Case Study 1 - Crop recognition, mapping and monitoring · Case study 1 „Crop recognition, mapping and monitoring” - general idea 4 Satellite data Winter rape Winter wheat Sugar

Case Study 1 - Crop recognition, mapping and monitoringCurrent work status

3rd Meeting of WPH Earth Observation

28.06.2019, Olsztyn1

Magdalena Mleczko, Przemysław Slesiński, Marek Morze

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

• Main goal• Crops mapping and area estimation

Case study 1 „Crop recognition, mapping and monitoring”- general idea

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spring barleywinter barleycorncereal mixes

oatspring wheat

winter wheatspring triticalewinter triticalewinter raperye

Crops map

Estimated area of crops [ha]

• How does the whole process look like?

Case study 1 „Crop recognition, mapping and monitoring”- general idea

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Satellite data

Winter rape Winter wheatCornSugar beet

Forest Urban

Machine learningalgorithms

Each pixel or object iscompared to sample and

then is classified

Crops map

Estimated area of crops [ha]Administrative data• cadastral parcels vector (LPIS)• information on crops declared by

farmers (ARMA)• agricultural plots borders from Land

Use\land Cover (ARMA)

Case study 1 „Crop recognition, mapping and monitoring”- research complexity

• What is the problem? Target complexity

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Agriculture fileds

Arable lands Grasslands Permanent crops

The factors which influence on thematic classes

PhenologyCultivationpractices

Externalinfluences

Outer class variability Inner class variability

Examples of crops types

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S2 2018.04.13 S2 2018.05.13 S2 2018.06.08 S1

Winter rape Winter rape Winter rape Winter rape

Winter rapeWinter rape Winter rape Winter rape

Winter rape Winter rape Winter rape Winter rape

Winter wheat Winter wheatWinter wheatWinter wheat

Examples of crops types

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S2 2018.04.13 S2 2018.05.13 S2 2018.06.08 S1

Winter wheat

Winter wheat

Winter wheat

Winter wheat

Winter wheat

Winter wheat

Winter wheat

Winter wheat

Winter wheatWinter wheatWinter wheat

Spring wheatWinter rape

Sugar beetSugar beet


Sugar beet


Sugar beet

Spring wheatWinter rape Spring wheatWinter rape Spring wheatWinter rape

Current work status of CS 1 in reference to methodological framework of WPH

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State of the Art

Statistical product definition

Data collection

Pilot production

Pre-works STAGE 1 STAGE 4

Main data processing- Methods and

procedure description

- Data processing

STAGE 3

Data source & Toolkit- Data sources

review

- Data access condition

- Toolkit and software

Quality assessment of collected data

Test site definition

Main findings

Quality assessment of results

Results analysis

IT Infrastructure for collecting data

IT Infrastructure for main data processing

Data pre-processing

STAGE 2

Quality assessment of pre-processed data

IT Infrastructure for pre-processing data

Metadata of collected data

Metadata of pre-processed data

Metadata of results

Assessment of the data timeliness, ensuring continuity of data sources and statistical information for longer time period

Validation

PRE-WORKS / State of the art – Literature review

• Over 100 meaningful articles of remote sensing in agriculture within last 10 years

• Dozen of research projects

• Platforms and local-, web- applications

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SEN4STAT -

SENTINELS FOR STATI

STICS

PRE-WORKS / Data source and Toolkit

• Satellite imaging nowadays

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SPOT (2 m) Sep 2016 Sentinel-2 (10 m) Oct 2016

OR

10 m • Medium spatial resolution

• Short revisit time

• Free of charge

• Huge coverage (270 x 100 km)

Several days (~ 5)

0.5 -5 m

One or several days

• Very high spatial resolution

• Short revisit time

• Expensive

• Small coverage(10 x 10, 60 x 60 km)


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• Optical vs Radar DataRadar Data

• Independent of sun light

• Insensitive to clouds

• Sensitive to crops type and cultivation (roughness of bare soil/vegetation surface)

Sentinel 2Optical data

Sentinel 1Radar data


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• Optical vs Radar Data

Optical data of Sentinel-2 every 5 days

Radar data of Sentinel-1 every 6 days(with different imagingparameters even 2-3 days)




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• Optical vs Radar Data

Example:Year: 2018 (April-September)Region: Warmian-Mazurian

OpticalData

RadarDatavs

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Optical data of Sentinel-2 every 5 days

Radar data of Sentinel-1 every 6 days(with different imagingparameters even 2-3 days)




• Rysunek o dostępnych systemach

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• Software need for:• Satellite data processing

• Administrative data processing (GIS and ASCII)

• Machine learning (for raster and vector data)

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Commercial:• ENVI/SARscape• GAMMA software• PCI Geomatics• Erdas Imagine• TerrSET• SARPROZ

