Co-inversion of island leaf area index combination morphological and spectral parameters based on UAV multi-source remote sensing data

The precise and rapid estimation of leaf area index (LAI) is crucial for quantitative ecological monitoring and assessment, as it serves as an important ecological indicator of plant growth state and canopy structure. The acquisition, integration, and application of multiple remote sensing data from Unmanned Aerial Vehicle (UAV) platforms in quantitative ecological research is a current research hotspot. This study aims to explore the potential of combining morphological and spectral parameters using the symbolic regression (SR) algorithm with allometric models to improve the estimation of Leaf Area Index (LAI) at a regional scale of island ecosystem using UAV multi-source remote sensing data. The Canopy Height Model (CHM), Vegetation Density (VD), and Vegetation Indices (VIs), derived from UAV multi-platforms, were used as independent variables in the development of the retrieval models. Subsequently, regression equations were used to construct the Singlet, Duplet, and Triplet models. The precise assessments indicated that the triplet model demonstrates the highest accuracy, with a mean absolute error (MAE) of 9.48% and root mean square error (RMSE) of 0.2345. This improvement was observed compared to the singlet model (MAE = 19.55%, RMSE = 0.4091) and duplet model (MAE = 15.88%, RMSE = 0.3450), which can be attributed to incorporating morphological parameters to reduce the impact of tree height and leaf density. The analysis also showed that the estimated LAI accuracy is generally higher for shrubs and herbs compared to trees, and that lower plants with high vegetation coverage are better retrieved. The methodology advaneced in this study demonstrates significant potential and implications for ecological monitoring and assessment of island ecosystems, thereby expanding the research scope in the field of island ecology. Furthermore, it provides scientific evidence and technical support for decision-makers and scholars to propose and implement various measures for the conservation and restoration of island ecosystems.