With the continuous improvement of living standards, residents’ requirements for urban vegetation are also increasing. Urban vegetation has become one of the important criteria to measure the livability of cities and plays a very important role in assessing and protecting urban biodiversity. Therefore, rational planning of urban vegetation is an important means of solving environmental problems and improve the quality of life. To sum up, monitoring urban vegetation becomes the main task, and the extraction of urban vegetation becomes the top priority. At present, the problems of urban vegetation extraction mainly focus on two aspects. Vegetation extraction is affected by region and species. On the other hand, Vegetation extraction is affected by topography and the shadow of buildings. In order to solve the above problems, this paper proposes a red edge-near infrared vegetation index model based on DEM. In this experiment, worldView-3 remote sensing images with red-edge bands and high spectral and spatial resolution after radiation calibration and atmospheric correction were first selected. Then, according to the high sensitivity of the Red Edge band to vegetation and the good correlation between the spectral data within the red edge and the parameters reflecting vegetation growth, the DEM model and the spectral difference between the red edge were adopted to remove the shadow of terrain and buildings effectively. Finally, the red-border spectrum-near-infrared spectrum is constructed based on the feature space within the visible band, and the red-border near-infrared vegetation Index model is constructed. At the same time, the urban vegetation extraction is compared and analyzed with NDVI and EVI. The analysis methods are qualitative and quantitative. The former is to extract vegetation images for visual analysis by using a real vegetation image reference map and model. The latter is a quantitative analysis using user accuracy, producer accuracy, overall accuracy and Kappa coefficient. The result of the qualitative experiment shows that the DEM model can effectively remove the shadow of buildings and terrain by combining with the different information of the red edge band between shadow and vegetation. After removing the shadows, NDVI and EVI were used to extract urban vegetation from the images, which made the buildings and road pixels confused in the vegetation, resulting in the problem of misclassification and omission. However, RENVI can effectively eliminate the confusion between shadow pixels and vegetation pixels, accurately extract urban vegetation, reduce redundancy, and increase vegetation index information. The quantitative experimental results show that the RENVI model can accurately extract urban vegetation compared with NDVI and RVI. The overall accuracy of the 3 images is 89%, 81.4% and 91.8% respectively, and the Kappa coefficient is 0.852 8, 0.791 3 and 0.905 2 respectively. In summer, this method can effectively improve the extraction precision of urban vegetation and obtain a better visual effect of extraction.