describe

Knowledge collection- Content of each data type

Meteorological dataA - aerological dataB - surface meteorological dataC - meteorological radiation dataD - marine meteorological dataE - agro-meteorological dataF - cryosphere dataG - atmospheric chemistry and atmospheric physics dataH - hydrological and meteorological dataI - terrestrial physics dataJ - analysis dataK - meteorological disaster informationL - history and alternate dataM - soil and vegetation dataS - radar dataT - satellite data

Hydrological elementsEvaporation and evaporation assisted data Water level Flow rate (water)SedimentWater temperature Ice Tides station properties Other

Temporal informationExtracted value date valuemid-valuemonthly values annual valuethe measured and survey values the constant rate valuebasic information data instructions other values

Geophysical dataGravity data, Magnetic data, Deep seismic reflection data, Broadband seismic data, Stress measurement data

Hydrological dataHydrological stationswater level stationsrainfall stationsevaporation stationsGroundwater Monitoring Stationwater quality stationsmoisture stationswater flow stationswater boundary datawater body datawater resources partition data water function zoning dataflood data

Airborne remote sensingAviation ScanningAerial PhotographyMicrowave radar imaging

Remote sensing satellite Terrestrial ObservationLandsat seriesSPOT seriesCanada Radarsat CBERSIkonosQuickbirdothers

Marine SatelliteOcean color satelliteOcean topography SatelliteOcean dynamic environment satellites

Meteorological satelliteLEO satellitesAmerican-TIROSAmerican-NIMBUSAmerican-ESSAAmerican-NOAAChina-FY1High-orbit satellitesAmerican—SMS/GOESJapan-GMSRussia-ELECTRO GOMS N1China-FY2

Reconnaissance satellitesCommunications satelliteinternational Communications satellitedomestic Communications satelliteregional Communications satellite

Navigation SatelliteGPSGLONASSGalileo Beidou

Ground measure dataVegetationsoil remote sensingwater environment remote sensingAtmospheric remote sensing

Spaceborne rem

ote sensing

Earth Observation dataThere is a possibility of linking Earth Observation and Communication and Navigation Satellites to develop global information infrastructure which could offer viable solutions to many of the problems related to disaster management; but it can be enhanced only with international cooperation.

socio-economic data Statistical yearbookGovernment reportsDisaster relief planDisaster report Economic and social development reportPost-disaster reconstruction plan

damaged featuresPopulation statisticsGDPBuildingCrop LivestockInfrastructureSchoolsPipeline RoadOthers

emergency and relief informationEmergency agenciesRelief suppliesRelief planEmergency rescue routesHospitalsOther materials

Reconstruction informationReconstruction planAnnual reportEtc

Socio-econom

ic inform

ation

Disater event

11

N N

Code

Disaster Type

Time

Place

Disaster Grade

Hazard Factor

Monitor

Center Point

Monitoring Stations

M M

AbbreviationStationNumber

Location

N N

Monitor

M M

SatelliteSatelliteName

Oribit Altitude

Sensor

Inclination

Orbit Period

SensorName

Scan Amplitude

Band Number

Repeat Circle

Local Time

N N

carry

1 1

Abbreviation

MM

Affected Areas

influent

NN Administration Code

GDP

Collapsed Houses

Area

Casualties Population

Damaged Crop Area Livestock

Casualties

Orbit Type

N N

containWave Band

Spectrum Range

Spetrum Resolution

SpatialResolutionwavaBandName

Cause

M M

Annual ReportRecord

Socio-economic information

Meteorological & hydrological & Geophysical information, etc

Remote sensing data information

Concept modelC

once

pt m

odel

of d

isas

ter

even

t and

da

ta

relationship

association

Time & location& hydrological features Hydrological stations

Time & location& meteorological features Meteorological stations

Time & location& observation requirements Sensor & satellite

Time & location& disaster-loss features

Socio-economic data

Time & location& Geological features Geophysical elements

match

Search for

match

match

Search for Socio-economic elements access

Geophysical dataaccess

Search for

Search for

Hydrological elements

Meteorological elements

Search for Remote sensing Application

access

access

access

Hydrological data

meteorological data

Remote sensing data

Mai

n co

ncep

t an

d re

latio

nshi

p of

the

infe

renc

e pr

oces

s

Inference steps

Flood showcase

Flood event information

Exposure Rainfall hazard Water hazard Damage information Respond information

Happen timePlace of occurrence

Population, GDP, farmland area, urban land, woodland, grass land, water land ,other land

Precipitation Duration, amount and coverage

Hydrological station name, river basin name, river stage and discharge ,historical highest level, warning level

Affected area, affected population, casualties, houses destroyed, housed damaged, direct economic loss, number of industrial and mining enterprises affected, number of infrastructure affected, number of transport and communication facilities affected,

