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Remote Sensing in Precision Agriculture. Remote Sensing The science and art of obtaining information about an object, area, or phenomenon through the.

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Presentation on theme: "Remote Sensing in Precision Agriculture. Remote Sensing The science and art of obtaining information about an object, area, or phenomenon through the."— Presentation transcript:

1 Remote Sensing in Precision Agriculture

2 Remote Sensing The science and art of obtaining information about an object, area, or phenomenon through the analysis of data acquired by a device that is not in contact with object, area, or phenomenon under investigation.The science and art of obtaining information about an object, area, or phenomenon through the analysis of data acquired by a device that is not in contact with object, area, or phenomenon under investigation.

3 Remote Sensing can divide into four stages or division based on altitude of the sensor. Remote Sensing can divide into four stages or division based on altitude of the sensor.Remote Sensing can divide into four stages or division based on altitude of the sensor. Ground Observation - approximately 0 - 50 ft.Ground Observation - approximately 0 - 50 ft. Low Altitude Airplane - <10,000 ftLow Altitude Airplane - <10,000 ft High Altitude Airplane - > 10,000 ftHigh Altitude Airplane - > 10,000 ft Satellite > 150 milesSatellite > 150 miles

4 Advantages of Ground Level Sensors Lowest per unit costLowest per unit cost With a self-contained light source, complete control over incident light which simplifies calibration and correction.With a self-contained light source, complete control over incident light which simplifies calibration and correction. Ability to collect data at any time.Ability to collect data at any time. Potential for very high resolution data collection.Potential for very high resolution data collection.

5 Data can be easily georeferenced for use in a GIS.Data can be easily georeferenced for use in a GIS.

6 Disadvantages of Ground Level Sensing Relatively high costs to scan large areas unless part of another field operation.Relatively high costs to scan large areas unless part of another field operation. Cannot simultaneously scan entire fields.Cannot simultaneously scan entire fields.

7 Turf Scanned with OSU Sensor

8 Possible Configuration of a Sensor/Applicator

9 Experimental OSU Sensor with GPS Mounted on an ATV

10 40 ac of Wheat Pasture Center Scanned with ATV

11 Interpolated Surface from ATV Scanned Data

12 Noble Foundation - Pepsi Field ATV Sensor Scan

13 Noble Foundation Pepsi Field NDVI Fixed Interval Scale

14 Noble Foundation Pepsi Field NDVI - Natural Breaks Scale

15 Advantages of Aerial Remote Sensing Can quickly scan large area.Can quickly scan large area. Cost/ac when scanning large areas is relatively low.Cost/ac when scanning large areas is relatively low. Data can be collected at high resolution < 1m.Data can be collected at high resolution < 1m.

16 Disadvantages of Aerial Remote Sensing Images must be rectified and georeferenced.Images must be rectified and georeferenced. Cost to scan small areas is high.Cost to scan small areas is high. Data can’t be collected at night or in bad weather.Data can’t be collected at night or in bad weather. Calibration must be performed on the images.Calibration must be performed on the images.

17 Methods of Optical Sensing PhotographicPhotographic Digital ImagingDigital Imaging

18 NDVI of OSU Experiment Station 1m Resolution

19 Detail of Southwest Corner

20 False Color Image Noble Foundation Red River Ranch

21 Corn at Shelton, NE NDVI Late Sept. 1997

22 3/25/98 Wheat Pasture Center 1-m Resolution NDVI Image

23 4/23/98 Wheat Pasture Center 1-m Resolution NDVI Image

24 False Color (green, red, NIR) Image < 1 m Resolution - Raw Radiometric Data (Courtesy F. Schiebe)

25 False Color (green, red, NIR) Image < 1 m Resolution – Reflectance Corrected Radiometric Data (Courtesy F. Schiebe)

26 Gray Scale Image < 1 m Resolution – Reflectance Corrected NDVI (Courtesy F. Schiebe)

27 Reflectance Corrected Gray Scale Image < 1 m Resolution – Green to Near Infrafed Ratio (Courtesy F. Schiebe)

28

29 Advantage of Satellite Sensing Historical data are readily available.Historical data are readily available. Cost/ac of large area images is vary low.Cost/ac of large area images is vary low. Very large areas can be scanned near instantaneously.Very large areas can be scanned near instantaneously. Data for radiometric bands up to 16 micro meters are available.Data for radiometric bands up to 16 micro meters are available.

