Intensity Data Correction for Long-Range Terrestrial Laser Scanners: A Case Study of Target Differentiation in an Intertidal Zone

The intensity data recorded by a terrestrial laser scanner (TLS) contain spectral characteristics of a scanned target and are mainly influenced by incidence angle and distance. In this study, an improved implementable method is proposed to empirically correct the intensity data of long-distance TLSs. Similar to existing methods, the incidence angle–intensity relationship is estimated using some reference targets scanned in the laboratory. By contrast, due to the length limit of indoor environments and the laborious data processing, the distance–intensity relationship is derived by selecting some natural homogeneous targets with distances covering the entire distance scale of the adopted long-distance TLS. A case study of intensity correction and point cloud classification in an intertidal zone in Chongming Island, Shanghai, China, is conducted to validate the feasibility of the improved method by using the intensity data of a long-distance TLS (Riegl VZ-4000). Results indicate that the improved method can accurately eliminate the effects of incidence angle and distance on the intensity data of long-distance TLSs; the coefficient of variation of the intensity data for the targets in the study intertidal zone can be reduced by approximately 54%. The classification results of the study intertidal zone show that the improved method can effectively eliminate the variations caused by the incidence angle and distance in the original intensity data of the same target to obtain a corrected intensity that merely depends on target characteristics for improving classification accuracy by 49%.

[1]  M. Fournier,et al.  The use of terrestrial LiDAR technology in forest science: application fields, benefits and challenges , 2011, Annals of Forest Science.

[2]  Diego González-Aguilera,et al.  Terrestrial laser scanning intensity data applied to damage detection for historical buildings , 2010 .

[3]  M. Menenti,et al.  Scanning geometry: Influencing factor on the quality of terrestrial laser scanning points , 2011 .

[4]  Pablo Rodríguez-Gonzálvez,et al.  Multispectral Radiometric Analysis of Façades to Detect Pathologies from Active and Passive Remote Sensing , 2016, Remote. Sens..

[5]  Patrick E. Clark,et al.  Estimating Sagebrush Biomass Using Terrestrial Laser Scanning , 2014 .

[6]  Scott R. Marion,et al.  Ecological niche and species distribution modelling of sea stars along the Pacific Northwest continental shelf , 2016 .

[7]  K. Tan,et al.  Modeling hemispherical reflectance for natural surfaces based on terrestrial laser scanning backscattered intensity data. , 2016, Optics express.

[8]  Craig Glennie,et al.  Review of Earth science research using terrestrial laser scanning , 2017 .

[9]  Stefano Girardi,et al.  Discrimination between marls and limestones using intensity data from terrestrial laser scanner , 2009 .

[10]  Rudolf Sailer,et al.  Glacier Snowline Determination from Terrestrial Laser Scanning Intensity Data , 2017 .

[11]  R. Billen,et al.  3D Point Clouds in Archaeology: Advances in Acquisition, Processing and Knowledge Integration Applied to Quasi-Planar Objects , 2017 .

[12]  Andrew K. Skidmore,et al.  3D leaf water content mapping using terrestrial laser scanner backscatter intensity with radiometric correction , 2015 .

[13]  M. James,et al.  Detecting the development of active lava flow fields with a very‐long‐range terrestrial laser scanner and thermal imagery , 2009 .

[14]  Kai Tan,et al.  Intensity data correction based on incidence angle and distance for terrestrial laser scanner , 2015 .

[15]  Anttoni Jaakkola,et al.  Analysis of Incidence Angle and Distance Effects on Terrestrial Laser Scanner Intensity: Search for Correction Methods , 2011, Remote. Sens..

[16]  G. Hoarau,et al.  The fate of the Arctic seaweed Fucus distichus under climate change: an ecological niche modeling approach , 2016, Ecology and evolution.

[17]  Ahmed Shaker,et al.  Radiometric normalization of overlapping LiDAR intensity data for reduction of striping noise , 2016, Int. J. Digit. Earth.

[18]  R. Ejrnæs,et al.  Lidar-derived variables as a proxy for fungal species richness and composition in temperate Northern Europe , 2017 .

[19]  Harri Kaartinen,et al.  Remote Sensing Radiometric Calibration of Terrestrial Laser Scanners with External Reference Targets , 2022 .

