TANG Xiying, LI Huazhe, CUI Lijuan, ZHAO Xinsheng, ZHAI Xiajie, LEI Yinru, LI Jing, WANG Jinzhi, LI Wei
Wetland plant species diversity, as a quantifiable indicator reflecting the level of organization in an ecosystem’s community, can reveal the community organization and stability of wetland ecosystems. Accurate assessments of wetland health, degradation, and restoration status are crucial for effective wetland management and protection. Therefore, timely understanding of the current status of wetland plant community species diversity is of great importance. However, traditional field survey methods are time-consuming and labor-intensive, limited by temporal costs, and cannot achieve large-scale synchronous observation. Meanwhile, hyperspectral technology, with its high resolution, can capture more abundant spectral information, providing an opportunity for the realization of this goal. To investigate how to accurately invert wetland plant species diversity using hyperspectral technology, we investigated the wetland plants in Hanzhong Crested Ibis National Nature Reserve in Shaanxi Province and simultaneously acquired hyperspectral images of the plant canopy. Species diversity was characterized by four indicators: Simpson (DS), Margalef (DM), Shannon-Weiner (H'), and Pielou (J). The inverse model was established using three methods: Random Forest (RF), Back Propagation Neural Network (BPNN), and Partial Least Squares (PLS). Finally, the inverse projection of regional species diversity was realized. The outcomes indicate that spectral differentiation complicates the association between spectra and species diversity indices, producing a range of sensitive bands. Notably, the first-order differential transform is superior in extracting sensitive bands compared to the second-order differential transform. Furthermore, correlating species diversity indices can be enhanced through the integration of vegetation indices from various bands. When applying the RF model to analyze differential spectra and vegetation indices, it was found that both using original features and combinations of features, the model's inversion results demonstrated similar and high accuracy (R2 > 0.40). Particularly, in predicting H' and J, the model exhibited strong precision (R2 > 0.6), and in terms of DS, R2 also exceeded 0.5, indicating potential predictive capabilities. However, in reverting another measurement of DM, the model showed lower accuracy (R2 < 0.5), suggesting challenges in improving the model's predictive power. This study demonstrates the effectiveness of UAV hyperspectral technology in the accurate inversion of wetland plant species diversity and confirms the reliability of the method for species diversity inversion at the Unmanned Aerial Vehicle (UAV) scale, achieved through spectral differential transformation and feature variable extraction combined with the random forest model. This technique can provide technical support for the large-scale detection of wetland biodiversity and offer references for decision-making by relevant management departments.