30 Disadvantages of Satellite Sensing Resolution is lower than other sources.Resolution is lower than other sources. Cannot control when an area is scanned, e.g. each area is scanned every 16 to 26 days.Cannot control when an area is scanned, e.g. each area is scanned every 16 to 26 days. Correction of radiometric data because of atmospheric interference is challenging.Correction of radiometric data because of atmospheric interference is challenging.

31 Remote Sensing System Measures of Performance Spatial ResolutionSpatial Resolution Spectral ResponseSpectral Response Spectral ResolutionSpectral Resolution Frequency of CoverageFrequency of Coverage

32 Landsat Satellite Program United States NASA satellitesUnited States NASA satellites Images from Landsat 5 available from Space Imaging Corporation - www.spaceimaging.com (formerly EOSATImages from Landsat 5 available from Space Imaging Corporation - www.spaceimaging.com (formerly EOSAT Images from Landsat 7 available from USGS, Sioux Falls, South DakotaImages from Landsat 7 available from USGS, Sioux Falls, South Dakota

33 Landsat Satellites Landsat Scene185 km x 185 kmLandsat Scene185 km x 185 km TM quantatization Range 256 (8 bits)TM quantatization Range 256 (8 bits) 16 day repeat cycle per satellite16 day repeat cycle per satellite Currently only one satellite is operationalCurrently only one satellite is operational Satellite crosses the equator at 9:45 local time (North to South Pass)Satellite crosses the equator at 9:45 local time (North to South Pass)

34 Sensor Used on Current Landsat Satellites

35 Landsat Thematic Mapper (TM)

36 TM Spectral Bands

37 Landsat TM Bands

38

39 TM Image North Central Oklahoma April, 1998

40 April 23,1998 TM Scene over North Central Oklahoma

41 Systeme Pour l’Observation de la Terre (SPOT) Orbit repeats every 26 daysOrbit repeats every 26 days 60 km wide field-of-view per camera or 117 km field of view with both units60 km wide field-of-view per camera or 117 km field of view with both units Quantatization Range 256 (8 bits)Quantatization Range 256 (8 bits) Images available through www.spot.comImages available through www.spot.com

42 Systeme Pour l’Observation de la Terre (SPOT)

43 SPOT XS image

44 SPOT Pan image

45 Indian Research Satellite IRS - LISS 3 Satellites 23 m Resolution 4 bands23 m Resolution 4 bands 5 m Resolution - Panchromatic5 m Resolution - Panchromatic 142 by 145 km Image Size142 by 145 km Image Size 24 day repeat cycle24 day repeat cycle Images available through Spaceimaging at www.spaceimaging.comImages available through Spaceimaging at www.spaceimaging.com

46 IRS-LISS

47 IKONIS ResolutionResolution –4 m multispectral –1 m Panchromatic Scene size is approximately 7 miles by 7 milesScene size is approximately 7 miles by 7 miles Scenes are available from Space ImagingScenes are available from Space Imaging Farm size images marketed by Earthscan Network, a subsidiary of DTNFarm size images marketed by Earthscan Network, a subsidiary of DTN

48 IKONOS

49 Steps to Utilize Remote Sensed Data (modified from JD text Collect dataCollect data Process image data (rectification, radiometric correction, and georeferencing)Process image data (rectification, radiometric correction, and georeferencing) Examine image and analyze statistical dataExamine image and analyze statistical data Perform ground truthing of remote- sensed dataPerform ground truthing of remote- sensed data

50 Steps to Utilize Remote Sensed Data (modified from JD text Incorporate remote sensed and ground truth data into a GISIncorporate remote sensed and ground truth data into a GIS Develop calibration equations for remote sensed dataDevelop calibration equations for remote sensed data Identify cause-effect relationships among measured variables and crop conditionsIdentify cause-effect relationships among measured variables and crop conditions Treat regions in fields (management zones) based on information generatedTreat regions in fields (management zones) based on information generated

51 Marshall Wheat Pasture Center Calibration Data


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