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Biomass Estimation of Subtropical Arboreal Forest at Single Tree Scale Based on Feature Fusion of Airborne LiDAR Data and Aerial Images

Author

Listed:
  • Min Yan

    (Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China)

  • Yonghua Xia

    (Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China
    City College, Kunming University of Science and Technology, Kunming 650051, China)

  • Xiangying Yang

    (Faculty of Public Administration, Yunnan University of Finance and Economics, Kunming 650221, China)

  • Xuequn Wu

    (Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China)

  • Minglong Yang

    (Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China
    City College, Kunming University of Science and Technology, Kunming 650051, China)

  • Chong Wang

    (Kunming Survey, Design and Research Institute Co., Ltd. of China Power Construction Group, Kunming 650200, China)

  • Yunhua Hou

    (City College, Kunming University of Science and Technology, Kunming 650051, China)

  • Dandan Wang

    (City College, Kunming University of Science and Technology, Kunming 650051, China)

Abstract

Low-cost UAV aerial photogrammetry and airborne lidar scanning have been widely used in forest biomass survey and mapping. However, the feature dimension after multisource remote sensing fusion is too high and screening key features to achieve feature dimension reduction is of great significance for improving the accuracy and efficiency of biomass estimation. In this study, UAV image and point cloud data were combined to estimate and map the biomass of subtropical forests. Firstly, a total of 173 dimensions of visible light vegetation index, texture, point cloud height, intensity, density, canopy, and topographic features were extracted as variables. Secondly, the Kendall Rank correlation coefficient and permutation importance (PI) index were used to identify the key features of biomass estimation among different tree species. The random forest (RF) model and XGBoost model finally were used to compare the accuracy of biomass estimation with different variable sets. The experimental results showed that the point cloud height, canopy features, and topographic factors were identified as the key parameters of the biomass estimate, which had a significant influence on the biomass estimation of the three dominant tree species in the study area. In addition, the differences in the importance of characteristics among the tree species were discussed. The fusion features combined with the PI index screening and RF model achieved the best estimation accuracy, the R2 of 0.7356, 0.8578, and 0.6823 were obtained for the three tree species, respectively.

Suggested Citation

  • Min Yan & Yonghua Xia & Xiangying Yang & Xuequn Wu & Minglong Yang & Chong Wang & Yunhua Hou & Dandan Wang, 2023. "Biomass Estimation of Subtropical Arboreal Forest at Single Tree Scale Based on Feature Fusion of Airborne LiDAR Data and Aerial Images," Sustainability, MDPI, vol. 15(2), pages 1-26, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:2:p:1676-:d:1036778
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    References listed on IDEAS

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    1. Umut Hasan & Mamat Sawut & Shuisen Chen, 2019. "Estimating the Leaf Area Index of Winter Wheat Based on Unmanned Aerial Vehicle RGB-Image Parameters," Sustainability, MDPI, vol. 11(23), pages 1-11, December.
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