Predictions of Spartina alterniflora leaf functional traits based on hyperspectral data and machine learning models

ABSTRACT

Investigating the functional traits of Spartina alterniflora can provide insights towards understanding its invasion mechanism, and developing a method leaves can improve its management in coastal wetlands. Here, we examined the relationship between 11 leaf functional traits of S. alterniflora and hyperspectral data and investigated the feature bands through importance score analysis. Using original spectral and first-order differential conversion data of feature bands, we established four prediction models: random forest (RF), support vector machine (SVM), eXtreme Gradient Boosting (XGBoost), and back propagation neural network (BPNN). The study results showed that: (1) the SVM model based on Random Forest Importance Score is well-suited for S. alterniflora leaf functional trait inversion; (2) the importance score of leaf functional traits differed, and first-order differential spectral data produced more bands with high scores compared with the original hyperspectral reflectance data; (3) first-order differential data modelling effects were slightly better than those of the original spectral data. However, the first-order differential treatment did not show a significant improvement in the validation accuracy compared with the original data, and the accuracy of some traits decreased. Our study provides a new methodological approach for improving the monitoring and management of S. alterniflora in coastal wetlands.

KEYWORDS:

• Coastal wetlands
• functional traits
• hyperspectral data
• machine learning
• spartina alterniflora

Acknowledgments

We would like to express our gratitude to Deputy Director Chen Hao, Section Chief Zhang Yanan and Mr. Li Zhenghao of the reserve for their valuable assistance in conducting our field experiments.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Hyperspectral data and functional trait data used in this study are available from the corresponding author upon request.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/22797254.2023.2294951

Additional information

Funding

This work was supported by the National Key R&D Program of China under Grant [2017YFC0506200].