Exploring the Impact of Species Participation Levels on the Performance of Dominant Plant Identification Models in the Sericite–Artemisia Desert Grassland by Using Deep Learning

作者：Wenhao Liu, Guili Jin, Wanqiang Han, Mengtian Chen, Wenxiong Li, Chao Li, Wenlin Du · 发表于：Agriculture · 年份：2025 · DOI：10.3390/agriculture15141547 · 被引用次数：1 · 研究领域：Species Distribution and Climate Change、Plant and animal studies、Genetic diversity and population structure

Accurate plant species identification in desert grasslands using hyperspectral data is a critical prerequisite for large-scale, high-precision grassland monitoring and management. However, due to prolonged overgrazing and the inherent ecological vulnerability of the environment, sericite–Artemisia desert grassland has experienced significant ecological degradation. Therefore, in this study, we obtained spectral images of the grassland in April 2022 using a Soc710 VP imaging spectrometer (Surface Optics Corporation, San Diego, CA, USA), which were classified into three levels (low, medium, and high) based on the level of participation of Seriphidium transiliense (Poljakov) Poljakov and Ceratocarpus arenarius L. in the community. The optimal index factor (OIF) was employed to synthesize feature band images, which were subsequently used as input for the DeepLabv3p, PSPNet, and UNet deep learning models in order to assess the influence of species participation on classification accuracy. The results indicated that species participation significantly impacted spectral information extraction and model classification performance. Higher participation enhanced the scattering of reflectivity in the canopy structure of S. transiliense, while the light saturation effect of C. arenarius was induced by its short stature. Band combinations—such as Blue, Red Edge, and NIR (BREN) and Red, Red Edge, and NIR (RREN)—exhibited strong capabilities in capturing structural vegetation information. T...