WPH Earth observation - Europa...2 Case study 1 (PL) Agriculture - Crop recognition, mapping and monitoring Stage 1 – data collection a) collection of time series (X 2017 – IX

WPH – Earth observation Case Study 1 (PL) Agriculture-Crop recognition, mapping and monitoring

05 – 06 December 2018, Vienna, Austria 1

Marek Morze

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Case study 1 (PL) Agriculture - Crop recognition, mapping and monitoring

Stage 1 – data collection a) collection of time series (X 2017 – IX 2018) Sentinel-1A/B SAR data in interferometric wide swath mode for crop recognition (provides continuous imagery day, night and all weather, revisit time – 6 days). Sentinel-1 working modes: • SM (Stripmap Mode) – acquires data with an 80 km swath at 5 m by 5 m spatial resolution, • IW (Interferometric Wideswath Mode) – acquires data with a 250 km swath at 5 m by 20 m spatial resolution, • EW (Extra-wide Swath Mode) – acquires data over a 400 km swath at 20 m by 40 m spatial resolution, • WV (Wave Mode) – acquires data in 20 km by 20 km area, at 5 m by 5 m under angle 23° and 36.5°.

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Stage 1 – data collection b) collection of Sentinel-2A/B VNIR/SWIR images for the same period for image segmentation suport and calculating vegetation indexes, (wide-swath - 290 km, high-resolution, revisit time– 6 days), Sentinel-2 spectral bands: • four bands at 10 metres spatial resolution– 490 nm (B2), 560 nm (B3), 665 nm (B4), 842 nm (B8), • six bands at 20 metres spatial resolution– 705 nm (B5), 740 nm (B6), 783 nm (B7), 865 nm (B8A), 1 610 nm (B11), 2 190 nm (B12), • three bands at 60 metres spatial resolution: – 443 nm (B1), 945 nm (B9) and 1 375 nm (B10).

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Stage 1 – data collection c) administrative data sources: • cadastral parcels vector data from Land Parcel Identification System (Agency for Restructuring and Modernisation of Agriculture ) – for image segmentation suport,

• agricultural plots borders from General Geographic Geodatabase (Head Office of Geodesy and Cartography) – for data reduction,

• information on crops declared by farmers (ARMA) – as samples for machine learning algorithms and validation


Stage 1 – data collection d) in situ data geodatabase for validation (CSO)

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Stage 1 – data collection f) lists of crops for recognition – 90% of all crops of Poland

Crop classes

sugar beets

buckwheat

spring barley

winter barley

corn

cereal mixes

oat

fruit trees plantations

fruit bushes plantations

spring wheat

winter wheat

spring triticale

winter triticale

spring rape

winter rape

grassland

potatoes

rye

mustard

leguminous crops

Number of classes: 20

Aggregated crop classes

sugar beets

buckwheat

spring cereals

winter cereals

corn

fruit trees plantations

fruit bushes plantations

spring rape

winter rape

grassland

potatoes

mustard

leguminous crops

Number of classes: 13

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Stage 2 – data preprocessing

a) preprocessing Sentinel-1 radar data to sigma0 (backscatter

coefficient) :

• time series data frame: from 10.2017 to 09.2018,

• number of acquisitions: approx. 28,

• number of radar scenes to be processed: approx. 56 (2 scenes for one

acquisition),

• raw data volume: approx. 100GB,

• intermediate data and individual processed products: approx. 2TB,

• open source software: ESA SNAP and CNES Orfeo Toolbox.

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example of SENTINEL-1 data processing workflow

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Example of processed Sentinel-1 data (false colour composition at VV polarization for 3 dates 20.05.2017, 25.06.2017, 19.07.2017, resolution 10x10m)

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Example of processed Sentinel-1 data (false colour composition at VV polarization for 3 dates 20.05.2017, 25.06.2017, 19.07.2017, spatial resolution 10x10m)

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a) preprocessing Sentinel-2 optical data:

• time series data frame: from 10.2017 to 08.2018,

• number of acquisitions: only 6 (due to weather conditions) ,

• number of tiles to be processed: approx. 60 (10 tiles for one

acquisition),

• raw data volume: approx. 60GB,

• intermediate data and individual processed products: approx. 200GB,

• open source software: ESA SNAP and CNES Orfeo Toolbox.

example of SENTINEL-2 data processing workflow

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Example of processed Sentinel-2 data (natural colour composition at 28.05.2017, spatial resolution 10x10m)

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Example of processed Sentinel-2 data (natural colour composition at 28.05.2017, spatial resolution 10x10m)

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Stage 3 – testing segmentation and classification alghoritms a) Segmentation - process of partitioning a digital image into multiple homogeneous segments Example of SENTINEL-1 image segmentation • large scale mean shift segmentation algorithm will be applied for calculating homogeneous areas on Sentinel-1 and Sentinel-2 preprocessed data, • several parameters need to be tested in the context of obtaining the highest quality results for object based image classification

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Stage 3 – testing segmentation and classification alghoritms b) Object Based Supervised Image Classification – several machine learning algorithms will be tested in the context of obtaining the best accuracy for crop recognition: • SVM - support vector machine classifier, • DT - decision tree classifier, • ANN - artificial neural network classifier, • RF - random forest classifier, • KNN - k-nearest neighbors classifier. For supervision and learning crops declared by farmers for direct payments in agriculture (ARMA) will be used. Database of approx. 1,5 mln records must be processed, filtered and georeferenced to obtain reliable samples.

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Stage 4 – accuracy assesment a) extraction of validation samples from ARMA and insitu data, b) computing confusion matrix for each classifier with ARMA and insitu validation data including: • producer accuracy per class, • user accuracy per class, • F-score measure per class, • OA - overall accuracy, • KIA - kappa index of agreement. c) comparing classified areas to the official statistics.

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Deliverables a) detailed description of methodology for Sentinel-1 and Sentinel-2 data processing at each stage - detailed description of methodology for Object Based Image Classification including segmentation and machine learning algorithms parameters, c) comparison of applied algorithms and their accuracy the context of obtaining best classification results, d) Crops area calculation and development of crops maps for the warmian-masurian voivodeship in 2018.

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Deliverables

Crops map - example of Object Based Image Classification result

spring barley winter barley corn cereal mixes

oat spring wheat

winter wheat spring triticale winter triticale winter rape rye

05 – 06 December 2018, Vienna, Austria

Marek Morze

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Documents

WPH Earth observation - Europa...2 Case study 1 (PL) Agriculture - Crop recognition, mapping and monitoring Stage 1 – data collection a) collection of time series (X 2017 – IX