[20]  Peter P. Flaig,et al.  Lidar intensity as a remote sensor of rock properties , 2011 .

[21]  Keqi Zhang,et al.  Application of terrestrial laser scanner on tidal flat morphology at a typhoon event timescale , 2017 .

[22]  Julian Podgórski,et al.  Revealing recent calving activity of a tidewater glacier with terrestrial LiDAR reflection intensity , 2018, Cold Regions Science and Technology.

[23]  Kai Tan,et al.  Correction of Incidence Angle and Distance Effects on TLS Intensity Data Based on Reference Targets , 2016, Remote. Sens..

[24]  N. Pfeifer,et al.  Correction of laser scanning intensity data: Data and model-driven approaches , 2007 .

[25]  Tee-Ann Teo,et al.  Empirical Radiometric Normalization of Road Points from Terrestrial Mobile Lidar System , 2015, Remote. Sens..

[26]  G. Ventura,et al.  Remote sensing of volcanic terrains by terrestrial laser scanner: preliminary reflectance and RGB implications for studying Vesuvius crater (Italy) , 2008 .

[27]  Kai Tan,et al.  Specular Reflection Effects Elimination in Terrestrial Laser Scanning Intensity Data Using Phong Model , 2017, Remote. Sens..

[28]  Yanjun Su,et al.  Improved progressive TIN densification filtering algorithm for airborne LiDAR data in forested areas , 2016 .

[29]  Antero Kukko,et al.  Effect of incidence angle on laser scanner intensity and surface data. , 2008, Applied optics.

[30]  Ahmed Shaker,et al.  Radiometric Correction and Normalization of Airborne LiDAR Intensity Data for Improving Land-Cover Classification , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[31]  Qi Zhang,et al.  Intensity Data Correction for the Distance Effect in Terrestrial Laser Scanners , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[32]  M. Vastaranta,et al.  Terrestrial laser scanning in forest inventories , 2016 .

[33]  Brian L. F. Daku,et al.  Temperature Compensation for Radiometric Correction of Terrestrial LiDAR Intensity Data , 2017, Remote. Sens..

[34]  Vivian Vimarlund,et al.  Applications of terrestrial laser scanning for tunnels : a review , 2014 .

[35]  B. C. Prooijen,et al.  Investigation of flocculation dynamics under changing hydrodynamic forcing on an intertidal mudflat , 2018 .

[36]  Wu Chen,et al.  Combination of overlap-driven adjustment and Phong model for LiDAR intensity correction , 2013 .

[37]  J. Revuelto,et al.  Using very long-range terrestrial laser scanner to analyze the temporal consistency of the snowpack distribution in a high mountain environment , 2017, Journal of Mountain Science.

[38]  Fan Zhang,et al.  Intensity Correction of Terrestrial Laser Scanning Data by Estimating Laser Transmission Function , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[39]  A. Kukko,et al.  TOPOGRAPHIC AND DISTANCE EFFECTS IN LASER SCANNER INTENSITY CORRECTION , 2009 .

[40]  Michel Jaboyedoff,et al.  Correction of terrestrial LiDAR intensity channel using Oren–Nayar reflectance model: An application to lithological differentiation , 2016 .

[41]  Lijun Xu,et al.  Terrestrial Laser Scanning Intensity Correction by Piecewise Fitting and Overlap-Driven Adjustment , 2017, Remote. Sens..

[42]  Bernhard Höfle,et al.  Radiometric Correction of Terrestrial LiDAR Point Cloud Data for Individual Maize Plant Detection , 2014, IEEE Geoscience and Remote Sensing Letters.

[43]  Stefano Fabbri,et al.  Geomorphological analysis and classification of foredune ridges based on Terrestrial Laser Scanning (TLS) technology , 2017 .

[44]  Nicholas Wilson,et al.  A Review of LIDAR Radiometric Processing: From Ad Hoc Intensity Correction to Rigorous Radiometric Calibration , 2015, Sensors.

[45]  Franck Garestier,et al.  Analysis of ALS Intensity Behavior as a Function of the Incidence Angle in Coastal Environments , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[46]  Guo Huadong,et al.  Digital Earth and Future Earth , 2016 .

[47]  Gerardo M. E. Perillo,et al.  Modern microbial mats in siliciclastic tidal flats: Evolution, structure and the role of hydrodynamics , 2014 .