Free/Open source tools:• ESA/SNAP• PolSARpro• RAT (Radar Tools)• Sen2Agri• Ilwis• MapReady

Satellite data processing

SAR and OpticalSARSAR and OpticalSAR and OpticalOpticalSAR

SAR and OpticalSARSAROpticalOpticalSAR and Optical

Admistrative data processing

Commercial GIS:

• ArcGIS

Free/Open source tools GIS:

• QGiS

• SAGA

• GRASS

Database tools

• Microsoft Office

• Libre Office

Machine learning

Commercial:

• eCognition

• ENVI

Free/Open source tools:

• ORFEO Toolbox

• LEOworks

STAGE 1 / Test site definition

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• Area of interest• Warmian-Masurian Voivodeship (24192 km2)

• Agricultural fields (40% of voivodeship)

• Time series• October 2017 – September 2018

• List of crop types for recognition1. sugar beets2. buckwheat3. spring barley4. winter barley5. corn6. cereal mixes7. oat8. fruit trees plantations9. fruit bushes plantations10. spring wheat

11. winter wheat12. spring triticale13. winter triticale14. spring rape15. winter rape16. grassland17. potatoes18. rye19. mustard20. leguminous crops

STAGE 1 / Data collection

• Satellite data• 26 acquisitions of Sentinel-1 (60 Gb)

• 3 acquisitions of Sentinel-2 (60 Gb)

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09.2017 10.2017 11.2017 12.2017 01.2018 02.2018 03.2018 04.2018 05.2018 06.2018 07.2018 08.2018 09.2018 10.2018

Data acquisitions

Sentinel-1 Sentinel-2

Sentinel-1 data characteristics

Imaging mode IW

Product GRD

Relative orbit 51

Pass direction descending

Polarization VV/VH

Resolution 10m

Scene size 270 x 200 km

File size 1.5-2 GB per scene

Sentinel-2 data characteristics

Processing level 2A

Product S2 MSI 2A

Relative orbit 79

Pass direction descending

Resolution 10m

Tile size 100 x 100 km

File size 1 GB per tile


• Administrative data source• cadastral parcels vector data from Land Parcel Identification

System (over 34 mln records, 13GB of data)1,

• agricultural plots vector data from Land Parcel Identification System (over 33 mln records, 23GB of data)1,

• information of crops declared by farmers (ARMA)

• IN-SITU 2

1 Agency for Restructuring and Modernisation of Agriculture (ARMA)

2 Statistics Poland

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• IN-SITU data geodatabase (CSO)

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• IN-SITU data geodatabase (CSO)

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Crop type Number of samples

sugar beets 270buckwheat 201spring barley 354winter barley 251corn 375cereal mixes 426oat 341fruit trees plantations 367fruit bushes plantations 261spring wheat 402winter wheat 396spring triticale 149winter triticale 363spring rape 125winter rape 353grassland 925potatoes 349rye 312Sum (2018) 6220


spring barley 481winter barley 340corn 509cereal mixes 655oat 426spring wheat 432winter wheat 559spring triticale 218winter triticale 448spring rape 134winter rape 476rye 406Sum (2017) 5084

STAGE 2 / Data pre-processing

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• Sentinel-1 data pre-processing

• 26 acquisitions of GRD were processed into 52 images of Sigma 0 (in VV and VH polarization)

• Sentinel-2 data pre-processing

• 3 acquisition of orthorecified data were mosaiced for each date (12 images)

• NDVI indices were calculated (4 images)

Pre-processing scheme of Sentinel-1 data Pre-processing scheme of Sentinel-2 data

1 Sigma0_VH_2017_10_17_db 2 Sigma0_VH_2017_10_23_db 3 Sigma0_VH_2018_04_03_db 4 Sigma0_VH_2018_04_09_db 5 Sigma0_VH_2018_04_15_db 6 Sigma0_VH_2018_04_21_db 7 Sigma0_VH_2018_04_27_db 8 Sigma0_VH_2018_05_03_db 9 Sigma0_VH_2018_05_09_db 10 Sigma0_VH_2018_05_15_db 11 Sigma0_VH_2018_05_21_db 12 Sigma0_VH_2018_05_27_db 13 Sigma0_VH_2018_06_08_db 14 Sigma0_VH_2018_06_14_db 15 Sigma0_VH_2018_06_20_db 16 Sigma0_VH_2018_06_26_db 17 Sigma0_VH_2018_07_02_db 18 Sigma0_VH_2018_07_08_db 19 Sigma0_VH_2018_07_14_db 20 Sigma0_VH_2018_07_20_db 21 Sigma0_VH_2018_08_01_db 22 Sigma0_VH_2018_08_07_db 23 Sigma0_VH_2018_08_19_db 24 Sigma0_VH_2018_08_25_db

