The use of Geodata for monitoring and …...IRSIIRRSSIRS----P6 LISSP6 LISSP6 LISS----III 06/08 u....

Company Data

The use of Geodata for

monitoring and

environmental services

Jörn Reike

Euromap GmbH

Augustastr. 18 A

17235 Neustrelitz

www.euromap.de

Company Data

1. Company profile

2. Geodata: fields of application

3. Practice example I

• Monitoring nature conservation: Biotope type mapping• Monitoring nature conservation: Biotope type mapping

4. Practice example II

• Modeling climate change: REGKLAM model region Dresden

Company Data

1. Company profile1. Company profile

Company Data

Company profile

• Corporate centre: Neustrelitz

• Founded 1996 as 100 % subsidiary of GAF AG

• Fields of work:

• Data acquisition and distribution (satellite data) • Data acquisition and distribution (satellite data)

• Data reception

• in close cooperation to DLR (German Aerospace Center)

• Data processing

• Image interpretation

Company Data

Department Data reception

Department data reception

• Situated in the area of the German Aerospace Center

(DLR) in Neustrelitz

• Kalkhorstweg 53, Mecklenburg-Western

Pomerania

• Operated by Euromap- und DLR-staff• Operated by Euromap- und DLR-staff

• Main focus

• Data reception

• Processing of satellite data

• Processing of mass data

Company Data

Department Image Interpretation

Department Image Interpretation

• situated in the city core of Neustrelitz, Augustastr. 18 A

Main focus:

• Classification on satellite images• Classification on satellite images

• Mapping on satellite images or

aerial photos

• Orthocorrection

• Quality control of ortho images

© 2011 TU Dresden, GAF AG, Euromap; Dresden REGKLAM region

Company Data

2. Geodata: Fields of application

Company Data

Projects

Telecommunication

• Land cover- and settlement classification for the network

planning within the telecommunication

Infrastructure planningInfrastructure planning

• Land use maps of large European cities

Forest sector

• Forest area analysis of south west Germany

• Forest area change analysis of Thuringia and Brandenburg

Company Data

Projects

Recultivation of open-cast minings

• Land cover map for Central Germany and Lausitz

region

Modeling of climate change

• Land cover map for the Dresden REGKLAM region

EU

• Delivery of remote sensing data (Indian satellite

data) within frame contracts

GMES Fast Track Land Service, GSC-DA

• Delivery of coverages for European countries of

with multispectral satellite data

Company Data

3. Practice example I

Monitoring nature protection: Biotope type mappingMonitoring nature protection: Biotope type mapping

Company Data

Practice example I: Biotope mapping

Destination

• Acquisition and homogenization of all natural biotopes and all further

anthropogenically affected objects of Brandenburg

• The result is an area-wide final data set (topologically correct!)

• It is important for the later users (environmental administration, nature conservation • It is important for the later users (environmental administration, nature conservation

authorities, agencies of planning) to get an overview e. g. of

• Arrangement of special classes

• Frequency of special classes

• Biotope network

• Work is done by a bidding consortium (LUP, GeoContent, BSF Swissphoto, Survey

offices Peick und Konopka)

• Purchaser: LUGV (Ministry of Environment, Health and Consumer Protection of

the Federal State of Brandenburg)

Company Data


Base data

• Color infrared aerial photos �� Ground resolution: 40 cm

• True color aerial photos �� Ground resolution: 20 cm

• Topographical maps (color and black/white)

• Additional data (swamp map, map of field survey, map of biotope mapping 1992)

Company Data


Workflow

• Biotope borders will be detected based on Aerial photos and after this mapped

digitally

• With help of additional data the interpreter has to define the type of biotope

• Afterwards, for each object class (grazing lands, grasslands, wetlands etc.) will be • Afterwards, for each object class (grazing lands, grasslands, wetlands etc.) will be

allocated a specific code in the attribute table

• Used software: ArcGIS 9.3

• Working with a topology within a geodatabase (*.gdb) makes it possible to do

quality checks (e. g. gap check, overlap check)

Company Data


Examples for objects which have to be mapped

�� each of them with a lot of subclasses

• Water bodies (permanent and flowing water bodies)

• Ruderal areas (Badlands, dumps, formerly cultivated areas with spare vegetation)

• Grass lands (swamp areas, fresh meadows)

• Grazing lands

• Dry lawn

• Fields

• Settlements

• Forest

Company Data


Example: great cane breaks at standing water bodies

© LUGV 2009

Company Data


Example: ruderal areas

© LUGV 2009

Company Data


Example: swamp

© LUGV 2009

Company Data


Example: wet grassland

© LUGV 2009

Company Data


Example: intensive grazing land

© LUGV 2009

Company Data


Example: dry lawn

© LUGV 2009

Company Data


Example: field fallow

© LUGV 2009

Company Data


Example: active/inactive pit, reduction area

© LUGV 2009

Company Data


Example: nursery garden

© LUGV 2009

Company Data


Example: groins

© LUGV 2009

Company Data


Example: duckweeds

© LUGV 2009

Company Data


Aerial stereo photo interpretation

• Very important for the biotope mapping

• It is easier to identify some object classes like reed or

shrubs, because one can see their heights in the stereo

image

• Takes place at a special stereo-station with stereo display

• It is not real „3D“, but only a two-dimensional Illustration,

which induce a spatial impression

• Two different two-dimensional images will be shown to the

left and the right eye from two angles of vision which are

minimal divergent

• A spatial impression arises© Schneider Digital, http://www.schneider-

digital.com/images/product_images/popup_

images/1707_0__planar_sd_2620w_taipeito

wer.jpg

Company Data

4. Practice example II

Modeling climate change: Dresden REGKLAM regionModeling climate change: Dresden REGKLAM region

