Pastures pro-rata coefficients

Semi –automatic classification in Italy

AGEA

• EC Regulation 640/2014 art. 10 allows MS to use “pro-rata” coefficients to calculate

non eligible areas to be excluded from pastures

• Italy has been using for several years this approach, to reduce the 100% eligibility

when necessary (4 classes: 0-5%; 5-20%; 20-50%, > 50%) on national LPIS/refresh

• However, undertaken Audits in the last years by EC have highlighted issues vs the

real permanent grazing areas correctness, derived by orthophotos CAPI

• In 2016 JRC officially presenting T. Guidances asked for MS actions to improve

systematic suitability, mapping and validation of national “pasture pro-rata systems

(see JRC slides 12-17)

• In summer 2016 tests of pro-rata classification by Drones (funded by It. Ministry of

Agriculture), in collaboration with JRC, have been performed and presented during EU

events

• Also starting from these results AGEA is implementing an “action Plan” to consolidate,

through a semi-automatic mode, the eligibility measures of permanent pastures and

existing pro-rata classes

Background

Semi automatic pro-rata goals

Italian LPIS semi automatic classification is focused on:

• Obtain new and objective pro-rata coefficients, as reference layer, to

assess and guide the updating of LPIS 2017 (a third of Italy) : DONE

• Provide homogenous and verified layers to support and guide the CwRS

chain and along the year back-office activities

• Provide to Regions, objective tools to support local PLT delineation

(Traditional Local Practices), aimed at Rural Development measures

determination

• Provide during EC Audits, objective “classification ranges” and measures

to be evaluate together

• Support the next “CAP monitoring” scenario, where Sentinel will be not

able to provide useful results

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Starting point: Pilot on Drone use with JRC- example on pro-rata pasture classification

woodland; bush, grass, sparse grass, bare/rock…water is missing through simple RGB

Natural

waterhole

Animal paths

Manmade

waterhole

Radicofani test site

Siena,

July, 28th 2016

2D classification, 6 cm pixel

Grazing sheep during the Drone flight

New Technologies: Drones 3D classification for better grazing analysis

1water

> 3m

2 -3m

1 – 2 m

0,5- 1m

0,26ha 2,0%

0,44ha 3,3%

0,37ha 2,8%

0,93ha 7,0%

0,08 ha 0,6%

0,2 ha 1,5%

Water

Roads

Total sample area: 13.31 ha

Total other than grass: 17,1%

To be surely excluded (not veg): 2,1%

To be excl. only after grazeability evaluation (bush):

9,8%

To be evaluated for possible reduction (trees): 5,5%

=> Pro-rata class: 5-15%

AGEA methodology for a cost effective pro-rata “land cover”

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1) Drone 5 cm and Airborne 20 cm present no main differences at 1:5,000 scale,

therefore starting from the LPIS pastures polygons with different pro-rata classes,

are extracting and fused, eliminating pre-existing borders

2) For each new larger polygon, 20 cm .tiff orthophotos are clipped by specific

application

3) Spectral signatures (zone by zone) guide the pixel based (3x3) classification inside

the selected polygons, considering: rocks, high vegetation (bush,trees), slope factors

4) Each pixel group is classified (all 4 bands used) creating 2 separate layers:

rocks - bush/trees

5) A second step eliminates the too small polygons (<2m), cleaning them for a

manageable mapping

6) The remaining are classified in 4 classes (0-5%; 5-20%, 20-50%,>50%) and

re-delineated as the existing LPIS rules

7) The last task is to overlap the polygons to national DTM by AGEA

GeoDataW, for detecting steep slopes to exclude as possible grazing land

8) All intermediate layers are maintained/archived for using in LPIS updating phases

2017 LPIS Regions (a third of Italy) concluded

Automatic rocks/high vegetation extraction

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Example= Starting from merging 3 adjacent polygons

(50% and 2 at 20%),

by using 20cm 4 bands

Example of successive classification through

Moran index for rocks and higher vegetation

Rocks and bush/trees polygons <2m

are cleaned

Automatic rocks/high vegetation extraction

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higher vegetation extraction using 321,421 and 431

bands composition

Output:

5 polygons instead 3

4 at 20% and 1 at 50%

Increasing of eligible surface

Example of pro-rata semi automatic extraction (Lazio)-1

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20 cm

Orthophoto

4 bands

1,2,3,4

Example of pro-rata semi automatic extraction (Lazio) - 2

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Rock extraction:

Spectral signature(adapted zone by zone)

Example of pro-rata semi automatic extraction (Lazio) -3

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Higher

vegetation

Extraction:

Spectral signature

Texture variability

Shadow gradient

(in improvement)

Example of pro-rata semi automatic extraction (Lazio) - 4

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Example of pro-rata semi automatic extraction (south: Calabria) -1

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Example of pro-rata semi automatic extraction (south: Calabria) -2

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Rock extraction

Example of pro-rata semi automatic extraction (south: Calabria) - 3

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Higher vegetation extraction

…residual improvements

for shadow calculation and reduction to be done

Morphologic issues overlapping

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Starting: 1 unique polygon 50% tare: 3,6 ha eligible surface

Output: 4 polygons, 3 at 100% tare and 1 at 50% => 2,1 ha eligible

The blu polygon indicates

a portion > 70% sloping

LPIS year N° Polygons Surface (SKM) Eligible (SKM)

2015 434894 10.786,79 8.085,89

2016 746142 21.091,56 15.823,12

2017 480113 10.934,02 8.060,84

Italian LPIS numbers

Automatic pro-rata land cover tasks:example of working time

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LAYER PRO-RATA semi-automatic classification

Example of processing for a Province N° LPIS polygons Surface (Ha) Eligible surface (Ha)

initial 3026 5108 initial 3489

final 2511 5108 final 3390

TASKS and working times

Task phases Activity Operator Hours Minutes

Crop_FilePyton procedure for imagery clipping Boundary Box of pro-rata polygon)

No 0 44

Buff&SimplifyPyton proc for working polygon

generation No 0 28

Gen_Work_FilePyton proc for image treatment: noise ,

cross correlation indexNo 3 42

Create_Hist_FileVisualStudio SW for tare generation

parametres No 2 54

Generation_Tare Pyton application for tares generation No 4 49

Quality control 25%15% for larger surfaces, 10% random;

Total 577 verified polygons for 3866 Ha => 76% of the total surface

YES 7 21

Evident errors correction

Visual verification and correction derived by QC (38 polygons)

YES 3 16

New polygons generation

Pyton app for new polygons generation No 16 2

Final Quality Controls Quality Control for 5% of the new

generated polygons YES 0 43

Gen_ReportVisualStudio SW for table of comparison

No 0 11

The working map

numbers

are selected when

the class

of permanent

pasture

presence is

> 5ha

Comments and perspectives

• This 2D semi-automatic classification uses already existing

and available data (orthophotos)

• It offers a general low cost and fast production/provision vs

suface

• The accuracy can appear no high, but surely each delineated

class remains within the 4 class ranges of pro-rata (near the

mid)

• Considering the new monitoring approach, it can be useful for

speeding up the updating process, reducing costs and

providing automatic support for the areas where S2 is NOT

usable

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