Cost-effective and accurate monitoring of flowering across multiple tropical tree species over two years with a time series of high-resolution drone imagery and deep learning

Calvin Ka Fai Lee, Guangqin Song, Helene C. Muller-Landau, Shengbiao Wu, S. Joseph Wright, K. C. Cushman, Raquel Fernandes Araujo, Stephanie Bohlman, Yingyi Zhao, Ziyu Lin, Zounachuan Sun, Peter Chuen Yan Cheng, Michael Kwok Po Ng, Jin Wu

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

Detection of flowering and quantification of flowering phenology are key to monitoring the reproduction of tropical trees and their response to global change. However, effective monitoring of flowering over various scales from individuals to forest ecosystem levels is lacking due to the relatively small sizes of flowers, diverse flowering strategies across species, and the short duration of flowering, making accurate flower detection difficult. Drone-based surveys require less time and human resources than traditional ground-based flower surveys and thus may be able to help address this in a cost-effective manner but remain underexplored in species-rich tropical forest ecosystems. Here, we developed a method that integrated the Residual Networks 50 (ResNet50) deep learning algorithm with high resolution imagery (c. 0.05 m) from monthly drone surveys done in a 50-ha tropical forest plot on Barro Colorado Island (BCI), Panamá, over 2018–2020 to detect a diversity of flowering species in this tree community and to track the timing of flowering throughout the year. We built a comprehensive training library of canopy components (flower, leaf, branch, and shade) from this forest plot throughout the study period, trained a single deep learning model across all drone imagery, and validated it using five-fold cross validation at the pixel scale. We further generated image- and tree-crown-specific supervised classifications to evaluate the deep learning model at the tree-crown scale. Our deep learning method accurately classified flowers (User's accuracy = 95.3 %, Producer's accuracy = 85.8 %) while maintaining high predictive power for the other three classes (Overall accuracy = 98.4 %). Our results also demonstrated high consistency against tree-crown-specific supervised classifications for flower (r2 = 0.85), leaf (r2 = 0.84), and branch (r2 = 0.92) components, with lower agreement observed for the shade component (r2 = 0.59). These results demonstrate the effectiveness of our method in advancing fine-scale flower monitoring in the tropics, with potential to be extended to other regions or other remote sensing platforms with frequent high-resolution monitoring. The method will allow us to better monitor flowering in tropical forests and improve our understanding of how phenology and reproductive success may be affected by climate change.

Original languageEnglish
Pages (from-to)92-103
Number of pages12
JournalISPRS Journal of Photogrammetry and Remote Sensing
Volume201
DOIs
StatePublished - Jul 2023
Externally publishedYes

Funding

This work was supported by National Natural Science Foundation of China (#31922090). C.K.F. Lee was in part supported by HKU seed fund for basic research (#202011159154) and the HKU 45th round PDF scheme. J. Wu was in part supported by the Innovation and Technology Fund (funding support to State Key Laboratories in Hong Kong of Agrobiotechnology) of the HKSAR, China. M. Ng's research is funded by HKRGC GRF 12300519, 17201020 and 17300021, HKRGC CRF C1013-21GF and C7004-21GF, and Joint NSFC and RGC N-HKU769/21. We thank Milton Garcia, Pablo Ramos, Paulino Villareal, Milton Solano, Areli Benito, Carrie Tribble, and Osvaldo Calderon for assistance with drone imagery collection and processing, crown mapping, and litter trap data collection and identification. Financial support for the data collection was provided by the Smithsonian Tropical Research Institute. This work was supported by National Natural Science Foundation of China (# 31922090 ). C.K.F. Lee was in part supported by HKU seed fund for basic research (# 202011159154 ) and the HKU 45th round PDF scheme . J. Wu was in part supported by the Innovation and Technology Fund (funding support to State Key Laboratories in Hong Kong of Agrobiotechnology) of the HKSAR, China. M. Ng's research is funded by HKRGC GRF 12300519 , 17201020 and 17300021 , HKRGC CRF C1013-21GF and C7004-21GF , and Joint NSFC and RGC N-HKU769/21 . We thank Milton Garcia, Pablo Ramos, Paulino Villareal, Milton Solano, Areli Benito, Carrie Tribble, and Osvaldo Calderon for assistance with drone imagery collection and processing, crown mapping, and litter trap data collection and identification. Financial support for the data collection was provided by the Smithsonian Tropical Research Institute .

FundersFunder number
HKRGC17300021, C7004-21GF, GRF 12300519, 17201020
HKU seed fund for basic research202011159154
State Key Laboratories in Hong Kong of Agrobiotechnology
Smithsonian Tropical Research Institute
National Natural Science Foundation of China31922090, RGC N-HKU769/21
Innovation and Technology Fund

    Keywords

    • Drone/UAV
    • Flower detection
    • Flower fraction
    • ResNet50
    • Tropical phenology

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