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Automated classification of land cover for the needs of CAP using Sentinel data. A proactive approach
- 1. Automated classification of land cover for the needs of CAP using Sentinel data. A proactive approach. PanagiotisILIAS(NP) p_ilias@neuropublic.gr DimitrisKAPNIAS(GAIA) d_kapnias@c-gaia.gr 22nd CAP/IACS conference, 24-25 November 2016, Lisbon - Portugal
- 2. Farmers’ Organizations 70 Coops 150 K Farmers 0.75 B € Turnover 0.5 M ha Area Neuropublic Gaia Cloud Platform AgriTech Know-How AgriTech Solutions Smart Farming Piraeus Bank No 1 GR Contract Farming AgriCard Young Farmer loans The shareholders legacy to the coalition Farmers Tech Bank
- 4. Society Management Subsidy (CAP) Commerce Smart Farming 92 Farmer Service Points (FSP) certified as AID Application assistants to the beneficiaries GAIA’s involvement in the Aid Application Scheme Nationwide Network of local service advisors (FSP) GAIA is certified as the coordination & support body (2014-2020)
- 5. SupportTraining Advice Facts Data Weather Forecast Remote Sensing Proximity Sensing Field Sensors Farm Data Data fusion Data assimilation Data interpolation Farmer Eligibility status? Parcel crop group? Coupled payments & greening requirements? Which choices to make? Which financial instruments to use? How can I support my business? How much water and when? When do I have to spray? What is the risk level? What is the precise type and the exact amount of fertilizers needed? Supporting Farmer using Data – Advisory Services
- 6. Atmospheric Soil Data GAIA Cloud RPAS IoT GAIA Sense Satellite Sensors Weather Data Fusion Decision Making Data Analysis API GAIA Smart Farm & GAIA Subsidy (CAP) Advisor Farmer Data Collection Other sources IoT and Cloud Infrastructure for Agricultural Monitoring Other Platforms Other Platforms large scale agricultural areas Sentinel-2 • Sort revisit • HR • MS • Open
- 7. Subsidies control Smart Farming Services The role of Sentinels On-the-fly automated cross checks of parcels declaration (proactive control): • Reducing errors in the location of parcels • Discouraging false claims Full scale automatic compliance cross checks (post- declaration control): • crop-wise in terms of modelling and time windows • Allows better risk analysis, less RFV, more effective controls • Reducing overall error rates Pest management/Early hazard warnings Irrigation Fertilisation CAP eligibility - Compliance Sentinel can been used for the multitemporal extraction of vegetation, soil and water indices and the monitoring of the phenological growth. Parcel Level
- 8. Multi-Temporal Object-based Monitoring Collect Store Define Objects Assign Properties Create Models Decision Making Take Actions Monitor Actions [ 0. 0.2 0.5 0.8 0.2 0.7 0.2 0.9 0.7 0. 0.74 0.97 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.39 1. 0.07 0.01 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.1 1. 0.26 0.06 0.04 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.14 0.03 0. 0. 0. 0. ] Time Space Vegetation – Soil – water Indices at parcel level, for (single or multi) crop growing period Crop Models Parcel = Object Cloud based services that integrate earth observation data with Image Processing, Machine Learning, Spatial Modeling
- 9. S2 Collection dates Extracted Indices patterns agree with phenological growth for the specific crop (beans) Multitemporal Visualization of the Prespa area, for the growth period, using indices values (R=NDVI, G=NDWI, B=SAVI) Temperature data for area Leukonas - Prespes Rainfall data for area Leukonas - Prespes Indices in Agricultural Monitoring Indices allows us too visualize the behavior of specific crops at parcel level through time.
