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Crop area estimation in Geoland 2 Ispra , 14-15/05/2012

Crop area estimation in Geoland 2 Ispra , 14-15/05/2012. I. Ukraine region II. North China Plain. I. Crop area estimates over Ukraine region. 1. Study Area. Test area: 3 oblasts around Kiev. ZH. K. KH. Crop distribution: % of crop area over main cropland area (2007 stats).

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Crop area estimation in Geoland 2 Ispra , 14-15/05/2012

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  1. Crop area estimation in Geoland 2Ispra, 14-15/05/2012 I. UkraineregionII. North China Plain

  2. I. Cropareaestimates over Ukraineregion

  3. 1. Study Area Test area: 3 oblasts around Kiev ZH K KH Crop distribution: % of crop area over main cropland area (2007 stats)

  4. 2. Hard classifications • Groundsurvey + high resolutionimagery land cover maps • Groundsurvey • Segments & along the road • HR imagery • AWIFS • Landsat 5 – TM • IRS LISS 3 • RapidEye (RE) • Problem: • Heavy cloud conditions in spring • 10 Classes: Artificial-urban, winter(winter & spring wheat, rapeseed), spring(winter & spring barley), summer(maize, potatoes, sugar beet, sunflower, soybean, vegetables), family gardens, other crops, woodland, permanent grassland, bare land, water & wetland • For the sub-pixel approach  merged winter & spring

  5. 1 Artificial 2 'winter' 3 'spring' 4 'summer' 5 Family garden 6 Other crops 7 woodland 8 Perm grassland 9 Bare land 10 Water-wetland 3. Choice of high resolution LC map Landsat 5-TM (Kiev Oblast) • 75 scenes from 04 to 09 • Overall accuracy MLP: 63% • Stripes at the overlapping areas of TM-frames •  Masked

  6. Hard classifcation map LR soft classification:Principle TRAINING DATA Creation of AFIs INPUT data MODIS/VGT NDVI time series [april-september] - 11 images NEURAL NETWORK Crop area % = f(NDVI) Training pixels sampled every 8 rows & columns Reference AF for each LR pixel derived from hard classification OUTPUT data Estimated AFIs (Area Fraction Images) winter crops summer crops Family gardens grassland forest Other crops

  7. 4. Neural network: choice of the number of hidden nodes Ukraine 2010 • Evolution of the determination coefficient of the regression, at pixel level, between the reference area fraction (derived from HR hard classification) and the soft classification area faction for all classes pooled together as a function of the number of hidden nodes • Model chosen: • 4 hidden nodes in Ukraine, 11 MODIS NDVI, 8 classes -> 88 weights

  8. 5. Assessment of crop area fractions at pixel level • For Kiev oblast • Winter & spring crops merged • All pixels considered for the correlation

  9. 6. Assessment at district level Correlation of class area fractions aggregated at district level for the Kiev oblast district

  10. 7. Use of soft classification with AFS Maize • Predicting crop % from ground survey from class (winter crops, summer crops) % from soft classification • Area fractions aggregated per segment Winter wheat

  11. II. North China Plain

  12. 1. Objective • Problem: • difficulty of acquiring HR imagery at optimal timing • ground survey = cost and time consuming • Estimation crop areas for ongoing season • Solution ? • Use the sub-pixel classification approach. • Spatial and temporal extrapolation of a Neural Network

  13. 2. Temporal extrapolation Referenceyear • 1. Perform a hard classification (groundsurvey, collection of high resolution data, classify) for a certainyear. • 2 .Use the hard classification and moderate resolution data of the referenceyear to train a neuralnetwork. • 3. Apply the neuralnetworkon moderate resolution data for the consecutiveyears. • Condition : Interannualvariation in temporal NDVI response is minor and has little effect onNeuralNetwork performance (recognizingcropspecific NDVI profiles).

  14. 3. Spatialextrapolation • 1. Perform a hard classification (groundsurvey, collection of high resolution data, classify) on a referencearea. • 2 .Use the hard classification to train a neuralnetwork. • 3. Apply the neuralnetworkon moderate resolution data for a widerarea. • Condition : Phenologicaldifferences over the region of interest is minor and has little effect onNeuralNetwork performance (recognizingcropspecific NDVI profiles). Training area

  15. 4. Collection of hard classifications • 2005: 2 TM classif • 2006: 1 LISS classif • 2007: 3 TM-classif • 2 AWiFSclassif • 2009: 1 TM classif • 1 AWiFSclassif • name = YYYYwt_sen • YYYY = year • wt = winter wheat • sen = sensor 2006 2005 2009 2007 Workon winter wheatestimations

  16. 5. CALIBRATION • 10 dekadal SPOT-VGT images • [11 feb – 31 may] • Training pixel sampling: every 8th row/column • Use the sameyear as the hard classification

  17. OUTPUT = 6 EstimatedAreaFraction images for winter wheat, oneforeachseason 6. APPLICATION 2005 2006 NEURAL NETWORK 4 hiddennodes 2007 2008 2009 2010

  18. 7. VALIDATION • Visual inspectioncroppatterns • Temporal and spatialconsitency check • Comparewith official statistics • Collection of official statisticsfor the 60 districts • [1994-2009]

  19. 7. VALIDATION – visualinspectioncroppatterns Unstablecroppattern – 05_tmb_NN Stablecroppattern – 07_awg_NN

  20. 7. VALIDATION – Comparewith official statistics • For the 60 districts: • Collection of official statistics [1994-2009] • Caclulate the estimatedcrop areas foreverysub-pixelclassificationper district

  21. 7. VALIDATION – Comparewith official statistics Example: ComparissonEstimatedAreaFractionfor 2005 with official statisticsfor 2005 For the NeuralNetwork 05_tmb_NN 1. Spatial 2. Scatterplot

  22. 7. VALIDATION – Comparewith official statistics 07_awa_NN, 07awg_NN, 09tm_NN  stable

  23. 7. VALIDATION – Comparewith official statistics LOW PERFORMANCE NeuralNetwork: 05_tmb_NN R2 = 0.0 R2 = 0.13 R2 = 0.07 R2 = 0.21 R2 = 0.10 HIGH PERFORMANCE NeuralNetwork: 09_tm_NN R2 = 0.74 R2 = 0.75 R2 = 0.64 R2 = 0.77 R2 = 0.68

  24. 8. DISCUSSION • Heterogeneous reference dataset • single TM-frame HR classifications are not capable to act as an input for the sub-pixel approach over the North China Plain • mountain areas are known by strong underestimations • Winter wheat area is less important • Impact on results • Remove from further analysis

  25. 8. DISCUSSION – phenology • Phenologicaldifferences – spatialy • North China Plain N-Z extent > 1000 km • Different agro-ecological zones • Reflected in NDVI-profile • Impact oncropspecificNeuralNetworkspectralprofilerecognition • divideNorth China Plain a priori in agro-ecological zones BaodingShi LiaochengShi ZhoukouShi Time windowused in sub-pixelapproach

  26. 8. DISCUSSION – phenology • Phenologicaldifferences – temporaly • Climatologicalconditionsdifferfromyear to year

  27. 9. Conclusion • Spatial and temporal extrapolation of the sub-pixel approach is limited to hard classifications with a sufficient high coverage of the region of interest • AWIFS classification of 2007 and TM classification of 2009 provide the best training data • The extent of the North China Plain is too large to use a single high resolution image as reference for the sub-pixel approach for the whole region • Divide the North China Plain in smaller agro-ecological zones or provinces • Focus on wheat dense areas

  28. THANK YOU

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