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ARS Home » Southeast Area » Mississippi State, Mississippi » Crop Science Research Laboratory » Genetics and Sustainable Agriculture Research » Research » Publications at this Location » Publication #189558

Title: A post-processing step error correction algorithm for overlapping LiDAR strips from agricultural landscapes

item Willers, Jeffrey
item Jenkins, Johnie

Submitted to: Computers and Electronics in Agriculture
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/25/2008
Publication Date: 9/1/2008
Citation: Willers, J.L., Jin, M., Eksioglu, B., Zusmanis, A., O'Hara, C., Jenkins, J.N. 2008. A post-processing step error correction algorithm for overlapping LiDAR strips from agricultural landscapes. Computers and Electronics in Agriculture. 64:183-193.

Interpretive Summary: Aircraft equipped with LIDAR (LIght Detection And Ranging) can measure elevation for agricultural purposes. At times, there are vertical deviation errors between LIDAR flight lines where they overlap. These deviations cause obvious error lines along the interface of overlap areas in the digital surface model of elevation, especially for low relief cropland. A method of analysis was developed to minimize the vertical deviations in elevation estimated by overlapping LIDAR flight lines. The method adjusts the elevation estimates of the measured points from each flight line with reference to tie-line points. The tie-line lies at a right angle to and crosses over near the center of other flight lines. The optimization technique successfully minimized the deviations and eliminated the offset bias. These results led to the creation of a better digital surface model of elevation for the agricultural fields of interest.

Technical Abstract: A mathematical programming approach estimates coefficients to be added or subtracted from the LIDAR point cloud of individual flight lines. The coefficients estimated for each line remove the offset bias in the overlap region of contiguous flightlines, with elevation points from a tie-line serving as a benchmark. These estimates adjust the elevation of the LIDAR points in the flight line strips to create a seamless data set. The derivation of the quadratic program that estimates these decision variable coefficients is described. Results of its application are compared as hillshade surfaces of elevation before and after adjustment.