Pastures pro-rata coefficients Semi automatic classification in Italy AGEA

Transcription

1 Pastures pro-rata coefficients Semi automatic classification in Italy AGEA

2 Background 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

3 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 3

4 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 Animal paths Natural waterhole Manmade waterhole 2D classification, 6 cm pixel Radicofani test site Siena, July, 28 th 2016 Grazing sheep during the Drone flight

5 New Technologies: Drones 3D classification for better grazing analysis > 3m 0,26ha 2,0% 2-3m 0,44ha 3,3% Total sample area: 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% Water Roads 1 2 m 1water 0,5-1m 0,37ha 2,8% 0,93ha 7,0% 0,08 ha 0,6% 0,2 ha 1,5%

6 AGEA methodology for a cost effective pro-rata land cover 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 6

7 Automatic rocks/high vegetation extraction 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 7

8 Automatic rocks/high vegetation extraction Output: 5 polygons instead 3 4 at 20% and 1 at 50% Increasing of eligible surface higher vegetation extraction using 321,421 and 431 bands composition 8

9 Example of pro-rata semi automatic extraction (Lazio)-1 20 cm Orthophoto 4 bands 1,2,3,4 9

10 Example of pro-rata semi automatic extraction (Lazio) - 2 Rock extraction: Spectral signature (adapted zone by zone) 10

11 Example of pro-rata semi automatic extraction (Lazio) -3 Higher vegetation Extraction: Spectral signature Texture variability Shadow gradient (in improvement) 11

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

13 Example of pro-rata semi automatic extraction (south: Calabria) -1 13

14 Example of pro-rata semi automatic extraction (south: Calabria) -2 Rock extraction 14

15 Example of pro-rata semi automatic extraction (south: Calabria) - 3 Higher vegetation extraction residual improvements for shadow calculation and reduction to be done 15

16 Morphologic issues overlapping 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 LPIS year N Polygons Surface (SKM) Eligible (SKM) , , , , , ,84 Italian LPIS numbers The blu polygon indicates a portion > 70% sloping 16

17 Automatic pro-rata land cover tasks: example of working time LAYER PRO-RATA semi-automatic classification Example of processing for a Province N LPIS polygons Surface (Ha) Eligible surface (Ha) initial initial 3489 final final 3390 TASKS and working times Task phases Activity Operator Hours Minutes Crop_File Pyton procedure for imagery clipping Boundary Box of pro-rata polygon) No 0 44 Buff&Simplify Pyton proc for working polygon generation No 0 28 Gen_Work_File Pyton proc for image treatment: noise, cross correlation index No 3 42 Create_Hist_File VisualStudio SW for tare generation parametres No 2 54 Generation_Tare Pyton application for tares generation No 4 49 Quality control 25% Evident errors correction New polygons generation Final Quality Controls 15% for larger surfaces, 10% random; Total 577 verified polygons for 3866 Ha => 76% of the total surface Visual verification and correction derived by QC (38 polygons) YES 7 21 YES 3 16 Pyton app for new polygons generation No 16 2 Quality Control for 5% of the new generated polygons YES 0 43 The working map numbers are selected when the class of permanent pasture presence is > 5ha Gen_Report VisualStudio SW for table of comparison No

18 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 18