Précédent|Prochain

Resolving leaf area index of individual trees based on multi-return terrestrial laser point cloud data

  • role： First author第一作者
  • Affiliation：

    College of Forestry, Nanjing Forestry University, Nanjing 210037, China

    Advanced Analysis and Testing Center, Nanjing Forestry University, Nanjing 210037, China

  • Email：757758952@qq.com
  • Introduction：黄星旻，1995年生，研究方向为森林资源动态监测。E-mail：757758952@qq.com
Xingming HUANG12,  
  • role： Corresponding author通信作者
  • Affiliation：

    College of Forestry, Nanjing Forestry University, Nanjing 210037, China

    Advanced Analysis and Testing Center, Nanjing Forestry University, Nanjing 210037, China

  • Email：sunyuan1123@126.com
  • Introduction：孙圆，1976年生，副教授，研究方向为森林资源动态监测。E-mail：sunyuan1123@126.com
Yuan SUN12*,  
  • Affiliation：

    College of Forestry, Nanjing Forestry University, Nanjing 210037, China

    Advanced Analysis and Testing Center, Nanjing Forestry University, Nanjing 210037, China

Huiqian LIU12,  
  • Affiliation：

    College of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, China

Fangzhou LIU3

résumé

This study aimed to extract the Leaf Area Index (LAI) of individual trees through multi-return terrestrial laser point cloud data, including single target, first-return waveform, intermediate, and last target data. The species was soapberry planted as street trees. An algorithm based on Beer–Lambert law was developed to divide return waveform, and the LAIs of individual trees with different projection resolutions of 0.01, 0.02, and 0.03 m were obtained. Two-dimensional digital hemispherical image data and LAI-2200 were used to extract the LAIs of corresponding single trees for accuracy comparison. Result shows that the resolutions and multiple return waveforms have significant influence on LAI. The results with 0.02 m and 0.03 m resolutions are similar to those obtained with LAI2200, and the difference is insignificant. However, the single target waveform point cloud data are used for the LAI solution and can be mutually verified with the results of the 2D image data of LAI2200. The LAI value of single target waveform point cloud was calculated with Beer– Lambert at 0.02 m resolution, and the intercept measured by LAI2200 data was subjected to a linear regression with an intercept of 0 and slope of 0.827, which is close to 1. Therefore, for the multi-return waveform ground laser scanning data, the single target waveform point cloud data with a projection resolution of 0.02 m can be used to obtain accurate LAI when using an extinction coefficient method. The algorithm for calculating the LAI based on multi-return waveform ground-based laser data in this study expands the application field of TLS and provides an important technical reference for the accurate extraction of the growth of standing trees and accurate tree modeling.

