WPH - Earth Observation€¦ · WPH Earth Observation – Case study description 10 Agriculture Case study 2 Monitoring of the off-season vegetation cover • Main goal • Monitoring

WPH - Earth Observation

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Marek Morze

05 – 06 December 2018, Vienna, Austria

Earth Observation

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OPERATIONAL SATELLITES = 1886

2 16 41

127

297

661

1995 2000 2005 2010 2015

30.04.2018

Earth Observation

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Earth Observation

35%

Communications 43%

Technology development

11%

Navigation 7%

Space Science

4%

WPH Earth Observation – crucial goals

• Goals • Facilitation and improvement the mandatory statistical registers

• The usage of the EO data in official statistical production

• Support the upcoming Census 2021 and Agricultural Census

• Implementation other commitments of European Commission or United Nations

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WPH Earth Observation

• Expectations • Identification and analysis of EO data sources for multiple

statistical themes product and development of an adequate Reference Methodological Framework for processing data

• Results • Final technical report: the evaluation of big data sources and

definition of possible statistical products from examined big data sources as well as associated with them the protection of privacy and confidentiality and other legal issues.

• Cooperation of 9 partners from 8 countries

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WPH Earth Observation – Thematic tasks & Case studies

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Agriculture

Build-up area

Land cover

Settlements, Enumeration Areas and Forestry

Crop recognition, mapping and monitoring

Monitoring of the off-season vegetation cover

Crop recognition with very high resolution aerial data

Implementing SDG indicator 11.7.1

Urban sprawl across urban areas in Europe

Combination of administrative and Earth Observation data to determine the quality of housing

Comparing «in-situ» and «remote-sensing» collection mode for land cover data

Land cover maps at very detailed scale

Update the INSPIRE Theme Statistical Units dataset and preventing forest fire

Case study 1

Case study 2

Case study 3

Case study 4

Case study 5

Case study 6

Case study 7

Case study 8

Case study 9

WPH Earth Observation – Activities of each case study

• For each of the case studies the following activities will be undertaken: • Statistical products description based on data sources and needs of

statistical data users; • Data access (ensuring continuity of data sources and statistical

information for longer time period); • Definition of business processes and derived metadata (auditable

steps including assurance of data security and confidentiality; ensuring data quality and its documentation);

• Quality assessment of the data; • Development of methodology for production of statistics; • IT infrastructures definition for data processing; • Treatment of legal issues (related to data access, processing and

output); • Pilot production of statistical data and assessment of quality

(including multi-purpose and multi-source aspects).

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• Main goal • Crops mapping and area estimation

WPH Earth Observation – Case study description

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Agriculture Crop recognition, mapping and monitoring Case study 1

spring barley winter barley corn cereal mixes

oat spring wheat

winter wheat spring triticale winter triticale winter rape rye

Satellite data

Administrative data • cadastral parcels vector

(LPIS) • information on crops

declared by farmers (ARMA) • agricultural plots borders

from General Geographic Geodatabase

Crops map

Machine learning

algorithms

Estimated area of crops [ha]

• Verification and improvement the accuracy of the crop recognition methodology (ESSnet 2016-2018)

• Crop recognition using long time series of Sentinel-1 data

• Assessment of the state of winter crops (overwintering) using Sentinel-2

• Testing machine learning algorithms


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Agriculture Crop recognition, mapping and monitoring Case study 1


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Agriculture Monitoring of the off-season vegetation cover Case study 2

• Main goal • Monitoring the off-season vegetation cover of

agricultural soils that gives important information on nutrients losses from fields to water bodies

• Proposed method would provide grounds for establishing an indicator on sustainable agriculture as land management practices closely relate to sustainability.


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Agriculture Crop recognition with very high resolution aerial data Case study 3

• Main goal • Use of the aerial photography with very high resolution (10-40

cm) to crop recognition in case of small size parcels

Small size parcels can not be recognized on images of 10 m resolution (Sentinel 1-2)

200 m

• Objectives • Implementation the UN-Habitat

methodology for the whole France


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Build-up area Implementing SDG indicator 11.7.1 Case study 4

• Bench marking this methodology with specifics data or concepts that are available in France or in Europe (French or European definition of cities, Sentinel 2, French Road maps layer)

• Promotion the results at the French level (CNIS), European level (Eurostat, UN-GGIM Europe), Global level (IAEG-SDG).


