سابِق|تال

Uncertainty analysis of the absolute radiometric calibration of full waveform airborne LiDAR

  • role： First author第一作者
  • Affiliation：

    School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China

    Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China

  • Email：luhao@bjfu.edu.cn
  • Introduction：卢昊，1990年生,男,讲师,研究方向为全波形机载激光雷达信号处理、林业遥感。E-mail: luhao@bjfu.edu.cn
LU Hao12,  
  • role： Corresponding author通信作者
  • Affiliation：

    Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China

  • Email：pangy@ifrit.ac.cn
  • Introduction：庞勇，1976年生，男,研究员，研究方向为地基、机载、星载激光雷达林业应用。E-mail: pangy@ifrit.ac.cn
PANG Yong2*,  
  • Affiliation：

    Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China

LI Zengyuan2,  
  • Affiliation：

    School of Engineering, Aalto University, Espoo 02150, Finland

WANG Di3,  
  • Affiliation：

    Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China

CHEN Bowei2,  
  • Affiliation：

    Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China

MA Zhenyu2

ملخص

The multiple return phenomenon of laser pulse in the technology of full waveform laser scanning has an inevitable impact on the radiometric signal of LiDAR data as well as their radiometric calibration. Previous studies have been made that characterized this phenomenon as an attenuation of laser pulse energy partially intersected by objects, such as canopy and building edges, through the travel path. In this study, we proposed a novel theoretical explanation of multiple returns by establishing a set of LiDAR equations, one for each sub-pulse that composed the original outgoing pulse. The energy attenuation of LiDAR signals through penetrable targets, such as forest canopy, and its influence on radiometric calibration were particularly analyzed. Comparative experiments were conducted with data from one laser instrument of Riegl LMS-Q680i LiDAR system in two different data collection campaigns. One data collection site was covered by LiDAR flight lines of 600 m and 1200 m Above Ground Level (AGL), and the other site with all 600 m AGL. During the data acquisition, ground earth surface with approximate Lambertian reflectivity behavior were measured with filed spectrometer, and the reflectance of ground reference objects were applied in the radiometric calibration process. The data were processed and radiometrically calibrated on the basis of classical LiDAR equation. In addition, multiple return point cloud of a scene with homogeneous ground surface with planted vegetation were extracted for further quantitative analysis. This process was implemented to reveal and characterize the influence of multiple returns on full waveform LiDAR echoes and subsequent target classification. Through the quantitative comparison of data strips, deviations of overlapping data of different flying altitude were calculated. It was demonstrated from the results that the systematic data deviations of LiDAR strip parameters are successfully eliminated. The overall relative errors of corrected diffuse reflectance of the two regions are less than 10% and 5.5%. The standard deviations of strip difference are 0.044 and 0.077 accordingly. Calibration constants in independent LiDAR surveying campaigns are compared. The constants were found to be with correlation to the LiDAR system and flying configurations. Moreover, it was found that LiDAR returns of different return number were not consistent, despite that they were reflected by the same object surface. Significant weakening was observed in the returns of higher orders. It was concluded that multiple return is the major cause of return intensity weakening on homogeneous surfaces and it has crucial effects on radiometric information based target recognition. This problem cannot be readily solved with the current LiDAR observation mechanism in typical mapping scenarios. Challenges from this phenomenon are inevitable to further target recognition and should be addressed for advanced multiple and hyperspectral LiDAR data in the future.

مفهوم

remote sensing;airborne LiDAR;radiometric calibration;radar equation;uncertainty analysis;classification

