application of multi-temporal & multi-frequency...
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Application of MultiApplication of Multi--temporal & Multitemporal & Multi--frequency frequency Polarimetric Polarimetric SAR for operational crop inventory SAR for operational crop inventory
in Canadain CanadaHeather McNairn, Jiali Shang, Xianfeng Jiao, Catherine Champagne
Research Branch, Agriculture and Agri-food Canada, Ottawa, [email protected], [email protected]
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Presentation Outline
• Agriculture and agriculture remote sensing in Canada
• Crop classification using Earth Observation data• Contribution of Radar to crop identification• Results and discussion• Future activities
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Agriculture in Canada
• Total population 33 million people
• 700,000 km2 farmland
• 5 acres of farmland per person
• provides 1 in 8 jobs and accounts for 8% of Canada’s GDP
• 5th largest exporter of agriculture products
• accounts for about 20% of the total world exports of wheat and wheat flour (10 year average)
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Need for Agriculture Land Use Information
• Annual information on agriculture land use would permit more efficient and effective delivery of agricultural programs and policies
• Land use map is needed for deriving agriculture indicators used in modelling
• Also needed for risk management, un-seeded acreage, hail damage
• To monitor environmental threats due to surface runoff of fertilizer, herbicide and pesticide for the safety of the population
• Annual crop inventory system is needed
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EO-Based Crop Inventory in AAFC
• AAFC is required to deliver information on agriculture land use annually
• For operational mapping, need to find a method that works consistently over different sites across Canada and repeatable over multiple years
• Specific research questions:– What satellite data (optical, SAR or both) are needed to
accurately classify crop types across Canadian landscapes? – When are the critical times during the growing season to
collect these data?• Target accuracy: 85%
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AAFC Pilot Studies for Method Testing (2004-2007)
Swift Current
Winnipeg
• Multi-temporal optical data or a combination of radar and optical data can successfully classify crops
• Overall accuracies of 85% were achieved, and most major crops (corn, wheat and soybean) were also classified to this level of accuracy
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Challenges and Opportunities
• When available, multi-temporal optical data are ideal for crop classification
• Timing of these acquisitions is critical. Optical data acquired later in the growing season has been found to provide the best overall classification accuracy, but this presents a challenge to operational crop mapping:– Dependency on late season imagery prevents the crop identification (and
potentially acreage estimation) at an earlier point in the growing season
• To circumvent this problem, a multi-frequency approaches was adopted:– Integration of multi-frequency SAR (C- and L-band) will provide richer
information content which could lead to successful crop identification earlier in the season
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Test on Multi-frequency SAR Synergy
• Study Site– CFIA Farm is centred at 45º13’N, 75 º 46’W, Ontario, Canada,
about 25km x 40km– The main crop types are corn, cereal, soybean, and
pasture/forage• Ground truth data
– 2 field visits were made over the growing season to ensure data quality and to note variations in growth stage, harvest time, and changes in crop type (due to recording errors and under-seeding)
– A total of 234 fields were visited, 67 fields were used for training and the other 67 were for testing the classification accuracy
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Satellite Data Collection
Table 1. Data Acquisitions over the 2006 growing season
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June 5July 7August 8
Early Season Mid Season Late Season
May 19July 4August 19
Hay/pastureCornSoybeanOats
L-5
ALOS
Impact on Timing of Data Acquisition
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Image Classification
• Supervised classification was performed on various combinations of optical and radar data using a decision tree classifier
• All classifications were preformed on a per-pixel basis without a null class
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• Single-date PALSAR is not adequate for crop classification• Multi-temporal and multi-polarization L-Band data alone can achieve an overall
accuracy of 70%• The cross-polarizations (HV or VH) produced the highest accuracies of the L-
Band linear polarizations
Results and Discussion: I
Table 2. Crop Classification Using Multi-temporal & Multi-polarization ALOS
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Results and Discussion: II
Table 3. L-Band, C-Band SAR Comparison and Integration
0.8186.192.996.676.976.73TM (Jun, Jul, Aug)
0.4056.325.662.550.883.93 RSAT C-band HH
0.5970.132.994.770.462.63 ALOS – all linear polarizations
0.4962.527.291.450.863.33 ALOS L-band HH
0.77
0.68
Kappa
76.544.996.471.879.13 ALOS + 3 RSAT
83.156.896.782.086.9All radar (3 ALOS, 3 RSAT-1, 6 ASAR, )
OverallCerealCornSoybeanHay-PastureProducer’s accuracies
• Comparing L- and C-Band at the same polarization (HH), L-Band is better• L-Band is much better for classifying large biomass crops (corn) while C-
Band is better for low biomass crops (hay-pasture)• Integrating multi-temporal L-band (ALOS) and C-band (RSAT, ASAR)
provided improved accuracy• Multi-temporal and multi-frequency radar can achieve overall accuracy of
83%. A radar alone method could be viable
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Results and Discussion: III
Table 4. Comparison of Classification Between Optical and SAR Data
• Multi-frequency and multi-polarization SAR provides competitive overall and crop level accuracies achieved using Landsat data.
