Yanbin Zhang

Utilizing a low-altitude unmanned aerial vehicle (UAV), a hyperspectral remote-sensing system can identify key grass species indicating grassland degradation, developing an ASI index and classification rules and leveraging spectral differences and plant senescence reflectance to effectively monitor and evaluate grassland conditions and degradation.

In this study, we propose a low-altitude unmanned aerial vehicle (UAV) hyperspectral visible near-infrared (vis-NIR) remote sensing hardware platform, which combines efficiency and accuracy for high-precision remote sensing-based ecological surveys and statistical data collection on grassland desertification.