- 10. Land Cover Classification using +Indices b1 b2 b3 bn Image (b1, b2, b3, … , bn) Object (reference parcel or holding LPIS) Land Cover Class System Crop Code & Crop Description 1.1.1 xxxxxxxxxxxx1.1.1 Segment (Polygon: x1y1, x2y2, x3y3, x4y4) PHASE 2: Descriptor Extraction for each object per Image Feature Extraction t22 All pixels All Bands or/ and Indices SIFT SURF ORB CHIST Method(s) [ 0, 0, 0, 32, 0 , 0, …., 123, 0 ] Descriptor (scaling) PHASE 1: Indices (i) extraction using atmospherically corrected bands (b) PHASE 3: Model Creation (data driven) Train set Input (time series data for polygons) Output( Labe)l [ 0, 0, 0, 22, 0, 5, …., 100, 0, 12/04/2016] [1.1.1] [ 0, 0, 0, 32, 0, 0, …., 123, 0, 07/05/2016] [1.1.1] ……. [ 4, 1, 1, 22, 0, 0, …., 123, 0, 22/10/2016] [1.1.1] Training SVM RF NN Evaluation PHASE 4: Crop estimation for other parcels using the data-driven input -output model(s) Input (time series data for a polygon) [ 0, 0, 0, 22, 0 , 5, …., 100, 0, 12/8/2016 ] ……. [ 4, 1, 1, 22, 0 , 0, …., 123, 0, 12/8/2016 ] Land Cover Classification Model Model Multi-Dimensional Dimension varies i1 i2 i3 in Image (i1, i2, i3, … , in) b1 b2 b3 bn Image (b1, b2, b3, … , bn) Object (reference parcel or holding LPIS) Land Cover Class System Crop Code & Crop Description 1.1.1 xxxxxxxxxxxx1.1.1 Segment (Polygon: x1y1, x2y2, x3y3, x4y4) PHASE 2: Descriptor Extraction for each object per Image Feature Extraction t22 All pixels All Bands or/ and Indices SIFT SURF ORB CHIST Method(s) [ 0, 0, 0, 32, 0 , 0, …., 123, 0 ] Descriptor (scaling) PHASE 1: Indices (i) extraction using atmospherically corrected bands (b) PHASE 3: Model Creation (data driven) Train set Input (time series data for polygons) Output( Labe)l [ 0, 0, 0, 22, 0, 5, …., 100, 0, 12/04/2016] [1.1.1] [ 0, 0, 0, 32, 0, 0, …., 123, 0, 07/05/2016] [1.1.1] ……. [ 4, 1, 1, 22, 0, 0, …., 123, 0, 22/10/2016] [1.1.1] Training SVM RF NN Evaluation PHASE 4: Crop estimation for other parcels using the data-driven input -output model(s) Input (time series data for a polygon) [ 0, 0, 0, 22, 0 , 5, …., 100, 0, 12/8/2016 ] ……. [ 4, 1, 1, 22, 0 , 0, …., 123, 0, 12/8/2016 ] Land Cover Classification Model Model Multi-Dimensional Dimension varies i1 i2 i3 in Image (i1, i2, i3, … , in) b1 b2 b3 bn Image (b1, b2, b3, … , bn) Object (reference parcel or holding LPIS) Land Cover Class System Crop Code & Crop Description 1.1.1 xxxxxxxxxxxx1.1.1 Segment (Polygon: x1y1, x2y2, x3y3, x4y4) PHASE 2: Descriptor Extraction for each object per Image Feature Extraction t22 All pixels All Bands or/ and Indices SIFT SURF ORB CHIST Method(s) [ 0, 0, 0, 32, 0 , 0, …., 123, 0 ] Descriptor (scaling) PHASE 1: Indices (i) extraction using atmospherically corrected bands (b) PHASE 3: Model Creation (data driven) Train set Input (time series data for polygons) Output( Labe)l [ 0, 0, 0, 22, 0, 5, …., 100, 0, 12/04/2016] [1.1.1] [ 0, 0, 0, 32, 0, 0, …., 123, 0, 07/05/2016] [1.1.1] ……. [ 4, 1, 1, 22, 0, 0, …., 123, 0, 22/10/2016] [1.1.1] Training SVM RF NN Evaluation PHASE 4: Crop estimation for other parcels using the data-driven input -output model(s) Input (time series data for a polygon) [ 0, 0, 0, 22, 0 , 5, …., 100, 0, 12/8/2016 ] ……. [ 4, 1, 1, 22, 0 , 0, …., 123, 0, 12/8/2016 ] Land Cover Classification Model Model Multi-Dimensional Dimension varies i1 i2 i3 in Image (i1, i2, i3, … , in) b1 b2 b3 bn Image (b1, b2, b3, … , bn) Object (reference parcel or holding LPIS) Land Cover Class System Crop Code & Crop Description 1.1.1 xxxxxxxxxxxx1.1.1 Segment (Polygon: x1y1, x2y2, x3y3, x4y4) PHASE 2: Descriptor Extraction for each object per Image Feature Extraction t22 All pixels All Bands or/ and Indices SIFT SURF ORB CHIST Method(s) [ 0, 0, 0, 32, 0 , 0, …., 123, 0 ] Descriptor (scaling) PHASE 1: Indices (i) extraction using atmospherically corrected bands (b) PHASE 3: Model Creation (data driven) Train set Input (time series data for polygons) Output( Labe)l [ 0, 0, 0, 22, 0, 5, …., 100, 0, 12/04/2016] [1.1.1] [ 0, 0, 0, 32, 0, 0, …., 123, 0, 07/05/2016] [1.1.1] ……. [ 4, 1, 1, 22, 0, 0, …., 123, 0, 22/10/2016] [1.1.1] Training SVM RF NN Evaluation PHASE 4: Crop estimation for other parcels using the data-driven input -output model(s) Input (time series data for a polygon) [ 0, 0, 0, 22, 0 , 5, …., 100, 0, 12/8/2016 ] ……. [ 4, 1, 1, 22, 0 , 0, …., 123, 0, 12/8/2016 ] Land Cover Classification Model Model Multi-Dimensional Dimension varies i1 i2 i3 in Image (i1, i2, i3, … , in)
- 11. TRAIN SET Land Cover Classification using Indices Crop Cover (ha) Parcels No. Wheat 545 498 Stone fruits 367 364 Legumes 158 142 Maize 229 164 Trees 244 210 Fallow 354 358 Pasture 1,976 80 Total 3,873 1,816 Total Area (ha): 282,600 Agricultural Areas (ha): 53,580
- 12. TEST SET Land Cover Classification using Indices Crop Acc. (%) Wheat 90% Stone fruits 81% Legumes 87% Maize 57% Forest Trees 61% Fallow 65% Pasture 78% Total 74% Crop Cover (ha) Parcels No. Wheat 2,164 4611 Stone fruits 2,400 5045 Legumes 1,344 1588 Maize 2,917 3823 Forest Trees 549 894 Fallow 3,258 8744 Pasture 1,167 51 Total 13,799 25,264
- 13. Two back-end web services that rely on EO-S2 data: • Polygon, time period -> Times series of indices, Estimated Crop Pattern • Polygon, time period -> Crop classification GAIA Smart Farm & GAIA Subsidy (CAP) Advisor Farmer GAIA Cloud API That allows: • On-the-fly automated cross checks of parcels declaration (proactive control) • Full scale automatic compliance cross checks (post-declaration control - locate the outliers) • Crop monitoring – Smart farming (Advices based on Facts) Plus Smart Farming data: • Digital data (Valid Train set). • Other type of data describing soil and atmospheric conditions (better mapping).
- 14. Next steps: • Period 2017 – 2018: Subsidy and Smart Farming services for 16 large scale areas (pilot sites) and for 15 significant crops. • Collaboration for specific sites and crops with the partners of DataBio H2020 project (3 years from 2017 to 2020). • Enrich the modelling process with more Descriptors (SL1 + L8). • Deep Learning (for specific time windows using VHR data). • Big Data Analytics (Outliers) Thoughts - Discussions : • IACS will take advantage of other Cloud Platforms (Food Security).
