Use Case IIUse Case IIReal-time fire monitoring

Part A: Introduction to the Use Case

2nd User workshopDarmstadt, 10-11 May 2012Darmstadt, 10 11 May 2012

P t H i K t & I i P t i (NOA)Presenters: Haris Kontoes & Ioannis Papoutsis (NOA)

GMES: Global Monitoring for Environment and SecurityBackground - GMES

The objective is to provide Earth Observation = satellite i f ti i t li d d i i kinformation services to policy and decision-makers

EARTH OBSERVATION PUBLIC

Satellite ImagesInformationOBSERVATION

SYSTEMS(satellite images,

PUBLICPOLICIES

(Environment & aerial photographs,

in-situ data)

(Security)Needs

(user driven)

Space AgenciesIn-situ Observing systems Scientific

National Governments and AgenciesEuropean Union Institutions

CommunityEO Value Adding Industry

Inter-Governmental Organisations (IGOs) Non Governmental Organisations (NGOs)

5/10/2012

Background - GMES

Space SystemsSpace SystemsSpace SystemsSpace Systems

Obser ational Net orksObser ational Net orksObser ational Net orksObser ational Net orks

Mission OperationsMission OperationsMission OperationsMission Operations

Observational NetworksObservational NetworksObservational NetworksObservational NetworksData CentersData CentersData CentersData Centers

GMESCore Services es

p.es

p.es

p.es

p.

rererereyyyy ange

ange

ange

ange

ency

Re

ency

Re

ency

Re

ency

Re

arin

ear

ine

arin

ear

ine

osph

eos

phe

osph

eos

phe

ecur

ityec

urity

ecur

ityec

urity

Land

Land

Land

Land

te C

hate

Cha

te C

hate

Cha

Urban AtlasUrban AtlasE

mer

geE

mer

geE

mer

geE

mer

geMMMM

Atm

oA

tmo

Atm

oA

tmo

Se

Se

Se

SeLLLL

Clim

atC

limat

Clim

atC

limatUrban AtlasUrban Atlas

EEEE CCCC

Downstream ServicesDownstream ServicesDownstream ServicesDownstream Services5/10/2012

SAFER - LinkER ERS projects rationale

The projects SAFER - LinkER aims to implement and validate a preoperationalp j p p pversion of the GMES Emergency Response Service, reinforcing the Europeancapacity to respond to such challenges

First priority: Validate an information i f i id i

SHORT-TERMUPGRADES

MID AND LONGER-TERMUPGRADES

SHORT-TERMUPGRADES

MID AND LONGER-TERMUPGRADES

service focusing on rapid mappingduring the response phase

Second priority: Enrich this service

UPGRADES

MID AND

UPGRADES

MID AND Second priority: Enrich this service with a wider set of thematic products

LONGER-TERM

UPGRADES

LONGER-TERM

UPGRADES

In the long term, ERCS provide tangible benefits for all citizens, in Europe andworldwide, in terms of better quality of life, better health, and increased safety.

5/10/2012

Fire monitoring applicationAvailable datasets

NOA hotspot service

• Raw data

Available datasets

Raw data

• Intermediate products

• Hotspots

MSG2 (15 minutes) & MSG1_RSS (5 minutes)

Ready products

• FMM-1 hotspot shapefiles & kml

Derived from MSG/SEVIRI on a 15 minutes basis Available in the Derived from MSG/SEVIRI, on a 15 minutes basis

Derived from MODIS, 2-3 times per day

• FMM-2 burnt area shapefiles & kml

Available in the framework of SAFER

Derived from MODIS, on a daily basis

• Burn Scar Mapping shapefiles

De i ed f om HR and VHR senso s on a seasonal basisDerived from HR and VHR sensors, on a seasonal basis

Auxiliary datasets

• GIS layers: administrative boundaries, road network, places of interest & toponymsy , , p p y

• Digital Elevation Model (DEM)

• CORINE Land Cover raster and vector layers

• Background mosaic, derived from LANDSAT 5 TM images

5/10/2012 5

Dataset examplesFMM-1 MODIS derived hotspotsFMM 1, MODIS derived hotspots

Shapefile & kmlShapefile & kml

Delivered 2-3 times per day

Summer seasons of 2009 2010 Summer seasons of 2009, 2010 & 2011

Spatial resolution 1×1km

5/10/2012 6

Dataset examplesFMM-2 MODIS derived burnt areasFMM 2, MODIS derived burnt areas

Shapefile & kml

A l t d b t Accumulated burnt areas

Daily burnt areas

Summer seasons of 2009 2010 & Summer seasons of 2009, 2010 & 2011

Spatial resolution 250×250m

5/10/2012 7Zip files of the form:

