Aiming at the problems of small plant size of degraded indicator species and mixed pixels caused by similar morphological characteristics between grass species， a two-stage classification method based on object detection and semantic segmentation is proposed according to the obtained low-altitude UAV data. Secondly， the segmentation model is lightweight improved. The RepVGG network with structural reparameterization is used to replace the Unet backbone network. The efficient channel attention mechanism ECA is introduced in the coding stage， and the feature extraction ability of the model is improved in the down-sampling link to achieve lightweight feature extraction. The block structure uses the ESE module to avoid the loss of channel information. The improved segmentation model has a good classification effect on the two types of grassland degradation indicator species of Artemisia frigida and Convolvulus ammannii in the typical grassland of Xilinhot. The MIoU can reach 0.91， which is about 0.11 higher than the original Unet model. The experimental results show that the UAV data and the two-stage classification method can classify the grassland degradation indicator species well， and the proposed lightweight improved model has a good effect.