Extracting Terrain Points from Airborne Laser Scanning Data in Densely Forested Areas
Airborne Laser Scanning (ALS) is one of the main technologies for generating high-resolution digital terrain models (DTMs). DTMs are crucial to several applications, such as topographic mapping, flood zone delineation, geographic information systems (GIS), hydrological modelling, spatial analysis, etc. Laser scanning system generates irregularly spaced three-dimensional cloud of points. Raw ALS data are mainly ground points (that represent the bare earth) and non-ground points (that represent buildings, trees, cars, etc.). Removing all the non-ground points from the raw data is referred to as filtering. Filtering heavily forested areas is considered a difficult and challenging task as the canopy stops laser pulses from reaching the terrain surface. This research presents an approach for removing non-ground points from raw ALS data in densely forested areas. Smoothing splines are exploited to interpolate and fit the noisy ALS data. The presented filter utilizes a weight function to allocate weights for each point of the data. Furthermore, unlike most of the methods, the presented filtering algorithm is designed to be automatic. Three different forested areas in the United Kingdom are used to assess the performance of the algorithm. The results show that the generated DTMs from the filtered data are accurate (when compared against reference terrain data) and the performance of the method is stable for all the heavily forested data samples. The average root mean square error (RMSE) value is 0.35 m.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 439
 Y. Quan, J. Song, X. Guo, Q. Miao, and Y. Yang, “Filtering LiDAR data based on adjacent triangle of triangulated irregular network,” Multimedia Tools and Applications, vol. 76, no. 8, pp. 11051–11063, 2017.
 Y. B. Yang, N. N. Zhang, and X. L. Li, “Adaptive slope filtering for airborne Light Detection and Ranging data in urban areas based on region growing rule,” Survey Review, vol. 6265, no. June, pp. 1–8, 2016.
 C. A. Silva, C. Klauberg, Â. M. K. Hentz, A. P. D. Corte, U. Ribeiro, and V. Liesenberg, “Comparing the performance of ground filtering algorithms for terrain modeling in a forest environment using airborne LiDAR data,” Floresta e Ambiente, vol. 25, no. 2, 2018.
 Z. Hui, Y. Hu, Y. Z. Yevenyo, and X. Yu, “An improved morphological algorithm for filtering airborne LiDAR point cloud based on multi-level kriging interpolation,” Remote Sensing, vol. 8, no. 1, pp. 12–16, 2016.
 G. Sithole and G. Vosselman, “Filtering of airborne laser scanner data based on segmented point clouds,” International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 36, no. 3/W3-4, pp. 66–71, 2005.
 A. Kobler, N. Pfeifer, P. Ogrinc, L. Todorovski, K. Oštir, and S. Džeroski, “Repetitive interpolation: A robust algorithm for DTM generation from Aerial Laser Scanner Data in forested terrain,” Remote Sensing of Environment, vol. 108, no. 1, pp. 9–23, 2007.
 Z. Chen, B. Gao, and B. Devereux, “State-of-the-Art: DTM Generation Using Airborne LIDAR Data,” Sensors, vol. 17, no. 1, p. 150, 2017.
 L. Liu and S. Lim, “A voxel-based multiscale morphological airborne lidar filtering algorithm for digital elevation models for forest regions,” Measurement: Journal of the International Measurement Confederation, vol. 123, pp. 135–144, 2018.
 W. Schickler and A. Thorpe, “Surface estimation based on LIDAR,” Proceedings of the ASPRS Annual Conference, no. April, pp. 23–27, 2001.
 H. Chen, M. Cheng, J. Li, and Y. Liu, “An Iterative Terrain Recovery Approach To Automated Dtm Generation From Airborne Lidar Point Clouds,” The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XXXIX-B4, no. September, pp. 363–368, 2012.
 Q. Chen, H. Wang, H. Zhang, M. Sun, and X. Liu, “A point cloud filtering approach to generating DTMs for steep mountainous areas and adjacent residential areas,” Remote Sensing, vol. 8, no. 71, 2016.
 Z. Hui, D. Li, S. Jin, Y. Y. Ziggah, L. Wang, and Y. Hu, “Automatic DTM extraction from airborne LiDAR based on expectation-maximization,” Optics and Laser Technology, vol. 112, pp. 43–55, 2019.
 S. Cai, Z. Wuming, L. Xinlian,W. Peng, Q. Jianbo, Y. Sisi, Y. Guangjian, S. Jie, “Filtering Airborne LiDAR Data Through Complementary Cloth Simulation and Progressive TIN Densification Filters,” Remote Sensing, vol. 11, no. 9, p. 1037, 2019.
 T. Ramsay, “Spline smoothing over difficult regions,” Journal of the Royal Statistical Society. Series B: Statistical Methodology, vol. 64, no. 2, pp. 307–319, 2002.
 B. W. Silverman, “Some Aspects of the Spline Smoothing Approach to Non-Parametric Regression Curve Fitting,” Journal of the Royal Statistical Society, vol. 74, no. 1, pp. 1–52, 1985.
 C. De Boor, “A Practical Guide to Splines,” Springer, pp. 207-240, 2001.
 K. Zhang, S. C. Chen, D. Whitman, M. L. Shyu, J. Yan, and C. Zhang, “A progressive morphological filter for removing nonground measurements from airborne LIDAR data,” IEEE Transactions on Geoscience and Remote Sensing (TGRS), vol. 41, no. 4 PART I, pp. 872–882, 2003.
 Z. Abdeldayem, "Automatic Weighted Splines Filter (AWSF): A New Algorithm for Extracting Terrain Measurements From Raw LiDAR Point Clouds," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13, pp. 60-71, 2020.
 Environment Agency, “Environment Agency LIDAR data,” Environment Agency LIDAR data, p. 16, 2016.