SAGRIWASENT - acdn.be · PowerPoint Presentation Author: MIGNON Vanessa (VMGN) Created Date: 4/25/2018 1:41:01 PM

SAGRIWASENT Management of Land Parcel Identification System and preparation of the

monitoring in the framework of the CAP

Emilie Beriaux, Cozmin Lucau

Walloon Agricultural Research Centre

Agriculture and Natural Environment Department

Farming systems, Territory and Information Technologies Unit [email protected]

24/04/2018

Walloon Agricultural Research Centre To address today’s questions and to prepare tomorrow’s challenges

www.cra.wallonie.be

Walloon Agricultural Research Center

410 including

120 scientists

300 ha experimental

fields, orchards,

greenhouses, laboratories…

3 sites Gembloux, Libramont, Mussy-la-

ville

150 research

projects at

regional, national and

european level

29 automatic

agricultural weather stations

‘PAMESEB network’

Regional public body

Walloon Agricultural Research Centre acts as active interface between farmers, universities, industries and administrations/authorities for society/individuals

issues


issues


issues

Universities and other research

institutions


issues

Bussiness/industry Universities and other research

institutions


issues

Bussiness/industry Universities and other research

institutions

http://www.blegny.be/avis-denquete-programme-wallon-de-reduction-des-pesticides/


issues

Bussiness/industry

Society Individuals

Universities and other research

institutions

http://www.blegny.be/avis-denquete-programme-wallon-de-reduction-des-pesticides/

SAGRIWASENT 1. THE PROJECT

2. OBJECTIVES

3. DATA SET

4. PRELIMINARY RESULTS


2. OBJECTIVES

3. DATA SET



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SAGRIWASENT

40 months project (started in May 2017)

Funded by Walloon Region

Carried out by Walloon Agricultural Research Centre Active collaboration with UCL-ELIE

2017 2018 2019 2020

12 months


2. OBJECTIVES

3. DATA SET



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SAGRIWASENT - objectives

1. Change detection of agricultural parcels

2. Check of crop diversification (crop classification)

3. Monitoring of land cover evolution

Management of Land Parcel Identification

System (LPIS) and preparation of the monitoring in the

framework of Integrated Agricultural Control System

Helping Farmers : for Geo Spatial Aid Applications (PAConWeb)

Wallon Paying Agency (LPIS management and monitoring)


2. OBJECTIVES

3. DATA SET



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Data set for LPIS management


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GSAA



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GSAA



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GSAA



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Source: https://ec.europa.eu/jrc/sites/jrcsh/files/09_int_mon_brncic.pdf


OTSC – On The Spot Checks IACS – Integrated Agricultural Control System


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Orthoimagery VHR images

LPIS Meteorological data

In situ data ....

S2 S1

Not useful for direct

LPIS update (parcel

borders)

OPEN SOURCE •R

•QGIS

•SNAP

•Sen2Agri

•Python

•…..

Data set – Sentinel 1 & 2 images


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Advantages and disadvantages of data

Aerial ortho-images

Copernicus

Sentinel 1 (A & B) (SAR)

Sentinel 2 (A & B) (optical)

+ - + - + -

Spatial resolution 0.25 m 10 m 10 m

Price high Only processing Only processing

Utilisation Easy Pre processing Easy

Temporal resolution

annuelle 2-3 days 3-5 days clouds

Availability n + 1

/ Retroactivity

Several times/year Corrections during the campaign

Several times/year Corrections during the campaign

LPIS needs Yes n+1 Yes Just identification Yes Just identification

Data set – Sentinel 1 & 2 images


2. OBJECTIVES

3. DATA SET



2. OBJECTIVES

3. DATA SET


- Change detection - Crop classification - Monitoring of land cover evolution


2. OBJECTIVES

3. DATA SET




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Preliminary results – change detection

Identification of areas (agricultural parcels) where a change

in homogeneity of land cover seems to be present

JUST FOR IDENTIFYING THE PARCELS !!! NOT FOR LPIS UPDATE !!!

