Sen2Agri Workshop Crop Type Indentification in South Africa

Transcription

1 Unleashing the power of imagery, improving your business intelligence Sen2Agri Workshop Crop Type Indentification in South Africa Fanie Ferreira

2 Content Introduction CropType: Current Approach Comparison with Sen2Agri Future Benefits of the Sen2Agri

3 DAFF: Crop Estimates Committee (CEC) National Crop Statistics Consortium (NCSC) Agricultural Research Council Institute Soil Climate &Water: Agro-meterology / Climatic conditions Summer Grain Institute: objective yield maize Field measurements: Small Grain Institute: objective yield wheat Field measurements SiQ PICES aerial surveys & interviews Statistical processing GeoTerraImage Satellite image processing Crop type classifications

4 Field Boundaries Stratification for PICES Surveys & CropTypes Centre Pivot Irrigation Irrigation (Non Pivot) Orchards: Fruit, Vineyards, Tea, Pineapples Strip Fields: Rooibos in Western Cape Groups of Small Scale / Subsistence Fields

5 SA coverage: 14 million ha

6 Mapped Fields

7 Use of satellite imagery Range of sensors SPOT 5 / SPOT 6 & 7 LANDSAT 8 & Sentinel 2A / (2B) Previous seasons 2006/7/8/9/10/11/12/13/14/15/? Winter 2016 / Summer 2017 Stratification: Update of Field crop boundaries Satellite image calibration to identify crop types PICES provincial level to determine cropped area Satellite image classification to generate crop types

8 Crop Calendar

9 Sentinel 2 Preparation Download for RSA Coverage Amazon Cloud Recording every 10 days Annual size: 50 TerraByte Output: 3 Products 4 Band 10m 6 Band 20m Landsat 8 equivalent: 10 Band 20m Derive crop specific information

10 Satellites: Landsat8 vs Sentinel2

11 Crop Type Identification Results & Output Provincial Crop Type District Summaries for Each Crop Type Track Farm Management & Crop Rotation Practices

12 Irrigation & Double Cropping Wheat Maize / Wheat - Soya Jul 2010 Aug 2010 Oct 2010 Nov 2010 Double Cropping Area (Ha): Winter Summer 2014 Province NorthWest FreeState Mpumalanga WheatMaize 10,371 25,060 3,462 WheatSoya 14,093 1,939 2,712 Dec 2010 Double Cropping Feb 2011 Area (Ha): Winter2014 Mar - Summer Apr 2011 Province NorthWest FreeState Mpumalanga WheatMaize 21,153 2,132 WheatSoya 8,446 3,099

13 District Level Summary Crop Type Area (Ha) North West District CropType Breakdown Winter 2012 / Summer 2013: Area Planted (Hectares) District Maize Pasture SoyaBeans Sunflower Wheat WheatMaize WheatSoya Fallow Lucern Total BAFOKENG BLOEMHOF BRITS CHRISTIANA COLOGNY DELAREYVILLE DITSOBOTLA GANYESA KLERKSDORP KOSTER KUDUMANE LEHURUTSHE LICHTENBURG MADIKWE MANKWE MARICO MOLOPO MORETELE POTCHEFSTROOM RUSTENBURG SCHWEIZER-RENEK SWARTRUGGENS TAUNG VENTERSDORP VRYBURG WOLMARANSSTAD Total

14 Comparison Components : GeoTerraImage / Sen2Agric Landsat 8 & Sentinel 2 Yes / Yes Calibration/Training: PICES Survey Yes / Yes Classifier: Random Forest Yes / Yes Cropland Mask No / Yes Leaf Area Index No / Yes Crop Calendar Yes / No Double Cropping Yes / No Provincial / Local Crop Region Yes / No

15 Comparison: F-Score Accuracy: GeoTerraImage / Sen2Agri Maize: 85% / 94% Sunflower: 77% / 80% SoyaBeans: 79% / 82% Planted Pasture: 86% / 89% Fallow / Bare: 81% / N/A

16 Future USDA Cropland Data Layer (CDL)

17 Sen2Agri Benefits Continuous Improvement Generate crop type inventory for all grain crop fields Cropland mask can support intentions to plant Increase accuracy Earlier delivery South African crop type inventory Sen2Agri System: improved accuracy Cloud Computing: improved delivery time

18 Next Steps Continuous Collaboration UCL Refine classification legend for South Africa Incorporate local crop distribution regions Training on Sen2Agri system Test in neighbouring SADC countries

19 Conclusion Sen2Agri Opportunity Further improvement RSA Crop Type South African crop type inventory Faster results Higher accuracy

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