References

  1. 1.
    Ahokas E, Hyyppä J, Yu X, Liang X, Matikainen L, Karila K, Litkey P, Kukko A, Jaakkola A, Kaartinen H, Holopainen M and Vastaranta M. 2016. Towards automatic single-sensor mapping by multispectral airborne laser scanning. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLI-B3: 155-162
  2. 2.
    Ahokas E, Kaasalainen S, Hyyppä J and Suomalainen J. 2006. Calibration of the Optech ALTM 3100 laser scanner intensity data using brightness targets. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 36: 1Á6
  3. 3.
    Alexander C, Tansey K, Kaduk J, Holland D and Tate N J. 2010. Backscatter coefficient as an attribute for the classification of full-waveform airborne laser scanning data in urban areas. ISPRS Journal of Photogrammetry and Remote Sensing, 65(5): 423-432
  4. 4.
    Höfle B and Pfeifer N. 2007. Correction of laser scanning intensity data: data and model-driven approaches. ISPRS Journal of Photogrammetry and Remote Sensing, 62(6): 415-433
  5. 5.
    Hovi A, Korhonen L, Vauhkonen J and Korpela I. 2016. LiDAR waveform features for tree species classification and their sensitivity to tree- and acquisition related parameters. Remote Sensing of Environment, 173: 224-237
  6. 6.
    Lehner H and Briese C. 2010. Radiometric calibration of full-waveform airborne laser scanning data based on natural surfaces//Wagner W and Székely B, eds. ISPRS TC VII Symposium - 100 Years ISPRS. Vienna, Austria: ISPRS
  7. 7.
    Li Z Y, Pang Y and Liu Q W. 2015. LiDAR Forest Parameter Inversion: Technology and Methods. Beijing: Science Press
  8. 8.
    Lin Y C. 2015. Normalization of echo features derived from full-waveform airborne laser scanning data. Remote Sensing, 7(3): 2731-2751
  9. 9.
    Lu H, Pang Y and Li Z Y. 2015b. Storage and access of full waveform LiDAR data of LAS1.3 format based on inverted index. Computer Engineering and Applications, 51(16): 243-247
  10. 10.
    Lu H, Pang Y, Li Z Y and Chen B W. 2015. An automatic range ambiguity solution in high-repetition-rate airborne laser scanner using priori terrain prediction. IEEE Geoscience and Remote Sensing Letters, 12(11): 2232-2236
  11. 11.
    Lu H, Pang Y, Xu G C and Li Z Y. 2015a. Quantitative analysis of differences between full waveform data and system point cloud data from airborne LiDAR. Geomatics and Information Science of Wuhan University, 40(5): 588-593
  12. 12.
    Pang Y, Li Z Y, Ju H B, Lu H, Jia W, Si L, Guo Y, Liu Q W, Li S M, Liu L X, Xie B B, Tan B X and Dian Y Y. 2016. LiCHy: The CAF’s LiDAR, CCD and hyperspectral integrated airborne observation system. Remote Sensing, 8(5): 398
  13. 13.
    Pfeifer N, Mandlburger G, Otepka J and Karel W. 2014. OPALS - A framework for Airborne Laser Scanning data analysis. Computers, Environment and Urban Systems, 45: 125-136
  14. 14.
    Qin Y C, Li B, Niu Z, Huang W J and Wang C Y. 2011. Stepwise decomposition and relative radiometric normalization for small footprint LiDAR waveform. Science China Earth Sciences, 54(4): 625-630
  15. 15.
    Tan K and Cheng X J. 2016. Correction of incidence angle and distance effects on TLS intensity data based on reference targets. Remote Sensing, 8(3): 251
  16. 16.
    Teo T A and Wu H M. 2015. Radiometric block adjustment for multi-strip airborne waveform lidar data. Remote Sensing, 7(12): 16831-16848
  17. 17.
    Wagner W, Ullrich A, Ducic V, Melzer T and Studnicka N. 2006. Gaussian decomposition and calibration of a novel small-footprint full-waveform digitising airborne laser scanner. ISPRS Journal of Photogrammetry and Remote Sensing, 60(2): 100-112
  18. 18.
    Wagner W. 2010. Radiometric calibration of small-footprint full-waveform airborne laser scanner measurements: Basic physical concepts. ISPRS Journal of Photogrammetry and Remote Sensing, 65(6): 505-513
  19. 19.
    Wang D, Hollaus M, Puttonen E and Pfeifer N. 2016. Automatic and self-adaptive stem reconstruction in landslide-affected forests. Remote Sensing, 8(12): 974
  20. 20.
    Xu P T, Liu D, Zhou Y D, Liu Q, Bai J, Liu Z P, Wu L, Shen Y B and Liu C. 2020. Modeling and analysis of oceanic lidar returns with multiple scattering. Journal of Remote Sensing (Chinese), 24(2):142-148
  21. 21.
    Xu G C, Pang Y, Li Z Y, Zhao D and Li D. 2013. Classifying land cover based on calibrated full-waveform airborne light detection and ranging data. Chinese Optics Letters, 11(8): 082801
  22. 22.
    Xu G C. 2013. Forest LAI and individual trees biomass estimation using small-footprint full-waveform LiDAR data. Beijing: Chinese Academy of Forestry
  23. 23.
    Zhang J X, Lin X G and Liang X L. 2017. Advances and prospects of information extraction from point clouds. Acta Geodaetica et Cartographica Sinica, 46(10): 1460-1469

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