In order to accurately monitor the dynamic changes of typical vegetation coverage at the landscape scale between dunes and meadows， this paper has utilized multi-temporal and high-definition RGB orthophotos acquired by unmanned aerial vehicle （UAV）. On the basis of U-shaped neural network structure of deep learning， a vegetation cover extraction model has been established. In addition， the characteristics of vegetation cover changes during the growing period and its response to environmental factors have been further investigated. The study showed that： （1） the accuracy of the vegetation coverage extraction model is high. The accuracy of the training set is 0.81 and the accuracy of the verification set is 0.86. The vegetation coverage of different topography and complex habitats can be extracted efficiently and conveniently. （2） During the growth period （May to October）， The vegetation coverage of semi-mobile sand dunes， farmland and meadow combinations， semi-fixed sand dunes and fixed sand dunes showed a single-peak trend over time， peaking in August and followed by 37.51%， 76.21%， 61.66% and 80.57% respectively. （3） Precipitation， air temperature and vegetation cover were significantly related （the correlation coefficients were 0.575， 0.602 and P is less than 0.01 respectively）， precipitation is the main factor affecting the change of vegetation cover in the sand dune-meadow phase， and air temperature is also an important environmental factor to limit its growth and distribution. （4） The impact of precipitation to vegetation coverage was from high to low， which were semi-mobile sand dunes， semi-fixed sand dunes， fixed sand dunes， the combinations of farmland and meadow. Using high-definition images of UAV to accurately monitor changes in vegetation cover can provide data support for the extraction of vegetation information in large-scale desert areas and theoretical basis for the scientific environment construction and management of desertification ecosystems.

Keywords： vegetation coverage ; UAV ; convolutional neural network ; environmental factors