Company DataREGKLAM

• Regional Program for Climate Change Adaption Model Region of Dresden

• One of seven model projects in Germany

• Dresden is one of the most dynamic economic regions in Germany’s eastern states

• It has to deal with the direct and indirect impact of climate change

Practice example II: Dresden REGKLAM region

• It has to deal with the direct and indirect impact of climate change

• A large consortium of actors (from politics, business, science) design strategies to

better cope with the regional impact of climate change

• Euromap creates for this purpose a land cover dataset which shows the actual land

cover and land use

Company Data• The land cover data is used to

• model climate scenarios

• develop strategies for an adaptation

to new situations


• An already existing data set was adapted

especially for this project

• „Euro-Maps Land Cover“

(explanation on next slide)

© 2009 GAF AG; Euro-Maps LC Germany

REGKLAM

region

Company DataEuro-Maps Land Cover

• Homogeneous thematic land cover data set (based on interpretation of satellite

imagery)

• 22 land cover classes

• allow a wide range of analytical applications


• Coverage: Germany

• Minimum mapping unit: 0.25 hectares

• Accuracy: > 90 % in each class

• Ground resolution: 25 m

• Base data: IRS-P6 Resourcesat-1 LISS-III

• Acquisition years: 2008-2006

© 2009 GAF AG; Classification of Halle (Saale)

Company DataStep 1: Insertion of class “urban local parks and leisure facilities”

• Was refined out of the previous classes shrubs/mixture fields-forests and

agriculture (open land)

• Includes all inner-city grassland- and lawn areas


Examples:

• Cemeteries

• Parks

• Allotment gardens

• Green spaces within golf

courses

• Sports fields or tree

nurseries

© TU Dresden, GAF AG, Euromap; REGKLAM Region

Company Data


LandsatLandsatLandsatLandsat----5 TM 06/065 TM 06/065 TM 06/065 TM 06/06

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Step 2: Insertion of class “grassland”

• For classifying the grassland, a pre-classification on multi-temporal satellite data took

place

• Basis: Landsat-5 TM and IRS-P6 LISS-III

Company Data• Unsupervised

classification with the

help of ERDAS

IMAGINE

• ERDAS uses the

ISODATA algorithm


ISODATA algorithm

(Iterative Self-

Organizing Data

Analysis Technique)

• The ISODATA clustering method uses the minimum spectral distance formula to

form clusters

Company Data• Potential grassland class on Landsat and LISS-III after recoding to one class out of

30 ISODATA classes


LandsatLandsatLandsatLandsat----5 TM 06/065 TM 06/065 TM 06/065 TM 06/06

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Company Data


Step 2: Insertion of class “grassland”

• Final result:


Company DataStep 3: Insertion of class “Young forest plantations, clear cut- and wind break areas”

• Concern all areas which are situated within forest areas


Example: military

training ground

„Königsbrücker Heide“


Company DataStep 4: Insertion of forest classes

• Was refined out of the previous

classes coniferous forest and

deciduous forest


• A shapefile containing 29 forest

classes was added automatically

• Borderlines between forest classes to

other classes and boundary effects

were eliminated manually (using

ERDAS IMAGINE)


Company DataStep 4: Insertion of forest classes

• Final result:



Company DataStep 5: Insertion of field classes

• Was refined out of the previous class

agriculture (open land)

• A shapefile containing 30 new field


• A shapefile containing 30 new field

classes was added automatically

• Borderlines between forest classes to

other classes and boundary effects

were eliminated manually (using

ERDAS IMAGINE)


Company DataStep 5: Insertion of field classes

• Final result:



Company DataQuality check and final refinement

• Filtering with several filters to the minimum mapping unit (0,25 ha)

• Elimination of single pixels or small pixel groups


• Visual controls and plausibility checks (with the help of quality check protocols)

• Final product, 84 classes



Company Data

• This was only a very small aperture of what you can do with Geodata

• Based on detailed Geodata: economy and administration planning, decision and action

• With the tools of today and the potential of special software there is much more

possible

Conclusion and forecast

• Image classification and visual interpretation can be done fast and efficient

• It is possible to realize individual project ideas

• Cooperation with public Institutions, research and companies have positive effects for

both sides

Company Data

Thank you very

much for your

interest!

The use of Geodata for monitoring and …...IRSIIRRSSIRS----P6 LISSP6 LISSP6 LISS----III 06/08 u....

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