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Build-up area Urban sprawl across urban areas in Europe Case study 5

https://sourceable.net/true-costs-sprawl/

• Objectives • Characterization of urban sprawl (SDG 11.7.1) across

Urban areas in Europe by means of data-driven machine learning methods.

• Evaluation what extent can national

statistic offices benefit from Earth observation to monitor and report on the SGDs at local to national level

• Investigation the possibility of providing temporal continuity on the basis of multiple datasets provided by satellites such as MODIS and SENTINEL 2.

• Objectives • Exploration of the quality of urban living

based on EO data combined with administrative data


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Build-up area Combination of administrative and Earth Observation data to determine the quality of housing

Case study 6

• Presentation that a more comprehensive understanding can be developed through their combination rather than by analyzing only a single data source.


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Land cover Comparing «in-situ» and «remote-sensing» collection mode for land cover data

Case study 7

• TERUTI - French statistical area-frame survey on land cover and land use

• Since 2017, the administrative, geographical data and in-situ are used

OSO map (land cover maps at country scale using high resolution optical image time series which is based on supervised classification and uses existing databases as reference

data for training and validation.

70%

30%

Administrative data (LPIS) and geographical databases (i.e. BD FORET from National Geographic Institute – IGN)

In-situ by a surveyour everyyear


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Land cover Comparing «in-situ» and «remote-sensing» collection mode for land cover data

Case study 7

• Objectives

• Analysing the differences (frequency, reasons) between land cover information collected on a geo-located point in Teruti survey and LC information provided by the automatic classification of the same pixel in the OSO map ;

• Identifying land cover types which could be automatically classified by remote sensing with an adjustment of the classification used into the OSO process ;

• Identifying with a high confidence pixels which contains a high probability of land cover change in order to send a Teruti's surveyor to collect and verify the LC change on the ground.

• Objectives

• Land cover map at various scale by four bands aerial and satellite (Sentinel and LANDSAT) images based on 1st LUCAS (Land Use/Cover Area frame Survey) level legend.

• Machine learning algorithms will be used (i.e a segmentation algorithm grounded on CNN and Unet in order to recognize built-up artificial areas).


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Land cover Land cover maps at very detailed scale Case study 8

Aerial and satellite data Land cover map at various scale with legend:

Machine learning

algorithms A00 – Artificial land B00 – Cropland C00 – Woodland D00 – Shrubland E00 – Grassland F00 – Bare land and lichens/moss G00 – Water areas H00 – Wetlands

• Objectives

• to update the INSPIRE Theme Statistical Units dataset, namely the Settlements and Enumeration Areas.


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Case study 9

• the process will contribute to build the geospatial framework to support 2021 Census.

• exploration the possibility of studying the forest and the eucalyptus plantation and its impact in preventing forest fire.


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Case study 9

• Big challenge is:

• the spatial resolution of Sentinel images

• the integration of these geospatial data requiring methodological development

• the inexistence of explicit procedures by ESS to use these data in the statistical production process

WPH Earth Observation – Final technical report

• Partners cooperation: • similar topics and indicators;

• data access: satellite imagery (Sentinel, Modis, Landsat) and aerial imagery;

• data processing workflow including machine learning algorithms for image classification and segmentation;

• exchange of experiences among the WPH partners

• accuracy assessment of the results;

• harmonization of administrative data sources as a reference data;

• IT infrastructure definition for data processing;

• treatment of legal issues related to data access, processing and output.

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05 – 06 December 2018, Vienna, Austria 21

Documents

WPH - Earth Observation€¦ · WPH Earth Observation – Case study description 10 Agriculture Case study 2 Monitoring of the off-season vegetation cover • Main goal • Monitoring