• Full list of generated images


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25 Sigma0_VH_2018_08_31_db 26 Sigma0_VH_2018_09_06_db 27 Sigma0_VV_2017_10_17_db 28 Sigma0_VV_2017_10_23_db 29 Sigma0_VV_2018_04_03_db 30 Sigma0_VV_2018_04_09_db 31 Sigma0_VV_2018_04_15_db 32 Sigma0_VV_2018_04_21_db 33 Sigma0_VV_2018_04_27_db 34 Sigma0_VV_2018_05_03_db 35 Sigma0_VV_2018_05_09_db 36 Sigma0_VV_2018_05_15_db 37 Sigma0_VV_2018_05_21_db 38 Sigma0_VV_2018_05_27_db 39 Sigma0_VV_2018_06_08_db 40 Sigma0_VV_2018_06_14_db 41 Sigma0_VV_2018_06_20_db 42 Sigma0_VV_2018_06_26_db 43 Sigma0_VV_2018_07_02_db 44 Sigma0_VV_2018_07_08_db 45 Sigma0_VV_2018_07_14_db 46 Sigma0_VV_2018_07_20_db 47 Sigma0_VV_2018_08_01_db 48 Sigma0_VV_2018_08_07_db

49 Sigma0_VV_2018_08_19_db 50 Sigma0_VV_2018_08_25_db 51 Sigma0_VV_2018_08_31_db 52 Sigma0_VV_2018_09_06_db53 B2_Blue_443nm_2018_03_19 54 B3_Green_560nm_2018_03_19 55 B4_Red_665nm_2018_03_19 56 B8_NIR_842nm_2018_03_19 57 B2_Blue_443nm_2018_04_13 58 B3_Green_560nm_2018_04_13 59 B4_Red_665nm_2018_04_13 60 B8_NIR_842nm_2018_04_13 61 B2_Blue_443nm_2018_05_08 62 B3_Green_560nm_2018_05_08 63 B4_Red_665nm_2018_05_08 64 B8_NIR_842nm_2018_05_08 65 B2_Blue_443nm_2018_06_07 66 B3_Green_560nm_2018_06_07 67 B4_Red_665nm_2018_06_07 68 B8_NIR_842nm_2018_06_07 69 NDVI_2018_03_19 70 NDVI_2018_04_13 71 NDVI_2018_05_08 72 NDVI_2018_06_07

• Color composition of Sentinel-2

13 April 2018, 10x10m

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8 May 2018, 10x10m

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13 May 2018, 10x10m

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7 June 2018, 10x10m

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9 May, 8 June, 7 August 2018, 10x10m, polarization VH

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13 April 2018, 10x10m

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8 May 2018, 10x10m

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13 May 2018, 10x10m

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7 June 2018, 10x10m

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9 May, 8 June, 7 August 2018, 10x10m, polarization VH

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• Administrative data• Task 1 To exclude non agri area

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Cadastral parcels (LPIS) Land Use and Land Cover (ARMA) Joined cadastralparcels & Land Use

and Land Cover


• Administrative data• Task 2 To choose representative parcels / samples

• Based on information on crops declared by farmers (ARMA)

• Based on cadastral parcels (LPIS)

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Conditions:

• Parcels > 1ha

• One type of crop on one cadastral parcel

• Declared type of crop should cover at least 98% of cadastral parcel


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• Administrative data• Task 2 To choose representative parcels / samples


Sum of area samples [ha]

% of agriculture area

sugar beets 75 520 0.04buckwheat 268 1174 0.10spring barley 848 3835 0.32winter barley 156 539 0.04corn 1163 5451 0.45cereal mixes 553 1862 0.15oat 610 2577 0.21fruit trees plantations 91 291 0.02fruit bushes plantations 85 227 0.02spring wheat 1097 5618 0.46winter wheat 2250 11404 0.94spring triticale 222 626 0.05winter triticale 1422 6033 0.50spring rape 147 792 0.07winter rape 1250 7335 0.60grassland 12013 58108 4.78potatoes 87 225 0.02rye 878 3264 0.27mustard 75 218 0.02leguminous crops 755 4097 0.34

SUM 24045 114195 9.40

What next?

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STAGE 3 August 2019 – April 2020

Description of segmentation and classification methods August – September 2019

Testing of segmentation methods September 2019 – April 2020

Testing of machine learning algorithms September 2019 – April 2020

Development of classification methodology September 2019 – April 2020

Accuracy assessment of segmentation and classification September 2019 – April 2020

Analysis of metadata, IT infrastructure and quality of processing results

September 2019 – April 2020

STAGE 4 May – July 2020

Pilot production May - June 2020

Validation of the pilot production results. May - June 2020

Describing of main findings June 2020

28.06.2019, Olsztyn

Thank you for attention

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Documents

Case Study 1 - Crop recognition, mapping and monitoring · Case study 1 „Crop recognition, mapping and monitoring” - general idea 4 Satellite data Winter rape Winter wheat Sugar