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.
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. • Low Altitude Airplane - <10,000 ft • High Altitude Airplane - > 10,000 ft • Satellite > 150 miles
Advantages of Ground Level Sensors • Lowest per unit cost • With a self-contained light source, complete control over incident light which simplifies calibration and correction. • Ability to collect data at any time. • Potential for very high resolution data collection.
Disadvantages of Ground Level Sensing • Relatively high costs to scan large areas unless part of another field operation. • Cannot simultaneously scan entire fields.
700.00 0.70 600.00 0.65 0.60 500.00 0.55 0.50 0.45 400.00 0.40 0.35 300.00 0.30 0.25 200.00 0.20 0.15 100.00 100.00 200.00 300.00 400.00 Turf Scanned with OSU Sensor
Advantages of Aerial Remote Sensing • Can quickly scan large area. • Cost/ac when scanning large areas is relatively low. • Data can be collected at high resolution < 1m.
Disadvantages of Aerial Remote Sensing • Images must be rectified and georeferenced. • Cost to scan small areas is high. • Data can’t be collected at night or in bad weather. • Calibration must be performed on the images.
Methods of Optical Sensing • Photographic • Digital Imaging
False Color (green, red, NIR) Image < 1 m Resolution - Raw Radiometric Data(Courtesy F. Schiebe)
False Color (green, red, NIR) Image < 1 m Resolution – Reflectance Corrected Radiometric Data (Courtesy F. Schiebe)
Gray Scale Image < 1 m Resolution – Reflectance Corrected NDVI (Courtesy F. Schiebe)
Reflectance Corrected Gray Scale Image < 1 m Resolution – Green to Near Infrafed Ratio(Courtesy F. Schiebe)
Advantage of Satellite Sensing • Historical data are readily available. • Cost/ac of large area images is vary low. • Very large areas can be scanned near instantaneously. • Data for radiometric bands up to 16 micro meters are available.
Disadvantages of Satellite Sensing • Resolution is lower than other sources. • 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.
Remote Sensing System Measures of Performance • Spatial Resolution • Spectral Response • Spectral Resolution • Frequency of Coverage
Landsat Satellite Program • United States NASA satellites • Images from Landsat 5 available from Space Imaging Corporation - www.spaceimaging.com (formerly EOSAT • Images from Landsat 7 available from USGS, Sioux Falls, South Dakota
Landsat Satellites • Landsat Scene 185 km x 185 km • TM quantatization Range 256 (8 bits) • 16 day repeat cycle per satellite • Currently only one satellite is operational • Satellite crosses the equator at 9:45 local time (North to South Pass)
Systeme Pour l’Observation de la Terre (SPOT) • Orbit repeats every 26 days • 60 km wide field-of-view per camera or 117 km field of view with both units • Quantatization Range 256 (8 bits) • Images available through www.spot.com
Indian Research SatelliteIRS - LISS 3 Satellites • 23 m Resolution 4 bands • 5 m Resolution - Panchromatic • 142 by 145 km Image Size • 24 day repeat cycle • Images available through Spaceimaging at www.spaceimaging.com
IKONIS • Resolution • 4 m multispectral • 1 m Panchromatic • Scene size is approximately 7 miles by 7 miles • Scenes are available from Space Imaging • Farm size images marketed by Earthscan Network, a subsidiary of DTN
Steps to Utilize Remote Sensed Data (modified from JD text • Collect data • Process image data (rectification, radiometric correction, and georeferencing) • Examine image and analyze statistical data • Perform ground truthing of remote-sensed data
Steps to Utilize Remote Sensed Data (modified from JD text • Incorporate remote sensed and ground truth data into a GIS • Develop calibration equations for remote sensed data • Identify cause-effect relationships among measured variables and crop conditions • Treat regions in fields (management zones) based on information generated