mots-clés

multi-return waveform;terrestrial laser scan;leaf area index;difference analysis

References

  1. 1.
    Calders K, Origo N, Disney M, Nightingale J, Woodgate W, Armston J and Lewis P. 2018. Variability and bias in active and passive ground-based measurements of effective plant, wood and leaf area index. Agricultural and Forest Meteorology, 252: 231–240
  2. 2.
    Cao L, She G H, Dai J S and Xu J X. 2013. Status and prospects of the LiDAR-based forest biomass estimation. Journal of Nanjing Forestry University (Natural Sciences Edition), 37(3): 163–169 (
  3. 3.
    Chen J M and Black T A. 1992. Defining leaf area index for non-flat leaves. Plant, Cell and Environment, 15(4): 421–429
  4. 4.
    Deng Y B, Liu J, Yu K Y, Chen Z H, Yu X Y and Lin T Z. 2017. Individual tree Lai inversion technique based on stratification theory and 3D laser scanning. Journal of Southwest Forestry University, 37(6): 148–155 (
  5. 5.
    Li Y M, Guo Q H, Su Y J, Tao S L, Zhao K G and Xu G C. 2017. Retrieving the gap fraction, element clumping index, and leaf area index of individual trees using single-scan data from a terrestrial laser scanner. ISPRS Journal of Photogrammetry and Remote Sensing, 130: 308–316
  6. 6.
    Liu Y, Liu R G, Chen J M, Cheng X and Zheng G. 2013. Current status and perspectives of leaf area index retrieval from optical remote sensing data. Journal of Geo-information science, 15(5): 734–743 (
  7. 7.
    Ma L X, Zheng G, Eitel J U H, Moskal L M, He W and Huang H B. 2016. Improved salient feature-based approach for automatically separating photosynthetic and nonphotosynthetic components within terrestrial lidar point cloud data of forest canopies. IEEE Transactions on Geoscience and Remote Sensing, 54(2): 679–696
  8. 8.
    Ma L X, Zheng G, Eitel J U H, Magney T S and Moskal L M. 2017. Retrieving forest canopy extinction coefficient from terrestrial and airborne lidar. Agricultural and Forest Meteorology, 236: 1–21
  9. 9.
    Ma Z Q, Liu Q J, Zeng H Q, Li X R, Chen Y R, Lin Y M, Zhang S H, Yang F T and Wang H Q. 2008. Estimation of leaf area index of planted forests in subtropical China by Photogrammetry. Acta Ecologica Sinica, 28(5): 1971–1980 (
  10. 10.
    Martens S N, Ustin S L and Rousseau R A. 1993. Estimation of tree canopy leaf area index by gap fraction analysis. Forest Ecology and Management, 61(1/2): 91–108
  11. 11.
    Pirotti F, Guarnieri A and Vettore A. 2013. Vegetation filtering of waveform terrestrial laser scanner data for DTM production. Applied Geomatics, 5(4): 311–322 [DOI:10.1007/s12518-013-0119-3]
  12. 12.
    Wang H S, Li S H, Guo J W and Liang Z Q. 2016. Retrieval of the leaf area density of Magnolia woody canopy with terrestrial Laser-scanning data. Journal of Remote Sensing, 20(4): 570–578 (
  13. 13.
    Wilkes P, Lau A, Disney M, Calders K, Burt A, de Tanago J G, Bartholomeus H, Brede B and Herold M. 2017. Data acquisition considerations for Terrestrial Laser Scanning of forest plots. Remote Sensing of Environment, 196: 140–153
  14. 14.
    Wu H S. 2015. Research and implementation of fisheye camera image processing. Xi’an: Xidian University
  15. 15.
    Xing Y Q, Yao S T, You H T, Tian X, Peng T, Li M Y, Xie J and Yan C. 2018. A inversion on birch forest LAI based on airborne LiDAR full waveform data. Journal of Northwest Forestry University, 33(1): 11–18 (
  16. 16.
    Yang W X, Fu W X, Zhou Z W, Yu W and Ma J. 2014. Fast three dimensional LIDAR target recognition based on projection dimension reduction. Infrared and Laser Engineering, 43(S1): 1–7 (
  17. 17.
    Yin G F, Liu Q H, Li J, Zeng Y L and Xu B D. 2014. Effect of crown shape on the estimation of gap probability and leaf area index. Journal of Remote Sensing, 18(4): 752–759 (
  18. 18.
    Yun T, An F, Li W Z, Sun Y, Cao L and Xue L F. 2016. A novel approach for retrieving tree leaf area from ground-based LiDAR. Remote Sensing, 8(11): 942
  19. 19.
    Zhao K G, García M, Liu S, Guo Q H, Chen G, Zhang X S, Zhou Y Y and Meng X L. 2015. Terrestrial lidar remote sensing of forests: Maximum likelihood estimates of canopy profile, leaf area index, and leaf angle distribution. Agricultural and Forest Meteorology, 209-210: 100–113
  20. 20.
    Zheng G, Moskal L M and Kim S H. 2013. Retrieval of effective leaf area index in heterogeneous forests with terrestrial laser scanning. IEEE Transactions on Geoscience and Remote Sensing, 51(2): 777–786
  21. 21.
    Zou J and Yan G J. 2010. Optical methods for in situ measuring leaf area index of forest canopy: a review. Chinese Journal of Applied Ecology, 21(11): 2971–2979 (

Lire l'article complet

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website