Dataset examplesBSM example in Greece

Rapid Fire Mapping – Greece 2007

BSM example in Greece

5/10/2012

Dataset examplesBSM derived from HR and VHR satellite imagesBSM, derived from HR and VHR satellite images

Shapefile

Delivered seasonally

2007, 2008, 2009, 2010 & 2012

5/10/2012 9High and Very High resolution satellite imagery (2m – 30m)

Dataset examplesBSM example in IoanninaBSM example in Ioannina

Dataset examplesBSM example in CorsicaBSM example in Corsica

Dataset examplesAuxiliary informationAuxiliary information

Contour lineAirfield

Background images

and mosaics from Place of interest

CLC 2000various sensors

GIS layers

(administrative (administrative,

places of interest,

toponyms, etc.)

Background image

Contour lines

derived from DEM

C i L d C Corine Land Cover,

in raster and vector

format, in 3 layers

f d t ilHospital

Administrative

of detail

5/10/2012 12

Administrative boundaries

Fire monitoringThe 2007 wildfiresThe 2007 wildfires

WP7 in TELEIOSRole with respect to the general architectureRole with respect to the general architecture

5/10/2012

Use Case IIObjectiveObjective

D i i l t d lid t f llDesign, implement, and validate a fullyautomatic fire monitoring processing chain, forreal time fire monitoring and rapid mapping,that combines in real-time:

i) Volumes of EO image acquisitions.

ii) Volumes of GMES fire monitoring productsii) Volumes of GMES fire monitoring products.

iii) Models/Algorithms for data exchange and processing

iv) Auxiliary geo-information.

v) Human evidence, in order to draw reliable decisionsand generate highly accurate fire products.

5/10/2012

1st User WorkshopRequirements gatheringRequirements gathering

5/10/2012 16

Use Case IIUse Case IIReal-time fire monitoring

Part B: An insight at the various components

2nd User workshopDarmstadt, 10-11 May 2012Darmstadt, 10 11 May 2012

P t H i K t & I i P t i (NOA)Presenters: Haris Kontoes & Ioannis Papoutsis (NOA)

Real-time Fire Monitoring

Refinement of hotspot serviceRefinement of hotspot service

• Automation of the processing and storage

Processing chain triggering

Systematic archiving of raw data and storage of hotspot products

• Geo-referencing with increased geometric accuracy

• Increased thematic accuracy using a dynamic threshold approach

Product refinement using semantics

Generation of thematic maps using Linked dataGeneration of thematic maps using Linked data

Easy integration of new processing modules using SciQL

TELEIOS system infrastructure - DEMO

5/10/2012 18

Fire monitoring applicationAdvancements – integration of the TELEIOS technologiesAdvancements integration of the TELEIOS technologies

EumetsatEumetsat @ @ 9.59.5°°EastEastFront End: GUIFront End: GUI Map ElementMap Element

Back End: MonetDB / Strabon

•• CorineCorine LandcoverLandcover•• Admin BoundariesAdmin Boundaries•• POIsPOIs

External Sources GeospatialGeospatial

OntologyOntologyWeb accessWeb access

based on Semanticsbased on Semantics

Cataloguing ServiceCataloguing Service& Metadata Creation& Metadata Creation

OntologyOntology based on Semanticsbased on Semantics

LinkedLinked

Data Vault

Linked Linked Geospatial DataGeospatial Data

SSemanticemantictechnologiestechnologies

HotSpotsHotSpots

Raw DataRaw Data• Search & Display• Search for raw & Processing

Processing ChainProcessing Chain((SciQLSciQL based)based)

• Real-time Fire Monitoring• Refinement (Post-Processing) • Linked Data

5/10/2012 19

Fire monitoring service Processing chainProcessing chain

Data Import

• Band Separation• Band Separation• Transformation of raw

imagery to appropriate internal format for

Raw data

processing Product generation

• Exports final products to raster and vector formats

Cropping

• Crops image to the area of interestof interest

Georeferencing ClassificationGeoreferencing

• Georeference input image bands

• Each pixel can be

Classification

• Derive physical indexes through band operations

• Assign each pixel a fire non-• Each pixel can be geographically identified

• Assign each pixel a fire nonfire flag with an associated level of confidence, via index thresholding

Fire monitoring serviceProcessing chain – intermediate productsProcessing chain intermediate products

Data Import

Cropping Georeferencing

Classification

Fire monitoring service ProductsProducts

Rasteraste

Vector

Fire monitoring serviceAlgorithmic improvementsAlgorithmic improvements

Classification approach #1: Fire detection is based on fixed thresholding applied on two spectral bands using simple band thresholding, applied on two spectral bands using simple band algebra.

