1 Crop area estimation in Geoland 2 Ispra, 15/12/2011 H. Kerdiles, J. Gallego, O. Léo, MARS Unit, JRC Ispra Q. Dong, I. Piccard, R. Van Hoolst, VITO, BE S. Skakun, O. Kravchenko, Space Research Inst. NASU-NSAU, UA Xu Zhenyu, Yang Qing, Anhui Inst. For Economical Research, CN Yang Yimeng, Wang Di, Inst. for Agricultural Resources & Regional Planning, CAAS, CN

2 / 28 Outline Test on the use of hard classification of High Resolution (HR) / Mid Resolution (MR) RS data in Area Frame Sampling Test on soft classification of Low Resolution (LR) RS data

3 / 28 How to estimate crop area? -Traditionally, through list survey of a sample of farmers or area frame survey based on a sample of fields, clustered fields (segments) or points - : statistical method, estimated accuracy (function of sampling size, crop distribution & importance in the region) -  : Possible bias (farm list), heavy field work difficult for monitoring foreign countries -EO imagery (through full coverage of the region of interest) may support stratification: sampling rate adjusted to stratum -> improved accuracy for a given sampling size – VHR / HR imagery help building the area frame (national stats) – mostly VHR imagery provide crop areas estimates through image classification + correction for classification bias (calibration estimator based on field data) – mostly HR imagery; possibly LR data (through subpixel classification) help Improving field survey estimates through classification of the whole region of interest and regression estimator – mostly HR imagery

4 / 28 Demonstration: Tests in the frame of Geoland2 Regression estimator: improving AFS with HR classification Ukraine 2010, 3 oblasts, 5 types of imagery (NASU) North China Plain 2011, 1 county, 2 images (CAAS, VITO) MR/LR soft classification based on HR classification on a calibration site (VITO) : North China Plain 2009 – wheat & maize seasons Ukraine 2010 MR (MODIS 250 m) / LR (VGT 1 km) data: daily coverage but mixed pixels -> need to derive crop percentage inside each LR pixel (soft classification) -> need to calibrate relationship between LR response and crop area fractions over “calibration areas” (for which HR classification is available)

5 / 28 ZH KH K Ukraine Area km2 % crop land Kyivska (K) % Khmelnitska (KH) % Zhytomyrska (ZH) % Test area: 3 oblasts around Kiev Crop typeKKHZHTotal Oilseed rape Sugar beet66 76 Soybean62 24 vegetables32 32 Sunflower31 12 Crop typeKKHZHTotal Winter wheat Spring barley Potato maize Crop distribution: % of crop area over main cropland area (2007 stats)

6 / 28 Ukraine test: AFS + HR classification Objective: assess the contribution of HR & MR imagery to crop area estimates obtained by AFS -> Test 5 types of imagery: Image typeGSDframeAcquisition datesClassified area MODIS (NDVI 10 day MVC) 250 mSwath: 2300 km 13 dekads:4 in 09-10/2009, 5 in 04-06/2010, 4 in 07-08/ oblasts AWIFS 60 m370 x 370 km 4 scenes: 5/6, 28/6, 15/8, 4/93 oblasts Landsat 5 - TM 30 m180 x 185 km 75 scenes from 04 to 093 oblasts IRS LISS 3 23 m140 x 140 km 3 scenes: 11/08 (x2), 4/09Part of K RapidEye (RE) 5 m70 x 70 km 2 windows: 25&30/04, 5&6/06Part of K Nearly no HR data in spring (due to heavy cloud conditions & technical problem), esp. for LISS-3 & AWiFS 04: 1 RE and 1 TM over oblast K 05: 1 TM over oblast ZH Many TM scenes cloudy -> 3-4 composites created for each oblast

7 / 28 Methodology 30 per oblast

8 8 Stratified Area Frame Sampling Sampling based on a regular grid of 40x40 km Selection of 3 segments of 4x4 km per grid (i.e ha), with fields / segment on average ( ha / field) 3 strata derived from ESA GLOBCOVER (300m) land cover map‏ (>50% agri, 0-50% agri, no agri) -> no segment in the non agricultural stratum & estimation of crop area % per stratum Oblast 4x4 km segments 2x2 km segments Total K (Kyivska) KH (Khmelnitska) ZH (Zhytomyrska) oblasts segments photointerpreted: Chernobyl area (2) & Belarus border (1) 2x2 km segments in areas with small parcels

