The use of Geodata for monitoring and …...IRSIIRRSSIRS----P6 LISSP6 LISSP6 LISS----III 06/08 u....
Transcript of The use of Geodata for monitoring and …...IRSIIRRSSIRS----P6 LISSP6 LISSP6 LISS----III 06/08 u....
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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
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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
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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
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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
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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
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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
Practice example I: Biotope mapping
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)
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Company Data
Practice example I: Biotope mapping
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
Practice example I: Biotope mapping
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
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Company Data
Practice example I: Biotope mapping
Example: great cane breaks at standing water bodies
© LUGV 2009
Company Data
Practice example I: Biotope mapping
Example: ruderal areas
© LUGV 2009
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Company Data
Practice example I: Biotope mapping
Example: swamp
© LUGV 2009
Company Data
Practice example I: Biotope mapping
Example: wet grassland
© LUGV 2009
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Company Data
Practice example I: Biotope mapping
Example: intensive grazing land
© LUGV 2009
Company Data
Practice example I: Biotope mapping
Example: dry lawn
© LUGV 2009
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Company Data
Practice example I: Biotope mapping
Example: field fallow
© LUGV 2009
Company Data
Practice example I: Biotope mapping
Example: active/inactive pit, reduction area
© LUGV 2009
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Company Data
Practice example I: Biotope mapping
Example: nursery garden
© LUGV 2009
Company Data
Practice example I: Biotope mapping
Example: groins
© LUGV 2009
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Company Data
Practice example I: Biotope mapping
Example: duckweeds
© LUGV 2009
Company Data
Practice example I: Biotope mapping
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
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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
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Company Data• The land cover data is used to
• model climate scenarios
• develop strategies for an adaptation
to new situations
Practice example II: Dresden REGKLAM region
• 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
Practice example II: Dresden REGKLAM region
• 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)
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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
Practice example II: Dresden REGKLAM region
Examples:
• Cemeteries
• Parks
• Allotment gardens
• Green spaces within golf
courses
• Sports fields or tree
nurseries
© TU Dresden, GAF AG, Euromap; REGKLAM Region
Company Data
Practice example II: Dresden REGKLAM region
LandsatLandsatLandsatLandsat----5 TM 06/065 TM 06/065 TM 06/065 TM 06/06
IRSIRSIRSIRS----P6 LISSP6 LISSP6 LISSP6 LISS----III 06/08 u. 09/06III 06/08 u. 09/06III 06/08 u. 09/06III 06/08 u. 09/06
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
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Company Data• Unsupervised
classification with the
help of ERDAS
IMAGINE
• ERDAS uses the
ISODATA algorithm
Practice example II: Dresden REGKLAM region
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
Practice example II: Dresden REGKLAM region
LandsatLandsatLandsatLandsat----5 TM 06/065 TM 06/065 TM 06/065 TM 06/06
IRSIRSIRSIRS----P6 LISSP6 LISSP6 LISSP6 LISS----III 06/08 u. 09/06III 06/08 u. 09/06III 06/08 u. 09/06III 06/08 u. 09/06
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Company Data
Practice example II: Dresden REGKLAM region
Step 2: Insertion of class “grassland”
• Final result:
© TU Dresden, GAF AG, Euromap; REGKLAM Region
Company DataStep 3: Insertion of class “Young forest plantations, clear cut- and wind break areas”
• Concern all areas which are situated within forest areas
Practice example II: Dresden REGKLAM region
Example: military
training ground
„Königsbrücker Heide“
© TU Dresden, GAF AG, Euromap; REGKLAM Region
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Company DataStep 4: Insertion of forest classes
• Was refined out of the previous
classes coniferous forest and
deciduous forest
Practice example II: Dresden REGKLAM region
• 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)
© TU Dresden, GAF AG, Euromap; REGKLAM Region
Company DataStep 4: Insertion of forest classes
• Final result:
Practice example II: Dresden REGKLAM region
© TU Dresden, GAF AG, Euromap; REGKLAM Region
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Company DataStep 5: Insertion of field classes
• Was refined out of the previous class
agriculture (open land)
• A shapefile containing 30 new field
Practice example II: Dresden REGKLAM region
• 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)
© TU Dresden, GAF AG, Euromap; REGKLAM Region
Company DataStep 5: Insertion of field classes
• Final result:
Practice example II: Dresden REGKLAM region
© TU Dresden, GAF AG, Euromap; REGKLAM Region
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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
Practice example II: Dresden REGKLAM region
• Visual controls and plausibility checks (with the help of quality check protocols)
• Final product, 84 classes
Practice example II: Dresden REGKLAM region
© TU Dresden, GAF AG, Euromap; REGKLAM Region
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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!