For LPIS update the use of aerial/satellite imagery VHR or adapted GNSS receivers remains main method


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Obj :

• helping farmers for online declarations

• helping administration to identify changes


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Calculation of standard deviation of NDVI for each parcel

High values of NDVI Std

Low values of NDVI Std


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Two S2 images in February 2018 : More than 2900 parcels with identified with high values of NDVI standard deviation


2. OBJECTIVES

3. DATA SET




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Preliminary results – crop classification

Check of compliance with greening requirements

(as number of crop types)


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Preliminary results – crop classification

Object oriented supervised

classification « random forest »

algorithm

Wallonia 2017 LPIS = ~279000 plots – 151 crop codes LPIS buffer -15 m = ~251425 plots – 143 crop codes LPIS buffer -15 m – plots > 0.5ha : 228258 – 138 crop codes

Images from 1/1/17 to 31/7/17 Negative buffer of 15 m to avoid mixed pixels Plots > 0.5 ha Crop code at least presents in 10 plots in each dataset Vegetation indices from S2 data (linear interpolation) HV, VV backscattering coefficient + ratio HV/VV Training sample = 50% LPIS Validation sample = 50% LPIS


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Reference

data

LPIS –

buffer 15m

Samples

selection

Training

sample

50%

Validation

sample

50% mask

LPIS –

buffer 15m

S1 Images

S2 Images

Selection period

Pre-

processing

Classification

méthod :

Supervised

Random

Forest

Objet

oriented

validation

Overall accuracy

< track

110,161,37,88

Jan- Jul

< meteo

Jan –Jul

Geocoding

Calibration

Conversion to dB

CNES

Stack- mosaïcs

training

Classification

optic VV HV

HV/VV

NDVI

NDWI

Brightne

ss

S1 Images

Sigma° (dB)

S2 Images Level2a

Indices

extractio

n

Linear

interpo-

lation

Classification

SAR

Classification

SAR/optic

Random selection

Confusion matrix

metrics


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Preliminary results – crop classification Optical data

Optic images OA Nr of fields Nr of crops

16 images +

linear

interpolation

0.83 208746 93

01 – 07/2017 (16 images+ linear interpolation)

94

8 51


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Preliminary results – crop classification SAR data

Orbit Track Time OA Nr of

fields

Nr of

crops

D 110 5 :58 0.87 111418 94

37 5 :50 0.86 170221 92

A 161 17 :32 0.86 172054 106

88 17 :24 0.85 131915 103

01 – 07/2017 (35 or 36 images / track)


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Preliminary results – crop classification Optic and SAR data

SAR

Orbit

SAR

Track

Optic nr

of images

OA Nr of fields Nr of crop

types

D 161 16

images

0.87 163121 93

A, D 110, 161,

37

16

images

0.88 38867 37

01 – 07/2017 (35 or 36 SAR images / track,

16 S2 images + linear interpolation)


2. OBJECTIVES

3. DATA SET




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Preliminary results – monitoring of land cover evolution

Field campaign Nov-dec 2017

with on-board dash cameras

Dates of winter covers destruction

S1 & S2 images


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High coherence (if no meteo impact)

Loss of coherence (if works carried out on the parcel

and no meteo impact)


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Orge (escourgeon)

Froment d'hiver/épeautre

Betterave sucrière

Betteraves fourragères

Maïs fourrager (ensilage)

Maïs grain

Pomme de terre (excepté primeurs)

Colza

Lin textile

Lin oléagineux

Chicorée à inuline

féverolle d'hiver

féverolle de printemps

pois protéagineux d'hiver

pois protéagineux de printemps

lupin

avril mai Décembreaoût septembre octobre novembre Décembre juin juillet août septembre octobre novembrejanvier février mars

3 months

Taking into account the crops calendar to estimate intercrops periods

sowing

growth

harvest


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QUESTIONS, REMARKS, DISCUSSIONS, FEEDBACK

Documents

SAGRIWASENT - acdn.be · PowerPoint Presentation Author: MIGNON Vanessa (VMGN) Created Date: 4/25/2018 1:41:01 PM