Optimizing Observation Plans for Identifying Faxon Fir (Abies fargesii var. Faxoniana) Using Monthly Unmanned Aerial Vehicle Imagery

Research output: Contribution to journalJournal articleResearchpeer-review


  • Fulltext

    Final published version, 8.28 MB, PDF document

  • Weibo Shi
  • Xiaohan Liao
  • Jia Sun
  • Zhengjian Zhang
  • Dongliang Wang
  • Shaoqiang Wang
  • Wenqiu Qu
  • Hongbo He
  • Huping Ye
  • Huanyin Yue
  • Tagesson, Håkan Torbern

Faxon fir (Abies fargesii var. faxoniana), as a dominant tree species in the subalpine coniferous forest of Southwest China, has strict requirements regarding the temperature and humidity of the growing environment. Therefore, the dynamic and continuous monitoring of Faxon fir distribution is very important to protect this highly sensitive ecological environment. Here, we combined unmanned aerial vehicle (UAV) imagery and convolutional neural networks (CNNs) to identify Faxon fir and explored the identification capabilities of multispectral (five bands) and red-green-blue (RGB) imagery under different months. For a case study area in Wanglang Nature Reserve, Southwest China, we acquired monthly RGB and multispectral images on six occasions over the growing season. We found that the accuracy of RGB imagery varied considerably (the highest intersection over union (IoU), 83.72%, was in April and the lowest, 76.81%, was in June), while the accuracy of multispectral imagery was consistently high (IoU > 81%). In April and October, the accuracy of the RGB imagery was slightly higher than that of multispectral imagery, but for the other months, multispectral imagery was more accurate (IoU was nearly 6% higher than those of the RGB imagery for June). Adding vegetation indices (VIs) improved the accuracy of the RGB models during summer, but there was still a gap to the multispectral model. Hence, our results indicate that the optimized time of the year for identifying Faxon fir using UAV imagery is during the peak of the growing season when using a multispectral imagery. During the non-growing season, RGB imagery was no worse or even slightly better than multispectral imagery for Faxon fir identification. Our study can provide guidance for optimizing observation plans regarding data collection time and UAV loads and could further help enhance the utility of UAVs in forestry and ecological research.

Original languageEnglish
Article number2205
JournalRemote Sensing
Issue number8
Number of pages17
Publication statusPublished - 2023

Bibliographical note

Publisher Copyright:
© 2023 by the authors.

    Research areas

  • convolutional neural networks, Faxon fir, forest inventory, tree species classification, unmanned aerial vehicles, vegetation indices

ID: 347745831