Spot Image and its partners add value to

satellite imagery in Agriculture

The use of Spot imagery

in support of crop area estimates

in South Africa Fanie FERREIRA

An applicative session on

The use of Spot imagery in support of crop area estimates in

South Africa

Fanie FERREIRA

GeoTerraImage

National Crop Statistics Consortium

• Agricultural Research Council– Institute Soil Climate &Water:

• Yield modelling research

– Summer Grain Institute: objective yield – maize• Field measurements:

– Small Grain Institute: objective yield – wheat• Field measurements

• SiQ– Aerial surveys & telephonic interviews – Statistical processing

• GeoTerraImage– Satellite image processing– Crop type classifications

Overview

• Field Boundary Mapping– Stratification of Agricultural activity

• PICES: Producer Independent Crop Estimate Survey– Aerial Surveys: crop type area per province– Verified during the Gauteng census project:

• difference < 1.8%

• Crop Calendar– Understanding crop evolution/phenology

• Crop Type Classification– Image processing & classification of satellite imagery

• Classified Field Boundaries– Various applications

Use of satellite imagerySPOT4 / LANDSAT

Previous seasons 2006/7/8

Area calculation @ field level

Field crop boundary

In-season 2009

Satellite Analysis

PICES survey @ provincial level

SPOT5

Stratification

• Rate of interview refusal increased

• Requirement: Develop new methodologyProducer Independent Crop Estimate SurveyStratification: Field Boundary on 2.5m Spot5

Full refusals

0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

6.00%

2002/2003 2003/2004 2004/2005 2005/2006 2006/2007

Field Boundary Mapping

• Digitising manual to ensure consistency

• Crop Field Boundaries: every cultivated field– Cultivation, Irrigation, Smallholdings– Orchards (Horti/Viticulture), Subsistence

• Irrigation– Centre pivots

Province Stratification MappedFields Reduction % ReductionFree State 10,794,982 3,712,625 7,082,357 65.61%North West 5,776,803 1,921,927 3,854,876 66.73%Mpumalanga 4,118,568 1,103,706 3,014,862 73.20%

Advantage of Field Boundaries: Survey Area (Ha) Reduced

SA coverage: 13 million ha

PICES: Sample selection

Shortest routing algorithmPoints to be surveyedGauteng Province

PICES Infrastructure

PICES: aerial survey

Additional points used for image training

Selected fields with identified crop types

• Vast improvement: survey efficiency

• Support image classification

• Statistical calculated of area

Season Selected Additional TotalWinter2006 1628 2500 4128Summer2007 1605 6211 7816Winter2007 1774 6112 7886Summer2008 1602 5568 7170

PICES Crop Type Points: Freestate Province

Province Selected Additional TotalMpumalanga 1048 1852 2900Freestate 1582 2618 4200NorthWest 1288 1812 3100Gauteng 319 230 549Total 4237 6512 10749

PICES Crop Type Points/Province: Summer 2006

Crop Type Development

• Seasons– Temperature: Summer vs Winter – Summer rainfall vs Winter rainfall

• Vegetative growth– Annual vs Perennial– Annual: germination, growth, senescence, harvest– Perennial: deciduous vs evergreen

• Cultivation Practices– Field preparation: fallow / bare soil– Planting dates

Crop Evolution: Multi Season

R/S Process Sequence

• Selecting cloud free images

• Ortho-rectification

• Mapping cloud areas

• Building image sets: optimal cloud free

• Indices: NDVI, Tasseled Cap, PCA, BSI

• Select bands for best discrimination

• Image Calibration: PICES Crop Types

• Classification: Supervised – user defined classes

• Field Boundaries (shp) populate: Zonal Majority

Freestate Province

SouthAfrica Freestate % of SAArea:ha 2,551,800 1,020,000 39.97%Yield(ton/ha) 2.79 2.80Production 7,125,000 2,855,000 40.07%

SouthAfrica Freestate % of SAArea:ha 2,799,000 1,170,000 41.80%Yield(ton/ha) 4.54 4.21Production 12,700,000 4,928,000 38.80%

SouthAfrica Freestate % of SAArea:ha 2,427,500 955,000 39.34%Yield(ton/ha) 4.74 4.53Production 11,513,950 4,323,750 37.55%

Maize 2009

Maize 2007

Maize 2008

Classification Procedure

– Erdas: Supervised classification• Maximium likelihood combined with Parallelepiped

– Calibration / Training• Generate signature file from PICES crop types

• Buffer field boundaries -60m: remove edge pixel

– Heterogenity within field cause confusion– Evaluate spectral parametres

• Select spectral bands from profile: bands vs crops

• Class confusion/conflict – Set Std Dev

– Field parcel (shp) populated: crop type – Zonal majority function & record majority fraction

– Irrigated maize vs rainfed maize: 12% higher fraction

Classification Analysis

Maize Dominant Area

CropType Count Area Ha Avg Ha % Area Count Area Ha Avg Ha % AreaFallowWeed 946 31306 33 5.31% 1494 58210 39 9.87%Groundnuts 546 20715 38 3.51% 551 21640 39 3.67%Maize 8699 349990 40 59.37% 7958 323826 41 54.93%MaizeWheatPivot 210 4290 20 0.73% 125 2759 22 0.47%Pasture 4167 93817 23 15.91% 4420 104479 24 17.72%SoyaBeans 4 217 54 0.04% 1 26 26 0.00%Sorghum 6 61 10 0.01% 0 0 0 0.00%Sunflower 598 20762 35 3.52% 1663 56631 34 9.61%Wheat 1813 66835 37 11.34% 895 24477 27 4.15%WinterGrazing 89 1509 17 0.26% 109 2460 23 0.42%Total 17078 589504 35 17216 594509 35

Cropping Season 2007 Cropping Season 2008

Wheat Dominant Area

CropType Count Area Ha Avg Ha %Area Count Area Ha Avg Ha %AreaFallowWeed 4133 71683 17 16.49% 3921 73450 19 16.90%Maize 6948 140457 20 32.32% 8430 164280 19 37.80%Pasture 7229 94960 13 21.85% 6992 96639 14 22.23%Sorghum 6 52 9 0.01% 91 946 10 0.22%SoyaBeans 1183 21736 18 5.00% 1059 18028 17 4.15%Sunflower 375 7030 19 1.62% 639 12002 19 2.76%Wheat 4210 90152 21 20.74% 3524 67004 19 15.42%WinterGrazing 634 8580 14 1.97% 126 1739 14 0.40%Total 24718 434650 18 24782 434088 18

Cropping Season 2007 Cropping Season 2008

Maize Comparison: 2007vs2008

Spatial Distribution Cultivated area

Crop type classification

District level comparison:

Maize area / district

SoyaBean Comparison: 2007vs2008Spatial Distribution Cultivated area

Crop type classification

District level comparison:

Soya area / district

Conclusion• Integrated Processing Chain

– Based on Spot 4 & 5 imagery

• Spot 5 imagery provided complete coverage– Field Boundaries: Improved stratification

• Large reduction in area to survey: Reduced costs (8X)• Accuracy increased

• Spot 4 imagery regular recordings– Complete cloud free coverage

• Calculation of district level area / field level• Similar zones can be calculated• Visualise cropping patterns & trends• Valuable for agro-industry planning

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