Remote Sens. 2010, 2(3), 833-860; doi:10.3390/rs2030833
Review

Ground Filtering Algorithms for Airborne LiDAR Data: A Review of Critical Issues

Xuelian Meng 1,2,* email, Nate Currit 1 email and Kaiguang Zhao 3 email
1 Department of Geography, Texas State University-San Marcos, 601 University Drive, San Marcos, TX 78666, USA 2 Department of Geography, Texas A&M University, 810 O&M Building, College Station, TX 77843-3147, USA 3 Spatial Sciences Lab., Department of Ecosystem Science and Management, Texas A&M University, College Station, TX 77843-3147, USA
* Author to whom correspondence should be addressed.
Received: 4 January 2010; in revised form: 20 February 2010 / Accepted: 27 February 2010 / Published: 22 March 2010
(This article belongs to the Special Issue LiDAR)
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Abstract: This paper reviews LiDAR ground filtering algorithms used in the process of creating Digital Elevation Models. We discuss critical issues for the development and application of LiDAR ground filtering algorithms, including filtering procedures for different feature types, and criteria for study site selection, accuracy assessment, and algorithm classification. This review highlights three feature types for which current ground filtering algorithms are suboptimal, and which can be improved upon in future studies: surfaces with rough terrain or discontinuous slope, dense forest areas that laser beams cannot penetrate, and regions with low vegetation that is often ignored by ground filters.
Keywords: LiDAR; ground filtering; terrain; DEM; forest; urban; review

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Cite This Article

MDPI and ACS Style

Meng, X.; Currit, N.; Zhao, K. Ground Filtering Algorithms for Airborne LiDAR Data: A Review of Critical Issues. Remote Sens. 2010, 2, 833-860.

AMA Style

Meng X, Currit N, Zhao K. Ground Filtering Algorithms for Airborne LiDAR Data: A Review of Critical Issues. Remote Sensing. 2010; 2(3):833-860.

Chicago/Turabian Style

Meng, Xuelian; Currit, Nate; Zhao, Kaiguang. 2010. "Ground Filtering Algorithms for Airborne LiDAR Data: A Review of Critical Issues." Remote Sens. 2, no. 3: 833-860.

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