Leaf Area Index Inversion of Spartina alterniflora Using UAV Hyperspectral Data Based on Multiple Optimized Machine Learning Algorithms

Abstract

The leaf area index (LAI) is an essential biophysical parameter for describing the vegetation canopy structure and predicting its growth and productivity. Using unmanned aerial vehicle (UAV) hyperspectral imagery to accurately estimate the LAI is of great significance for Spartina alterniflora (S. alterniflora) growth status monitoring. In this study, UAV hyperspectral imagery and the LAI of S. alterniflora during the flourishing growth period were acquired. The hyperspectral data were preprocessed with Savitzky–Golay (SG) smoothing, and the first derivative (FD) and the second derivative (SD) spectral transformations of the data were then carried out. Then, using the band combination index (BCI) method, the characteristic bands related to the LAI were extracted from the hyperspectral image data obtained with the UAV, and spectral indices (SIs) were constructed through the characteristic bands. Finally, three machine learning (ML) regression methods—optimized support vector regression (OSVR), optimized random forest regression (ORFR), and optimized extreme gradient boosting regression (OXGBoostR)—were used to establish LAI estimation models. The results showed the following: (1) the three ML methods accurately predicted the LAI, and the optimal model was provided by the ORFR method, with an R2 of 0.85, an RMSE of 0.19, and an RPD of 4.33; (2) the combination of FD SIs improved the model accuracy, with the R2 value improving by 41.7%; (3) the band combinations screened using the BCI method were mainly concentrated in the red and near-infrared bands; (4) the higher LAI was distributed on the seaward side of the study area, while the lower LAI was located at the junction between the S. alterniflora and the tidal flat. This study serves as both theoretical and technological support for research on the LAI of S. alterniflora and as a solid foundation for the use of UAV remote sensing technologies in the supervisory control of S. alterniflora.

Estimation of Anthocyanins in Winter Wheat Based on Band Screening Method and Genetic Algorithm Optimization Models

“…The FDS transformation can limit the noisy backgrounds, such as soil, to a certain extent [75], and the optimal band combinations of OS may pay attention to the sensitivities of some specific bands to the anthocyanin value. In comparison, the optimal band combinations of FDS may attach more importance to the trends of the spectral curves [27]. Additionally, the band combinations selected for the same vegetation index were not identical for different wheat growth stages, mainly associated with different sensitivities that anthocyanin values generated to spectra at various stages of winter wheat.…”

“…Moreover, it has been shown that using VIo has achieved favorable outcomes in hyperspectral inversion. Fang et al [27] respectively screened five VIo3 (ESI, MCARI, SIPI, TSI, NSI) based on OS, FDS, and second-order differential data, using hyperspectral data of Spartina alterniflora, which captured the vegetation information that could not be covered by the traditional VIs and improved the sensitivity of the VIs to LAI. Deng et al [28] searched for the optimal combination of bands for VIs in a study that used hyperspectral to assess wheat stripe rust.…”

Inversion of Glycyrrhiza Chlorophyll Content Based on Hyperspectral Imagery

“…PLSR is particularly advantageous when internal variables exhibit high linear relationships and is suitable for scenarios where the number of variables exceeds the number of samples, as well as handling multicollinearity issues [16]. SVR is based on statistical learning theory, using kernel functions to transform the initial input space into a new feature space with higher dimensions, thus converting nonlinear regression problems into linear relationships, and performs exceptionally well with smaller samples [49]. RF is an ensemble learning method that combines multiple decision trees through bootstrap sampling.…”