Journal of East China Normal University(Natural Science) >

2022 , Vol. 2022 >Issue 1: 122 - 134

DOI: https://doi.org/10.3969/j.issn.1000-5641.2022.01.014

Geography

Quick estimation of three-dimensional vegetation volume based on images from an unmanned aerial vehicle: A case study on Shanghai Botanical Garden

  • Jiabei LUO ,
  • Yingfei ZHOU ,
  • Hanbing LENG ,
  • Chen MENG ,
  • Zhengyang HOU ,
  • Tongtong SONG ,
  • Zhengyun HU ,
  • Chao ZHANG ,
  • Shucheng FENG
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  • 1. Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai　200241, China
    2. Shanghai Urban Plant Resources Development and Application Engineering Technology Research Center, Shanghai Botanical Garden, Shanghai　200231, China
    3. School of Ecological and Environmental Sciences, East China Normal University, Shanghai　200241, China
    4. College of Forestry, Beijing Forestry University, Beijing　100083, China
    5. The Joint Laboratory for Environmental Remote Sensing and Data Assimilation, East China Normal University, Shanghai　200241, China

Received date: 2020-11-23

  Online published: 2022-01-18

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Abstract

Three-dimensional vegetation volume is a comprehensive index that can be used to represent the ecological benefits of urban vegetation. However, the challenge of how to accurately and quickly carry out three-dimensional vegetation volume monitoring in highly heterogeneous urban habitats is an urgent problem that requires attention. In this paper, we used Shanghai Botanical Garden as a case study. We acquired low-altitude, high-resolution images of Shanghai Botanical Garden through a UAV aerial photography system; after extracting the data, we calculated the surface elevation and canopy height models, estimated the three-dimensional vegetation volume, and analyzed the spatial distribution pattern. The results showed that: ① The overall plane and elevation accuracy of UAV images was better than 0.1 m, and the average error and standard deviation of the canopy height model accuracy was 0.27 m and 0.58 m, respectively. ② The vegetation volume of Shanghai Botanical Garden was distributed in a pattern from northeast low to southwest high, with a total vegetation volume of 3538944.50 m3. The average green density of the botanical garden was 6.51 m3/m2. The three gardens with the highest vegetation volume were: Peony Garden (289491.00 m3), Pinetum Garden (338322.10 m3), and the Green Space Attached to The Greenhouse (360587.50 m3). The three gardens with the lowest vegetation volume were: Recreational Green Space (24761.50 m3), Monocotyledon Botanical Garden (31621.40 m3), and Rose Garden (74607.30 m3). The three gardens with the highest vegetation volume density were: Tropical Orchid Room (9.23 m3/m2), Fern Garden (11.30 m3/m2), and Magnolia and Camphor Avenue (13.11 m3/m2). The three gardens with the lowest vegetation volume density were Recreational Green Space (1.57 m3/m2), Scientific Research Center Green Space (1.81 m3/m2), and Rose Garden (2.58 m3/m2). ③ The vegetation volume of each specialized garden was significantly related to the distribution area of the arbor community, the height of the constructive species, and the product thereof. The vegetation volume density of each specialized garden was significantly related to the proportion of the area of the arbor community in the specialized garden, the height of the constructive species, and the product thereof. This research can serve as a methodology reference for the quick estimation of urban vegetation volume, and provide basic data vegetation volume estimates and spatial pattern optimization for Shanghai Botanical Garden.

Key words： urban vegetation; three-dimensional vegetation volume; unmanned aerial vehicle (UAV); botanical garden

Cite this article

Jiabei LUO , Yingfei ZHOU , Hanbing LENG , Chen MENG , Zhengyang HOU , Tongtong SONG , Zhengyun HU , Chao ZHANG , Shucheng FENG . Quick estimation of three-dimensional vegetation volume based on images from an unmanned aerial vehicle: A case study on Shanghai Botanical Garden[J]. Journal of East China Normal University(Natural Science), 2022 , 2022(1) : 122 -134 . DOI: 10.3969/j.issn.1000-5641.2022.01.014

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