Redundant point clouds are inevitable in the overlapping area of adjacent airborne Li DAR strips. Data redundancy c auses problems in terms of data density and accuracy consistency compared with other regions. Reduction of airborne Li DAR data isan important step in Li DAR data pre-processing to distribute the laser points more evenly and eliminate accuracy inconsistency.This paper aims to reduce point cloud without auxiliary data. An algorithm based on time and texture information extracted from point cloud is proposed.（ 1） Point clouds are partitioned into different strips using cluster analysis method according to their GPS time.（ 2） A texture graph is generated by analyzing the Li DAR scanning mode and projecting point coordinates to scanning detection

from which the key points are extracted and connected into strip edge lines.（ 3） When holes caused by the occlusion of high objects appear: Ground and non-ground points are separated automatically by adaptive TIN filtering

followed by classifying high object points based on region-growing method

and finally detecting the edges of the high objects by Alpha-Shapes algorithm. A fter combining with other the information contained in point clouds

the holes occluded by high objects can be detected.（ 4） Remove of the redundant point clouds with low accuracy in the overlapping area by comparing the distances of the points to their strip edge lines. Thus

the closer distance points from their strip edge lines

the lower accuracy these points have. The redundant and low accuracy point clouds in the overlapping area were efficiently removed and the hole areas were reserved. Meanwhile

the accuracy of the DEM generated from the simplified data slightly improved. Experiments results showed the effectiveness of our method

which outperformed the conventional data reduction approaches in terms of the fact that flight trajectory information is necessary in those methods.