EGU2020-14009, updated on 20 Jan 2021
EGU General Assembly 2020
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Understory biomass measurement based on SfM data by a manual low-flying drone under the canopy

Yupan Zhang1, Yuichi Onda1, Hiroaki Kato1, Xinchao Sun2, and Takashi Gomi3
Yupan Zhang et al.
  • 1Center for Research in Isotopes and Environmental Dynamics, University of Tsukuba, Ibaraki, Japan (
  • 2Institute of Surface-Earth System Science, Tianjin University, Tianjin , PR China (
  • 3Department of International Environmental and Agricultural Science, Tokyo University of Agriculture and Technology, Tokyo , Japan (

Understory vegetation has the important effect that cannot be ignored on Evapotranspiration. In previous studies, laser scanner was used to measure small-scale biomass and airborne LiDAR was used to assess light availability to understory vegetation, which in turn was converted to understory biomass production. However, it is difficult to measure watershed-scale understory biomass with high resolution. In this study, Structure from Motion (SfM) was used to reconstruct understory vegetation structure by a manual low-flying drone under the canopy with radial paths in a line thinning plantation and a spot thinning plantation made by Japanese cedar and cypress. By generating Orthomosaic image and dense point cloud data, we then extracted Excess Green Index (ExG) and Canopy Height Model (CHM), combining with understory biomass data from field harvesting to establish a quantitative relationship between the CHM and biomass, which was then used to map biomass and vegetation coverage in the study area. The results indicated that (1) a flight height of 7-10 meters is more conducive to understory vegetation reconstruction, with a photo quality greater than 0.8 and a point cloud density of more than 20 points/cm2. (2) a regression cubic model based on the CHM has acceptable accuracy and biomass estimate capability (P<0.01), with a coefficient of determination of 0.75. (3) compared with the spot thinning, the understory biomass under the line thinning scenario was higher(average biomass 3.03kg/m2). (4) vegetation coverage based on the ExG index of visible light analysis was affected by ambient light(strong sunlight on a sunny day), and it cannot reflect the seasonal changes of understory vegetation biomass. These results disclosed the potential of the dense point cloud from drone SfM for estimating understory biomass. With this method, we will measure more than 5000m2 of headwater catchment and output a understory biomass map.


How to cite: Zhang, Y., Onda, Y., Kato, H., Sun, X., and Gomi, T.: Understory biomass measurement based on SfM data by a manual low-flying drone under the canopy, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-14009,, 2020

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Presentation version 3 – uploaded on 06 May 2020
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  • CC1: Comment on EGU2020-14009, Hamideh Nouri, 06 May 2020

    Very interesting research. Can you please explain how including understory vegetation improve ET estimation? To what extent?

    • AC1: Reply to CC1, Yupan Zhang, 06 May 2020

      Thanks for your comment, about ET we have values calculated by the Total Soil Moisture measurement in two small plot(4m*10m) , and we hope to analyze the relationship between biomass and ET in the same factors.

      • CC3: Reply to AC1, Hamideh Nouri, 07 May 2020

        Thanks Yupan. All the best.

  • CC2: Comment on EGU2020-14009, Stenka Vulova, 07 May 2020

    Interesting study! Did you capture any other data with your UAV (spectral, thermal)? Do you know if biomass can be integrated into any models modelling evapotranspiration?

    • AC2: Reply to CC2, Yupan Zhang, 07 May 2020

      Thanks for your comment. The small drone used in the study only collected visible light. As you said, I am reading some paper that combines biomass simulation with ET, but more paper focus on some specific species, such as crops and grass, rather than the complex undergrowth vegetation.And please let me know if you have any good Suggestions.

Presentation version 2 – uploaded on 01 May 2020 , no comments
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Presentation version 1 – uploaded on 30 Apr 2020 , no comments