National relief, input of flood resistance supplies, Number of Flood resistance people

Related KnowledgeTypes of flood: river floods, flash floods, dam-break floods or coastal floodsDifferent characteristics of flood: time of occurrence, the magnitude, frequency, duration, flow velocity and the areal extensionFactors: the intensity and duration of rainfall, snowmelt, deforestation, land use practices, sedimentation in riverbeds, and natural or man made obstructions.Parameters: depth of water during flood, the duration of flood, the flow velocity, the rate of rise and decline, and the frequency of occurrence. Required information: Time information: time of occurrence, Rainfall duration， flood duration, submerged durationSpatial information : place of occurrence, submerged area and scopeMeteorological information : rainfall(precipitation, levels)Hydrological information: water situation, river stage, discharge, flow velocity, water temperature, peak water level, peak flowSocial and economic information: injuries and deaths, Collapsed buildings; Livestock casualties; crop lossesNatural and environmental effects

Related information analysis of Flood event

Flood showcase: spatial and temporal resolution requirement of flood detection

Application Phase Threshold OptimumLand use post-flood pre-flood 30 meter (MSI) 4-5 meter (MSI)Infrastructure status post-flood pre-flood 5 meter (pan-vis) <= 1 meter (pan-vis)Vegetation post-flood pre-flood <= 250 meter (M/HSI) <= 30 meter (M/HSI)Soil Moisture pre-flood 1 km 100 meterSnow Pack pre-flood 1 km 100 meterDEM (vertical) post-flood pre-flood 1-3 meter (INSAR/pan-vis) 0.10-0.15 meterFlood development and flood peak during flood post-flood <= 30 meter (SAR/MSI/ vis-pan/IR) <= 5 meter

Damage assessment (incl. feedback/lessons learned)

post flood 2-5 meter (MSI/pan-vis/ SAR) 0.3 meter

Bathymetry (near-shore) < 1 km (SAR/MSI) 90 meter

Spatial Resolution Requirements (by application

MSI = multi-spectral imagery (2 to 50 bands) HSI = hyper-spectral imagery (> 50 bands) pan-vis = panchromatic visible imagery SAR = synthetic aperture radar INSAR = interferometric SAR

Application Image refresh rate(Threshold/Optimum)

Image delivery time(Threshold/Optimum)

Infrastructure status 1-3 yrs / 6 months monthsLand use 1-3 yrs / 6 months monthsVegetation 3 months / 1 month monthsSoil Moisture 1 week/daily 1 daySnow Pack 2 month/1 week 1 dayDEM pre- and post-flood 1-3 yrs / months monthsFlood development, Flood peak, 24-hr from tasking to delivery

hours-days (function of drainage basin) hours-days (function of drainage basin) /

Damage assessment n/a 2-3 days / < 1 dayBathymetry pre- and post-flood 1-3 yrs / months months

Temporal resolution requirements (by application)

Knowledge base: Concept and property

Next step is to complete the concept and property ontology, further to build relationship between flood and data in order to realize the inference process

Yearly indicesObservation period (day)

NOAA0.5

Landsat TM16

Spot26

ERS-1/2 JERS-144

Radarsat3-4

Real-time aviation

Spatial resolution(meters) 1100 30 20 30 18 10-100Imaging width 80Weather capability Submerged area Submerged water depth Last time Flooded area bottom Working condition monitoring Disaster assessment

Suitability of remote sensing data to flood monitoring

WaveLength Wave band Application Sensor examplesVisible 0.4-0.7mm Vegetation mapping SPOT; Landsat TM

Assessment of building AVHRR; MODIS; IKONOSPopulation density IKONOS; MODISDigital elevation models ASTER; PRISM

NIR 0.7-1.0mm Vegetation mapping SPOT; Landsat TM; AVHRR; MODISFlood mapping MODIS

SWIR 0.7-3.0mm Water vapor AIRSThermal infrared 3.0-14mm Active fire detection MODIS

Fire slash mapping MODIShotspots MODIS; AVHRRVolcanic activity Hyperion

Microwave (radar) 0.1-100cm Earth deformation and ground motion Radarsat SAR; PALSARrainfall Meteosat; Microwave Imager (carried by TRMM)Streamflow AMSR-EFlood mapping and forecasting AMSR-ESurface wind QuikScat RardarThree-dimensional storm structure (carried by TRMM)

Application of different wavebands on disaster management

Knowledge base : remote sensing data for flood detection

Outlook

Disaster phenomenon is the combined effect of the natural environment on human kind, the related elements are complex and intricate. The related information almost involves all kind of data, so it is urgent to link open data for disaster research which call for cooperation between multi-discipline agencies.

I need help from field expert for the knowledge base building and inference rules of hydrological data & meteorological data as well as socio-economic data.

Also welcome suggestions on the inference ontology construction .