ARS | Publication request: Methods for georectification and spectral scaling of remote imagery using ArcView, ArcGIS, and ENVI

Submitted to: ASABE Annual International Meeting
Publication Type: Abstract Only
Publication Acceptance Date: June 21, 2009
Publication Date: July 23, 2009
Citation: Bright Jr, J.R., Thomson, S.J., Huang, Y. 2009. Methods for georectification and spectral scaling of remote imagery using ArcView, ArcGIS, and ENVI. ASABE Annual International Meeting. Paper no. 09-5549, 9 pp.

Technical Abstract: Remote sensing images can be used to support variable-rate (VR) application of material from aircraft. Geographic coordinates must be assigned to an image (georeferenced) so that the variable-rate system can determine where in the field to apply these inputs and adjust the system when a zone has been traversed. Resulting imagery obtained aerially can exhibit position distortions due to airplane roll or tilt. Depending on the position accuracy required for the image and assuming that rotational variables (roll, tilt, yaw, pitch) can be measured in the airplane with an Inertial Measurement Unit (IMU), image pixels can subsequently be shifted (georectified) to remove these distortions. However, a preliminary study using images obtained from aircraft has shown minimal improvement to positioning accuracy by compensating for these variables. This is coupled with the fact that effective experimental positioning accuracy on the ground for on-off rate changes has been found to be no better than + 5 m (with a standard deviation of error magnitude up to 7 m). This result was typical of experiments conducted using GPS receiver position updating of 0.2 s. Distortion correction under these error conditions might provide only minimal relative improvement in positioning accuracy. Rapid procedures for georeferencing and rectifying imagery are outlined using ground control points obtained by RTK GPS. Both ArcView 3.2a and ArcGIS 9.3 were used, and interesting procedural differences between the two GIS are discussed that could dictate preference of one method over the other. Positioning error due to rotational attributes was not factored into the processing procedure, and both absolute and relative error are discussed relative to a precision spraying problem. Another type of processing involves image scaling. We have used a thermal imaging camera in an effort to detect the onset of crop water stress on a temporal basis. Obtaining the proper image representation of canopy temperature has been quite a challenge since differences in incoming solar radiation and wind from day to day can alter that representation. Inconsistent results have thus been obtained from imagery over long-term drying cycles that should have shown a clear trend (increase) in canopy temperature. A method that could be useful for scaling imagery to account for varying solar radiation is proposed using ENVI 4.4 image analysis software. Example imagery taken of soybean and cotton canopies, before and after processing, serves to illustrate the methodology.