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ISPRS Int. J. Geo-Inf. 2016, 5(6), 97; doi:10.3390/ijgi5060097

Parallel Landscape Driven Data Reduction & Spatial Interpolation Algorithm for Big LiDAR Data

1
Department of Computer Science, University of Iowa, Iowa city, IA 52246, USA
2
Department of Geography & GIS, University of Illinois at Urbana-Champaign, IL 61801, USA
3
Department of Geography, University of Iowa, Iowa City, IA 52246, USA
4
Department of Computer Science, University of Iowa, Iowa city, IA 52246, USA
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editor: Wolfgang Kainz
Received: 30 April 2016 / Revised: 8 June 2016 / Accepted: 12 June 2016 / Published: 17 June 2016
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Abstract

Airborne Light Detection and Ranging (LiDAR) topographic data provide highly accurate digital terrain information, which is used widely in applications like creating flood insurance rate maps, forest and tree studies, coastal change mapping, soil and landscape classification, 3D urban modeling, river bank management, agricultural crop studies, etc. In this paper, we focus mainly on the use of LiDAR data in terrain modeling/Digital Elevation Model (DEM) generation. Technological advancements in building LiDAR sensors have enabled highly accurate and highly dense LiDAR point clouds, which have made possible high resolution modeling of terrain surfaces. However, high density data result in massive data volumes, which pose computing issues. Computational time required for dissemination, processing and storage of these data is directly proportional to the volume of the data. We describe a novel technique based on the slope map of the terrain, which addresses the challenging problem in the area of spatial data analysis, of reducing this dense LiDAR data without sacrificing its accuracy. To the best of our knowledge, this is the first ever landscape-driven data reduction algorithm. We also perform an empirical study, which shows that there is no significant loss in accuracy for the DEM generated from a 52% reduced LiDAR dataset generated by our algorithm, compared to the DEM generated from an original, complete LiDAR dataset. For the accuracy of our statistical analysis, we perform Root Mean Square Error (RMSE) comparing all of the grid points of the original DEM to the DEM generated by reduced data, instead of comparing a few random control points. Besides, our multi-core data reduction algorithm is highly scalable. We also describe a modified parallel Inverse Distance Weighted (IDW) spatial interpolation method and show that the DEMs it generates are time-efficient and have better accuracy than the one’s generated by the traditional IDW method. View Full-Text
Keywords: spatial data analysis; LiDAR big data; parallel algorithm; DEM; GIS spatial data analysis; LiDAR big data; parallel algorithm; DEM; GIS
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Sharma, R.; Xu, Z.; Sugumaran, R.; Oliveira, S. Parallel Landscape Driven Data Reduction & Spatial Interpolation Algorithm for Big LiDAR Data. ISPRS Int. J. Geo-Inf. 2016, 5, 97.

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