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Estimating canopy chlorophyll in slash pine using multitemporal vegetation indices from uncrewed aerial vehicles (UAVs)

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Abstract

Canopy Chlorophyll Content (CCC) is an important physiological indicator that reflects the growth stage of trees. Accurate estimation of CCC facilitates dynamic monitoring and efficient forest management. In this study, we used high-resolution remote sensing images obtained by uncrewed aerial vehicles (UAVs) equipped with multispectral sensors (red, green, blue, near-infrared, and red-edge) to estimate CCC of lodgepole pine (Pinus elliottii). Our aim was to determine the optimal machine learning model between support vector regression (SVR) and random forest regression (RFR) for predicting CCC and to evaluate the effectiveness of multispectral bands along with 21 vegetation indices (VIs) in the estimation process. Individual tree boundaries were derived from the canopy height model (CHM) based on three-dimensional (3D) point clouds generated using structure from motion. These images, combined with continuous field measurements from January to December, provided comprehensive data for our analysis. The results showed that the SVR method outperformed the RFR method in estimating leaf chlorophyll content (LCC), with fitting R2 values up to 0.692 and RMSE values up to 0.168 mg⋅g−1. Overall, the study highlights the potential of UAV-based remote sensing for multitemporal forest monitoring, offering advances in precision forestry and tree breeding.

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Data availability

The data used for this paper is freely available upon permission of Research Institute of Subtropical Forestry, Chinese Academy of Forestry.

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Funding

This work was supported by the Science and Technology Innovation 2030-Agricultural Biological Breeding Major Project (2023ZD040580105), Fundamental Research Funds of Chinese Forestry Academy (CAFYBB2022QA001) and Zhejiang Science and Technology Major Program on Agricultural New Variety Breeding (2021C02070-8).

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  1. Research Institute of Subtropical Forestry, Chinese Academy of Forestry (National Forestry and Grassland Engineering Technology Research Center of Exotic Pine Cultivation), No. 73, Daqiao Road, Fuyang, Hangzhou, 311400, Zhejiang, China

    Qifu Luan, Xueyu Tao, Jingmin Jiang & Yanjie Li

  2. New Zealand School of Forestry, University of Canterbury, Private Bag 4800, Christchurch, 8140, New Zealand

    Cong Xu

  3. Hangzhou Academy of Forestry Sciences, Hangzhou, 310022, Zhejiang, China

    Lihua Chen

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  1. Qifu Luan
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  2. Cong Xu
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  3. Xueyu Tao
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  4. Lihua Chen
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  5. Jingmin Jiang
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Contributions

QL and XT conducted the experiment and wrote the manuscript. YL designed the study, supervised experiments, supported the data collection and performed revisions of the manuscript. CX, LC and JJ revised the manuscript, and all authors read and approved the final manuscript.

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Correspondence to Yanjie Li.

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Luan, Q., Xu, C., Tao, X. et al. Estimating canopy chlorophyll in slash pine using multitemporal vegetation indices from uncrewed aerial vehicles (UAVs). Precision Agric 25, 1086–1105 (2024). https://doi.org/10.1007/s11119-023-10106-9

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