• To gather such a SAR data set together still requires the integration of data from several satellite platforms.
• Building the frequency diversity still requires acquisitions from multiple satellites. With the quad-pol capability of RADARSAT-2, fewer images will be needed.
Hay-Pasture Soybean Corn Cereals U = User’s Accuracy P = Producer’s Accuracy
U P U P U P U P Overall
3 Landsat Images 84.5 78.1 96.9 79.6 88.7 98.2 80.0 94.9 88.0 All SAR Images (L- and C-Band) 68.1 98.1 96.2 88.0 96.6 98.1 97.7 64.5 88.7
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Results and Discussion: IV
Table 5. L-Band Polarimetric Decomposition for Crop Classification
• Polarimetric decompositions provided better results than linear polarizations • For single-date L-band, the Cloude-Pottier decomposition achieved pronounced
improvement in classification accuracy for the July acquisition• When two or more dates of PALSAR data were used in the classifier, accuracies
improved significantly
May 19
July 4 Aug 19
JulyAug
MayJulyAug
Linear Polarization(HH, HV, VV, VH)
49.4 53.4 50.1 60.5 70.1
Cloude-Pottier(H/α/A)
53.5 64.7 53.3 71.2 74.4
Freeman-Durden (double, volume, single bounce)
49.0 55.7 50.2 66.4 73.8
Krogager (helical, double, single bounce)
50.7 60.9 56.4 68.2 77.2
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Final Map Product (Post-classification Filtered) South Ottawa area (2006)
0 4 6 km
75º51’34” W 75º28’19” W75º39’57” W45º22’27” N
45º18’56” N
45º01’18” N
45º15’24” N
45º11’53” N
45º04’49” N
45º08’21” N
Water
UrbanShrub landWetlandHay-PastureSoybeanCornCerealBuckwheatForestRoad Network
Bare
Legend
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Conclusions
• L-band data outperforms the C-band data for larger biomass crops (corn with 86% of producer’s accuracy and 95% of user’s accuracy)
• For lower biomass crops (cereals), C-band data are needed• With the current method, even with a temporal rich series of images, the
collection of SAR data in multi-frequencies is needed• Using parameters derived from multi-temporal polarimetric PALSAR data,
all three decomposition approaches produced superior crop classification accuracies relative to L-Band linear polarizations
• Results from this study emphasize the value of polarimetric as well as multi-frequency SAR data for crop classification
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Future Activities
• Integrate additional radar frequency (TerraSAR-x) • Apply polarimetric SAR decomposition to ALOS
and RADARSAT-2 data• Incorporating radar texture information• Introducing a hierarchical approach into the
classifications
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Acknowledgements
• Dr. Ridha Touzi (Canada Centre for Remote Sensing)
• Alaska Satellite Facility• JAXA• Canadian Space Agency
Thank You