Estimating Community-Level Plant Functional Traits in a Species-Rich Alpine Meadow Using UAV Image Spectroscopy

Abstract

Plant functional traits at the community level (plant community traits hereafter) are commonly used in trait-based ecology for the study of vegetation–environment relationships. Previous studies have shown that a variety of plant functional traits at the species or community level can be successfully retrieved by airborne or spaceborne imaging spectrometer in homogeneous, species-poor ecosystems. However, findings from these studies may not apply to heterogeneous, species-rich ecosystems. Here, we aim to determine whether unmanned aerial vehicle (UAV)-based hyperspectral imaging could adequately estimate plant community traits in a species-rich alpine meadow ecosystem on the Qinghai–Tibet Plateau. To achieve this, we compared the performance of four non-parametric regression models, i.e., partial least square regression (PLSR), the generic algorithm integrated with the PLSR (GA-PLSR), random forest (RF) and extreme gradient boosting (XGBoost) for the retrieval of 10 plant community traits using visible and near-infrared (450–950 nm) UAV hyperspectral imaging. Our results show that chlorophyll a, chlorophyll b, carotenoid content, starch content, specific leaf area and leaf thickness were estimated with good accuracies, with the highest R2 values between 0.64 (nRMSE = 0.16) and 0.83 (nRMSE = 0.11). Meanwhile, the estimation accuracies for nitrogen content, phosphorus content, plant height and leaf dry matter content were relatively low, with the highest R2 varying from 0.3 (nRMSE = 0.24) to 0.54 (nRMSE = 0.20). Among the four tested algorithms, the GA-PLSR produced the highest accuracy, followed by PLSR and XGBoost, and RF showed the poorest performance. Overall, our study demonstrates that UAV-based visible and near-infrared hyperspectral imaging has the potential to accurately estimate multiple plant community traits for the natural grassland ecosystem at a fine scale.