Assessing mangrove leaf traits under different pest and disease severity with hyperspectral imaging spectroscopy

Hyperspectral imaging data have been rarely focused on studies of mangrove pests and diseases. With leaf hyperspectral imaging data, this study aims to extract the sensitive spectral and textural features related to information of mangrove pest and disease using successive projection algorithm (SPA), and to model and visualize leaf traits in response to different pest and disease severity using random forest (RF). The results showed that multiple repetitions of SPA and RF modeling operations could provide a robust set of sensitive features and reliable accuracies of vegetation parameter estimation. Among the five types of features (450 bands of original and first derivative reflectance, 52 vegetation indices, 112 texture features, and all coupling features), the RF models with 33 sensitive features chosen from the coupling of all the 1064 features, 13 sensitive wavelengths with first derivative reflectance, and 30 sensitive wavelengths with first derivative reflectance reported the optimal validation performance (mean R2Val = 0.752, 0.671, and 0.658) in estimating pest and disease severity, leaf SPAD-502, and leaf NBI values, respectively. Moreover, the two leaf trait values increased with decreasing severity of pest and disease based on the leaf trait visualization map using the optimal SPA-RF model. We conclude that the combination of SPA-RF model and hyperspectral imaging had great potential in detecting the spatial distribution of leaf traits under different pest and disease severity. The leaf-level study could lay foundation for early warning and monitoring of mangrove pests and diseases at the landscape or region level.

Automated summary

This study utilized hyperspectral imaging data and algorithms to extract sensitive features, model and visualize mangrove leaf traits in response to pest and disease severity, demonstrating the effectiveness of this method in predicting pest and disease severity.