- 15. Thank you for your attention Any questions? 15 PanagiotisILIAS(NP) p_ilias@neuropublic.gr DimitrisKAPNIAS(GAIA) d_kapnias@c-gaia.gr 22nd CAP/IACS conference, 24-25 November 2016, Lisbon - Portugal
Editor's Notes
- What is GAIA-Business S.A? GAIA-Business S.A. is a coalition between the farmers, the banking sector and the industry, aiming to the growth and transforming of Agriculture in Greece.
- GAIA EPICHEIREIN activities? GAIA EPICHEIREIN is certified - Farmer Service Offices (FSOs) - by the Greek Payment Agency until 2020 to coordinate the collection process, perform various cross compliance checks and develop and apply correction services. GAIA EPICHEIREIN advisory services? GAIA EPICHEIREIN also provides to the Greek farmers advisory, through the GAIA Cloud Platform. What is GAIA Subsidy? The GAIA Subsidy provide of advisory services to the farmers related to the two pillars of CAP aiming to promote the main objectives of CAP (2014-2020) aiming to simplify the whole process (including the declaration process) maximizing the effectiveness of the EU budget. What is GAIA Smart Farm? GAIA Smart Farm allow the farmers to make data-informed decisions regarding their cultivation practices. These services focus on the precise and timely application of inputs, such as fertilizers, pesticides and irrigation.
- What are the main data sources? For both services (Subsidy – Smart Farming) Generally speaking there are 5 main data sources. And our core business is to collect the data and transform them to facts and finally to advices (How, When, What, Why). What are the similarities between Subsidy – Smart Farming? Both services make use of similar cloud services and take in advance the same data sources. In a generic perspective view IACS is subset of the farming data (parcel limits, farmer id Both services trying to support the Farmer using data.
- Why it is possible to provide advices to farmers for large scale agricultural areas? The IoT and Cloud Infrastructure allows the monitoring of large scale agricultural areas and the provision advisory services to the farmer related to pest management, irrigation, fertilization and subsidy through an integrate. What is GAIA Sense? GAIA Sense is a network of GAIAtron telemetric stations, which are designed and built by NEUROPUBLIC for collecting soil and atmospheric data through the integrated sensors and for controlling actuators (e.g. for automating irrigation). The Role of Sentinel Data? Sentinels provide open, sort revisit (means high collections frequency), high resolution, multispectral data.
- The Role of Sentinel in CAP implementation? Sentinel can been used online during the declaration process, as part of a cross-check that analyses the data to examine the correctness of the declaration and provide other valuable information in terms of compliance. It can be used either to support the farmer’s decision making or provide, if need, conclusive proof to the paying agencies. The farmer can use the GAIA Subsidy services through the GAIA platform, not only to provide ancillary data relative to the control process, but also to validate its compliance during the declaration process. Through the use of Sentinel data, GAIA Subsidy services enriched the declaration process and adopted a more pre-active approach that follows the amendments of EU regulations (809/2014, 640/2014) for preliminary checks and increase of efficiency. This approach also promotes the use of data coming from other Food Security platforms, additionally to IACS data, for the agricultural policy monitoring.
- Gaia Observation integrates Remote sensing with Image Processing, Machine Learning, Spatial Modeling and Data Analytics in a cloud platform. Offering: More data More info Higher spatial and time resolution
- Phenological phases detection? And to combine vegetation's indices with the phenological stages of each crop To calculate the the NDWI from Sentinel-2, we’ll be using band 8A as the NIR band, and band 11 as the SWIR band. Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) soil adjusted vegetation index (SAVI), green soil adjusted vegetation index (GSAVI),
- Declare vs Predicted Crop