Classification approach #2: The thresholds are dynamically pp y ycalculated for each new image and for every pixel of the raw imagery.

Step 1: Calculation of latitude and longitude for each pixel using bilinear interpolation.Step 2: Estimation of Solar Zenith Angle per image, per pixel.

Step 3: Definition of new thresholds depending on this angle, with a linear interpolation.

Fire monitoring serviceAlgorithmic improvements – an exampleAlgorithmic improvements an example

Discussion on TELEIOS technologies Benefits from using MonetDBBenefits from using MonetDB

Hide completely the details/complexity of the raw data Hide completely the details/complexity of the raw data format, using the Data Vault

Use higher-level languages stop worrying about how to Use higher-level languages, stop worrying about how to store and manage data, just focus on the actual processing

Express common earth observation operations easily using Express common earth observation operations easily using the purpose-build SciQL instead of using a lengthy C programg

Easily handle massive loads due to advanced processing capabilities of column-store MonetDB p

Allow algorithm execution to be optimized by the DBMS’s query optimizer.query optimizer.

5/10/2012

Discussion on TELEIOS technologies Benefits from using Semantic technologiesBenefits from using Semantic technologies

Refine products by using spatio-temporal queries expressed in tSPARQLstSPARQL:

o Identify and eliminate hotspots occurring in the sea

o Decide for hotspots that are partly located in non-consistent underlying land use

Attribute a variable confidence level according the the spatioo Attribute a variable confidence level according the the spatio-temporal persistence of a hotspot

Express semantic queriesExpress semantic queries

o “Find hotspots within 2 km from a major archeological site”

Use other Linked Data together with fire products to further enhance our products’ value:

G eek go e nment data (geodata go g ) Administ ati e o Greek government data (geodata.gov.gr), Administrative Geography of Greece, Open Street Map, Wikipedia, Gazeteers (e.g. Geonames)

5/10/2012

Discussion on TELEIOS technologies Benefits from using Semantic technologiesBenefits from using Semantic technologies

5/10/2012

Burn Scar Mapping serviceProcessing chain

Geo-

Processing chain

Pre-processing

Input dataHigh & Very high spatial resolution

referencingIdentification of reference control

Cloud maskingspatial resolution reference control

points

Core processingClassification

NBR, ALBEDO, Near-Noise removalRaster to t

Core processing

, O, eaInfrared and NDVI difference indexes

are used

Application of two spatial filters

vector conversion

Attributes Post-processing

Visual refinement

Use of auxiliary GIS

Attributes enrichmentInvolves spatial

Map productionand delivery to Use of auxiliary GIS

layers

poperations with geo-information layers

and delivery to end users

LT51840342011218MOR00

LT5 S t llit

The LEDAPS preprocessing software performs • calibration & atmospheric

The Automated Precise Orthorectification Package (AROP) provides generalLT5 : Satellite

184 : Row034 : Path2011 : Year

pcorrection and additionally

• computes water-land and cloud mask products

Procedure 1

(AROP) provides general image georegistration and orthorectification for Landsat-like data sources.

Procedure 2218 : Day of the YearProcedure 1Procedure 2

Base Images DTM

7 TIF BANDS & *MTF ASCII7 TIF BANDS Base = BAND 5

B1 B2 B3 B4 B5 B6 B7

Metadata File

For each LANDSAT Image

400+LANDSAT-4 & LANDSAT-5Satellite Image Archive

Temporal Coverage:1984-1991 & 1998-2011

1. Orthorectified Landsat Image (*.IMG)2. Water-Land-Cloud (0-1-2) Mask (*.TIF)

Burn Scar Mapping serviceCore processingCore processing

Classification based on trained thresholdsClassification based on trained thresholds

o NBR, ALBEDO, Near-Infrared, NDVI difference indexes

Filtering of the classified pixels

o Spatial filtering for gap filling through a 3×3 (variable) p g g p g g ( )window

o Grouping to pixel clusters

o Elimination of clusters where area < 1ha (variable)

C t i hb i l t ith di t h t th o Connect neighboring clusters with distance shorter than 2 pixels (variable)

Raster to vector conversionRaster to vector conversion

Burn Scar Mapping servicePost-processingPost-processing

Application of refinement queries:Application of refinement queries:

o False alarms near the coastline

o Burnt crops during the summer

Attribute enrichment with the use of Linked data

Ability to produce rapid mapping products in rush Ability to produce rapid mapping products in rush mode

l h bl lProcess large archives enabling a time-series analysis

Pose queries on historical products for long-term ose que es o sto ca p oducts o o g teanalysis