9 / 28 Ground survey: segments & fields along the road Segments  30 / oblast Along the road ground survey ( ~ 2x1000 fields) Data collected: GPS location, crop type and photos ZH KH K Timing: 07/2010

10 / 28 v Segment example Zhytomir region. ZH28 segment RapidEye, 06/2010 Fields below 100 m x 100 m not surveyed

11 / 28 Image pre-processing Orthorectification using GCPs taken during ground survey Conversion to TOA reflectance cloud & cloud shadow removal (Landsat Automatic Cloud Cover Assessment) Image classification using training data from the along-the-road survey Neural Network (MultiLayered Perceptron - MLP)‏ Support Vector Machine (SVM)‏ Decision Tree (See5) 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 Regression estimator applied with MODIS, TM and AWiFS MLP classifications Image Processing

12 / 28 Overall accuracies Computed on segments independent from training data Best classifier: MLP in most cases; SVM close to MLP Best image type for the tested classifiers: TM for oblast K, AWiFS for ZH and TM & MODIS for KH, due to better acquisition timing

13 / 28 Classification Ground truth Landsat-5: Classification (oblast K) 1Artificial 2'winter' 3'spring' 4'summer' 5Family garden 6Other crops 7woodland 8Perm grassland 9Bare land 10Water-wetland User Accuracy: % of pixels assigned to class X correctly classified Producer Accuracy: % of ground truth pixels of class X correctly classified Artificial Winter crops Spring crops Summer crops Fam. Garden Other crops Woodlands Perm. Grass Bare land Wetland Overall accuracy MLP: 63%

14 / 28 Regression estimator Mean % of crop C in the segments from survey Mean % of crop C in the segments from classification Mean % of crop C over the region from classification Mean % of crop C estimated over the region The reduced regression estimator variance may be obtained with a sample of n 1 segments and no imagery: Regression estimator cost efficient if the cost of RS is less than surveying n 1 -n additional segments: RS cost < (n 1 -n).p or RS cost < n(  reg -1).p where p = survey cost of 1 segment Relative efficiency R 2 = 0.71 rel_eff = 3.4 Y = a + bx + 

15 / 28 R 2 = 0.71 rel_eff = 3.4 R 2 = 0.32 rel_eff = 1.45 R 2 = 0.29 rel_eff = 1.4 R 2 = 0.48 rel_eff = 1.9 Winter wheat Maize Soya Soya + maize TM data, MLP, oblast K (34 segments)

16 / 28 Relative efficiencies Mean relative efficiencies (4 crops, 3 oblasts): TM: 1.59, MODIS: 1.54, AWiFS: 1.49

17 / 28 Comparison with official statistics ( for oblast K ) official stats within 2*σ interval of estimates Winter wheat Maize Soya Barley

18 / 28 Cost efficiency analysis Mean relative efficiency for major crops around 1.5 for all tested images RS cost efficient for free data (MODIS, Landsat), for AWiFS above 47 segments for the AFS basic survey with a relative efficiency of RS of 1.49 Type of imagery Mean rel. eff. No of segs n image cost per oblast Basic ground survey** cost without RS cost with RS Cost ratio Cost reduction due to RS MODIS % TM % AWiFS % LISS-III1.50* ,47-112% RE1.50* ,22-364% * Assumed relative efficiency * * € / segment Image processing cost: 1500€ (1 week field survey 2 staff, 1 week processing 1 staff) Reduced variance RS  segments

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20 / 28BarleyMaizeSoyaWheat July 2010

21 / 28 ZH 1Artificial 2'winter' 3'spring' 4'summer' 5Family garden 6Other crops 7woodland 8Perm grassland 9Bare land 10Water-wetland MODIS classificationKH Overall accuracy MLP & SVM: 55% Overall accuracy MLP & SVM: 40%