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A Method to Identify Dacrydium pierrei Hickel Using Unmanned Aerial Vehicle Multi-source Remote Sensing Data in a Chinese Tropical Rainforest

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Abstract

Identifying special species in tropical forests is an important topic in forest resource management, and the use of a single type of remote sensing data for identification of species has limited accuracy. To analyze the ability of various unmanned aerial vehicle (UAV) remote sensing data for identifying target species, this study used three types of UAV remote sensing data (light detection and ranging (LiDAR), red, green, blue (RGB), and multispectral) to identify Dacrydium pierrei Hickel (D. pierrei) in Chinese tropical forests. The study compared the effects of using various combinations of UAV remote sensing data on the accuracy of D. pierrei identification and identified the optimal combination. (1) Random forest feature selection improved the accuracy of identification of D. pierrei by UAV multiple source remote sensing data: The producer accuracy (PA) was increased up to by 4.62%. (2) The following eight features were most useful for identifying D. pierrei: four features from multispectral images (DR_Standard, RE_Standard, DR_Mean, and B_Brightne), two features from RGB images (B_Standard and B_Mean), and two features from LiDAR images (INT_kurtosis and INT_aad). (3) Combining remote sensing data by integrating up to three types of data sources improved the accuracy of D. pierrei identification. When using a single type of remote sensing data, multispectral data gave the highest identification accuracy. When combining two types of remote sensing data, RGB and multispectral data achieved the best overall effect, and the highest overall identification accuracy, of more than 90%, was obtained by combining three types of remote sensing data.

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Data Availability

Data used in this study are available from the Research Institute of Forest Resources Information Techniques, Chinese Academy of Forestry.

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Acknowledgements

We thank Liyong Fu, Qiuwang Liu, Mengxi Wang, Guangyu Zhu, Jiazheng Liu, Qingqing Yang, and Yihui Chen et al. for assisting in the fieldwork. We thank the management of Diaoluo Natural Reserve of Hainan Island, Hainan Province, China, for their support during the study.

Funding

This work was supported by the Fundamental Research Funds for the Central Non-profit Research Institution of CAF (No. CAFBB2017ZB004) and the Fundamental Research Funds for the Central Non-profit Research Institution of CAF (No. CAFYBB2020GC006).

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Author notes

  1. Xi Peng and Haodong Liu have contributed equally to this work.

Authors and Affiliations

  1. Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing, 100091, People’s Republic of China

    Xi Peng, Haodong Liu, Yongfu Chen, Qiao Chen, Juan Wang & Huayu Li

  2. College of Forestry, Sichuan Aricultural University, Sichuan Province, Chengdu, 611130, People’s Republic of China

    Xi Peng & Anjiu Zhao

  3. College of Forestry, Southwest Forestry University, Yunnan Province, Kunming, 650224, People’s Republic of China

    Juan Wang & Huayu Li

  4. Key Laboratory of Forestry Remote Sensing and Information System, NFGA, Beijing, China

    Xi Peng, Haodong Liu, Yongfu Chen, Qiao Chen, Juan Wang & Huayu Li

Authors
  1. Xi Peng
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  2. Haodong Liu
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  3. Yongfu Chen
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  4. Qiao Chen
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  5. Juan Wang
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Correspondence to Qiao Chen.

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Peng, X., Liu, H., Chen, Y. et al. A Method to Identify Dacrydium pierrei Hickel Using Unmanned Aerial Vehicle Multi-source Remote Sensing Data in a Chinese Tropical Rainforest. J Indian Soc Remote Sens 50, 25–35 (2022). https://doi.org/10.1007/s12524-021-01453-z

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  • DOI: https://doi.org/10.1007/s12524-021-01453-z

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