Abstract: Grassland degradation is a critical issue in the Three Rivers Source Region that cannot be overlooked. Employing deep learning techniques for the evaluating of grassland degradation in the Three Rivers Source Region is a pivotal step towards intelligent grassland assessment. However， a challenge in semantic segmentation lies in the potential inconsistency of altitudes in UAV-captured imagery， which can lead to discrepancies between computed proportions of poisonous weed cover and actual conditions， consequently introducing errors in grassland degradation assessment. This study proposes a method to calculate the Actual Ground Proportion （AGP） for both known and unknown heights of captured grassland images. For images with known heights， we select to utilize the captured altitude for AGP calculation and then map images of varying altitudes to a common height for coverage computation. For images with unknown heights， we train a sorrel instance segmentation model to calculate AGP based on instance segmentation results， followed by coverage computation. Experimental restlts demonstrate that， in comparison to direct coverage calculation， the use of instance segmentation reduces the error from 2.7% to 0.39%. This approach holds significant importance in enhancing the accuracy of intelligent grassland degradation assessment.

Key words:  , deep learning, instance segmentation, UAV imagery, grassland degradation